From 247cada644c059df4575063d2ed3010a0c6a753b Mon Sep 17 00:00:00 2001 From: Heng Pan Date: Fri, 20 Sep 2024 16:32:04 +0100 Subject: [PATCH 01/23] fix(examples:skip) Specify `min_fit_clients` in `FedAvg` in the Secure Aggregation example (#4247) --- examples/flower-secure-aggregation/secaggexample/server_app.py | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/flower-secure-aggregation/secaggexample/server_app.py b/examples/flower-secure-aggregation/secaggexample/server_app.py index 0f1b594317fa..0b95d68e4183 100644 --- a/examples/flower-secure-aggregation/secaggexample/server_app.py +++ b/examples/flower-secure-aggregation/secaggexample/server_app.py @@ -40,6 +40,7 @@ def main(driver: Driver, context: Context) -> None: strategy = FedAvg( # Select all available clients fraction_fit=1.0, + min_fit_clients=5, # Disable evaluation in demo fraction_evaluate=(0.0 if is_demo else context.run_config["fraction-evaluate"]), min_available_clients=5, From b6babe95addcbd1380f3894cc3ea71772119b72c Mon Sep 17 00:00:00 2001 From: Heng Pan Date: Mon, 23 Sep 2024 11:37:28 +0100 Subject: [PATCH 02/23] feat(framework) Add new RPCs to `Control` service (#4241) --- src/proto/flwr/proto/control.proto | 7 +++ src/proto/flwr/proto/run.proto | 20 ++++++ src/py/flwr/proto/control_pb2.py | 6 +- src/py/flwr/proto/control_pb2_grpc.py | 68 ++++++++++++++++++++ src/py/flwr/proto/control_pb2_grpc.pyi | 26 ++++++++ src/py/flwr/proto/run_pb2.py | 32 +++++++--- src/py/flwr/proto/run_pb2.pyi | 86 ++++++++++++++++++++++++++ 7 files changed, 233 insertions(+), 12 deletions(-) diff --git a/src/proto/flwr/proto/control.proto b/src/proto/flwr/proto/control.proto index 9747c2e3ea11..8b75c66fccaa 100644 --- a/src/proto/flwr/proto/control.proto +++ b/src/proto/flwr/proto/control.proto @@ -22,4 +22,11 @@ import "flwr/proto/run.proto"; service Control { // Request to create a new run rpc CreateRun(CreateRunRequest) returns (CreateRunResponse) {} + + // Get the status of a given run + rpc GetRunStatus(GetRunStatusRequest) returns (GetRunStatusResponse) {} + + // Update the status of a given run + rpc UpdateRunStatus(UpdateRunStatusRequest) + returns (UpdateRunStatusResponse) {} } diff --git a/src/proto/flwr/proto/run.proto b/src/proto/flwr/proto/run.proto index ada72610e182..2c9bd877f66c 100644 --- a/src/proto/flwr/proto/run.proto +++ b/src/proto/flwr/proto/run.proto @@ -28,6 +28,15 @@ message Run { string fab_hash = 5; } +message RunStatus { + // "starting", "running", "finished" + string status = 1; + // "completed", "failed", "stopped" or "" (non-finished) + string sub_status = 2; + // failure details + string details = 3; +} + // CreateRun message CreateRunRequest { string fab_id = 1; @@ -40,3 +49,14 @@ message CreateRunResponse { uint64 run_id = 1; } // GetRun message GetRunRequest { uint64 run_id = 1; } message GetRunResponse { Run run = 1; } + +// UpdateRunStatus +message UpdateRunStatusRequest { + uint64 run_id = 1; + RunStatus run_status = 2; +} +message UpdateRunStatusResponse {} + +// GetRunStatus +message GetRunStatusRequest { repeated uint64 run_ids = 1; } +message GetRunStatusResponse { map run_status_dict = 1; } diff --git a/src/py/flwr/proto/control_pb2.py b/src/py/flwr/proto/control_pb2.py index 2b8776509d32..eb1c18d8dcff 100644 --- a/src/py/flwr/proto/control_pb2.py +++ b/src/py/flwr/proto/control_pb2.py @@ -15,13 +15,13 @@ from flwr.proto import run_pb2 as flwr_dot_proto_dot_run__pb2 -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x18\x66lwr/proto/control.proto\x12\nflwr.proto\x1a\x14\x66lwr/proto/run.proto2U\n\x07\x43ontrol\x12J\n\tCreateRun\x12\x1c.flwr.proto.CreateRunRequest\x1a\x1d.flwr.proto.CreateRunResponse\"\x00\x62\x06proto3') +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x18\x66lwr/proto/control.proto\x12\nflwr.proto\x1a\x14\x66lwr/proto/run.proto2\x88\x02\n\x07\x43ontrol\x12J\n\tCreateRun\x12\x1c.flwr.proto.CreateRunRequest\x1a\x1d.flwr.proto.CreateRunResponse\"\x00\x12S\n\x0cGetRunStatus\x12\x1f.flwr.proto.GetRunStatusRequest\x1a .flwr.proto.GetRunStatusResponse\"\x00\x12\\\n\x0fUpdateRunStatus\x12\".flwr.proto.UpdateRunStatusRequest\x1a#.flwr.proto.UpdateRunStatusResponse\"\x00\x62\x06proto3') _globals = globals() _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.control_pb2', _globals) if _descriptor._USE_C_DESCRIPTORS == False: DESCRIPTOR._options = None - _globals['_CONTROL']._serialized_start=62 - _globals['_CONTROL']._serialized_end=147 + _globals['_CONTROL']._serialized_start=63 + _globals['_CONTROL']._serialized_end=327 # @@protoc_insertion_point(module_scope) diff --git a/src/py/flwr/proto/control_pb2_grpc.py b/src/py/flwr/proto/control_pb2_grpc.py index 987b9d8d7433..a59f90f15935 100644 --- a/src/py/flwr/proto/control_pb2_grpc.py +++ b/src/py/flwr/proto/control_pb2_grpc.py @@ -19,6 +19,16 @@ def __init__(self, channel): request_serializer=flwr_dot_proto_dot_run__pb2.CreateRunRequest.SerializeToString, response_deserializer=flwr_dot_proto_dot_run__pb2.CreateRunResponse.FromString, ) + self.GetRunStatus = channel.unary_unary( + '/flwr.proto.Control/GetRunStatus', + request_serializer=flwr_dot_proto_dot_run__pb2.GetRunStatusRequest.SerializeToString, + response_deserializer=flwr_dot_proto_dot_run__pb2.GetRunStatusResponse.FromString, + ) + self.UpdateRunStatus = channel.unary_unary( + '/flwr.proto.Control/UpdateRunStatus', + request_serializer=flwr_dot_proto_dot_run__pb2.UpdateRunStatusRequest.SerializeToString, + response_deserializer=flwr_dot_proto_dot_run__pb2.UpdateRunStatusResponse.FromString, + ) class ControlServicer(object): @@ -31,6 +41,20 @@ def CreateRun(self, request, context): context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') + def GetRunStatus(self, request, context): + """Get the status of a given run + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def UpdateRunStatus(self, request, context): + """Update the status of a given run + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + def add_ControlServicer_to_server(servicer, server): rpc_method_handlers = { @@ -39,6 +63,16 @@ def add_ControlServicer_to_server(servicer, server): request_deserializer=flwr_dot_proto_dot_run__pb2.CreateRunRequest.FromString, response_serializer=flwr_dot_proto_dot_run__pb2.CreateRunResponse.SerializeToString, ), + 'GetRunStatus': grpc.unary_unary_rpc_method_handler( + servicer.GetRunStatus, + request_deserializer=flwr_dot_proto_dot_run__pb2.GetRunStatusRequest.FromString, + response_serializer=flwr_dot_proto_dot_run__pb2.GetRunStatusResponse.SerializeToString, + ), + 'UpdateRunStatus': grpc.unary_unary_rpc_method_handler( + servicer.UpdateRunStatus, + request_deserializer=flwr_dot_proto_dot_run__pb2.UpdateRunStatusRequest.FromString, + response_serializer=flwr_dot_proto_dot_run__pb2.UpdateRunStatusResponse.SerializeToString, + ), } generic_handler = grpc.method_handlers_generic_handler( 'flwr.proto.Control', rpc_method_handlers) @@ -65,3 +99,37 @@ def CreateRun(request, flwr_dot_proto_dot_run__pb2.CreateRunResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) + + @staticmethod + def GetRunStatus(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/flwr.proto.Control/GetRunStatus', + flwr_dot_proto_dot_run__pb2.GetRunStatusRequest.SerializeToString, + flwr_dot_proto_dot_run__pb2.GetRunStatusResponse.FromString, + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) + + @staticmethod + def UpdateRunStatus(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary(request, target, '/flwr.proto.Control/UpdateRunStatus', + flwr_dot_proto_dot_run__pb2.UpdateRunStatusRequest.SerializeToString, + flwr_dot_proto_dot_run__pb2.UpdateRunStatusResponse.FromString, + options, channel_credentials, + insecure, call_credentials, compression, wait_for_ready, timeout, metadata) diff --git a/src/py/flwr/proto/control_pb2_grpc.pyi b/src/py/flwr/proto/control_pb2_grpc.pyi index c38b7f15d125..7817e2b12e31 100644 --- a/src/py/flwr/proto/control_pb2_grpc.pyi +++ b/src/py/flwr/proto/control_pb2_grpc.pyi @@ -13,6 +13,16 @@ class ControlStub: flwr.proto.run_pb2.CreateRunResponse] """Request to create a new run""" + GetRunStatus: grpc.UnaryUnaryMultiCallable[ + flwr.proto.run_pb2.GetRunStatusRequest, + flwr.proto.run_pb2.GetRunStatusResponse] + """Get the status of a given run""" + + UpdateRunStatus: grpc.UnaryUnaryMultiCallable[ + flwr.proto.run_pb2.UpdateRunStatusRequest, + flwr.proto.run_pb2.UpdateRunStatusResponse] + """Update the status of a given run""" + class ControlServicer(metaclass=abc.ABCMeta): @abc.abstractmethod @@ -23,5 +33,21 @@ class ControlServicer(metaclass=abc.ABCMeta): """Request to create a new run""" pass + @abc.abstractmethod + def GetRunStatus(self, + request: flwr.proto.run_pb2.GetRunStatusRequest, + context: grpc.ServicerContext, + ) -> flwr.proto.run_pb2.GetRunStatusResponse: + """Get the status of a given run""" + pass + + @abc.abstractmethod + def UpdateRunStatus(self, + request: flwr.proto.run_pb2.UpdateRunStatusRequest, + context: grpc.ServicerContext, + ) -> flwr.proto.run_pb2.UpdateRunStatusResponse: + """Update the status of a given run""" + pass + def add_ControlServicer_to_server(servicer: ControlServicer, server: grpc.Server) -> None: ... diff --git a/src/py/flwr/proto/run_pb2.py b/src/py/flwr/proto/run_pb2.py index 99ca4df5c44c..d59cc26fbb48 100644 --- a/src/py/flwr/proto/run_pb2.py +++ b/src/py/flwr/proto/run_pb2.py @@ -16,7 +16,7 @@ from flwr.proto import transport_pb2 as flwr_dot_proto_dot_transport__pb2 -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x14\x66lwr/proto/run.proto\x12\nflwr.proto\x1a\x14\x66lwr/proto/fab.proto\x1a\x1a\x66lwr/proto/transport.proto\"\xd5\x01\n\x03Run\x12\x0e\n\x06run_id\x18\x01 \x01(\x04\x12\x0e\n\x06\x66\x61\x62_id\x18\x02 \x01(\t\x12\x13\n\x0b\x66\x61\x62_version\x18\x03 \x01(\t\x12<\n\x0foverride_config\x18\x04 \x03(\x0b\x32#.flwr.proto.Run.OverrideConfigEntry\x12\x10\n\x08\x66\x61\x62_hash\x18\x05 \x01(\t\x1aI\n\x13OverrideConfigEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12!\n\x05value\x18\x02 \x01(\x0b\x32\x12.flwr.proto.Scalar:\x02\x38\x01\"\xeb\x01\n\x10\x43reateRunRequest\x12\x0e\n\x06\x66\x61\x62_id\x18\x01 \x01(\t\x12\x13\n\x0b\x66\x61\x62_version\x18\x02 \x01(\t\x12I\n\x0foverride_config\x18\x03 \x03(\x0b\x32\x30.flwr.proto.CreateRunRequest.OverrideConfigEntry\x12\x1c\n\x03\x66\x61\x62\x18\x04 \x01(\x0b\x32\x0f.flwr.proto.Fab\x1aI\n\x13OverrideConfigEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12!\n\x05value\x18\x02 \x01(\x0b\x32\x12.flwr.proto.Scalar:\x02\x38\x01\"#\n\x11\x43reateRunResponse\x12\x0e\n\x06run_id\x18\x01 \x01(\x04\"\x1f\n\rGetRunRequest\x12\x0e\n\x06run_id\x18\x01 \x01(\x04\".\n\x0eGetRunResponse\x12\x1c\n\x03run\x18\x01 \x01(\x0b\x32\x0f.flwr.proto.Runb\x06proto3') +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x14\x66lwr/proto/run.proto\x12\nflwr.proto\x1a\x14\x66lwr/proto/fab.proto\x1a\x1a\x66lwr/proto/transport.proto\"\xd5\x01\n\x03Run\x12\x0e\n\x06run_id\x18\x01 \x01(\x04\x12\x0e\n\x06\x66\x61\x62_id\x18\x02 \x01(\t\x12\x13\n\x0b\x66\x61\x62_version\x18\x03 \x01(\t\x12<\n\x0foverride_config\x18\x04 \x03(\x0b\x32#.flwr.proto.Run.OverrideConfigEntry\x12\x10\n\x08\x66\x61\x62_hash\x18\x05 \x01(\t\x1aI\n\x13OverrideConfigEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12!\n\x05value\x18\x02 \x01(\x0b\x32\x12.flwr.proto.Scalar:\x02\x38\x01\"@\n\tRunStatus\x12\x0e\n\x06status\x18\x01 \x01(\t\x12\x12\n\nsub_status\x18\x02 \x01(\t\x12\x0f\n\x07\x64\x65tails\x18\x03 \x01(\t\"\xeb\x01\n\x10\x43reateRunRequest\x12\x0e\n\x06\x66\x61\x62_id\x18\x01 \x01(\t\x12\x13\n\x0b\x66\x61\x62_version\x18\x02 \x01(\t\x12I\n\x0foverride_config\x18\x03 \x03(\x0b\x32\x30.flwr.proto.CreateRunRequest.OverrideConfigEntry\x12\x1c\n\x03\x66\x61\x62\x18\x04 \x01(\x0b\x32\x0f.flwr.proto.Fab\x1aI\n\x13OverrideConfigEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12!\n\x05value\x18\x02 \x01(\x0b\x32\x12.flwr.proto.Scalar:\x02\x38\x01\"#\n\x11\x43reateRunResponse\x12\x0e\n\x06run_id\x18\x01 \x01(\x04\"\x1f\n\rGetRunRequest\x12\x0e\n\x06run_id\x18\x01 \x01(\x04\".\n\x0eGetRunResponse\x12\x1c\n\x03run\x18\x01 \x01(\x0b\x32\x0f.flwr.proto.Run\"S\n\x16UpdateRunStatusRequest\x12\x0e\n\x06run_id\x18\x01 \x01(\x04\x12)\n\nrun_status\x18\x02 \x01(\x0b\x32\x15.flwr.proto.RunStatus\"\x19\n\x17UpdateRunStatusResponse\"&\n\x13GetRunStatusRequest\x12\x0f\n\x07run_ids\x18\x01 \x03(\x04\"\xb1\x01\n\x14GetRunStatusResponse\x12L\n\x0frun_status_dict\x18\x01 \x03(\x0b\x32\x33.flwr.proto.GetRunStatusResponse.RunStatusDictEntry\x1aK\n\x12RunStatusDictEntry\x12\x0b\n\x03key\x18\x01 \x01(\x04\x12$\n\x05value\x18\x02 \x01(\x0b\x32\x15.flwr.proto.RunStatus:\x02\x38\x01\x62\x06proto3') _globals = globals() _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) @@ -27,18 +27,32 @@ _globals['_RUN_OVERRIDECONFIGENTRY']._serialized_options = b'8\001' _globals['_CREATERUNREQUEST_OVERRIDECONFIGENTRY']._options = None _globals['_CREATERUNREQUEST_OVERRIDECONFIGENTRY']._serialized_options = b'8\001' + _globals['_GETRUNSTATUSRESPONSE_RUNSTATUSDICTENTRY']._options = None + _globals['_GETRUNSTATUSRESPONSE_RUNSTATUSDICTENTRY']._serialized_options = b'8\001' _globals['_RUN']._serialized_start=87 _globals['_RUN']._serialized_end=300 _globals['_RUN_OVERRIDECONFIGENTRY']._serialized_start=227 _globals['_RUN_OVERRIDECONFIGENTRY']._serialized_end=300 - _globals['_CREATERUNREQUEST']._serialized_start=303 - _globals['_CREATERUNREQUEST']._serialized_end=538 + _globals['_RUNSTATUS']._serialized_start=302 + _globals['_RUNSTATUS']._serialized_end=366 + _globals['_CREATERUNREQUEST']._serialized_start=369 + _globals['_CREATERUNREQUEST']._serialized_end=604 _globals['_CREATERUNREQUEST_OVERRIDECONFIGENTRY']._serialized_start=227 _globals['_CREATERUNREQUEST_OVERRIDECONFIGENTRY']._serialized_end=300 - _globals['_CREATERUNRESPONSE']._serialized_start=540 - _globals['_CREATERUNRESPONSE']._serialized_end=575 - _globals['_GETRUNREQUEST']._serialized_start=577 - _globals['_GETRUNREQUEST']._serialized_end=608 - _globals['_GETRUNRESPONSE']._serialized_start=610 - _globals['_GETRUNRESPONSE']._serialized_end=656 + _globals['_CREATERUNRESPONSE']._serialized_start=606 + _globals['_CREATERUNRESPONSE']._serialized_end=641 + _globals['_GETRUNREQUEST']._serialized_start=643 + _globals['_GETRUNREQUEST']._serialized_end=674 + _globals['_GETRUNRESPONSE']._serialized_start=676 + _globals['_GETRUNRESPONSE']._serialized_end=722 + _globals['_UPDATERUNSTATUSREQUEST']._serialized_start=724 + _globals['_UPDATERUNSTATUSREQUEST']._serialized_end=807 + _globals['_UPDATERUNSTATUSRESPONSE']._serialized_start=809 + _globals['_UPDATERUNSTATUSRESPONSE']._serialized_end=834 + _globals['_GETRUNSTATUSREQUEST']._serialized_start=836 + _globals['_GETRUNSTATUSREQUEST']._serialized_end=874 + _globals['_GETRUNSTATUSRESPONSE']._serialized_start=877 + _globals['_GETRUNSTATUSRESPONSE']._serialized_end=1054 + _globals['_GETRUNSTATUSRESPONSE_RUNSTATUSDICTENTRY']._serialized_start=979 + _globals['_GETRUNSTATUSRESPONSE_RUNSTATUSDICTENTRY']._serialized_end=1054 # @@protoc_insertion_point(module_scope) diff --git a/src/py/flwr/proto/run_pb2.pyi b/src/py/flwr/proto/run_pb2.pyi index 26b69e7eed27..cec90c4d2d4c 100644 --- a/src/py/flwr/proto/run_pb2.pyi +++ b/src/py/flwr/proto/run_pb2.pyi @@ -52,6 +52,29 @@ class Run(google.protobuf.message.Message): def ClearField(self, field_name: typing_extensions.Literal["fab_hash",b"fab_hash","fab_id",b"fab_id","fab_version",b"fab_version","override_config",b"override_config","run_id",b"run_id"]) -> None: ... global___Run = Run +class RunStatus(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + STATUS_FIELD_NUMBER: builtins.int + SUB_STATUS_FIELD_NUMBER: builtins.int + DETAILS_FIELD_NUMBER: builtins.int + status: typing.Text + """"starting", "running", "finished" """ + + sub_status: typing.Text + """"completed", "failed", "stopped" or "" (non-finished)""" + + details: typing.Text + """failure details""" + + def __init__(self, + *, + status: typing.Text = ..., + sub_status: typing.Text = ..., + details: typing.Text = ..., + ) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["details",b"details","status",b"status","sub_status",b"sub_status"]) -> None: ... +global___RunStatus = RunStatus + class CreateRunRequest(google.protobuf.message.Message): """CreateRun""" DESCRIPTOR: google.protobuf.descriptor.Descriptor @@ -126,3 +149,66 @@ class GetRunResponse(google.protobuf.message.Message): def HasField(self, field_name: typing_extensions.Literal["run",b"run"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["run",b"run"]) -> None: ... global___GetRunResponse = GetRunResponse + +class UpdateRunStatusRequest(google.protobuf.message.Message): + """UpdateRunStatus""" + DESCRIPTOR: google.protobuf.descriptor.Descriptor + RUN_ID_FIELD_NUMBER: builtins.int + RUN_STATUS_FIELD_NUMBER: builtins.int + run_id: builtins.int + @property + def run_status(self) -> global___RunStatus: ... + def __init__(self, + *, + run_id: builtins.int = ..., + run_status: typing.Optional[global___RunStatus] = ..., + ) -> None: ... + def HasField(self, field_name: typing_extensions.Literal["run_status",b"run_status"]) -> builtins.bool: ... + def ClearField(self, field_name: typing_extensions.Literal["run_id",b"run_id","run_status",b"run_status"]) -> None: ... +global___UpdateRunStatusRequest = UpdateRunStatusRequest + +class UpdateRunStatusResponse(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + def __init__(self, + ) -> None: ... +global___UpdateRunStatusResponse = UpdateRunStatusResponse + +class GetRunStatusRequest(google.protobuf.message.Message): + """GetRunStatus""" + DESCRIPTOR: google.protobuf.descriptor.Descriptor + RUN_IDS_FIELD_NUMBER: builtins.int + @property + def run_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ... + def __init__(self, + *, + run_ids: typing.Optional[typing.Iterable[builtins.int]] = ..., + ) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["run_ids",b"run_ids"]) -> None: ... +global___GetRunStatusRequest = GetRunStatusRequest + +class GetRunStatusResponse(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + class RunStatusDictEntry(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + key: builtins.int + @property + def value(self) -> global___RunStatus: ... + def __init__(self, + *, + key: builtins.int = ..., + value: typing.Optional[global___RunStatus] = ..., + ) -> None: ... + def HasField(self, field_name: typing_extensions.Literal["value",b"value"]) -> builtins.bool: ... + def ClearField(self, field_name: typing_extensions.Literal["key",b"key","value",b"value"]) -> None: ... + + RUN_STATUS_DICT_FIELD_NUMBER: builtins.int + @property + def run_status_dict(self) -> google.protobuf.internal.containers.MessageMap[builtins.int, global___RunStatus]: ... + def __init__(self, + *, + run_status_dict: typing.Optional[typing.Mapping[builtins.int, global___RunStatus]] = ..., + ) -> None: ... + def ClearField(self, field_name: typing_extensions.Literal["run_status_dict",b"run_status_dict"]) -> None: ... +global___GetRunStatusResponse = GetRunStatusResponse From b370b0618fe53d6f01e5486c3269ee09dc0dcc6e Mon Sep 17 00:00:00 2001 From: Chong Shen Ng Date: Mon, 23 Sep 2024 13:54:22 +0100 Subject: [PATCH 03/23] feat(framework) Add `flwr log` (#3577) Co-authored-by: Charles Beauville Co-authored-by: jafermarq Co-authored-by: Taner Topal --- src/py/flwr/cli/app.py | 2 + src/py/flwr/cli/log.py | 196 +++++++++++++++++++++++++++++++++++++++++ 2 files changed, 198 insertions(+) create mode 100644 src/py/flwr/cli/log.py diff --git a/src/py/flwr/cli/app.py b/src/py/flwr/cli/app.py index 93effea6df98..8baccb4638fc 100644 --- a/src/py/flwr/cli/app.py +++ b/src/py/flwr/cli/app.py @@ -19,6 +19,7 @@ from .build import build from .install import install +from .log import log from .new import new from .run import run @@ -35,6 +36,7 @@ app.command()(run) app.command()(build) app.command()(install) +app.command()(log) typer_click_object = get_command(app) diff --git a/src/py/flwr/cli/log.py b/src/py/flwr/cli/log.py new file mode 100644 index 000000000000..6915de1e00c5 --- /dev/null +++ b/src/py/flwr/cli/log.py @@ -0,0 +1,196 @@ +# Copyright 2024 Flower Labs GmbH. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Flower command line interface `log` command.""" + +import sys +import time +from logging import DEBUG, ERROR, INFO +from pathlib import Path +from typing import Annotated, Optional + +import grpc +import typer + +from flwr.cli.config_utils import load_and_validate +from flwr.common.grpc import GRPC_MAX_MESSAGE_LENGTH, create_channel +from flwr.common.logger import log as logger + +CONN_REFRESH_PERIOD = 60 # Connection refresh period for log streaming (seconds) + + +# pylint: disable=unused-argument +def stream_logs(run_id: int, channel: grpc.Channel, period: int) -> None: + """Stream logs from the beginning of a run with connection refresh.""" + + +# pylint: disable=unused-argument +def print_logs(run_id: int, channel: grpc.Channel, timeout: int) -> None: + """Print logs from the beginning of a run.""" + + +def on_channel_state_change(channel_connectivity: str) -> None: + """Log channel connectivity.""" + logger(DEBUG, channel_connectivity) + + +def log( + run_id: Annotated[ + int, + typer.Argument(help="The Flower run ID to query"), + ], + app: Annotated[ + Path, + typer.Argument(help="Path of the Flower project to run"), + ] = Path("."), + federation: Annotated[ + Optional[str], + typer.Argument(help="Name of the federation to run the app on"), + ] = None, + stream: Annotated[ + bool, + typer.Option( + "--stream/--show", + help="Flag to stream or print logs from the Flower run", + ), + ] = True, +) -> None: + """Get logs from a Flower project run.""" + typer.secho("Loading project configuration... ", fg=typer.colors.BLUE) + + pyproject_path = app / "pyproject.toml" if app else None + config, errors, warnings = load_and_validate(path=pyproject_path) + + if config is None: + typer.secho( + "Project configuration could not be loaded.\n" + "pyproject.toml is invalid:\n" + + "\n".join([f"- {line}" for line in errors]), + fg=typer.colors.RED, + bold=True, + ) + sys.exit() + + if warnings: + typer.secho( + "Project configuration is missing the following " + "recommended properties:\n" + "\n".join([f"- {line}" for line in warnings]), + fg=typer.colors.RED, + bold=True, + ) + + typer.secho("Success", fg=typer.colors.GREEN) + + federation = federation or config["tool"]["flwr"]["federations"].get("default") + + if federation is None: + typer.secho( + "❌ No federation name was provided and the project's `pyproject.toml` " + "doesn't declare a default federation (with a SuperExec address or an " + "`options.num-supernodes` value).", + fg=typer.colors.RED, + bold=True, + ) + raise typer.Exit(code=1) + + # Validate the federation exists in the configuration + federation_config = config["tool"]["flwr"]["federations"].get(federation) + if federation_config is None: + available_feds = { + fed for fed in config["tool"]["flwr"]["federations"] if fed != "default" + } + typer.secho( + f"❌ There is no `{federation}` federation declared in the " + "`pyproject.toml`.\n The following federations were found:\n\n" + + "\n".join(available_feds), + fg=typer.colors.RED, + bold=True, + ) + raise typer.Exit(code=1) + + if "address" not in federation_config: + typer.secho( + "❌ `flwr log` currently works with `SuperExec`. Ensure that the correct" + "`SuperExec` address is provided in the `pyproject.toml`.", + fg=typer.colors.RED, + bold=True, + ) + raise typer.Exit(code=1) + + _log_with_superexec(federation_config, run_id, stream) + + +# pylint: disable-next=too-many-branches +def _log_with_superexec( + federation_config: dict[str, str], + run_id: int, + stream: bool, +) -> None: + insecure_str = federation_config.get("insecure") + if root_certificates := federation_config.get("root-certificates"): + root_certificates_bytes = Path(root_certificates).read_bytes() + if insecure := bool(insecure_str): + typer.secho( + "❌ `root_certificates` were provided but the `insecure` parameter" + "is set to `True`.", + fg=typer.colors.RED, + bold=True, + ) + raise typer.Exit(code=1) + else: + root_certificates_bytes = None + if insecure_str is None: + typer.secho( + "❌ To disable TLS, set `insecure = true` in `pyproject.toml`.", + fg=typer.colors.RED, + bold=True, + ) + raise typer.Exit(code=1) + if not (insecure := bool(insecure_str)): + typer.secho( + "❌ No certificate were given yet `insecure` is set to `False`.", + fg=typer.colors.RED, + bold=True, + ) + raise typer.Exit(code=1) + + channel = create_channel( + server_address=federation_config["address"], + insecure=insecure, + root_certificates=root_certificates_bytes, + max_message_length=GRPC_MAX_MESSAGE_LENGTH, + interceptors=None, + ) + channel.subscribe(on_channel_state_change) + + if stream: + try: + while True: + logger(INFO, "Starting logstream for run_id `%s`", run_id) + stream_logs(run_id, channel, CONN_REFRESH_PERIOD) + time.sleep(2) + logger(DEBUG, "Reconnecting to logstream") + except KeyboardInterrupt: + logger(INFO, "Exiting logstream") + except grpc.RpcError as e: + # pylint: disable=E1101 + if e.code() == grpc.StatusCode.NOT_FOUND: + logger(ERROR, "Invalid run_id `%s`, exiting", run_id) + if e.code() == grpc.StatusCode.CANCELLED: + pass + finally: + channel.close() + else: + logger(INFO, "Printing logstream for run_id `%s`", run_id) + print_logs(run_id, channel, timeout=5) From fc6962d4983edc8172d8d8a38a2918a3632ab857 Mon Sep 17 00:00:00 2001 From: Charles Beauville Date: Mon, 23 Sep 2024 16:39:19 +0200 Subject: [PATCH 04/23] ci(framework:skip) Add job to update translation text (#3154) --- .github/workflows/update_translations.yml | 79 +++++++++++++++++++++++ 1 file changed, 79 insertions(+) create mode 100644 .github/workflows/update_translations.yml diff --git a/.github/workflows/update_translations.yml b/.github/workflows/update_translations.yml new file mode 100644 index 000000000000..9419f4aaef25 --- /dev/null +++ b/.github/workflows/update_translations.yml @@ -0,0 +1,79 @@ +name: Translations + +on: + schedule: + - cron: '0 0 * * *' # Runs every day at midnight + workflow_dispatch: # Allows to manually trigger the workflow + +jobs: + update-and-pr: + runs-on: ubuntu-22.04 + permissions: + contents: write + pull-requests: write + env: + branch-name: auto-update-trans-text + name: Update text + steps: + - uses: actions/checkout@v4 + + - name: Bootstrap + uses: ./.github/actions/bootstrap + with: + python-version: '3.10' + + - name: Install dependencies + run: | + python -m poetry install + pip install sphinx==7.3.7 + + - name: Install pandoc + uses: nikeee/setup-pandoc@v1 + + - name: Update text and translations for all locales + run: | + cd doc + make update-text + for langDir in locales/*; do + if [ -d "$langDir" ]; then + lang=$(basename $langDir) + echo "Updating language $lang" + make update-lang lang=$lang + fi + done + + - name: Commit changes + run: | + git config --local user.email "41898282+github-actions[bot]@users.noreply.github.com" + git config --local user.name "github-actions[bot]" + git add doc/locales + git commit -m "Update text and language files" + continue-on-error: true + + - name: Calculate diff # Even without doc changes the update-lang command will generate 228 additions and 60 deletions, so we only want to open a PR when there is more + id: calculate_diff + run: | + additions=$(git diff --numstat HEAD^1 | awk '{s+=$1} END {print s}') + deletions=$(git diff --numstat HEAD^1 | awk '{s+=$2} END {print s}') + echo "Additions: $additions" + echo "Deletions: $deletions" + echo "additions=$additions" >> $GITHUB_OUTPUT + echo "deletions=$deletions" >> $GITHUB_OUTPUT + + - name: Push changes + if: steps.calculate_diff.outputs.additions > 228 && steps.calculate_diff.outputs.deletions > 60 + uses: ad-m/github-push-action@master + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + branch: '${{ env.branch-name }}' + + - name: Create Pull Request + if: steps.calculate_diff.outputs.additions > 228 && steps.calculate_diff.outputs.deletions > 60 + uses: peter-evans/create-pull-request@v6 + with: + token: ${{ secrets.GITHUB_TOKEN }} + branch: '${{ env.branch-name }}' + delete-branch: true + title: 'docs(framework:skip) Update source texts for translations (automated)' + body: 'This PR is auto-generated to update text and language files.' + draft: false From 5e87ce88a411898f94b0b108fc8080d79e87918e Mon Sep 17 00:00:00 2001 From: Robert Steiner Date: Mon, 23 Sep 2024 23:29:59 +0200 Subject: [PATCH 05/23] docs(framework:skip) Update docker quickstart table (#4251) Signed-off-by: Robert Steiner --- .../docker/run-quickstart-examples-docker-compose.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/source/docker/run-quickstart-examples-docker-compose.rst b/doc/source/docker/run-quickstart-examples-docker-compose.rst index b279fb66c45b..5bdb33e991dd 100644 --- a/doc/source/docker/run-quickstart-examples-docker-compose.rst +++ b/doc/source/docker/run-quickstart-examples-docker-compose.rst @@ -98,8 +98,8 @@ Limitations - Limitations * - quickstart-fastai - None - * - examples/quickstart-huggingface - - For CPU-only environments, it requires at least 32GB of memory. + * - quickstart-huggingface + - None * - quickstart-jax - The example has not yet been updated to work with the latest ``flwr`` version. * - quickstart-mlcube @@ -109,7 +109,7 @@ Limitations * - quickstart-monai - None * - quickstart-pandas - - The example has not yet been updated to work with the latest ``flwr`` version. + - None * - quickstart-pytorch-lightning - Requires an older pip version that is not supported by the Flower Docker images. * - quickstart-pytorch From 2339b5180336c8496e361c37b59080cd3d584c0d Mon Sep 17 00:00:00 2001 From: Yan Gao Date: Tue, 24 Sep 2024 07:34:28 +0100 Subject: [PATCH 06/23] refactor(benchmarks) Update llm leaderboard readmes (#4249) --- benchmarks/flowertune-llm/README.md | 4 ++-- benchmarks/flowertune-llm/evaluation/README.md | 3 +++ 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/benchmarks/flowertune-llm/README.md b/benchmarks/flowertune-llm/README.md index f45b7a6198b7..45cd8a828a89 100644 --- a/benchmarks/flowertune-llm/README.md +++ b/benchmarks/flowertune-llm/README.md @@ -13,13 +13,13 @@ As the first step, please register for a Flower account on [flower.ai/login](htt Then, create a new Python environment and install Flower. > [!TIP] -> We recommend using `pyenv` and the `virtualenv` plugin to create your environment. Other manager such as Conda would likely work too. Check the [documentation](https://flower.ai/docs/framework/how-to-install-flower.html) for alternative ways of installing Flower. +> We recommend using `pyenv` with the `virtualenv` plugin to create your environment. Other managers, such as Conda, will likely work as well. Check the [documentation](https://flower.ai/docs/framework/how-to-install-flower.html) for alternative ways to install Flower. ```shell pip install flwr ``` -On the new environment, create a new Flower project using the `FlowerTune` template. You will be prompted for a name to give to your project, your username, and for your choice of LLM challenge: +In the new environment, create a new Flower project using the `FlowerTune` template. You will be prompted for a name to give to your project, your username, and for your choice of LLM challenge: ```shell flwr new --framework=FlowerTune ``` diff --git a/benchmarks/flowertune-llm/evaluation/README.md b/benchmarks/flowertune-llm/evaluation/README.md index e2a7477fca76..c99ad640203a 100644 --- a/benchmarks/flowertune-llm/evaluation/README.md +++ b/benchmarks/flowertune-llm/evaluation/README.md @@ -39,6 +39,9 @@ The default template generated by `flwr new` (see the [Project Creation Instruct |:----------:|:-----:|:---------:|:--------------:|:---------------:|:-----:| | Pass@1 (%) | 31.60 | 23.78 | 28.57 | 25.47 | 27.36 | +> [!NOTE] +> In the LLM Leaderboard, we rank the submissions based on the **average** value derived from different evaluation datasets for each challenge. + ## Make submission on FlowerTune LLM Leaderboard From 983217f69b021b6804def1bbe3a0833caa419880 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Tue, 24 Sep 2024 06:41:09 +0000 Subject: [PATCH 07/23] Update text and language files (#4252) --- doc/locales/fr/LC_MESSAGES/framework-docs.po | 2194 +++++++++++------ doc/locales/ko/LC_MESSAGES/framework-docs.po | 1989 ++++++++++----- .../pt_BR/LC_MESSAGES/framework-docs.po | 1939 ++++++++++----- .../zh_Hans/LC_MESSAGES/framework-docs.po | 2179 ++++++++++------ 4 files changed, 5574 insertions(+), 2727 deletions(-) diff --git a/doc/locales/fr/LC_MESSAGES/framework-docs.po b/doc/locales/fr/LC_MESSAGES/framework-docs.po index bee09019489f..681916e78ed5 100644 --- a/doc/locales/fr/LC_MESSAGES/framework-docs.po +++ b/doc/locales/fr/LC_MESSAGES/framework-docs.po @@ -3,7 +3,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower Docs\n" "Report-Msgid-Bugs-To: EMAIL@ADDRESS\n" -"POT-Creation-Date: 2024-09-15 09:09+0200\n" +"POT-Creation-Date: 2024-09-24 00:29+0000\n" "PO-Revision-Date: 2023-09-05 17:54+0000\n" "Last-Translator: Charles Beauville \n" "Language: fr\n" @@ -13,7 +13,7 @@ msgstr "" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" -"Generated-By: Babel 2.15.0\n" +"Generated-By: Babel 2.16.0\n" #: ../../source/contributor-explanation-public-and-private-apis.rst:2 msgid "Public and private APIs" @@ -1522,7 +1522,7 @@ msgstr "" msgid "Setting up the repository" msgstr "Mise en place du référentiel" -#: ../../source/contributor-tutorial-contribute-on-github.rst:12 +#: ../../source/contributor-tutorial-contribute-on-github.rst:21 msgid "**Create a GitHub account and setup Git**" msgstr "**Créer un compte GitHub et configurer Git**" @@ -1571,7 +1571,7 @@ msgstr "" " des modifications localement et tu en gardes une trace à l'aide de Git, " "puis tu télécharges ton nouvel historique à nouveau sur GitHub." -#: ../../source/contributor-tutorial-contribute-on-github.rst:23 +#: ../../source/contributor-tutorial-contribute-on-github.rst:32 msgid "**Forking the Flower repository**" msgstr "**Fourche le dépôt de Flower**" @@ -1601,7 +1601,7 @@ msgstr "" " devrais voir dans le coin supérieur gauche que tu es en train de " "regarder ta propre version de Flower." -#: ../../source/contributor-tutorial-contribute-on-github.rst:34 +#: ../../source/contributor-tutorial-contribute-on-github.rst:47 msgid "**Cloning your forked repository**" msgstr "**Clonage de ton dépôt forké**" @@ -1635,7 +1635,7 @@ msgstr "" "Cela créera un dossier `flower/` (ou le nom de ta fourche si tu l'as " "renommée) dans le répertoire de travail actuel." -#: ../../source/contributor-tutorial-contribute-on-github.rst:49 +#: ../../source/contributor-tutorial-contribute-on-github.rst:66 msgid "**Add origin**" msgstr "**Ajouter l'origine**" @@ -1663,7 +1663,7 @@ msgstr "" "Une fois que le \\ est copié, nous pouvons taper la commande " "suivante dans notre terminal :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:68 +#: ../../source/contributor-tutorial-contribute-on-github.rst:90 msgid "**Add upstream**" msgstr "**Ajouter en amont**" @@ -1739,7 +1739,7 @@ msgstr "" msgid "And with Flower's repository:" msgstr "Et avec le référentiel de Flower :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:114 +#: ../../source/contributor-tutorial-contribute-on-github.rst:122 msgid "**Create a new branch**" msgstr "**Créer une nouvelle branche**" @@ -1761,7 +1761,7 @@ msgstr "" "Pour ce faire, il suffit d'exécuter la commande suivante dans le " "répertoire du référentiel :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:124 +#: ../../source/contributor-tutorial-contribute-on-github.rst:125 msgid "**Make changes**" msgstr "**Apporter des modifications**" @@ -1771,7 +1771,7 @@ msgstr "" "Écris du bon code et crée de merveilleuses modifications à l'aide de ton " "éditeur préféré !" -#: ../../source/contributor-tutorial-contribute-on-github.rst:127 +#: ../../source/contributor-tutorial-contribute-on-github.rst:138 msgid "**Test and format your code**" msgstr "**Teste et mets en forme ton code**" @@ -1789,7 +1789,7 @@ msgstr "" msgid "To do so, we have written a few scripts that you can execute:" msgstr "Pour ce faire, nous avons écrit quelques scripts que tu peux exécuter :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:140 +#: ../../source/contributor-tutorial-contribute-on-github.rst:150 msgid "**Stage changes**" msgstr "**Changements de scène**" @@ -1815,7 +1815,7 @@ msgstr "" "version (last commit) et pour voir quels fichiers sont mis à disposition " "pour le commit, tu peux utiliser la commande :code:`git status`." -#: ../../source/contributor-tutorial-contribute-on-github.rst:152 +#: ../../source/contributor-tutorial-contribute-on-github.rst:160 msgid "**Commit changes**" msgstr "**Commit changes**" @@ -1838,7 +1838,7 @@ msgstr "" "commit. Il doit être écrit dans un style impératif et être concis. Un " "exemple serait :code:`git commit -m \"Ajouter des images au README\"`." -#: ../../source/contributor-tutorial-contribute-on-github.rst:162 +#: ../../source/contributor-tutorial-contribute-on-github.rst:171 msgid "**Push the changes to the fork**" msgstr "**Pousser les changements vers la fourche**" @@ -1865,7 +1865,7 @@ msgstr "" msgid "Creating and merging a pull request (PR)" msgstr "Créer et fusionner une pull request (PR)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:176 +#: ../../source/contributor-tutorial-contribute-on-github.rst:206 msgid "**Create the PR**" msgstr "**Créer le PR**" @@ -1949,7 +1949,7 @@ msgstr "" " personne, tu as la possibilité de créer un brouillon de demande de " "traction :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:209 msgid "**Making new changes**" msgstr "**Faire de nouveaux changements**" @@ -1963,7 +1963,7 @@ msgstr "" "toujours y pousser de nouveaux commits de la même manière qu'auparavant, " "en apportant des modifications à la branche associée au PR." -#: ../../source/contributor-tutorial-contribute-on-github.rst:211 +#: ../../source/contributor-tutorial-contribute-on-github.rst:231 msgid "**Review the PR**" msgstr "**Review the PR**" @@ -2008,7 +2008,7 @@ msgstr "" "Une fois que toutes les conversations ont été résolues, tu peux " "redemander un examen." -#: ../../source/contributor-tutorial-contribute-on-github.rst:233 +#: ../../source/contributor-tutorial-contribute-on-github.rst:251 msgid "**Once the PR is merged**" msgstr "**Une fois que le PR est fusionné**" @@ -2340,6 +2340,7 @@ msgstr "Devenez un·e contributeur·ice" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 #: ../../source/docker/run-as-subprocess.rst:11 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:15 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:12 #: ../../source/docker/tutorial-quickstart-docker.rst:11 msgid "Prerequisites" @@ -3098,6 +3099,241 @@ msgid "" " the SuperNode to execute the ClientApp as a subprocess:" msgstr "" +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:2 +#, fuzzy +msgid "Run Flower Quickstart Examples with Docker Compose" +msgstr "Démarrage rapide XGBoost" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:4 +msgid "" +"Flower provides a set of `quickstart examples " +"`_ to help you get " +"started with the framework. These examples are designed to demonstrate " +"the capabilities of Flower and by default run using the Simulation " +"Engine. This guide demonstrates how to run them using Flower's Deployment" +" Engine via Docker Compose." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:11 +msgid "" +"Some quickstart examples may have limitations or requirements that " +"prevent them from running on every environment. For more information, " +"please see `Limitations`_." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:17 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:14 +#: ../../source/docker/tutorial-quickstart-docker.rst:13 +msgid "Before you start, make sure that:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:19 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 +#: ../../source/docker/tutorial-quickstart-docker.rst:15 +msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:20 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:17 +#: ../../source/docker/tutorial-quickstart-docker.rst:16 +msgid "The Docker daemon is running." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:21 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:18 +msgid "Docker Compose is `installed `_." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:24 +#, fuzzy +msgid "Run the Quickstart Example" +msgstr "Demande pour un nouveau Flower Example" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:26 +msgid "" +"Clone the quickstart example you like to run. For example, ``quickstart-" +"pytorch``:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:34 +msgid "" +"Download the `compose.yml " +"`_" +" file into the example directory:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:41 +#, fuzzy +msgid "Build and start the services using the following command:" +msgstr "Active la virtualenv en exécutant la commande suivante :" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:47 +#, fuzzy +msgid "" +"Append the following lines to the end of the ``pyproject.toml`` file and " +"save it:" +msgstr "Augmente la version mineure de ``pyproject.toml`` d'une unité." + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:49 +#: ../../source/docker/tutorial-quickstart-docker.rst:319 +msgid "pyproject.toml" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:58 +msgid "" +"You can customize the string that follows ``tool.flwr.federations.`` to " +"fit your needs. However, please note that the string cannot contain a dot" +" (``.``)." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:61 +msgid "" +"In this example, ``local-deployment`` has been used. Just remember to " +"replace ``local-deployment`` with your chosen name in both the " +"``tool.flwr.federations.`` string and the corresponding ``flwr run .`` " +"command." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:65 +#, fuzzy +msgid "Run the example:" +msgstr "Fédérer l'exemple" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:71 +msgid "Follow the logs of the SuperExec service:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:77 +msgid "" +"That is all it takes! You can monitor the progress of the run through the" +" logs of the SuperExec." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:80 +msgid "Run a Different Quickstart Example" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:82 +msgid "" +"To run a different quickstart example, such as ``quickstart-tensorflow``," +" first, shut down the Docker Compose services of the current example:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:89 +msgid "After that, you can repeat the steps above." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:92 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:98 +#, fuzzy +msgid "Limitations" +msgstr "Simulation de moniteur" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:97 +#, fuzzy +msgid "Quickstart Example" +msgstr "Démarrage rapide de JAX" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:99 +#, fuzzy +msgid "quickstart-fastai" +msgstr "Démarrage rapide fastai" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:100 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:102 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:110 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:112 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:116 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:118 +#: ../../source/ref-changelog.md:33 ../../source/ref-changelog.md:399 +#: ../../source/ref-changelog.md:676 ../../source/ref-changelog.md:740 +#: ../../source/ref-changelog.md:798 ../../source/ref-changelog.md:867 +#: ../../source/ref-changelog.md:929 +msgid "None" +msgstr "Aucun" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:101 +#, fuzzy +msgid "quickstart-huggingface" +msgstr "Quickstart tutorials" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:103 +#, fuzzy +msgid "quickstart-jax" +msgstr "Démarrage rapide de JAX" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:104 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:106 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:120 +#, fuzzy +msgid "" +"The example has not yet been updated to work with the latest ``flwr`` " +"version." +msgstr "" +"Les exemples de code couvrant scikit-learn et PyTorch Lightning ont été " +"mis à jour pour fonctionner avec la dernière version de Flower." + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:105 +#, fuzzy +msgid "quickstart-mlcube" +msgstr "Démarrage rapide de JAX" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:107 +#, fuzzy +msgid "quickstart-mlx" +msgstr "Démarrage rapide de JAX" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:108 +msgid "" +"`Requires to run on macOS with Apple Silicon `_." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:109 +#, fuzzy +msgid "quickstart-monai" +msgstr "Démarrage rapide de JAX" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:111 +#, fuzzy +msgid "quickstart-pandas" +msgstr "Démarrage rapide des Pandas" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:113 +#, fuzzy +msgid "quickstart-pytorch-lightning" +msgstr "Démarrage rapide de PyTorch Lightning" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:114 +msgid "" +"Requires an older pip version that is not supported by the Flower Docker " +"images." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:115 +#, fuzzy +msgid "quickstart-pytorch" +msgstr "Démarrage rapide de PyTorch" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:117 +#, fuzzy +msgid "quickstart-sklearn-tabular" +msgstr "Démarrage rapide de scikit-learn" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:119 +#, fuzzy +msgid "quickstart-tabnet" +msgstr "Démarrage rapide de JAX" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:121 +#, fuzzy +msgid "quickstart-tensorflow" +msgstr "Démarrage rapide de TensorFlow" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:122 +msgid "Only runs on AMD64." +msgstr "" + #: ../../source/docker/set-environment-variables.rst:2 #, fuzzy msgid "Set Environment Variables" @@ -3128,21 +3364,6 @@ msgid "" " understanding the basic workflow that uses the minimum configurations." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:14 -#: ../../source/docker/tutorial-quickstart-docker.rst:13 -msgid "Before you start, make sure that:" -msgstr "" - -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 -#: ../../source/docker/tutorial-quickstart-docker.rst:15 -msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." -msgstr "" - -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:17 -#: ../../source/docker/tutorial-quickstart-docker.rst:16 -msgid "The Docker daemon is running." -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 #: ../../source/docker/tutorial-quickstart-docker.rst:19 msgid "Step 1: Set Up" @@ -3568,10 +3789,6 @@ msgstr "" msgid "Add the following lines to the ``pyproject.toml``:" msgstr "Augmente la version mineure de ``pyproject.toml`` d'une unité." -#: ../../source/docker/tutorial-quickstart-docker.rst:319 -msgid "pyproject.toml" -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker.rst:326 msgid "Run the ``quickstart-docker`` project by executing the command:" msgstr "" @@ -3621,6 +3838,7 @@ msgstr "" msgid "Remove the containers and the bridge network:" msgstr "" +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:401 #: ../../source/docker/tutorial-quickstart-docker.rst:399 #, fuzzy msgid "Where to Go Next" @@ -3657,10 +3875,6 @@ msgid "" "configuration that best suits your project's needs." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:18 -msgid "Docker Compose is `installed `_." -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:23 msgid "Clone the Docker Compose ``complete`` directory:" msgstr "" @@ -3856,7 +4070,7 @@ msgstr "" #: ../../source/docker/tutorial-quickstart-docker-compose.rst:188 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:241 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:362 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:369 msgid "Rerun the ``quickstart-compose`` project:" msgstr "" @@ -3920,75 +4134,80 @@ msgstr "" msgid "compose.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:303 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:310 msgid "" "If you also want to enable TLS for the new SuperNodes, duplicate the " "SuperNode definition for each new SuperNode service in the ``with-" "tls.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:306 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:313 msgid "" "Make sure that the names of the services match with the one in the " "``compose.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:308 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:315 msgid "In ``with-tls.yml``, add the following:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:310 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:317 msgid "with-tls.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:332 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:339 msgid "Step 8: Persisting the SuperLink State and Enabling TLS" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:334 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:341 msgid "" "To run Flower with persisted SuperLink state and enabled TLS, a slight " "change in the ``with-state.yml`` file is required:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:337 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:344 msgid "Comment out the lines 2-4 and uncomment the lines 5-9:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:339 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:346 msgid "with-state.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:356 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:363 #, fuzzy msgid "Restart the services:" msgstr "Démarrer le serveur" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:370 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:377 msgid "Step 9: Merge Multiple Compose Files" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:372 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:379 msgid "" "You can merge multiple Compose files into a single file. For instance, if" " you wish to combine the basic configuration with the TLS configuration, " "execute the following command:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:380 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:387 msgid "" "This will merge the contents of ``compose.yml`` and ``with-tls.yml`` into" " a new file called ``my_compose.yml``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:384 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:391 msgid "Step 10: Clean Up" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:386 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:393 msgid "Remove all services and volumes:" msgstr "" +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:403 +#, fuzzy +msgid ":doc:`run-quickstart-examples-docker-compose`" +msgstr "Démarrage rapide XGBoost" + #: ../../source/docker/use-a-different-version.rst:2 msgid "Use a Different Flower Version" msgstr "" @@ -4389,7 +4608,7 @@ msgstr "" "getting/setting model parameters, one method for training the model, and " "one method for testing the model:" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:218 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:219 msgid ":code:`set_parameters`" msgstr ":code:`set_parameters`" @@ -4425,9 +4644,9 @@ msgstr "" ":code:`ndarray` NumPy (ce qui correspond à ce que " ":code:`flwr.client.NumPyClient` attend)" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:223 -#: ../../source/tutorial-quickstart-jax.rst:171 -#: ../../source/tutorial-quickstart-scikitlearn.rst:123 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:225 +#: ../../source/tutorial-quickstart-jax.rst:173 +#: ../../source/tutorial-quickstart-scikitlearn.rst:125 msgid ":code:`fit`" msgstr ":code:`fit`" @@ -4451,9 +4670,9 @@ msgstr "entraîne le modèle sur l'ensemble d'apprentissage local" msgid "get the updated local model weights and return them to the server" msgstr "récupère les poids du modèle local mis à jour et les renvoie au serveur" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:227 -#: ../../source/tutorial-quickstart-jax.rst:175 -#: ../../source/tutorial-quickstart-scikitlearn.rst:127 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 +#: ../../source/tutorial-quickstart-jax.rst:178 +#: ../../source/tutorial-quickstart-scikitlearn.rst:128 msgid ":code:`evaluate`" msgstr ":code:`évaluer`" @@ -4572,7 +4791,7 @@ msgid "" " individual's information remains hidden in the crowd." msgstr "" -#: ../../source/explanation-differential-privacy.rst:16 +#: ../../source/explanation-differential-privacy.rst:-1 msgid "DP Intro" msgstr "" @@ -4685,8 +4904,8 @@ msgid "" "the client's data." msgstr "" +#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:68 -#: ../../source/explanation-differential-privacy.rst:71 #: ../../source/how-to-use-differential-privacy.rst:11 #, fuzzy msgid "Central Differential Privacy" @@ -4714,7 +4933,7 @@ msgid "" "that larger updates are scaled down to fit within the norm `S`." msgstr "" -#: ../../source/explanation-differential-privacy.rst:84 +#: ../../source/explanation-differential-privacy.rst:-1 msgid "clipping" msgstr "" @@ -4759,8 +4978,8 @@ msgid "" "others." msgstr "" +#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:105 -#: ../../source/explanation-differential-privacy.rst:110 #: ../../source/how-to-use-differential-privacy.rst:96 #, fuzzy msgid "Local Differential Privacy" @@ -5047,7 +5266,7 @@ msgstr "" msgid "This is sometimes called a hub-and-spoke topology:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:18 +#: ../../source/explanation-flower-architecture.rst:24 #, fuzzy msgid "Hub-and-spoke topology in federated learning" msgstr "Qu'est-ce que l'apprentissage fédéré ?" @@ -5120,7 +5339,7 @@ msgid "" "`missing link` between all those SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:65 +#: ../../source/explanation-flower-architecture.rst:71 #, fuzzy msgid "Basic Flower architecture" msgstr "Architecture florale" @@ -5158,7 +5377,7 @@ msgid "" "SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:91 +#: ../../source/explanation-flower-architecture.rst:97 #, fuzzy msgid "Multi-tenancy federated learning architecture" msgstr "Stratégie de moyenne fédérée." @@ -5182,7 +5401,7 @@ msgid "" "their corresponding ``ClientApp``\\s:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:107 +#: ../../source/explanation-flower-architecture.rst:113 #, fuzzy msgid "Multi-tenancy federated learning architecture - Run 1" msgstr "Stratégie de moyenne fédérée." @@ -5199,7 +5418,7 @@ msgid "" " to participate in the training:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:119 +#: ../../source/explanation-flower-architecture.rst:125 #, fuzzy msgid "Multi-tenancy federated learning architecture - Run 2" msgstr "Stratégie de moyenne fédérée." @@ -5236,7 +5455,7 @@ msgid "" "developer machine." msgstr "" -#: ../../source/explanation-flower-architecture.rst:145 +#: ../../source/explanation-flower-architecture.rst:151 msgid "Flower Deployment Engine with SuperExec" msgstr "" @@ -8526,7 +8745,7 @@ msgid "" "adaptive clipping." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:25 +#: ../../source/how-to-use-differential-privacy.rst:-1 #, fuzzy msgid "server side clipping" msgstr "Logique côté serveur" @@ -8557,7 +8776,7 @@ msgid "" ":code:`DifferentialPrivacyClientSideAdaptiveClipping`." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:57 +#: ../../source/how-to-use-differential-privacy.rst:-1 #, fuzzy msgid "client side clipping" msgstr "Logique côté client" @@ -8585,7 +8804,7 @@ msgid "" "clipping norm value, sensitivity, epsilon, and delta." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:99 +#: ../../source/how-to-use-differential-privacy.rst:-1 msgid "local DP mod" msgstr "" @@ -9047,11 +9266,33 @@ msgstr "" msgid "Arguments" msgstr "Amélioration de la documentation" -#: ../../flwr install:1 new:1 run:1 +#: ../../flwr install:1 log:1 new:1 run:1 #, fuzzy msgid "Optional argument" msgstr "Améliorations facultatives" +#: ../../flwr log:1 +msgid "Get logs from a Flower project run." +msgstr "" + +#: ../../flwr log:1 +msgid "Flag to stream or print logs from the Flower run" +msgstr "" + +#: ../../flwr log +#, fuzzy +msgid "default" +msgstr "Flux de travail" + +#: ../../flwr log:1 +msgid "``True``" +msgstr "" + +#: ../../flwr log:1 +#, fuzzy +msgid "Required argument" +msgstr "Amélioration de la documentation" + #: ../../flwr new:1 #, fuzzy msgid "Create new Flower App." @@ -9143,7 +9384,7 @@ msgstr "" #: ../../source/ref-api/flwr.rst:35::1 #, fuzzy -msgid ":py:obj:`client `\\" +msgid ":py:obj:`flwr.client `\\" msgstr "serveur.stratégie.Stratégie" #: ../../source/ref-api/flwr.rst:35::1 flwr.client:1 of @@ -9153,7 +9394,7 @@ msgstr "Client de Flower" #: ../../source/ref-api/flwr.rst:35::1 #, fuzzy -msgid ":py:obj:`common `\\" +msgid ":py:obj:`flwr.common `\\" msgstr "serveur.stratégie.Stratégie" #: ../../source/ref-api/flwr.rst:35::1 flwr.common:1 of @@ -9162,7 +9403,7 @@ msgstr "Composants communs partagés entre le serveur et le client." #: ../../source/ref-api/flwr.rst:35::1 #, fuzzy -msgid ":py:obj:`server `\\" +msgid ":py:obj:`flwr.server `\\" msgstr "serveur.stratégie.Stratégie" #: ../../source/ref-api/flwr.rst:35::1 @@ -9174,7 +9415,7 @@ msgstr "Serveur de Flower" #: ../../source/ref-api/flwr.rst:35::1 #, fuzzy -msgid ":py:obj:`simulation `\\" +msgid ":py:obj:`flwr.simulation `\\" msgstr "serveur.stratégie.Stratégie" #: ../../source/ref-api/flwr.rst:35::1 flwr.simulation:1 of @@ -9258,7 +9499,7 @@ msgstr "" #: ../../source/ref-api/flwr.client.rst:50::1 #, fuzzy -msgid ":py:obj:`mod `\\" +msgid ":py:obj:`flwr.client.mod `\\" msgstr "serveur.stratégie.Stratégie" #: ../../source/ref-api/flwr.client.rst:50::1 flwr.client.mod:1 of @@ -9459,48 +9700,57 @@ msgstr "" msgid "Getter for `Context` client attribute." msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst -#: ../../source/ref-api/flwr.client.NumPyClient.rst -#: ../../source/ref-api/flwr.client.mod.LocalDpMod.rst -#: ../../source/ref-api/flwr.common.Array.rst -#: ../../source/ref-api/flwr.common.ConfigsRecord.rst -#: ../../source/ref-api/flwr.common.Context.rst -#: ../../source/ref-api/flwr.common.Error.rst -#: ../../source/ref-api/flwr.common.Message.rst -#: ../../source/ref-api/flwr.common.Metadata.rst -#: ../../source/ref-api/flwr.common.MetricsRecord.rst #: ../../source/ref-api/flwr.common.Parameters.rst:2 -#: ../../source/ref-api/flwr.common.ParametersRecord.rst -#: ../../source/ref-api/flwr.common.RecordSet.rst -#: ../../source/ref-api/flwr.server.ClientManager.rst -#: ../../source/ref-api/flwr.server.Driver.rst -#: ../../source/ref-api/flwr.server.ServerAppComponents.rst -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst -#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideAdaptiveClipping.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideFixedClipping.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideAdaptiveClipping.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideFixedClipping.rst -#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst -#: ../../source/ref-api/flwr.server.strategy.FedAdam.rst -#: ../../source/ref-api/flwr.server.strategy.FedAvg.rst -#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst -#: ../../source/ref-api/flwr.server.strategy.FedAvgM.rst -#: ../../source/ref-api/flwr.server.strategy.FedOpt.rst -#: ../../source/ref-api/flwr.server.strategy.FedProx.rst -#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst -#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst -#: ../../source/ref-api/flwr.server.strategy.Krum.rst -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst -#: ../../source/ref-api/flwr.server.workflow.SecAggPlusWorkflow.rst -#: ../../source/ref-api/flwr.server.workflow.SecAggWorkflow.rst -#: ../../source/ref-api/flwr.simulation.run_simulation.rst -#: ../../source/ref-api/flwr.simulation.start_simulation.rst #: flwr.client.app.start_client flwr.client.app.start_numpy_client -#: flwr.server.app.start_server -#: flwr.server.driver.driver.Driver.send_and_receive of +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.mod.localdp_mod.LocalDpMod +#: flwr.client.numpy_client.NumPyClient.evaluate +#: flwr.client.numpy_client.NumPyClient.fit +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.context.Context flwr.common.message.Error +#: flwr.common.message.Message flwr.common.message.Message.create_error_reply +#: flwr.common.message.Message.create_reply flwr.common.message.Metadata +#: flwr.common.record.configsrecord.ConfigsRecord +#: flwr.common.record.metricsrecord.MetricsRecord +#: flwr.common.record.parametersrecord.Array +#: flwr.common.record.parametersrecord.ParametersRecord +#: flwr.common.record.recordset.RecordSet flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.ClientManager.unregister +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.unregister +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.serverapp_components.ServerAppComponents +#: flwr.server.strategy.bulyan.Bulyan +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.fedadagrad.FedAdagrad +#: flwr.server.strategy.fedadam.FedAdam flwr.server.strategy.fedavg.FedAvg +#: flwr.server.strategy.fedavg_android.FedAvgAndroid +#: flwr.server.strategy.fedavgm.FedAvgM flwr.server.strategy.fedopt.FedOpt +#: flwr.server.strategy.fedprox.FedProx +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg +#: flwr.server.strategy.fedyogi.FedYogi flwr.server.strategy.krum.Krum +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow +#: flwr.simulation.run_simulation.run_simulation of #, fuzzy msgid "Parameters" msgstr "Paramètres du modèle." @@ -9512,21 +9762,31 @@ msgid "" "customize the local evaluation process." msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst -#: ../../source/ref-api/flwr.client.NumPyClient.rst -#: ../../source/ref-api/flwr.common.ConfigsRecord.rst -#: ../../source/ref-api/flwr.common.Message.rst -#: ../../source/ref-api/flwr.common.MetricsRecord.rst -#: ../../source/ref-api/flwr.common.ParametersRecord.rst -#: ../../source/ref-api/flwr.server.ClientManager.rst -#: ../../source/ref-api/flwr.server.Driver.rst -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst -#: ../../source/ref-api/flwr.simulation.start_simulation.rst -#: flwr.server.app.start_server -#: flwr.server.driver.driver.Driver.send_and_receive of +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.numpy_client.NumPyClient.evaluate +#: flwr.client.numpy_client.NumPyClient.fit +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.message.Message.create_reply flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.num_available +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.SimpleClientManager.num_available +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters of #, fuzzy msgid "Returns" msgstr "Ressources" @@ -9537,18 +9797,29 @@ msgid "" "details such as the number of local data examples used for evaluation." msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst -#: ../../source/ref-api/flwr.client.NumPyClient.rst -#: ../../source/ref-api/flwr.common.Message.rst -#: ../../source/ref-api/flwr.server.ClientManager.rst -#: ../../source/ref-api/flwr.server.Driver.rst -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst -#: ../../source/ref-api/flwr.simulation.start_simulation.rst -#: flwr.server.app.start_server -#: flwr.server.driver.driver.Driver.send_and_receive of +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.message.Message.create_reply flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.num_available +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.SimpleClientManager.num_available +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters of msgid "Return type" msgstr "" @@ -9879,6 +10150,11 @@ msgstr "Logique côté client" msgid ":py:obj:`make_ffn `\\ \\(ffn\\, mods\\)" msgstr "serveur.stratégie.Stratégie" +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.utils.make_ffn:1 of +msgid "." +msgstr "" + #: ../../source/ref-api/flwr.client.mod.rst:28::1 msgid "" ":py:obj:`message_size_mod `\\ \\(msg\\," @@ -10046,10 +10322,6 @@ msgstr "" msgid "make\\_ffn" msgstr "" -#: flwr.client.mod.utils.make_ffn:1 of -msgid "." -msgstr "" - #: ../../source/ref-api/flwr.client.mod.message_size_mod.rst:2 msgid "message\\_size\\_mod" msgstr "" @@ -10078,15 +10350,6 @@ msgstr "" msgid "secaggplus\\_mod" msgstr "Flux de travail" -#: ../../source/ref-api/flwr.client.run_client_app.rst:2 -msgid "run\\_client\\_app" -msgstr "" - -#: ../../source/ref-api/flwr.client.run_supernode.rst:2 -#, fuzzy -msgid "run\\_supernode" -msgstr "flower-superlink" - #: ../../source/ref-api/flwr.client.start_client.rst:2 #, fuzzy msgid "start\\_client" @@ -10822,17 +11085,12 @@ msgstr "" #: collections.abc.MutableMapping.clear:1::1 of #, fuzzy -msgid ":py:obj:`get `\\ \\(key\\[\\, default\\]\\)" +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr "" "Flower 1.0 : ``start_server(..., " "config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " "...)``" -#: collections.abc.Mapping.get:1 -#: collections.abc.MutableMapping.clear:1::1 of -msgid "Retrieve the corresponding layout by the string key." -msgstr "" - #: collections.abc.MutableMapping.clear:1::1 of msgid ":py:obj:`items `\\ \\(\\)" msgstr "" @@ -10889,22 +11147,6 @@ msgstr "" msgid "This function counts booleans as occupying 1 Byte." msgstr "" -#: collections.abc.Mapping.get:3 of -msgid "" -"When there isn't an exact match, all the existing keys in the layout map " -"will be treated as a regex and map against the input key again. The first" -" match will be returned, based on the key insertion order. Return None if" -" there isn't any match found." -msgstr "" - -#: collections.abc.Mapping.get:8 of -msgid "the string key as the query for the layout." -msgstr "" - -#: collections.abc.Mapping.get:10 of -msgid "Corresponding layout based on the query." -msgstr "" - #: ../../source/ref-api/flwr.common.Context.rst:2 msgid "Context" msgstr "" @@ -11663,7 +11905,7 @@ msgstr "" msgid "The encoding in which to encode the string." msgstr "" -#: flwr.common.EventType.encode:5 of +#: flwr.common.EventType.encode:9 of msgid "errors" msgstr "" @@ -11839,7 +12081,7 @@ msgid "" "string." msgstr "" -#: flwr.common.EventType.replace:3 of +#: flwr.common.EventType.replace:5 of msgid "count" msgstr "" @@ -11875,7 +12117,7 @@ msgid "" "strings and the original string." msgstr "" -#: flwr.common.EventType.rsplit:3 flwr.common.EventType.split:3 of +#: flwr.common.EventType.rsplit:7 flwr.common.EventType.split:7 of msgid "sep" msgstr "" @@ -11890,7 +12132,7 @@ msgid "" " empty strings from the result." msgstr "" -#: flwr.common.EventType.rsplit:9 flwr.common.EventType.split:9 of +#: flwr.common.EventType.rsplit:11 flwr.common.EventType.split:11 of msgid "maxsplit" msgstr "" @@ -11931,7 +12173,7 @@ msgid "" "remaining cased characters have lower case." msgstr "" -#: flwr.common.EventType.translate:3 of +#: flwr.common.EventType.translate:5 of #, fuzzy msgid "table" msgstr "Database" @@ -12354,7 +12596,7 @@ msgstr "" #: collections.abc.MutableMapping.clear:1::1 of #, fuzzy -msgid ":py:obj:`get `\\ \\(key\\[\\, default\\]\\)" +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr "serveur.stratégie.Stratégie" #: collections.abc.MutableMapping.clear:1::1 of @@ -12490,9 +12732,7 @@ msgstr "" #: collections.abc.MutableMapping.clear:1::1 of #, fuzzy -msgid "" -":py:obj:`get `\\ \\(key\\[\\, " -"default\\]\\)" +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr "serveur.stratégie.Stratégie" #: collections.abc.MutableMapping.clear:1::1 of @@ -12839,7 +13079,7 @@ msgstr "" #: ../../source/ref-api/flwr.server.rst:56::1 #, fuzzy -msgid ":py:obj:`strategy `\\" +msgid ":py:obj:`flwr.server.strategy `\\" msgstr "serveur.stratégie.Stratégie" #: ../../source/ref-api/flwr.server.rst:56::1 @@ -12849,7 +13089,7 @@ msgstr "" #: ../../source/ref-api/flwr.server.rst:56::1 #, fuzzy -msgid ":py:obj:`workflow `\\" +msgid ":py:obj:`flwr.server.workflow `\\" msgstr "serveur.stratégie.Stratégie" #: ../../source/ref-api/flwr.server.rst:56::1 @@ -13353,8 +13593,7 @@ msgid "" msgstr "" #: flwr.server.app.start_server:9 -#: flwr.server.serverapp_components.ServerAppComponents:6 -#: flwr.simulation.app.start_simulation:29 of +#: flwr.server.serverapp_components.ServerAppComponents:6 of msgid "" "Currently supported values are `num_rounds` (int, default: 1) and " "`round_timeout` in seconds (float, default: None)." @@ -13478,15 +13717,6 @@ msgstr "" msgid "**success**" msgstr "" -#: ../../source/ref-api/flwr.server.run_server_app.rst:2 -msgid "run\\_server\\_app" -msgstr "" - -#: ../../source/ref-api/flwr.server.run_superlink.rst:2 -#, fuzzy -msgid "run\\_superlink" -msgstr "flower-superlink" - #: ../../source/ref-api/flwr.server.start_server.rst:2 #, fuzzy msgid "start\\_server" @@ -16567,16 +16797,16 @@ msgid "Run a Flower App using the Simulation Engine." msgstr "" #: ../../source/ref-api/flwr.simulation.rst:18::1 +#, fuzzy msgid "" -":py:obj:`start_simulation `\\ \\(\\*\\," -" client\\_fn\\, num\\_clients\\)" -msgstr "" +":py:obj:`start_simulation `\\ " +"\\(\\*args\\, \\*\\*kwargs\\)" +msgstr "serveur.stratégie.Stratégie" #: ../../source/ref-api/flwr.simulation.rst:18::1 -#: flwr.simulation.app.start_simulation:1 of -#, fuzzy -msgid "Start a Ray-based Flower simulation server." -msgstr "Simulation de moniteur" +#: flwr.simulation.start_simulation:1 of +msgid "Log error stating that module `ray` could not be imported." +msgstr "" #: ../../source/ref-api/flwr.simulation.run_simulation.rst:2 #, fuzzy @@ -16638,120 +16868,6 @@ msgstr "" msgid "start\\_simulation" msgstr "démarrer_simulation" -#: flwr.simulation.app.start_simulation:3 of -msgid "" -"A function creating `Client` instances. The function must have the " -"signature `client_fn(context: Context). It should return a single client " -"instance of type `Client`. Note that the created client instances are " -"ephemeral and will often be destroyed after a single method invocation. " -"Since client instances are not long-lived, they should not attempt to " -"carry state over method invocations. Any state required by the instance " -"(model, dataset, hyperparameters, ...) should be (re-)created in either " -"the call to `client_fn` or the call to any of the client methods (e.g., " -"load evaluation data in the `evaluate` method itself)." -msgstr "" - -#: flwr.simulation.app.start_simulation:13 of -msgid "The total number of clients in this simulation." -msgstr "" - -#: flwr.simulation.app.start_simulation:15 of -msgid "" -"UNSUPPORTED, WILL BE REMOVED. USE `num_clients` INSTEAD. List " -"`client_id`s for each client. This is only required if `num_clients` is " -"not set. Setting both `num_clients` and `clients_ids` with " -"`len(clients_ids)` not equal to `num_clients` generates an error. Using " -"this argument will raise an error." -msgstr "" - -#: flwr.simulation.app.start_simulation:21 of -msgid "" -"CPU and GPU resources for a single client. Supported keys are `num_cpus` " -"and `num_gpus`. To understand the GPU utilization caused by `num_gpus`, " -"as well as using custom resources, please consult the Ray documentation." -msgstr "" - -#: flwr.simulation.app.start_simulation:26 of -msgid "" -"An implementation of the abstract base class `flwr.server.Server`. If no " -"instance is provided, then `start_server` will create one." -msgstr "" - -#: flwr.simulation.app.start_simulation:32 of -msgid "" -"An implementation of the abstract base class `flwr.server.Strategy`. If " -"no strategy is provided, then `start_server` will use " -"`flwr.server.strategy.FedAvg`." -msgstr "" - -#: flwr.simulation.app.start_simulation:36 of -msgid "" -"An implementation of the abstract base class `flwr.server.ClientManager`." -" If no implementation is provided, then `start_simulation` will use " -"`flwr.server.client_manager.SimpleClientManager`." -msgstr "" - -#: flwr.simulation.app.start_simulation:40 of -msgid "" -"Optional dictionary containing arguments for the call to `ray.init`. If " -"ray_init_args is None (the default), Ray will be initialized with the " -"following default args: { \"ignore_reinit_error\": True, " -"\"include_dashboard\": False } An empty dictionary can be used " -"(ray_init_args={}) to prevent any arguments from being passed to " -"ray.init." -msgstr "" - -#: flwr.simulation.app.start_simulation:40 of -msgid "" -"Optional dictionary containing arguments for the call to `ray.init`. If " -"ray_init_args is None (the default), Ray will be initialized with the " -"following default args:" -msgstr "" - -#: flwr.simulation.app.start_simulation:44 of -msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" -msgstr "" - -#: flwr.simulation.app.start_simulation:46 of -msgid "" -"An empty dictionary can be used (ray_init_args={}) to prevent any " -"arguments from being passed to ray.init." -msgstr "" - -#: flwr.simulation.app.start_simulation:49 of -msgid "" -"Set to True to prevent `ray.shutdown()` in case " -"`ray.is_initialized()=True`." -msgstr "" - -#: flwr.simulation.app.start_simulation:51 of -msgid "" -"Optionally specify the type of actor to use. The actor object, which " -"persists throughout the simulation, will be the process in charge of " -"executing a ClientApp wrapping input argument `client_fn`." -msgstr "" - -#: flwr.simulation.app.start_simulation:55 of -msgid "" -"If you want to create your own Actor classes, you might need to pass some" -" input argument. You can use this dictionary for such purpose." -msgstr "" - -#: flwr.simulation.app.start_simulation:58 of -msgid "" -"(default: \"DEFAULT\") Optional string (\"DEFAULT\" or \"SPREAD\") for " -"the VCE to choose in which node the actor is placed. If you are an " -"advanced user needed more control you can use lower-level scheduling " -"strategies to pin actors to specific compute nodes (e.g. via " -"NodeAffinitySchedulingStrategy). Please note this is an advanced feature." -" For all details, please refer to the Ray documentation: " -"https://docs.ray.io/en/latest/ray-core/scheduling/index.html" -msgstr "" - -#: flwr.simulation.app.start_simulation:67 of -msgid "**hist** -- Object containing metrics from training." -msgstr "" - #: ../../source/ref-changelog.md:1 msgid "Changelog" msgstr "Changelog" @@ -16889,13 +17005,6 @@ msgstr "" msgid "Incompatible changes" msgstr "Changements incompatibles" -#: ../../source/ref-changelog.md:33 ../../source/ref-changelog.md:399 -#: ../../source/ref-changelog.md:676 ../../source/ref-changelog.md:740 -#: ../../source/ref-changelog.md:798 ../../source/ref-changelog.md:867 -#: ../../source/ref-changelog.md:929 -msgid "None" -msgstr "Aucun" - #: ../../source/ref-changelog.md:35 #, fuzzy msgid "v1.11.0 (2024-08-30)" @@ -23786,7 +23895,20 @@ msgstr "" "Oui, bien sûr, une liste d'exemples disponibles utilisant Flower dans un " "environnement blockchain est disponible ici :" -#: ../../source/ref-faq.rst:28 +#: ../../source/ref-faq.rst:29 +msgid "`FLock: A Decentralised AI Training Platform `_." +msgstr "" + +#: ../../source/ref-faq.rst:29 +msgid "Contribute to on-chain training the model and earn rewards." +msgstr "" + +#: ../../source/ref-faq.rst:30 +#, fuzzy +msgid "Local blockchain with federated learning simulation." +msgstr "Mise à l'échelle de l'apprentissage fédéré" + +#: ../../source/ref-faq.rst:31 msgid "" "`Flower meets Nevermined GitHub Repository `_." @@ -23794,7 +23916,7 @@ msgstr "" "`Flower meets Nevermined GitHub Repository `_." -#: ../../source/ref-faq.rst:29 +#: ../../source/ref-faq.rst:32 msgid "" "`Flower meets Nevermined YouTube video " "`_." @@ -23802,7 +23924,7 @@ msgstr "" "`Flower rencontre Nevermined vidéo YouTube " "`_." -#: ../../source/ref-faq.rst:30 +#: ../../source/ref-faq.rst:33 #, fuzzy msgid "" "`Flower meets KOSMoS `_." -#: ../../source/ref-faq.rst:31 +#: ../../source/ref-faq.rst:34 msgid "" "`Flower meets Talan blog post `_ ." -#: ../../source/ref-faq.rst:32 +#: ../../source/ref-faq.rst:35 msgid "" "`Flower meets Talan GitHub Repository " "`_ ." @@ -24147,239 +24269,325 @@ msgstr "" "`_ " "pour en savoir plus." -#: ../../source/tutorial-quickstart-fastai.rst:-1 -msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with FastAI to train a vision model on CIFAR-10." -msgstr "" - #: ../../source/tutorial-quickstart-fastai.rst:5 msgid "Quickstart fastai" msgstr "Démarrage rapide fastai" -#: ../../source/tutorial-quickstart-fastai.rst:10 -msgid "Let's build a federated learning system using fastai and Flower!" +#: ../../source/tutorial-quickstart-fastai.rst:7 +#, fuzzy +msgid "" +"In this federated learning tutorial we will learn how to train a " +"SqueezeNet model on MNIST using Flower and fastai. It is recommended to " +"create a virtual environment and run everything within a :doc:`virtualenv" +" `." msgstr "" -"Construisons un système d'apprentissage fédéré en utilisant fastai et " -"Flower !" +"Tout d'abord, il est recommandé de créer un environnement virtuel et de " +"tout exécuter au sein d'un `virtualenv `_." #: ../../source/tutorial-quickstart-fastai.rst:12 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 +msgid "Then, clone the code example directly from GitHub:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:20 +msgid "" +"This will create a new directory called `quickstart-fastai` containing " +"the following files:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:33 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:33 #, fuzzy +msgid "Next, activate your environment, then run:" +msgstr "et active l'environnement virtuel avec :" + +#: ../../source/tutorial-quickstart-fastai.rst:43 msgid "" -"Please refer to the `full code example " -"`_ " -"to learn more." +"This example by default runs the Flower Simulation Engine, creating a " +"federation of 10 nodes using `FedAvg `_ " +"as the aggregation strategy. The dataset will be partitioned using Flower" +" Dataset's `IidPartitioner `_." +" Let's run the project:" msgstr "" -"Réfère-toi à l'exemple de code complet " -"`_ " -"pour en savoir plus." + +#: ../../source/tutorial-quickstart-fastai.rst:56 +#: ../../source/tutorial-quickstart-huggingface.rst:65 +#: ../../source/tutorial-quickstart-mlx.rst:64 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:56 +#: ../../source/tutorial-quickstart-pytorch.rst:64 +#: ../../source/tutorial-quickstart-tensorflow.rst:65 +msgid "With default arguments you will see an output like this one:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:100 +#: ../../source/tutorial-quickstart-huggingface.rst:116 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:106 +#: ../../source/tutorial-quickstart-pytorch.rst:105 +#: ../../source/tutorial-quickstart-tensorflow.rst:106 +msgid "" +"You can also override the parameters defined in the " +"``[tool.flwr.app.config]`` section in ``pyproject.toml`` like this:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:110 +#, fuzzy +msgid "" +"Check the `source code `_ of this tutorial in ``examples/quickstart-fasai`` " +"in the Flower GitHub repository." +msgstr "" +"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " +"premier système d'apprentissage fédéré. Le code source complet " +"`_ de cet exemple se trouve dans :code:`examples" +"/quickstart-mxnet`." #: ../../source/tutorial-quickstart-huggingface.rst:-1 msgid "" "Check out this Federating Learning quickstart tutorial for using Flower " -"with HuggingFace Transformers in order to fine-tune an LLM." +"with 🤗 HuggingFace Transformers in order to fine-tune an LLM." msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:5 msgid "Quickstart 🤗 Transformers" msgstr "Démarrage rapide 🤗 Transformateurs" -#: ../../source/tutorial-quickstart-huggingface.rst:10 +#: ../../source/tutorial-quickstart-huggingface.rst:7 +#, fuzzy msgid "" -"Let's build a federated learning system using Hugging Face Transformers " -"and Flower!" +"In this federated learning tutorial we will learn how to train a large " +"language model (LLM) on the `IMDB " +"`_ dataset using Flower" +" and the 🤗 Hugging Face Transformers library. It is recommended to create" +" a virtual environment and run everything within a :doc:`virtualenv " +"`." msgstr "" -"Construisons un système d'apprentissage fédéré à l'aide des " -"transformateurs Hugging Face et de Flower !" +"Tout d'abord, il est recommandé de créer un environnement virtuel et de " +"tout exécuter au sein d'un `virtualenv `_." -#: ../../source/tutorial-quickstart-huggingface.rst:12 +#: ../../source/tutorial-quickstart-huggingface.rst:14 msgid "" -"We will leverage Hugging Face to federate the training of language models" -" over multiple clients using Flower. More specifically, we will fine-tune" -" a pre-trained Transformer model (distilBERT) for sequence classification" -" over a dataset of IMDB ratings. The end goal is to detect if a movie " -"rating is positive or negative." +"Let's use ``flwr new`` to create a complete Flower+🤗 Hugging Face " +"project. It will generate all the files needed to run, by default with " +"the Flower Simulation Engine, a federation of 10 nodes using |fedavg|_ " +"The dataset will be partitioned using |flowerdatasets|_'s " +"|iidpartitioner|_." msgstr "" -"Nous nous appuierons sur Hugging Face pour fédérer l'entraînement de " -"modèles de langage sur plusieurs clients à l'aide de Flower. Plus " -"précisément, nous mettrons au point un modèle Transformer pré-entraîné " -"(distilBERT) pour la classification de séquences sur un ensemble de " -"données d'évaluations IMDB. L'objectif final est de détecter si " -"l'évaluation d'un film est positive ou négative." - -#: ../../source/tutorial-quickstart-huggingface.rst:18 -msgid "Dependencies" -msgstr "Dépendances" #: ../../source/tutorial-quickstart-huggingface.rst:20 +#: ../../source/tutorial-quickstart-mlx.rst:19 +#: ../../source/tutorial-quickstart-pytorch.rst:19 +#: ../../source/tutorial-quickstart-tensorflow.rst:20 +#, fuzzy msgid "" -"To follow along this tutorial you will need to install the following " -"packages: :code:`datasets`, :code:`evaluate`, :code:`flwr`, " -":code:`torch`, and :code:`transformers`. This can be done using " -":code:`pip`:" +"Now that we have a rough idea of what this example is about, let's get " +"started. First, install Flower in your new environment:" msgstr "" -"Pour suivre ce tutoriel, tu devras installer les paquets suivants : " -":code:`datasets`, :code:`evaluate`, :code:`flwr`, :code:`torch`, et " -":code:`transformers`. Cela peut être fait en utilisant :code:`pip` :" +"Maintenant que nous avons une idée approximative de ce qui se passe, " +"commençons. Nous devons d'abord installer Flower. Tu peux le faire en " +"lançant :" -#: ../../source/tutorial-quickstart-huggingface.rst:30 -msgid "Standard Hugging Face workflow" -msgstr "Flux de travail standard pour le visage" +#: ../../source/tutorial-quickstart-huggingface.rst:28 +msgid "" +"Then, run the command below. You will be prompted to select one of the " +"available templates (choose ``HuggingFace``), give a name to your " +"project, and type in your developer name:" +msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:33 -msgid "Handling the data" -msgstr "Traitement des données" +#: ../../source/tutorial-quickstart-huggingface.rst:36 +#: ../../source/tutorial-quickstart-mlx.rst:35 +#: ../../source/tutorial-quickstart-pytorch.rst:35 +#: ../../source/tutorial-quickstart-tensorflow.rst:36 +msgid "" +"After running it you'll notice a new directory with your project name has" +" been created. It should have the following structure:" +msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:35 +#: ../../source/tutorial-quickstart-huggingface.rst:50 +#: ../../source/tutorial-quickstart-mlx.rst:49 +#: ../../source/tutorial-quickstart-pytorch.rst:49 +#: ../../source/tutorial-quickstart-tensorflow.rst:50 msgid "" -"To fetch the IMDB dataset, we will use Hugging Face's :code:`datasets` " -"library. We then need to tokenize the data and create :code:`PyTorch` " -"dataloaders, this is all done in the :code:`load_data` function:" +"If you haven't yet installed the project and its dependencies, you can do" +" so by:" +msgstr "" + +#: ../../source/tutorial-quickstart-huggingface.rst:58 +#: ../../source/tutorial-quickstart-pytorch.rst:57 +#: ../../source/tutorial-quickstart-tensorflow.rst:58 +msgid "To run the project, do:" +msgstr "" + +#: ../../source/tutorial-quickstart-huggingface.rst:106 +msgid "You can also run the project with GPU as follows:" msgstr "" -"Pour récupérer le jeu de données IMDB, nous utiliserons la bibliothèque " -":code:`datasets` de Hugging Face. Nous devons ensuite tokeniser les " -"données et créer des :code:`PyTorch` dataloaders, ce qui est fait dans la" -" fonction :code:`load_data` :" -#: ../../source/tutorial-quickstart-huggingface.rst:81 -msgid "Training and testing the model" -msgstr "Former et tester le modèle" +#: ../../source/tutorial-quickstart-huggingface.rst:113 +msgid "" +"This will use the default arguments where each ``ClientApp`` will use 2 " +"CPUs and at most 4 ``ClientApp``\\s will run in a given GPU." +msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:83 +#: ../../source/tutorial-quickstart-huggingface.rst:124 +#: ../../source/tutorial-quickstart-mlx.rst:114 +#: ../../source/tutorial-quickstart-pytorch.rst:113 msgid "" -"Once we have a way of creating our trainloader and testloader, we can " -"take care of the training and testing. This is very similar to any " -":code:`PyTorch` training or testing loop:" +"What follows is an explanation of each component in the project you just " +"created: dataset partition, the model, defining the ``ClientApp`` and " +"defining the ``ServerApp``." msgstr "" -"Une fois que nous avons trouvé un moyen de créer notre trainloader et " -"notre testloader, nous pouvons nous occuper de l'entraînement et du test." -" C'est très similaire à n'importe quelle boucle d'entraînement ou de test" -" :code:`PyTorch` :" -#: ../../source/tutorial-quickstart-huggingface.rst:121 -msgid "Creating the model itself" -msgstr "Créer le modèle lui-même" +#: ../../source/tutorial-quickstart-huggingface.rst:130 +#: ../../source/tutorial-quickstart-mlx.rst:120 +#: ../../source/tutorial-quickstart-pytorch.rst:119 +#: ../../source/tutorial-quickstart-tensorflow.rst:116 +#, fuzzy +msgid "The Data" +msgstr "Chargement des données" -#: ../../source/tutorial-quickstart-huggingface.rst:123 +#: ../../source/tutorial-quickstart-huggingface.rst:132 msgid "" -"To create the model itself, we will just load the pre-trained distillBERT" -" model using Hugging Face’s :code:`AutoModelForSequenceClassification` :" +"This tutorial uses |flowerdatasets|_ to easily download and partition the" +" `IMDB `_ dataset. In " +"this example you'll make use of the |iidpartitioner|_ to generate " +"``num_partitions`` partitions. You can choose |otherpartitioners|_ " +"available in Flower Datasets. To tokenize the text, we will also load the" +" tokenizer from the pre-trained Transformer model that we'll use during " +"training - more on that in the next section. Each ``ClientApp`` will call" +" this function to create dataloaders with the data that correspond to " +"their data partition." msgstr "" -"Pour créer le modèle lui-même, nous allons simplement charger le modèle " -"distillBERT pré-entraîné en utilisant le " -":code:`AutoModelForSequenceClassification` de Hugging Face :" -#: ../../source/tutorial-quickstart-huggingface.rst:136 -msgid "Federating the example" -msgstr "Fédérer l'exemple" +#: ../../source/tutorial-quickstart-huggingface.rst:178 +#: ../../source/tutorial-quickstart-mlx.rst:164 +#: ../../source/tutorial-quickstart-pytorch.rst:157 +#: ../../source/tutorial-quickstart-tensorflow.rst:145 +#, fuzzy +msgid "The Model" +msgstr "Entraîne le modèle" -#: ../../source/tutorial-quickstart-huggingface.rst:139 -msgid "Creating the IMDBClient" -msgstr "Création du client IMDBC" +#: ../../source/tutorial-quickstart-huggingface.rst:180 +#, fuzzy +msgid "" +"We will leverage 🤗 Hugging Face to federate the training of language " +"models over multiple clients using Flower. More specifically, we will " +"fine-tune a pre-trained Transformer model (|berttiny|_) for sequence " +"classification over the dataset of IMDB ratings. The end goal is to " +"detect if a movie rating is positive or negative. If you have access to " +"larger GPUs, feel free to use larger models!" +msgstr "" +"Nous nous appuierons sur Hugging Face pour fédérer l'entraînement de " +"modèles de langage sur plusieurs clients à l'aide de Flower. Plus " +"précisément, nous mettrons au point un modèle Transformer pré-entraîné " +"(distilBERT) pour la classification de séquences sur un ensemble de " +"données d'évaluations IMDB. L'objectif final est de détecter si " +"l'évaluation d'un film est positive ou négative." -#: ../../source/tutorial-quickstart-huggingface.rst:141 +#: ../../source/tutorial-quickstart-huggingface.rst:193 msgid "" -"To federate our example to multiple clients, we first need to write our " -"Flower client class (inheriting from :code:`flwr.client.NumPyClient`). " -"This is very easy, as our model is a standard :code:`PyTorch` model:" +"Note that here, ``model_name`` is a string that will be loaded from the " +"``Context`` in the ClientApp and ServerApp." msgstr "" -"Pour fédérer notre exemple à plusieurs clients, nous devons d'abord " -"écrire notre classe de client Flower (héritant de " -":code:`flwr.client.NumPyClient`). C'est très facile, car notre modèle est" -" un modèle :code:`PyTorch` standard :" -#: ../../source/tutorial-quickstart-huggingface.rst:169 +#: ../../source/tutorial-quickstart-huggingface.rst:196 msgid "" -"The :code:`get_parameters` function lets the server get the client's " -"parameters. Inversely, the :code:`set_parameters` function allows the " -"server to send its parameters to the client. Finally, the :code:`fit` " -"function trains the model locally for the client, and the " -":code:`evaluate` function tests the model locally and returns the " -"relevant metrics." +"In addition to loading the pretrained model weights and architecture, we " +"also include two utility functions to perform both training (i.e. " +"``train()``) and evaluation (i.e. ``test()``) using the above model. " +"These functions should look fairly familiar if you have some prior " +"experience with PyTorch. Note these functions do not have anything " +"specific to Flower. That being said, the training function will normally " +"be called, as we'll see later, from a Flower client passing its own data." +" In summary, your clients can use standard training/testing functions to " +"perform local training or evaluation:" msgstr "" -"La fonction :code:`get_parameters` permet au serveur d'obtenir les " -"paramètres du client. Inversement, la fonction :code:`set_parameters` " -"permet au serveur d'envoyer ses paramètres au client. Enfin, la fonction " -":code:`fit` forme le modèle localement pour le client, et la fonction " -":code:`evaluate` teste le modèle localement et renvoie les mesures " -"correspondantes." -#: ../../source/tutorial-quickstart-huggingface.rst:175 -msgid "Starting the server" -msgstr "Démarrer le serveur" +#: ../../source/tutorial-quickstart-huggingface.rst:239 +#: ../../source/tutorial-quickstart-mlx.rst:210 +#: ../../source/tutorial-quickstart-pytorch.rst:234 +#: ../../source/tutorial-quickstart-tensorflow.rst:176 +#, fuzzy +msgid "The ClientApp" +msgstr "client" -#: ../../source/tutorial-quickstart-huggingface.rst:177 +#: ../../source/tutorial-quickstart-huggingface.rst:241 msgid "" -"Now that we have a way to instantiate clients, we need to create our " -"server in order to aggregate the results. Using Flower, this can be done " -"very easily by first choosing a strategy (here, we are using " -":code:`FedAvg`, which will define the global weights as the average of " -"all the clients' weights at each round) and then using the " -":code:`flwr.server.start_server` function:" +"The main changes we have to make to use 🤗 Hugging Face with Flower will " +"be found in the ``get_weights()`` and ``set_weights()`` functions. Under " +"the hood, the ``transformers`` library uses PyTorch, which means we can " +"reuse the ``get_weights()`` and ``set_weights()`` code that we defined in" +" the :doc:`Quickstart PyTorch ` tutorial. As" +" a reminder, in ``get_weights()``, PyTorch model parameters are extracted" +" and represented as a list of NumPy arrays. The ``set_weights()`` " +"function that's the opposite: given a list of NumPy arrays it applies " +"them to an existing PyTorch model. Doing this in fairly easy in PyTorch." msgstr "" -"Maintenant que nous avons un moyen d'instancier les clients, nous devons " -"créer notre serveur afin d'agréger les résultats. Avec Flower, cela peut " -"être fait très facilement en choisissant d'abord une stratégie (ici, nous" -" utilisons :code:`FedAvg`, qui définira les poids globaux comme la " -"moyenne des poids de tous les clients à chaque tour) et en utilisant " -"ensuite la fonction :code:`flwr.server.start_server` :" -#: ../../source/tutorial-quickstart-huggingface.rst:205 +#: ../../source/tutorial-quickstart-huggingface.rst:254 +#: ../../source/tutorial-quickstart-pytorch.rst:245 msgid "" -"The :code:`weighted_average` function is there to provide a way to " -"aggregate the metrics distributed amongst the clients (basically this " -"allows us to display a nice average accuracy and loss for every round)." +"The specific implementation of ``get_weights()`` and ``set_weights()`` " +"depends on the type of models you use. The ones shown below work for a " +"wide range of PyTorch models but you might need to adjust them if you " +"have more exotic model architectures." msgstr "" -"La fonction :code:`weighted_average` est là pour fournir un moyen " -"d'agréger les mesures réparties entre les clients (en gros, cela nous " -"permet d'afficher une belle moyenne de précision et de perte pour chaque " -"tour)." -#: ../../source/tutorial-quickstart-huggingface.rst:209 -msgid "Putting everything together" -msgstr "Tout assembler" - -#: ../../source/tutorial-quickstart-huggingface.rst:211 -msgid "We can now start client instances using:" -msgstr "Nous pouvons maintenant démarrer des instances de clients en utilisant :" +#: ../../source/tutorial-quickstart-huggingface.rst:269 +#: ../../source/tutorial-quickstart-pytorch.rst:261 +msgid "" +"The rest of the functionality is directly inspired by the centralized " +"case. The ``fit()`` method in the client trains the model using the local" +" dataset. Similarly, the ``evaluate()`` method is used to evaluate the " +"model received on a held-out validation set that the client might have:" +msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:221 +#: ../../source/tutorial-quickstart-huggingface.rst:296 msgid "" -"And they will be able to connect to the server and start the federated " -"training." -msgstr "Et ils pourront se connecter au serveur et démarrer la formation fédérée." +"Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " +"defined above by means of a ``client_fn()`` callback. Note that the " +"`context` enables you to get access to hyperparemeters defined in your " +"``pyproject.toml`` to configure the run. In this tutorial we access the " +"``local-epochs`` setting to control the number of epochs a ``ClientApp`` " +"will perform when running the ``fit()`` method. You could define " +"additional hyperparameters in ``pyproject.toml`` and access them here." +msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:223 +#: ../../source/tutorial-quickstart-huggingface.rst:330 +#: ../../source/tutorial-quickstart-mlx.rst:376 +#: ../../source/tutorial-quickstart-pytorch.rst:321 +#: ../../source/tutorial-quickstart-tensorflow.rst:245 #, fuzzy +msgid "The ServerApp" +msgstr "serveur" + +#: ../../source/tutorial-quickstart-huggingface.rst:332 msgid "" -"If you want to check out everything put together, you should check out " -"the `full code example `_ ." +"To construct a ``ServerApp`` we define a ``server_fn()`` callback with an" +" identical signature to that of ``client_fn()`` but the return type is " +"|serverappcomponents|_ as opposed to a |client|_ In this example we use " +"the `FedAvg` strategy. To it we pass a randomly initialized model that " +"will server as the global model to federated. Note that the value of " +"``fraction_fit`` is read from the run config. You can find the default " +"value defined in the ``pyproject.toml``." msgstr "" -"Si tu veux voir tout ce qui est mis ensemble, tu devrais consulter " -"l'exemple de code complet : " -"[https://github.com/adap/flower/tree/main/examples/quickstart-" -"huggingface](https://github.com/adap/flower/tree/main/examples" -"/quickstart-huggingface)." -#: ../../source/tutorial-quickstart-huggingface.rst:226 +#: ../../source/tutorial-quickstart-huggingface.rst:371 msgid "" -"Of course, this is a very basic example, and a lot can be added or " -"modified, it was just to showcase how simply we could federate a Hugging " -"Face workflow using Flower." +"Congratulations! You've successfully built and run your first federated " +"learning system for an LLM." msgstr "" -"Bien sûr, c'est un exemple très basique, et beaucoup de choses peuvent " -"être ajoutées ou modifiées, il s'agissait juste de montrer avec quelle " -"simplicité on pouvait fédérer un flux de travail Hugging Face à l'aide de" -" Flower." -#: ../../source/tutorial-quickstart-huggingface.rst:229 +#: ../../source/tutorial-quickstart-huggingface.rst:376 msgid "" -"Note that in this example we used :code:`PyTorch`, but we could have very" -" well used :code:`TensorFlow`." +"Check the source code of the extended version of this tutorial in " +"|quickstart_hf_link|_ in the Flower GitHub repository. For a " +"comprehensive example of a federated fine-tuning of an LLM with Flower, " +"refer to the |flowertune|_ example in the Flower GitHub repository." msgstr "" -"Notez que dans cet exemple, nous avons utilisé :code:`PyTorch`, mais nous" -" aurions très bien pu utiliser :code:`TensorFlow`." #: ../../source/tutorial-quickstart-ios.rst:-1 msgid "" @@ -24455,7 +24663,6 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:34 #: ../../source/tutorial-quickstart-scikitlearn.rst:40 -#: ../../source/tutorial-quickstart-tensorflow.rst:29 #: ../../source/tutorial-quickstart-xgboost.rst:55 msgid "Flower Client" msgstr "Client de la fleur" @@ -24529,13 +24736,11 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:129 #: ../../source/tutorial-quickstart-scikitlearn.rst:167 -#: ../../source/tutorial-quickstart-tensorflow.rst:98 #: ../../source/tutorial-quickstart-xgboost.rst:341 msgid "Flower Server" msgstr "Serveur de Flower" #: ../../source/tutorial-quickstart-ios.rst:131 -#: ../../source/tutorial-quickstart-tensorflow.rst:100 msgid "" "For simple workloads we can start a Flower server and leave all the " "configuration possibilities at their default values. In a file named " @@ -24548,12 +24753,10 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:142 #: ../../source/tutorial-quickstart-scikitlearn.rst:230 -#: ../../source/tutorial-quickstart-tensorflow.rst:112 msgid "Train the model, federated!" msgstr "Entraîne le modèle, fédéré !" #: ../../source/tutorial-quickstart-ios.rst:144 -#: ../../source/tutorial-quickstart-tensorflow.rst:114 #: ../../source/tutorial-quickstart-xgboost.rst:567 msgid "" "With both client and server ready, we can now run everything and see " @@ -24806,7 +25009,7 @@ msgstr "" "paramètres du modèle, une méthode pour former le modèle, et une méthode " "pour tester le modèle :" -#: ../../source/tutorial-quickstart-jax.rst:165 +#: ../../source/tutorial-quickstart-jax.rst:167 msgid ":code:`set_parameters (optional)`" msgstr ":code:`set_parameters (optional)`" @@ -24920,17 +25123,6 @@ msgid "" "api/flwr_datasets.partitioner.IidPartitioner.html#flwr_datasets.partitioner.IidPartitioner>`_." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:19 -#: ../../source/tutorial-quickstart-pytorch.rst:19 -#, fuzzy -msgid "" -"Now that we have a rough idea of what this example is about, let's get " -"started. First, install Flower in your new environment:" -msgstr "" -"Maintenant que nous avons une idée approximative de ce qui se passe, " -"commençons. Nous devons d'abord installer Flower. Tu peux le faire en " -"lançant :" - #: ../../source/tutorial-quickstart-mlx.rst:27 msgid "" "Then, run the command below. You will be prompted to select of the " @@ -24938,49 +25130,16 @@ msgid "" "type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:35 -#: ../../source/tutorial-quickstart-pytorch.rst:35 -msgid "" -"After running it you'll notice a new directory with your project name has" -" been created. It should have the following structure:" -msgstr "" - -#: ../../source/tutorial-quickstart-mlx.rst:49 -#: ../../source/tutorial-quickstart-pytorch.rst:49 -msgid "" -"If you haven't yet installed the project and its dependencies, you can do" -" so by:" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:57 msgid "To run the project do:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:64 -#: ../../source/tutorial-quickstart-pytorch.rst:64 -msgid "With default arguments you will see an output like this one:" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:106 msgid "" "You can also override the parameters defined in " "``[tool.flwr.app.config]`` section in the ``pyproject.toml`` like this:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:114 -#: ../../source/tutorial-quickstart-pytorch.rst:113 -msgid "" -"What follows is an explanation of each component in the project you just " -"created: dataset partition, the model, defining the ``ClientApp`` and " -"defining the ``ServerApp``." -msgstr "" - -#: ../../source/tutorial-quickstart-mlx.rst:120 -#: ../../source/tutorial-quickstart-pytorch.rst:119 -#, fuzzy -msgid "The Data" -msgstr "Chargement des données" - #: ../../source/tutorial-quickstart-mlx.rst:122 msgid "" "We will use `Flower Datasets `_ to " @@ -24992,12 +25151,6 @@ msgid "" "api/flwr_datasets.partitioner.html>`_ available in Flower Datasets:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:164 -#: ../../source/tutorial-quickstart-pytorch.rst:157 -#, fuzzy -msgid "The Model" -msgstr "Entraîne le modèle" - #: ../../source/tutorial-quickstart-mlx.rst:166 msgid "" "We define the model as in the `centralized MLX example " @@ -25011,12 +25164,6 @@ msgid "" "over batches." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:210 -#: ../../source/tutorial-quickstart-pytorch.rst:234 -#, fuzzy -msgid "The ClientApp" -msgstr "client" - #: ../../source/tutorial-quickstart-mlx.rst:212 msgid "" "The main changes we have to make to use `MLX` with `Flower` will be found" @@ -25085,12 +25232,6 @@ msgid "" "method." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:376 -#: ../../source/tutorial-quickstart-pytorch.rst:321 -#, fuzzy -msgid "The ServerApp" -msgstr "serveur" - #: ../../source/tutorial-quickstart-mlx.rst:378 msgid "" "To construct a ``ServerApp``, we define a ``server_fn()`` callback with " @@ -25104,6 +25245,7 @@ msgstr "" #: ../../source/tutorial-quickstart-mlx.rst:402 #: ../../source/tutorial-quickstart-pytorch.rst:360 +#: ../../source/tutorial-quickstart-tensorflow.rst:279 msgid "" "Congratulations! You've successfully built and run your first federated " "learning system." @@ -25184,16 +25326,6 @@ msgid "" "and type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:57 -msgid "To run the project, do:" -msgstr "" - -#: ../../source/tutorial-quickstart-pytorch.rst:105 -msgid "" -"You can also override the parameters defined in the " -"``[tool.flwr.app.config]`` section in ``pyproject.toml`` like this:" -msgstr "" - #: ../../source/tutorial-quickstart-pytorch.rst:121 msgid "" "This tutorial uses `Flower Datasets `_ " @@ -25237,22 +25369,6 @@ msgid "" "PyTorch model. Doing this in fairly easy in PyTorch." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:245 -msgid "" -"The specific implementation of ``get_weights()`` and ``set_weights()`` " -"depends on the type of models you use. The ones shown below work for a " -"wide range of PyTorch models but you might need to adjust them if you " -"have more exotic model architectures." -msgstr "" - -#: ../../source/tutorial-quickstart-pytorch.rst:261 -msgid "" -"The rest of the functionality is directly inspired by the centralized " -"case. The ``fit()`` method in the client trains the model using the local" -" dataset. Similarly, the ``evaluate()`` method is used to evaluate the " -"model received on a held-out validation set that the client might have:" -msgstr "" - #: ../../source/tutorial-quickstart-pytorch.rst:294 msgid "" "Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " @@ -25292,6 +25408,7 @@ msgstr "" "/quickstart-mxnet`." #: ../../source/tutorial-quickstart-pytorch.rst:372 +#: ../../source/tutorial-quickstart-tensorflow.rst:295 #, fuzzy msgid "Video tutorial" msgstr "Tutoriel" @@ -25303,35 +25420,57 @@ msgid "" "that shows the new APIs (as the content above does)" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:-1 -msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with PyTorch Lightning to train an Auto Encoder model on MNIST." -msgstr "" - #: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 msgid "Quickstart PyTorch Lightning" msgstr "Démarrage rapide de PyTorch Lightning" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:10 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:7 #, fuzzy msgid "" -"Let's build a horizontal federated learning system using PyTorch " -"Lightning and Flower!" +"In this federated learning tutorial we will learn how to train an " +"AutoEncoder model on MNIST using Flower and PyTorch Lightning. It is " +"recommended to create a virtual environment and run everything within a " +":doc:`virtualenv `." msgstr "" -"Construisons un système d'apprentissage fédéré en utilisant PyTorch " -"Lightning et Flower !" +"Tout d'abord, il est recommandé de créer un environnement virtuel et de " +"tout exécuter au sein d'un `virtualenv `_." -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:20 +msgid "" +"This will create a new directory called `quickstart-pytorch-lightning` " +"containing the following files:" +msgstr "" + +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:43 +msgid "" +"By default, Flower Simulation Engine will be started and it will create a" +" federation of 4 nodes using `FedAvg `_ " +"as the aggregation strategy. The dataset will be partitioned using Flower" +" Dataset's `IidPartitioner `_." +" To run the project, do:" +msgstr "" + +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:94 +msgid "" +"Each simulated `ClientApp` (two per round) will also log a summary of " +"their local training process. Expect this output to be similar to:" +msgstr "" + +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:116 #, fuzzy msgid "" -"Please refer to the `full code example " -"`_ to learn more." +"Check the `source code `_ of this tutorial in ``examples" +"/quickstart-pytorch-lightning`` in the Flower GitHub repository." msgstr "" -"Réfère-toi à l'exemple de code complet " -"`_ pour en savoir plus." +"Félicitations ! Tu as réussi à construire et à faire fonctionner ton " +"premier système d'apprentissage fédéré. Le code source complet " +"`_ de cet exemple se trouve dans :code:`examples" +"/quickstart-mxnet`." #: ../../source/tutorial-quickstart-scikitlearn.rst:-1 msgid "" @@ -25439,7 +25578,7 @@ msgstr ":code:`set_model_params()`" msgid "Sets the parameters of a :code:`sklearn` LogisticRegression model" msgstr "Définit les paramètres d'un modèle de régression logistique :code:`sklean`" -#: ../../source/tutorial-quickstart-scikitlearn.rst:49 +#: ../../source/tutorial-quickstart-scikitlearn.rst:50 msgid ":code:`set_initial_params()`" msgstr ":code:`set_initial_params()`" @@ -25514,7 +25653,7 @@ msgstr "" msgid "return the model weight as a list of NumPy ndarrays" msgstr "renvoie le poids du modèle sous la forme d'une liste de ndarrays NumPy" -#: ../../source/tutorial-quickstart-scikitlearn.rst:120 +#: ../../source/tutorial-quickstart-scikitlearn.rst:121 msgid ":code:`set_parameters` (optional)" msgstr ":code:`set_parameters` (optionnel)" @@ -25644,7 +25783,6 @@ msgstr "" " commencer par lancer le serveur :" #: ../../source/tutorial-quickstart-scikitlearn.rst:239 -#: ../../source/tutorial-quickstart-tensorflow.rst:122 #: ../../source/tutorial-quickstart-xgboost.rst:575 msgid "" "Once the server is running we can start the clients in different " @@ -25655,7 +25793,6 @@ msgstr "" "premier client :" #: ../../source/tutorial-quickstart-scikitlearn.rst:246 -#: ../../source/tutorial-quickstart-tensorflow.rst:129 #: ../../source/tutorial-quickstart-xgboost.rst:582 msgid "Open another terminal and start the second client:" msgstr "Ouvre un autre terminal et démarre le deuxième client :" @@ -25688,144 +25825,118 @@ msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:-1 msgid "" "Check out this Federated Learning quickstart tutorial for using Flower " -"with TensorFlow to train a MobilNetV2 model on CIFAR-10." +"with TensorFlow to train a CNN model on CIFAR-10." msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:5 msgid "Quickstart TensorFlow" msgstr "Démarrage rapide de TensorFlow" -#: ../../source/tutorial-quickstart-tensorflow.rst:13 -msgid "Let's build a federated learning system in less than 20 lines of code!" -msgstr "" -"Construisons un système d'apprentissage fédéré en moins de 20 lignes de " -"code !" - -#: ../../source/tutorial-quickstart-tensorflow.rst:15 -msgid "Before Flower can be imported we have to install it:" -msgstr "Avant de pouvoir importer une fleur, nous devons l'installer :" - -#: ../../source/tutorial-quickstart-tensorflow.rst:21 +#: ../../source/tutorial-quickstart-tensorflow.rst:7 +#, fuzzy msgid "" -"Since we want to use the Keras API of TensorFlow (TF), we have to install" -" TF as well:" -msgstr "" -"Comme nous voulons utiliser l'API Keras de TensorFlow (TF), nous devons " -"également installer TF :" - -#: ../../source/tutorial-quickstart-tensorflow.rst:31 -msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:" +"In this tutorial we will learn how to train a Convolutional Neural " +"Network on CIFAR-10 using the Flower framework and TensorFlow. First of " +"all, it is recommended to create a virtual environment and run everything" +" within a :doc:`virtualenv `." msgstr "" -"Ensuite, dans un fichier appelé :code:`client.py`, importe Flower et " -"TensorFlow :" +"Tout d'abord, il est recommandé de créer un environnement virtuel et de " +"tout exécuter au sein d'un `virtualenv `_." -#: ../../source/tutorial-quickstart-tensorflow.rst:38 +#: ../../source/tutorial-quickstart-tensorflow.rst:13 msgid "" -"We use the Keras utilities of TF to load CIFAR10, a popular colored image" -" classification dataset for machine learning. The call to " -":code:`tf.keras.datasets.cifar10.load_data()` downloads CIFAR10, caches " -"it locally, and then returns the entire training and test set as NumPy " -"ndarrays." +"Let's use `flwr new` to create a complete Flower+TensorFlow project. It " +"will generate all the files needed to run, by default with the Flower " +"Simulation Engine, a federation of 10 nodes using `FedAvg " +"`_. The " +"dataset will be partitioned using Flower Dataset's `IidPartitioner " +"`_." msgstr "" -"Nous utilisons les utilitaires Keras de TF pour charger CIFAR10, un " -"ensemble de données de classification d'images colorées populaire pour " -"l'apprentissage automatique. L'appel à " -":code:`tf.keras.datasets.cifar10.load_data()` télécharge CIFAR10, le met " -"en cache localement, puis renvoie l'ensemble d'entraînement et de test " -"sous forme de NumPy ndarrays." -#: ../../source/tutorial-quickstart-tensorflow.rst:47 +#: ../../source/tutorial-quickstart-tensorflow.rst:28 msgid "" -"Next, we need a model. For the purpose of this tutorial, we use " -"MobilNetV2 with 10 output classes:" +"Then, run the command below. You will be prompted to select one of the " +"available templates (choose ``TensorFlow``), give a name to your project," +" and type in your developer name:" msgstr "" -"Ensuite, nous avons besoin d'un modèle. Pour les besoins de ce tutoriel, " -"nous utilisons MobilNetV2 avec 10 classes de sortie :" -#: ../../source/tutorial-quickstart-tensorflow.rst:54 +#: ../../source/tutorial-quickstart-tensorflow.rst:118 msgid "" -"The Flower server interacts with clients through an interface called " -":code:`Client`. When the server selects a particular client for training," -" it sends training instructions over the network. The client receives " -"those instructions and calls one of the :code:`Client` methods to run " -"your code (i.e., to train the neural network we defined earlier)." +"This tutorial uses `Flower Datasets `_ " +"to easily download and partition the `CIFAR-10` dataset. In this example " +"you'll make use of the `IidPartitioner `_" +" to generate `num_partitions` partitions. You can choose `other " +"partitioners `_ available in Flower Datasets. Each " +"``ClientApp`` will call this function to create the ``NumPy`` arrays that" +" correspond to their data partition." msgstr "" -"Le serveur Flower interagit avec les clients par le biais d'une interface" -" appelée :code:`Client`. Lorsque le serveur sélectionne un client " -"particulier pour la formation, il envoie des instructions de formation " -"sur le réseau. Le client reçoit ces instructions et appelle l'une des " -"méthodes :code:`Client` pour exécuter ton code (c'est-à-dire pour former " -"le réseau neuronal que nous avons défini plus tôt)." -#: ../../source/tutorial-quickstart-tensorflow.rst:60 +#: ../../source/tutorial-quickstart-tensorflow.rst:147 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses Keras. The :code:`NumPyClient` interface defines three " -"methods which can be implemented in the following way:" +"Next, we need a model. We defined a simple Convolutional Neural Network " +"(CNN), but feel free to replace it with a more sophisticated model if " +"you'd like:" msgstr "" -"Flower fournit une classe de commodité appelée :code:`NumPyClient` qui " -"facilite la mise en œuvre de l'interface :code:`Client` lorsque ta charge" -" de travail utilise Keras. L'interface :code:`NumPyClient` définit trois " -"méthodes qui peuvent être mises en œuvre de la manière suivante :" -#: ../../source/tutorial-quickstart-tensorflow.rst:82 +#: ../../source/tutorial-quickstart-tensorflow.rst:178 msgid "" -"We can now create an instance of our class :code:`CifarClient` and add " -"one line to actually run this client:" +"With `TensorFlow`, we can use the built-in ``get_weights()`` and " +"``set_weights()`` functions, which simplifies the implementation with " +"`Flower`. The rest of the functionality in the ClientApp is directly " +"inspired by the centralized case. The ``fit()`` method in the client " +"trains the model using the local dataset. Similarly, the ``evaluate()`` " +"method is used to evaluate the model received on a held-out validation " +"set that the client might have:" msgstr "" -"Nous pouvons maintenant créer une instance de notre classe " -":code:`CifarClient` et ajouter une ligne pour exécuter ce client :" -#: ../../source/tutorial-quickstart-tensorflow.rst:90 -#, fuzzy +#: ../../source/tutorial-quickstart-tensorflow.rst:212 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " -"implement a client of type :code:`NumPyClient` you'll need to first call " -"its :code:`to_client()` method. The string :code:`\"[::]:8080\"` tells " -"the client which server to connect to. In our case we can run the server " -"and the client on the same machine, therefore we use " -":code:`\"[::]:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we point the client at." +"Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " +"defined above by means of a ``client_fn()`` callback. Note that the " +"`context` enables you to get access to hyperparameters defined in your " +"``pyproject.toml`` to configure the run. For example, in this tutorial we" +" access the `local-epochs` setting to control the number of epochs a " +"``ClientApp`` will perform when running the ``fit()`` method, in addition" +" to `batch-size`. You could define additional hyperparameters in " +"``pyproject.toml`` and access them here." msgstr "" -"C'est tout pour le client. Il nous suffit d'implémenter :code:`Client` ou" -" :code:`NumPyClient` et d'appeler :code:`fl.client.start_client()`. La " -"chaîne :code:`\"[: :]:8080\"` indique au client à quel serveur se " -"connecter. Dans notre cas, nous pouvons exécuter le serveur et le client " -"sur la même machine, c'est pourquoi nous utilisons :code:`\"[: " -":]:8080\"`. Si nous exécutons une charge de travail véritablement fédérée" -" avec le serveur et les clients fonctionnant sur des machines " -"différentes, tout ce qui doit changer est l'adresse " -":code:`server_address` vers laquelle nous dirigeons le client." - -#: ../../source/tutorial-quickstart-tensorflow.rst:135 -msgid "Each client will have its own dataset." -msgstr "Chaque client aura son propre ensemble de données." -#: ../../source/tutorial-quickstart-tensorflow.rst:137 +#: ../../source/tutorial-quickstart-tensorflow.rst:247 msgid "" -"You should now see how the training does in the very first terminal (the " -"one that started the server):" +"To construct a ``ServerApp`` we define a ``server_fn()`` callback with an" +" identical signature to that of ``client_fn()`` but the return type is " +"`ServerAppComponents `_ as " +"opposed to a `Client `_. In this example we use the " +"`FedAvg`. To it we pass a randomly initialized model that will serve as " +"the global model to federate." msgstr "" -"Tu devrais maintenant voir comment la formation se déroule dans le tout " -"premier terminal (celui qui a démarré le serveur) :" -#: ../../source/tutorial-quickstart-tensorflow.rst:169 +#: ../../source/tutorial-quickstart-tensorflow.rst:284 #, fuzzy msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this can be found in :code:`examples" -"/quickstart-tensorflow/client.py`." +"Check the source code of the extended version of this tutorial in " +"|quickstart_tf_link|_ in the Flower GitHub repository." msgstr "" "Félicitations ! Tu as réussi à construire et à faire fonctionner ton " -"premier système d'apprentissage fédéré. Le `code source complet " +"premier système d'apprentissage fédéré. Le code source complet " "`_ pour cela se trouve dans :code:`examples" -"/quickstart-tensorflow/client.py`." +"mxnet/client.py>`_ de cet exemple se trouve dans :code:`examples" +"/quickstart-mxnet`." + +#: ../../source/tutorial-quickstart-tensorflow.rst:299 +msgid "" +"The video shown below shows how to setup a TensorFlow + Flower project " +"using our previously recommended APIs. A new video tutorial will be " +"released that shows the new APIs (as the content above does)" +msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:-1 msgid "" @@ -28377,7 +28488,7 @@ msgstr "" "chose d'autre, comme la régression linéaire classique." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 -msgid "|e5918c1c06a4434bbe4bf49235e40059|" +msgid "|e87b69b2ada74ea49412df16f4a0b9cc|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 @@ -28396,7 +28507,7 @@ msgstr "" " Go." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 -msgid "|c0165741bd1944f09ec55ce49032377d|" +msgid "|33cacb7d985c4906b348515c1a5cd993|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 @@ -28427,7 +28538,7 @@ msgstr "" "chanson." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 -msgid "|0a0ac9427ac7487b8e52d75ed514f04e|" +msgid "|cc080a555947492fa66131dc3a967603|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 @@ -28448,7 +28559,7 @@ msgstr "" " données pour la même tâche." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 -msgid "|5defee3ea4ca40d99fcd3e4ea045be25|" +msgid "|085c3e0fb8664c6aa06246636524b20b|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 @@ -28469,7 +28580,7 @@ msgstr "" "cloud." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 -msgid "|74f26ca701254d3db57d7899bd91eb55|" +msgid "|bfe69c74e48c45d49b50251c38c2a019|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 @@ -28490,7 +28601,7 @@ msgstr "" "appuyés." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 -msgid "|bda79f21f8154258a40e5766b2634ad7|" +msgid "|ebbecd651f0348d99c6511ea859bf4ca|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 @@ -28515,7 +28626,7 @@ msgstr "" " sur un serveur centralisé." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 -msgid "|89d30862e62e4f9989e193483a08680a|" +msgid "|163117eb654a4273babba413cf8065f5|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 @@ -28534,7 +28645,7 @@ msgstr "" "suffisantes pour former un bon modèle." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 -msgid "|77e9918671c54b4f86e01369c0785ce8|" +msgid "|452ac3ba453b4cd1be27be1ba7560d64|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 @@ -28756,7 +28867,7 @@ msgstr "" "partir d'un point de contrôle précédemment sauvegardé." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 -msgid "|7e4ccef37cc94148a067107b34eb7447|" +msgid "|f403fcd69e4e44409627e748b404c086|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 @@ -28791,7 +28902,7 @@ msgstr "" "rendements décroissants." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 -msgid "|28e47e4cded14479a0846c8e5f22c872|" +msgid "|4b00fe63870145968f8443619a792a42|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 @@ -28824,7 +28935,7 @@ msgstr "" "données locales, ou même de quelques étapes (mini-batchs)." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 -msgid "|4b8c5d1afa144294b76ffc76e4658a38|" +msgid "|368378731066486fa4397e89bc6b870c|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 @@ -28855,7 +28966,7 @@ msgstr "" " l'entraînement local." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 -msgid "|9dbdb3a0f6cb4a129fac863eaa414c17|" +msgid "|a66aa83d85bf4ffba7ed660b718066da|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 @@ -28914,7 +29025,7 @@ msgstr "" "times as much as each of the 100 examples." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 -msgid "|81749d0ac0834c36a83bd38f433fea31|" +msgid "|82324b9af72a4582a81839d55caab767|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 @@ -29057,7 +29168,7 @@ msgstr "" "quel cadre de ML et n'importe quel langage de programmation." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 -msgid "|ed9aae51da70428eab7eef32f21e819e|" +msgid "|fbf2da0da3cc4f8ab3b3eff852d80c41|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 @@ -36805,3 +36916,618 @@ msgstr "" #~ msgid "|c00bf2750bc24d229737a0fe1395f0fc|" #~ msgstr "" +#~ msgid "run\\_client\\_app" +#~ msgstr "" + +#~ msgid "run\\_supernode" +#~ msgstr "flower-superlink" + +#~ msgid "Retrieve the corresponding layout by the string key." +#~ msgstr "" + +#~ msgid "" +#~ "When there isn't an exact match, " +#~ "all the existing keys in the " +#~ "layout map will be treated as a" +#~ " regex and map against the input " +#~ "key again. The first match will be" +#~ " returned, based on the key insertion" +#~ " order. Return None if there isn't" +#~ " any match found." +#~ msgstr "" + +#~ msgid "the string key as the query for the layout." +#~ msgstr "" + +#~ msgid "Corresponding layout based on the query." +#~ msgstr "" + +#~ msgid "run\\_server\\_app" +#~ msgstr "" + +#~ msgid "run\\_superlink" +#~ msgstr "flower-superlink" + +#~ msgid "" +#~ ":py:obj:`start_simulation `\\" +#~ " \\(\\*\\, client\\_fn\\, num\\_clients\\)" +#~ msgstr "" + +#~ msgid "" +#~ "A function creating `Client` instances. " +#~ "The function must have the signature " +#~ "`client_fn(context: Context). It should return" +#~ " a single client instance of type " +#~ "`Client`. Note that the created client" +#~ " instances are ephemeral and will " +#~ "often be destroyed after a single " +#~ "method invocation. Since client instances " +#~ "are not long-lived, they should " +#~ "not attempt to carry state over " +#~ "method invocations. Any state required " +#~ "by the instance (model, dataset, " +#~ "hyperparameters, ...) should be (re-)created" +#~ " in either the call to `client_fn`" +#~ " or the call to any of the " +#~ "client methods (e.g., load evaluation " +#~ "data in the `evaluate` method itself)." +#~ msgstr "" + +#~ msgid "The total number of clients in this simulation." +#~ msgstr "" + +#~ msgid "" +#~ "UNSUPPORTED, WILL BE REMOVED. USE " +#~ "`num_clients` INSTEAD. List `client_id`s for" +#~ " each client. This is only required" +#~ " if `num_clients` is not set. Setting" +#~ " both `num_clients` and `clients_ids` with" +#~ " `len(clients_ids)` not equal to " +#~ "`num_clients` generates an error. Using " +#~ "this argument will raise an error." +#~ msgstr "" + +#~ msgid "" +#~ "CPU and GPU resources for a single" +#~ " client. Supported keys are `num_cpus` " +#~ "and `num_gpus`. To understand the GPU" +#~ " utilization caused by `num_gpus`, as " +#~ "well as using custom resources, please" +#~ " consult the Ray documentation." +#~ msgstr "" + +#~ msgid "" +#~ "Optionally specify the type of actor " +#~ "to use. The actor object, which " +#~ "persists throughout the simulation, will " +#~ "be the process in charge of " +#~ "executing a ClientApp wrapping input " +#~ "argument `client_fn`." +#~ msgstr "" + +#~ msgid "" +#~ "If you want to create your own " +#~ "Actor classes, you might need to " +#~ "pass some input argument. You can " +#~ "use this dictionary for such purpose." +#~ msgstr "" + +#~ msgid "" +#~ "(default: \"DEFAULT\") Optional string " +#~ "(\"DEFAULT\" or \"SPREAD\") for the VCE" +#~ " to choose in which node the " +#~ "actor is placed. If you are an " +#~ "advanced user needed more control you" +#~ " can use lower-level scheduling " +#~ "strategies to pin actors to specific " +#~ "compute nodes (e.g. via " +#~ "NodeAffinitySchedulingStrategy). Please note this" +#~ " is an advanced feature. For all " +#~ "details, please refer to the Ray " +#~ "documentation: https://docs.ray.io/en/latest/ray-" +#~ "core/scheduling/index.html" +#~ msgstr "" + +#~ msgid "" +#~ "Check out this Federated Learning " +#~ "quickstart tutorial for using Flower " +#~ "with FastAI to train a vision " +#~ "model on CIFAR-10." +#~ msgstr "" + +#~ msgid "Let's build a federated learning system using fastai and Flower!" +#~ msgstr "" +#~ "Construisons un système d'apprentissage fédéré" +#~ " en utilisant fastai et Flower !" + +#~ msgid "" +#~ "Please refer to the `full code " +#~ "example `_ to learn more." +#~ msgstr "" +#~ "Réfère-toi à l'exemple de code " +#~ "complet `_ pour en savoir plus." + +#~ msgid "" +#~ "Check out this Federating Learning " +#~ "quickstart tutorial for using Flower " +#~ "with HuggingFace Transformers in order " +#~ "to fine-tune an LLM." +#~ msgstr "" + +#~ msgid "" +#~ "Let's build a federated learning system" +#~ " using Hugging Face Transformers and " +#~ "Flower!" +#~ msgstr "" +#~ "Construisons un système d'apprentissage fédéré" +#~ " à l'aide des transformateurs Hugging " +#~ "Face et de Flower !" + +#~ msgid "Dependencies" +#~ msgstr "Dépendances" + +#~ msgid "" +#~ "To follow along this tutorial you " +#~ "will need to install the following " +#~ "packages: :code:`datasets`, :code:`evaluate`, " +#~ ":code:`flwr`, :code:`torch`, and " +#~ ":code:`transformers`. This can be done " +#~ "using :code:`pip`:" +#~ msgstr "" +#~ "Pour suivre ce tutoriel, tu devras " +#~ "installer les paquets suivants : " +#~ ":code:`datasets`, :code:`evaluate`, :code:`flwr`, " +#~ ":code:`torch`, et :code:`transformers`. Cela " +#~ "peut être fait en utilisant :code:`pip`" +#~ " :" + +#~ msgid "Standard Hugging Face workflow" +#~ msgstr "Flux de travail standard pour le visage" + +#~ msgid "Handling the data" +#~ msgstr "Traitement des données" + +#~ msgid "" +#~ "To fetch the IMDB dataset, we will" +#~ " use Hugging Face's :code:`datasets` " +#~ "library. We then need to tokenize " +#~ "the data and create :code:`PyTorch` " +#~ "dataloaders, this is all done in " +#~ "the :code:`load_data` function:" +#~ msgstr "" +#~ "Pour récupérer le jeu de données " +#~ "IMDB, nous utiliserons la bibliothèque " +#~ ":code:`datasets` de Hugging Face. Nous " +#~ "devons ensuite tokeniser les données et" +#~ " créer des :code:`PyTorch` dataloaders, ce" +#~ " qui est fait dans la fonction " +#~ ":code:`load_data` :" + +#~ msgid "Training and testing the model" +#~ msgstr "Former et tester le modèle" + +#~ msgid "" +#~ "Once we have a way of creating " +#~ "our trainloader and testloader, we can" +#~ " take care of the training and " +#~ "testing. This is very similar to " +#~ "any :code:`PyTorch` training or testing " +#~ "loop:" +#~ msgstr "" +#~ "Une fois que nous avons trouvé un" +#~ " moyen de créer notre trainloader et" +#~ " notre testloader, nous pouvons nous " +#~ "occuper de l'entraînement et du test." +#~ " C'est très similaire à n'importe " +#~ "quelle boucle d'entraînement ou de test" +#~ " :code:`PyTorch` :" + +#~ msgid "Creating the model itself" +#~ msgstr "Créer le modèle lui-même" + +#~ msgid "" +#~ "To create the model itself, we " +#~ "will just load the pre-trained " +#~ "distillBERT model using Hugging Face’s " +#~ ":code:`AutoModelForSequenceClassification` :" +#~ msgstr "" +#~ "Pour créer le modèle lui-même, " +#~ "nous allons simplement charger le modèle" +#~ " distillBERT pré-entraîné en utilisant le" +#~ " :code:`AutoModelForSequenceClassification` de Hugging" +#~ " Face :" + +#~ msgid "Creating the IMDBClient" +#~ msgstr "Création du client IMDBC" + +#~ msgid "" +#~ "To federate our example to multiple " +#~ "clients, we first need to write " +#~ "our Flower client class (inheriting from" +#~ " :code:`flwr.client.NumPyClient`). This is very" +#~ " easy, as our model is a " +#~ "standard :code:`PyTorch` model:" +#~ msgstr "" +#~ "Pour fédérer notre exemple à plusieurs" +#~ " clients, nous devons d'abord écrire " +#~ "notre classe de client Flower (héritant" +#~ " de :code:`flwr.client.NumPyClient`). C'est très" +#~ " facile, car notre modèle est un " +#~ "modèle :code:`PyTorch` standard :" + +#~ msgid "" +#~ "The :code:`get_parameters` function lets the" +#~ " server get the client's parameters. " +#~ "Inversely, the :code:`set_parameters` function " +#~ "allows the server to send its " +#~ "parameters to the client. Finally, the" +#~ " :code:`fit` function trains the model " +#~ "locally for the client, and the " +#~ ":code:`evaluate` function tests the model " +#~ "locally and returns the relevant " +#~ "metrics." +#~ msgstr "" +#~ "La fonction :code:`get_parameters` permet au" +#~ " serveur d'obtenir les paramètres du " +#~ "client. Inversement, la fonction " +#~ ":code:`set_parameters` permet au serveur " +#~ "d'envoyer ses paramètres au client. " +#~ "Enfin, la fonction :code:`fit` forme le" +#~ " modèle localement pour le client, et" +#~ " la fonction :code:`evaluate` teste le " +#~ "modèle localement et renvoie les mesures" +#~ " correspondantes." + +#~ msgid "Starting the server" +#~ msgstr "Démarrer le serveur" + +#~ msgid "" +#~ "Now that we have a way to " +#~ "instantiate clients, we need to create" +#~ " our server in order to aggregate " +#~ "the results. Using Flower, this can " +#~ "be done very easily by first " +#~ "choosing a strategy (here, we are " +#~ "using :code:`FedAvg`, which will define " +#~ "the global weights as the average " +#~ "of all the clients' weights at " +#~ "each round) and then using the " +#~ ":code:`flwr.server.start_server` function:" +#~ msgstr "" +#~ "Maintenant que nous avons un moyen " +#~ "d'instancier les clients, nous devons " +#~ "créer notre serveur afin d'agréger les" +#~ " résultats. Avec Flower, cela peut " +#~ "être fait très facilement en choisissant" +#~ " d'abord une stratégie (ici, nous " +#~ "utilisons :code:`FedAvg`, qui définira les " +#~ "poids globaux comme la moyenne des " +#~ "poids de tous les clients à chaque" +#~ " tour) et en utilisant ensuite la " +#~ "fonction :code:`flwr.server.start_server` :" + +#~ msgid "" +#~ "The :code:`weighted_average` function is there" +#~ " to provide a way to aggregate " +#~ "the metrics distributed amongst the " +#~ "clients (basically this allows us to " +#~ "display a nice average accuracy and " +#~ "loss for every round)." +#~ msgstr "" +#~ "La fonction :code:`weighted_average` est là" +#~ " pour fournir un moyen d'agréger les" +#~ " mesures réparties entre les clients " +#~ "(en gros, cela nous permet d'afficher" +#~ " une belle moyenne de précision et" +#~ " de perte pour chaque tour)." + +#~ msgid "Putting everything together" +#~ msgstr "Tout assembler" + +#~ msgid "We can now start client instances using:" +#~ msgstr "" +#~ "Nous pouvons maintenant démarrer des " +#~ "instances de clients en utilisant :" + +#~ msgid "" +#~ "And they will be able to connect" +#~ " to the server and start the " +#~ "federated training." +#~ msgstr "" +#~ "Et ils pourront se connecter au " +#~ "serveur et démarrer la formation " +#~ "fédérée." + +#~ msgid "" +#~ "If you want to check out " +#~ "everything put together, you should " +#~ "check out the `full code example " +#~ "`_ ." +#~ msgstr "" +#~ "Si tu veux voir tout ce qui " +#~ "est mis ensemble, tu devrais consulter" +#~ " l'exemple de code complet : " +#~ "[https://github.com/adap/flower/tree/main/examples/quickstart-" +#~ "huggingface](https://github.com/adap/flower/tree/main/examples" +#~ "/quickstart-huggingface)." + +#~ msgid "" +#~ "Of course, this is a very basic" +#~ " example, and a lot can be " +#~ "added or modified, it was just to" +#~ " showcase how simply we could " +#~ "federate a Hugging Face workflow using" +#~ " Flower." +#~ msgstr "" +#~ "Bien sûr, c'est un exemple très " +#~ "basique, et beaucoup de choses peuvent" +#~ " être ajoutées ou modifiées, il " +#~ "s'agissait juste de montrer avec quelle" +#~ " simplicité on pouvait fédérer un " +#~ "flux de travail Hugging Face à " +#~ "l'aide de Flower." + +#~ msgid "" +#~ "Note that in this example we used" +#~ " :code:`PyTorch`, but we could have " +#~ "very well used :code:`TensorFlow`." +#~ msgstr "" +#~ "Notez que dans cet exemple, nous " +#~ "avons utilisé :code:`PyTorch`, mais nous " +#~ "aurions très bien pu utiliser " +#~ ":code:`TensorFlow`." + +#~ msgid "" +#~ "Check out this Federated Learning " +#~ "quickstart tutorial for using Flower " +#~ "with PyTorch Lightning to train an " +#~ "Auto Encoder model on MNIST." +#~ msgstr "" + +#~ msgid "" +#~ "Let's build a horizontal federated " +#~ "learning system using PyTorch Lightning " +#~ "and Flower!" +#~ msgstr "" +#~ "Construisons un système d'apprentissage fédéré" +#~ " en utilisant PyTorch Lightning et " +#~ "Flower !" + +#~ msgid "" +#~ "Please refer to the `full code " +#~ "example `_ to learn " +#~ "more." +#~ msgstr "" +#~ "Réfère-toi à l'exemple de code " +#~ "complet `_ pour en " +#~ "savoir plus." + +#~ msgid "" +#~ "Check out this Federated Learning " +#~ "quickstart tutorial for using Flower " +#~ "with TensorFlow to train a MobilNetV2" +#~ " model on CIFAR-10." +#~ msgstr "" + +#~ msgid "Let's build a federated learning system in less than 20 lines of code!" +#~ msgstr "" +#~ "Construisons un système d'apprentissage fédéré" +#~ " en moins de 20 lignes de code" +#~ " !" + +#~ msgid "Before Flower can be imported we have to install it:" +#~ msgstr "Avant de pouvoir importer une fleur, nous devons l'installer :" + +#~ msgid "" +#~ "Since we want to use the Keras " +#~ "API of TensorFlow (TF), we have to" +#~ " install TF as well:" +#~ msgstr "" +#~ "Comme nous voulons utiliser l'API Keras" +#~ " de TensorFlow (TF), nous devons " +#~ "également installer TF :" + +#~ msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:" +#~ msgstr "" +#~ "Ensuite, dans un fichier appelé " +#~ ":code:`client.py`, importe Flower et " +#~ "TensorFlow :" + +#~ msgid "" +#~ "We use the Keras utilities of TF" +#~ " to load CIFAR10, a popular colored" +#~ " image classification dataset for machine" +#~ " learning. The call to " +#~ ":code:`tf.keras.datasets.cifar10.load_data()` downloads " +#~ "CIFAR10, caches it locally, and then " +#~ "returns the entire training and test " +#~ "set as NumPy ndarrays." +#~ msgstr "" +#~ "Nous utilisons les utilitaires Keras de" +#~ " TF pour charger CIFAR10, un ensemble" +#~ " de données de classification d'images " +#~ "colorées populaire pour l'apprentissage " +#~ "automatique. L'appel à " +#~ ":code:`tf.keras.datasets.cifar10.load_data()` télécharge " +#~ "CIFAR10, le met en cache localement, " +#~ "puis renvoie l'ensemble d'entraînement et " +#~ "de test sous forme de NumPy " +#~ "ndarrays." + +#~ msgid "" +#~ "Next, we need a model. For the " +#~ "purpose of this tutorial, we use " +#~ "MobilNetV2 with 10 output classes:" +#~ msgstr "" +#~ "Ensuite, nous avons besoin d'un modèle." +#~ " Pour les besoins de ce tutoriel, " +#~ "nous utilisons MobilNetV2 avec 10 " +#~ "classes de sortie :" + +#~ msgid "" +#~ "The Flower server interacts with clients" +#~ " through an interface called " +#~ ":code:`Client`. When the server selects " +#~ "a particular client for training, it " +#~ "sends training instructions over the " +#~ "network. The client receives those " +#~ "instructions and calls one of the " +#~ ":code:`Client` methods to run your code" +#~ " (i.e., to train the neural network" +#~ " we defined earlier)." +#~ msgstr "" +#~ "Le serveur Flower interagit avec les " +#~ "clients par le biais d'une interface " +#~ "appelée :code:`Client`. Lorsque le serveur " +#~ "sélectionne un client particulier pour " +#~ "la formation, il envoie des instructions" +#~ " de formation sur le réseau. Le " +#~ "client reçoit ces instructions et " +#~ "appelle l'une des méthodes :code:`Client` " +#~ "pour exécuter ton code (c'est-à-dire " +#~ "pour former le réseau neuronal que " +#~ "nous avons défini plus tôt)." + +#~ msgid "" +#~ "Flower provides a convenience class " +#~ "called :code:`NumPyClient` which makes it " +#~ "easier to implement the :code:`Client` " +#~ "interface when your workload uses Keras." +#~ " The :code:`NumPyClient` interface defines " +#~ "three methods which can be implemented" +#~ " in the following way:" +#~ msgstr "" +#~ "Flower fournit une classe de commodité" +#~ " appelée :code:`NumPyClient` qui facilite " +#~ "la mise en œuvre de l'interface " +#~ ":code:`Client` lorsque ta charge de " +#~ "travail utilise Keras. L'interface " +#~ ":code:`NumPyClient` définit trois méthodes qui" +#~ " peuvent être mises en œuvre de " +#~ "la manière suivante :" + +#~ msgid "" +#~ "We can now create an instance of" +#~ " our class :code:`CifarClient` and add " +#~ "one line to actually run this " +#~ "client:" +#~ msgstr "" +#~ "Nous pouvons maintenant créer une " +#~ "instance de notre classe :code:`CifarClient`" +#~ " et ajouter une ligne pour exécuter" +#~ " ce client :" + +#~ msgid "" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` or " +#~ ":code:`NumPyClient` and call " +#~ ":code:`fl.client.start_client()`. If you implement" +#~ " a client of type :code:`NumPyClient` " +#~ "you'll need to first call its " +#~ ":code:`to_client()` method. The string " +#~ ":code:`\"[::]:8080\"` tells the client which" +#~ " server to connect to. In our " +#~ "case we can run the server and " +#~ "the client on the same machine, " +#~ "therefore we use :code:`\"[::]:8080\"`. If " +#~ "we run a truly federated workload " +#~ "with the server and clients running " +#~ "on different machines, all that needs" +#~ " to change is the :code:`server_address`" +#~ " we point the client at." +#~ msgstr "" +#~ "C'est tout pour le client. Il nous" +#~ " suffit d'implémenter :code:`Client` ou " +#~ ":code:`NumPyClient` et d'appeler " +#~ ":code:`fl.client.start_client()`. La chaîne " +#~ ":code:`\"[: :]:8080\"` indique au client " +#~ "à quel serveur se connecter. Dans " +#~ "notre cas, nous pouvons exécuter le " +#~ "serveur et le client sur la même" +#~ " machine, c'est pourquoi nous utilisons " +#~ ":code:`\"[: :]:8080\"`. Si nous exécutons " +#~ "une charge de travail véritablement " +#~ "fédérée avec le serveur et les " +#~ "clients fonctionnant sur des machines " +#~ "différentes, tout ce qui doit changer" +#~ " est l'adresse :code:`server_address` vers " +#~ "laquelle nous dirigeons le client." + +#~ msgid "Each client will have its own dataset." +#~ msgstr "Chaque client aura son propre ensemble de données." + +#~ msgid "" +#~ "You should now see how the " +#~ "training does in the very first " +#~ "terminal (the one that started the " +#~ "server):" +#~ msgstr "" +#~ "Tu devrais maintenant voir comment la" +#~ " formation se déroule dans le tout" +#~ " premier terminal (celui qui a " +#~ "démarré le serveur) :" + +#~ msgid "" +#~ "Congratulations! You've successfully built and" +#~ " run your first federated learning " +#~ "system. The full `source code " +#~ "`_ for this can be " +#~ "found in :code:`examples/quickstart-" +#~ "tensorflow/client.py`." +#~ msgstr "" +#~ "Félicitations ! Tu as réussi à " +#~ "construire et à faire fonctionner ton" +#~ " premier système d'apprentissage fédéré. Le" +#~ " `code source complet " +#~ "`_ pour cela se trouve" +#~ " dans :code:`examples/quickstart-tensorflow/client.py`." + +#~ msgid "|e5918c1c06a4434bbe4bf49235e40059|" +#~ msgstr "" + +#~ msgid "|c0165741bd1944f09ec55ce49032377d|" +#~ msgstr "" + +#~ msgid "|0a0ac9427ac7487b8e52d75ed514f04e|" +#~ msgstr "" + +#~ msgid "|5defee3ea4ca40d99fcd3e4ea045be25|" +#~ msgstr "" + +#~ msgid "|74f26ca701254d3db57d7899bd91eb55|" +#~ msgstr "" + +#~ msgid "|bda79f21f8154258a40e5766b2634ad7|" +#~ msgstr "" + +#~ msgid "|89d30862e62e4f9989e193483a08680a|" +#~ msgstr "" + +#~ msgid "|77e9918671c54b4f86e01369c0785ce8|" +#~ msgstr "" + +#~ msgid "|7e4ccef37cc94148a067107b34eb7447|" +#~ msgstr "" + +#~ msgid "|28e47e4cded14479a0846c8e5f22c872|" +#~ msgstr "" + +#~ msgid "|4b8c5d1afa144294b76ffc76e4658a38|" +#~ msgstr "" + +#~ msgid "|9dbdb3a0f6cb4a129fac863eaa414c17|" +#~ msgstr "" + +#~ msgid "|81749d0ac0834c36a83bd38f433fea31|" +#~ msgstr "" + +#~ msgid "|ed9aae51da70428eab7eef32f21e819e|" +#~ msgstr "" + diff --git a/doc/locales/ko/LC_MESSAGES/framework-docs.po b/doc/locales/ko/LC_MESSAGES/framework-docs.po index db201f613126..4c738e16b434 100644 --- a/doc/locales/ko/LC_MESSAGES/framework-docs.po +++ b/doc/locales/ko/LC_MESSAGES/framework-docs.po @@ -7,7 +7,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower main\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-09-15 09:09+0200\n" +"POT-Creation-Date: 2024-09-24 00:29+0000\n" "PO-Revision-Date: 2024-08-23 13:09+0000\n" "Last-Translator: Seulki Yun \n" "Language: ko\n" @@ -17,7 +17,7 @@ msgstr "" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" -"Generated-By: Babel 2.15.0\n" +"Generated-By: Babel 2.16.0\n" #: ../../source/contributor-explanation-public-and-private-apis.rst:2 msgid "Public and private APIs" @@ -1468,7 +1468,7 @@ msgstr "" msgid "Setting up the repository" msgstr "레포지토리 설정하기" -#: ../../source/contributor-tutorial-contribute-on-github.rst:12 +#: ../../source/contributor-tutorial-contribute-on-github.rst:21 msgid "**Create a GitHub account and setup Git**" msgstr "**GitHub 계정을 만들고 Git을 설정합니다**" @@ -1509,7 +1509,7 @@ msgstr "" "일반적인 Git 및 GitHub 워크플로우의 기본 개념은 다음과 같이 요약됩니다. GitHub의 원격 레포지토리에서 코드를 " "다운로드하고 로컬에서 변경한 후 Git을 사용하여 추적한 다음 새 기록을 다시 GitHub에 업로드하는 것입니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:23 +#: ../../source/contributor-tutorial-contribute-on-github.rst:32 msgid "**Forking the Flower repository**" msgstr "**Flower 레포지토리 포크하기**" @@ -1534,7 +1534,7 @@ msgstr "" "원하는 경우 이름을 변경할 수 있지만, 이 버전의 Flower는 자신의 계정(즉, 자신의 리포지토리 목록)에 위치하게 되므로 변경할" " 필요는 없습니다. 만들기가 완료되면 왼쪽 상단에Flower 버전이 표시되는 것을 볼 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:34 +#: ../../source/contributor-tutorial-contribute-on-github.rst:47 msgid "**Cloning your forked repository**" msgstr "**포크된 레포지토리 클론하기**" @@ -1560,7 +1560,7 @@ msgid "" "it) folder in the current working directory." msgstr "현재 작업 디렉터리에``flower/``(또는 포크 이름을 변경한 경우 포크 이름) 폴더가 생성됩니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:49 +#: ../../source/contributor-tutorial-contribute-on-github.rst:66 msgid "**Add origin**" msgstr "**origin 추가**" @@ -1584,7 +1584,7 @@ msgid "" "terminal:" msgstr "\\ 이 복사되면 터미널에 다음 명령을 입력하면 됩니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:68 +#: ../../source/contributor-tutorial-contribute-on-github.rst:90 msgid "**Add upstream**" msgstr "**Upstream 추가하기**" @@ -1645,7 +1645,7 @@ msgstr "변경하기 전에 레포지토리를 최신 상태로 유지하세요: msgid "And with Flower's repository:" msgstr "Flower의 레포지토리도 있습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:114 +#: ../../source/contributor-tutorial-contribute-on-github.rst:122 msgid "**Create a new branch**" msgstr "**새 브랜치 만들기**" @@ -1662,7 +1662,7 @@ msgid "" "directory:" msgstr "이렇게 하려면 레포지토리 디렉토리에서 다음 명령을 실행하면 됩니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:124 +#: ../../source/contributor-tutorial-contribute-on-github.rst:125 msgid "**Make changes**" msgstr "**변경하기**" @@ -1670,7 +1670,7 @@ msgstr "**변경하기**" msgid "Write great code and create wonderful changes using your favorite editor!" msgstr "선호하는 편집기를 사용하여 멋진 코드를 작성하고 훌륭한 변화를 만들어 보세요!" -#: ../../source/contributor-tutorial-contribute-on-github.rst:127 +#: ../../source/contributor-tutorial-contribute-on-github.rst:138 msgid "**Test and format your code**" msgstr "**코드 테스트 및 서식 지정**" @@ -1687,7 +1687,7 @@ msgstr "" msgid "To do so, we have written a few scripts that you can execute:" msgstr "이를 위해 실행할 수 있는 몇 가지 스크립트를 작성했습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:140 +#: ../../source/contributor-tutorial-contribute-on-github.rst:150 msgid "**Stage changes**" msgstr "**변경사항 스테이징**" @@ -1710,7 +1710,7 @@ msgstr "" "마지막 버전(마지막 커밋)과 비교하여 수정된 파일을 확인하고 커밋을 위해 스테이징된 파일을 확인하려면 :code:`git " "status` 명령을 사용하면 됩니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:152 +#: ../../source/contributor-tutorial-contribute-on-github.rst:160 msgid "**Commit changes**" msgstr "**변경사항 커밋**" @@ -1729,7 +1729,7 @@ msgstr "" "커밋의 내용을 다른 사람에게 설명하기 위해 \\가 있습니다. 명령형 스타일로 작성해야 하며 간결해야" " 합니다. 예를 들면 :code:`git commit -m \"Add images to README\"`." -#: ../../source/contributor-tutorial-contribute-on-github.rst:162 +#: ../../source/contributor-tutorial-contribute-on-github.rst:171 msgid "**Push the changes to the fork**" msgstr "**변경 사항을 포크에 푸시**" @@ -1752,7 +1752,7 @@ msgstr "이 작업이 완료되면 변경한 내용으로 포크된 레포지토 msgid "Creating and merging a pull request (PR)" msgstr "pull request(PR) 만들기 및 병합하기" -#: ../../source/contributor-tutorial-contribute-on-github.rst:176 +#: ../../source/contributor-tutorial-contribute-on-github.rst:206 msgid "**Create the PR**" msgstr "**PR 만들기**" @@ -1823,7 +1823,7 @@ msgid "" "anyone, you have the option to create a draft pull request:" msgstr "PR이 아직 검토할 준비가 되지 않았고 다른 사람에게 알리고 싶지 않은 경우 pull request 초안을 만드는 옵션이 있습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:209 msgid "**Making new changes**" msgstr "**new changes 만들기**" @@ -1834,7 +1834,7 @@ msgid "" " associated with the PR." msgstr "PR이 초안으로 열렸든 아니든, PR과 연결된 브랜치를 변경하여 이전과 같은 방식으로 새 커밋을 푸시할 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:211 +#: ../../source/contributor-tutorial-contribute-on-github.rst:231 msgid "**Review the PR**" msgstr "**PR 검토하기**" @@ -1870,7 +1870,7 @@ msgid "" "review." msgstr "모든 대화가 해결되면 검토를 다시 요청할 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:233 +#: ../../source/contributor-tutorial-contribute-on-github.rst:251 msgid "**Once the PR is merged**" msgstr "**PR이 병합되면**" @@ -2157,6 +2157,7 @@ msgstr "기여자로 시작하기" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 #: ../../source/docker/run-as-subprocess.rst:11 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:15 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:12 #: ../../source/docker/tutorial-quickstart-docker.rst:11 msgid "Prerequisites" @@ -2941,6 +2942,239 @@ msgid "" " the SuperNode to execute the ClientApp as a subprocess:" msgstr "" +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:2 +#, fuzzy +msgid "Run Flower Quickstart Examples with Docker Compose" +msgstr "빠른 시작 튜토리얼" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:4 +msgid "" +"Flower provides a set of `quickstart examples " +"`_ to help you get " +"started with the framework. These examples are designed to demonstrate " +"the capabilities of Flower and by default run using the Simulation " +"Engine. This guide demonstrates how to run them using Flower's Deployment" +" Engine via Docker Compose." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:11 +msgid "" +"Some quickstart examples may have limitations or requirements that " +"prevent them from running on every environment. For more information, " +"please see `Limitations`_." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:17 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:14 +#: ../../source/docker/tutorial-quickstart-docker.rst:13 +#, fuzzy +msgid "Before you start, make sure that:" +msgstr "시작하기 전에 Docker daemon이 실행 중인지 확인하세요:" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:19 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 +#: ../../source/docker/tutorial-quickstart-docker.rst:15 +msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:20 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:17 +#: ../../source/docker/tutorial-quickstart-docker.rst:16 +#, fuzzy +msgid "The Docker daemon is running." +msgstr "Docker 데몬이 실행 중인지 확인하십시오." + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:21 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:18 +msgid "Docker Compose is `installed `_." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:24 +#, fuzzy +msgid "Run the Quickstart Example" +msgstr "예시 요청" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:26 +msgid "" +"Clone the quickstart example you like to run. For example, ``quickstart-" +"pytorch``:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:34 +msgid "" +"Download the `compose.yml " +"`_" +" file into the example directory:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:41 +#, fuzzy +msgid "Build and start the services using the following command:" +msgstr "다음 명령을 실행하여 가상 환경을 활성화합니다:" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:47 +#, fuzzy +msgid "" +"Append the following lines to the end of the ``pyproject.toml`` file and " +"save it:" +msgstr "``pyproject.toml``에 다음 버전 제약 조건을 설정했는지 확인하세요:" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:49 +#: ../../source/docker/tutorial-quickstart-docker.rst:319 +#, fuzzy +msgid "pyproject.toml" +msgstr "또는 ``pyproject.toml``:" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:58 +msgid "" +"You can customize the string that follows ``tool.flwr.federations.`` to " +"fit your needs. However, please note that the string cannot contain a dot" +" (``.``)." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:61 +msgid "" +"In this example, ``local-deployment`` has been used. Just remember to " +"replace ``local-deployment`` with your chosen name in both the " +"``tool.flwr.federations.`` string and the corresponding ``flwr run .`` " +"command." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:65 +#, fuzzy +msgid "Run the example:" +msgstr "전체 코드 예제" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:71 +msgid "Follow the logs of the SuperExec service:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:77 +msgid "" +"That is all it takes! You can monitor the progress of the run through the" +" logs of the SuperExec." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:80 +msgid "Run a Different Quickstart Example" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:82 +msgid "" +"To run a different quickstart example, such as ``quickstart-tensorflow``," +" first, shut down the Docker Compose services of the current example:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:89 +msgid "After that, you can repeat the steps above." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:92 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:98 +#, fuzzy +msgid "Limitations" +msgstr "동기" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:97 +#, fuzzy +msgid "Quickstart Example" +msgstr "빠른 시작" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:99 +#, fuzzy +msgid "quickstart-fastai" +msgstr "빠른 시작 튜토리얼" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:100 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:102 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:110 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:112 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:116 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:118 +#: ../../source/ref-changelog.md:33 ../../source/ref-changelog.md:399 +#: ../../source/ref-changelog.md:676 ../../source/ref-changelog.md:740 +#: ../../source/ref-changelog.md:798 ../../source/ref-changelog.md:867 +#: ../../source/ref-changelog.md:929 +msgid "None" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:101 +#, fuzzy +msgid "quickstart-huggingface" +msgstr "빠른 시작 튜토리얼" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:103 +#, fuzzy +msgid "quickstart-jax" +msgstr "빠른 시작" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:104 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:106 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:120 +msgid "" +"The example has not yet been updated to work with the latest ``flwr`` " +"version." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:105 +#, fuzzy +msgid "quickstart-mlcube" +msgstr "빠른 시작" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:107 +#, fuzzy +msgid "quickstart-mlx" +msgstr "빠른 시작" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:108 +msgid "" +"`Requires to run on macOS with Apple Silicon `_." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:109 +#, fuzzy +msgid "quickstart-monai" +msgstr "빠른 시작" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:111 +#, fuzzy +msgid "quickstart-pandas" +msgstr "빠른 시작 튜토리얼" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:113 +msgid "quickstart-pytorch-lightning" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:114 +msgid "" +"Requires an older pip version that is not supported by the Flower Docker " +"images." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:115 +#, fuzzy +msgid "quickstart-pytorch" +msgstr "빠른 시작 튜토리얼" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:117 +msgid "quickstart-sklearn-tabular" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:119 +#, fuzzy +msgid "quickstart-tabnet" +msgstr "빠른 시작 튜토리얼" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:121 +#, fuzzy +msgid "quickstart-tensorflow" +msgstr "빠른 시작 튜토리얼" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:122 +msgid "Only runs on AMD64." +msgstr "" + #: ../../source/docker/set-environment-variables.rst:2 #, fuzzy msgid "Set Environment Variables" @@ -2972,23 +3206,6 @@ msgid "" " understanding the basic workflow that uses the minimum configurations." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:14 -#: ../../source/docker/tutorial-quickstart-docker.rst:13 -#, fuzzy -msgid "Before you start, make sure that:" -msgstr "시작하기 전에 Docker daemon이 실행 중인지 확인하세요:" - -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 -#: ../../source/docker/tutorial-quickstart-docker.rst:15 -msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." -msgstr "" - -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:17 -#: ../../source/docker/tutorial-quickstart-docker.rst:16 -#, fuzzy -msgid "The Docker daemon is running." -msgstr "Docker 데몬이 실행 중인지 확인하십시오." - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 #: ../../source/docker/tutorial-quickstart-docker.rst:19 msgid "Step 1: Set Up" @@ -3419,11 +3636,6 @@ msgstr "" msgid "Add the following lines to the ``pyproject.toml``:" msgstr "``pyproject.toml``에 다음 버전 제약 조건을 설정했는지 확인하세요:" -#: ../../source/docker/tutorial-quickstart-docker.rst:319 -#, fuzzy -msgid "pyproject.toml" -msgstr "또는 ``pyproject.toml``:" - #: ../../source/docker/tutorial-quickstart-docker.rst:326 msgid "Run the ``quickstart-docker`` project by executing the command:" msgstr "" @@ -3472,6 +3684,7 @@ msgstr "" msgid "Remove the containers and the bridge network:" msgstr "" +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:401 #: ../../source/docker/tutorial-quickstart-docker.rst:399 #, fuzzy msgid "Where to Go Next" @@ -3508,10 +3721,6 @@ msgid "" "configuration that best suits your project's needs." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:18 -msgid "Docker Compose is `installed `_." -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:23 msgid "Clone the Docker Compose ``complete`` directory:" msgstr "" @@ -3707,7 +3916,7 @@ msgstr "" #: ../../source/docker/tutorial-quickstart-docker-compose.rst:188 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:241 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:362 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:369 msgid "Rerun the ``quickstart-compose`` project:" msgstr "" @@ -3771,76 +3980,81 @@ msgstr "" msgid "compose.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:303 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:310 msgid "" "If you also want to enable TLS for the new SuperNodes, duplicate the " "SuperNode definition for each new SuperNode service in the ``with-" "tls.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:306 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:313 msgid "" "Make sure that the names of the services match with the one in the " "``compose.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:308 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:315 msgid "In ``with-tls.yml``, add the following:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:310 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:317 msgid "with-tls.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:332 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:339 msgid "Step 8: Persisting the SuperLink State and Enabling TLS" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:334 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:341 msgid "" "To run Flower with persisted SuperLink state and enabled TLS, a slight " "change in the ``with-state.yml`` file is required:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:337 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:344 msgid "Comment out the lines 2-4 and uncomment the lines 5-9:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:339 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:346 msgid "with-state.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:356 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:363 #, fuzzy msgid "Restart the services:" msgstr "이미 *서버*를 시작할 수 있습니다:" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:370 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:377 msgid "Step 9: Merge Multiple Compose Files" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:372 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:379 msgid "" "You can merge multiple Compose files into a single file. For instance, if" " you wish to combine the basic configuration with the TLS configuration, " "execute the following command:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:380 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:387 msgid "" "This will merge the contents of ``compose.yml`` and ``with-tls.yml`` into" " a new file called ``my_compose.yml``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:384 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:391 msgid "Step 10: Clean Up" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:386 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:393 #, fuzzy msgid "Remove all services and volumes:" msgstr "R에서 모든 항목을 제거합니다." +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:403 +#, fuzzy +msgid ":doc:`run-quickstart-examples-docker-compose`" +msgstr "빠른 시작 튜토리얼" + #: ../../source/docker/use-a-different-version.rst:2 #, fuzzy msgid "Use a Different Flower Version" @@ -4184,7 +4398,7 @@ msgstr "" "code:`CifarClient`는 모델 파라미터를 가져오거나 설정하는 메서드 2개, 모델 학습을 위한 메서드 1개, 모델 테스트를" " 위한 메서드 1개 등 네 가지 메서드를 구현해야 합니다:" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:218 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:219 msgid ":code:`set_parameters`" msgstr ":code:`set_parameters`" @@ -4217,9 +4431,9 @@ msgstr "" "모델 매개변수를 가져와서 NumPy :code:`ndarray`의 목록으로 반환합니다(이는 " ":code:`flwr.client.NumPyClient`가 기대하는 바와 같습니다)" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:223 -#: ../../source/tutorial-quickstart-jax.rst:171 -#: ../../source/tutorial-quickstart-scikitlearn.rst:123 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:225 +#: ../../source/tutorial-quickstart-jax.rst:173 +#: ../../source/tutorial-quickstart-scikitlearn.rst:125 msgid ":code:`fit`" msgstr ":code:`fit`" @@ -4241,9 +4455,9 @@ msgstr "로컬 훈련 세트에서 모델을 훈련합니다" msgid "get the updated local model weights and return them to the server" msgstr "업데이트된 로컬 모델 가중치를 가져와 서버로 반환합니다" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:227 -#: ../../source/tutorial-quickstart-jax.rst:175 -#: ../../source/tutorial-quickstart-scikitlearn.rst:127 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 +#: ../../source/tutorial-quickstart-jax.rst:178 +#: ../../source/tutorial-quickstart-scikitlearn.rst:128 msgid ":code:`evaluate`" msgstr ":code:`evaluate`" @@ -4354,7 +4568,7 @@ msgstr "" "평균 소득 계산과 같은 모든 분석(M)이 두 데이터 세트에 대해 거의 동일한 결과를 산출하도록 보장합니다(O와 O' 는 비슷할 " "것입니다). 이렇게 하면 그룹 패턴은 보존하면서 개별 세부 정보는 가려져 개인의 정보가 군중 속에 숨겨집니다." -#: ../../source/explanation-differential-privacy.rst:16 +#: ../../source/explanation-differential-privacy.rst:-1 msgid "DP Intro" msgstr "DP 소개" @@ -4493,8 +4707,8 @@ msgstr "" "**로컬 차등 프라이버시**: DP는 정보를 서버로 보내기 전에 클라이언트 측에서 적용되며, 서버로 전송되는 업데이트가 클라이언트 " "데이터에 대한 정보를 유출하는 것을 방지하는 것이 목표입니다." +#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:68 -#: ../../source/explanation-differential-privacy.rst:71 #: ../../source/how-to-use-differential-privacy.rst:11 msgid "Central Differential Privacy" msgstr "중앙 차등 프라이버시" @@ -4529,7 +4743,7 @@ msgstr "" "개인정보 보호에 중요하고 견고성에 도움이 되는 경우가 많습니다. 이를 달성하기 위한 일반적인 접근 방식은 클라이언트 모델 업데이트의" " `L2` 규범을 제한하여 더 큰 업데이트가 규범 `S`에 맞도록 축소되도록 하는 것입니다." -#: ../../source/explanation-differential-privacy.rst:84 +#: ../../source/explanation-differential-privacy.rst:-1 msgid "clipping" msgstr "클리핑" @@ -4581,8 +4795,8 @@ msgid "" "others." msgstr "고정 클리핑과 조정 클리핑 중 선택은 개인정보 보호 요구 사항, 데이터 배포, 모델 복잡성 등 다양한 요인에 따라 달라집니다." +#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:105 -#: ../../source/explanation-differential-privacy.rst:110 #: ../../source/how-to-use-differential-privacy.rst:96 msgid "Local Differential Privacy" msgstr "로컬 차등 프라이버시" @@ -4850,7 +5064,7 @@ msgstr "" msgid "This is sometimes called a hub-and-spoke topology:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:18 +#: ../../source/explanation-flower-architecture.rst:24 #, fuzzy msgid "Hub-and-spoke topology in federated learning" msgstr "연합 학습이란 무엇입니까?" @@ -4923,7 +5137,7 @@ msgid "" "`missing link` between all those SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:65 +#: ../../source/explanation-flower-architecture.rst:71 #, fuzzy msgid "Basic Flower architecture" msgstr "Flower 아키텍처" @@ -4960,7 +5174,7 @@ msgid "" "SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:91 +#: ../../source/explanation-flower-architecture.rst:97 msgid "Multi-tenancy federated learning architecture" msgstr "" @@ -4982,7 +5196,7 @@ msgid "" "their corresponding ``ClientApp``\\s:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:107 +#: ../../source/explanation-flower-architecture.rst:113 msgid "Multi-tenancy federated learning architecture - Run 1" msgstr "" @@ -4998,7 +5212,7 @@ msgid "" " to participate in the training:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:119 +#: ../../source/explanation-flower-architecture.rst:125 msgid "Multi-tenancy federated learning architecture - Run 2" msgstr "" @@ -5034,7 +5248,7 @@ msgid "" "developer machine." msgstr "" -#: ../../source/explanation-flower-architecture.rst:145 +#: ../../source/explanation-flower-architecture.rst:151 msgid "Flower Deployment Engine with SuperExec" msgstr "" @@ -8205,7 +8419,7 @@ msgstr "" " 위한 :code:`DifferentialPrivacyServerSideFixedClipping`과 " ":code:`DifferentialPrivacyServerSideAdaptiveClipping`입니다." -#: ../../source/how-to-use-differential-privacy.rst:25 +#: ../../source/how-to-use-differential-privacy.rst:-1 msgid "server side clipping" msgstr "서버 측 클리핑" @@ -8244,7 +8458,7 @@ msgstr "" " 해당 서버 측 래퍼 :code:`DifferentialPrivacyClientSideFixedClipping` 및 " ":code:`DifferentialPrivacyClientSideAdaptiveClipping`이 있습니다." -#: ../../source/how-to-use-differential-privacy.rst:57 +#: ../../source/how-to-use-differential-privacy.rst:-1 msgid "client side clipping" msgstr "클라이언트 측 클리핑" @@ -8278,7 +8492,7 @@ msgstr "" "로컬 차분 프라이버시(DP)를 활용하고 클라이언트 모델 파라미터를 서버로 전송하기 전에 노이즈를 추가하려면 `LocalDpMod`를" " 사용하면 됩니다. 클리핑 노멀 값, 감도, 엡실론, 델타 등의 하이퍼파라미터를 설정해야 합니다." -#: ../../source/how-to-use-differential-privacy.rst:99 +#: ../../source/how-to-use-differential-privacy.rst:-1 msgid "local DP mod" msgstr "로컬 DP mod" @@ -8704,11 +8918,33 @@ msgstr "" msgid "Arguments" msgstr "빌드 전달인자" -#: ../../flwr install:1 new:1 run:1 +#: ../../flwr install:1 log:1 new:1 run:1 #, fuzzy msgid "Optional argument" msgstr "선택적 개선 사항" +#: ../../flwr log:1 +msgid "Get logs from a Flower project run." +msgstr "" + +#: ../../flwr log:1 +msgid "Flag to stream or print logs from the Flower run" +msgstr "" + +#: ../../flwr log +msgid "default" +msgstr "" + +#: ../../flwr log:1 +#, fuzzy +msgid "``True``" +msgstr "``DISTRO``" + +#: ../../flwr log:1 +#, fuzzy +msgid "Required argument" +msgstr "빌드 전달인자" + #: ../../flwr new:1 #, fuzzy msgid "Create new Flower App." @@ -8797,7 +9033,7 @@ msgstr "Modules" #: ../../source/ref-api/flwr.rst:35::1 #, fuzzy -msgid ":py:obj:`client `\\" +msgid ":py:obj:`flwr.client `\\" msgstr ":py:obj:`flwr.client `\\" #: ../../source/ref-api/flwr.rst:35::1 flwr.client:1 of @@ -8806,7 +9042,7 @@ msgstr "Flower 클라이언트." #: ../../source/ref-api/flwr.rst:35::1 #, fuzzy -msgid ":py:obj:`common `\\" +msgid ":py:obj:`flwr.common `\\" msgstr ":py:obj:`flwr.common `\\" #: ../../source/ref-api/flwr.rst:35::1 flwr.common:1 of @@ -8815,7 +9051,7 @@ msgstr "서버와 클라이언트 간에 공유되는 공통 구성 요소입니 #: ../../source/ref-api/flwr.rst:35::1 #, fuzzy -msgid ":py:obj:`server `\\" +msgid ":py:obj:`flwr.server `\\" msgstr ":py:obj:`flwr.server `\\" #: ../../source/ref-api/flwr.rst:35::1 @@ -8826,7 +9062,7 @@ msgstr "Flower 서버." #: ../../source/ref-api/flwr.rst:35::1 #, fuzzy -msgid ":py:obj:`simulation `\\" +msgid ":py:obj:`flwr.simulation `\\" msgstr ":py:obj:`flwr.simulation `\\" #: ../../source/ref-api/flwr.rst:35::1 flwr.simulation:1 of @@ -8913,7 +9149,7 @@ msgstr "NumPy를 사용하는 Flower 클라이언트를 위한 추상 베이스 #: ../../source/ref-api/flwr.client.rst:50::1 #, fuzzy -msgid ":py:obj:`mod `\\" +msgid ":py:obj:`flwr.client.mod `\\" msgstr ":py:obj:`flwr.client.mod `\\" #: ../../source/ref-api/flwr.client.rst:50::1 flwr.client.mod:1 of @@ -9110,48 +9346,57 @@ msgstr ":py:obj:`context `\\" msgid "Getter for `Context` client attribute." msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst -#: ../../source/ref-api/flwr.client.NumPyClient.rst -#: ../../source/ref-api/flwr.client.mod.LocalDpMod.rst -#: ../../source/ref-api/flwr.common.Array.rst -#: ../../source/ref-api/flwr.common.ConfigsRecord.rst -#: ../../source/ref-api/flwr.common.Context.rst -#: ../../source/ref-api/flwr.common.Error.rst -#: ../../source/ref-api/flwr.common.Message.rst -#: ../../source/ref-api/flwr.common.Metadata.rst -#: ../../source/ref-api/flwr.common.MetricsRecord.rst #: ../../source/ref-api/flwr.common.Parameters.rst:2 -#: ../../source/ref-api/flwr.common.ParametersRecord.rst -#: ../../source/ref-api/flwr.common.RecordSet.rst -#: ../../source/ref-api/flwr.server.ClientManager.rst -#: ../../source/ref-api/flwr.server.Driver.rst -#: ../../source/ref-api/flwr.server.ServerAppComponents.rst -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst -#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideAdaptiveClipping.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideFixedClipping.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideAdaptiveClipping.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideFixedClipping.rst -#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst -#: ../../source/ref-api/flwr.server.strategy.FedAdam.rst -#: ../../source/ref-api/flwr.server.strategy.FedAvg.rst -#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst -#: ../../source/ref-api/flwr.server.strategy.FedAvgM.rst -#: ../../source/ref-api/flwr.server.strategy.FedOpt.rst -#: ../../source/ref-api/flwr.server.strategy.FedProx.rst -#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst -#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst -#: ../../source/ref-api/flwr.server.strategy.Krum.rst -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst -#: ../../source/ref-api/flwr.server.workflow.SecAggPlusWorkflow.rst -#: ../../source/ref-api/flwr.server.workflow.SecAggWorkflow.rst -#: ../../source/ref-api/flwr.simulation.run_simulation.rst -#: ../../source/ref-api/flwr.simulation.start_simulation.rst #: flwr.client.app.start_client flwr.client.app.start_numpy_client -#: flwr.server.app.start_server -#: flwr.server.driver.driver.Driver.send_and_receive of +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.mod.localdp_mod.LocalDpMod +#: flwr.client.numpy_client.NumPyClient.evaluate +#: flwr.client.numpy_client.NumPyClient.fit +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.context.Context flwr.common.message.Error +#: flwr.common.message.Message flwr.common.message.Message.create_error_reply +#: flwr.common.message.Message.create_reply flwr.common.message.Metadata +#: flwr.common.record.configsrecord.ConfigsRecord +#: flwr.common.record.metricsrecord.MetricsRecord +#: flwr.common.record.parametersrecord.Array +#: flwr.common.record.parametersrecord.ParametersRecord +#: flwr.common.record.recordset.RecordSet flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.ClientManager.unregister +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.unregister +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.serverapp_components.ServerAppComponents +#: flwr.server.strategy.bulyan.Bulyan +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.fedadagrad.FedAdagrad +#: flwr.server.strategy.fedadam.FedAdam flwr.server.strategy.fedavg.FedAvg +#: flwr.server.strategy.fedavg_android.FedAvgAndroid +#: flwr.server.strategy.fedavgm.FedAvgM flwr.server.strategy.fedopt.FedOpt +#: flwr.server.strategy.fedprox.FedProx +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg +#: flwr.server.strategy.fedyogi.FedYogi flwr.server.strategy.krum.Krum +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow +#: flwr.simulation.run_simulation.run_simulation of msgid "Parameters" msgstr "파라미터" @@ -9162,21 +9407,31 @@ msgid "" "customize the local evaluation process." msgstr "서버에서 받은 (전역) 모델 파라미터와 로컬 평가 프로세스를 사용자 지정하는 데 사용되는 구성 값 사전이 포함된 평가 지침입니다." -#: ../../source/ref-api/flwr.client.Client.rst -#: ../../source/ref-api/flwr.client.NumPyClient.rst -#: ../../source/ref-api/flwr.common.ConfigsRecord.rst -#: ../../source/ref-api/flwr.common.Message.rst -#: ../../source/ref-api/flwr.common.MetricsRecord.rst -#: ../../source/ref-api/flwr.common.ParametersRecord.rst -#: ../../source/ref-api/flwr.server.ClientManager.rst -#: ../../source/ref-api/flwr.server.Driver.rst -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst -#: ../../source/ref-api/flwr.simulation.start_simulation.rst -#: flwr.server.app.start_server -#: flwr.server.driver.driver.Driver.send_and_receive of +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.numpy_client.NumPyClient.evaluate +#: flwr.client.numpy_client.NumPyClient.fit +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.message.Message.create_reply flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.num_available +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.SimpleClientManager.num_available +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters of msgid "Returns" msgstr "반환" @@ -9186,18 +9441,29 @@ msgid "" "details such as the number of local data examples used for evaluation." msgstr "로컬 데이터 세트의 손실 및 평가에 사용된 로컬 데이터 예제 수와 같은 기타 세부 정보가 포함된 평가 결과입니다." -#: ../../source/ref-api/flwr.client.Client.rst -#: ../../source/ref-api/flwr.client.NumPyClient.rst -#: ../../source/ref-api/flwr.common.Message.rst -#: ../../source/ref-api/flwr.server.ClientManager.rst -#: ../../source/ref-api/flwr.server.Driver.rst -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst -#: ../../source/ref-api/flwr.simulation.start_simulation.rst -#: flwr.server.app.start_server -#: flwr.server.driver.driver.Driver.send_and_receive of +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.message.Message.create_reply flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.num_available +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.SimpleClientManager.num_available +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters of msgid "Return type" msgstr "반환 타입" @@ -9554,6 +9820,11 @@ msgstr "클라이언트 측 고정 클리핑 수정자." msgid ":py:obj:`make_ffn `\\ \\(ffn\\, mods\\)" msgstr ":py:obj:`make_ffn `\\ \\(ffn\\, mods\\)" +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.utils.make_ffn:1 of +msgid "." +msgstr "." + #: ../../source/ref-api/flwr.client.mod.rst:28::1 msgid "" ":py:obj:`message_size_mod `\\ \\(msg\\," @@ -9730,10 +10001,6 @@ msgstr "일반적으로 fixedclipping_mod는 매개변수에서 가장 마지막 msgid "make\\_ffn" msgstr "make\\_ffn" -#: flwr.client.mod.utils.make_ffn:1 of -msgid "." -msgstr "." - #: ../../source/ref-api/flwr.client.mod.message_size_mod.rst:2 msgid "message\\_size\\_mod" msgstr "message\\_size\\_mod" @@ -9760,14 +10027,6 @@ msgstr "secagg\\_mod" msgid "secaggplus\\_mod" msgstr "secaggplus\\_mod" -#: ../../source/ref-api/flwr.client.run_client_app.rst:2 -msgid "run\\_client\\_app" -msgstr "run\\_client\\_app" - -#: ../../source/ref-api/flwr.client.run_supernode.rst:2 -msgid "run\\_supernode" -msgstr "run\\_supernode" - #: ../../source/ref-api/flwr.client.start_client.rst:2 msgid "start\\_client" msgstr "start\\_client" @@ -10569,14 +10828,9 @@ msgstr "이 객체에 저장된 바이트 수를 반환합니다." #: collections.abc.MutableMapping.clear:1::1 of #, fuzzy -msgid ":py:obj:`get `\\ \\(key\\[\\, default\\]\\)" +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" -#: collections.abc.Mapping.get:1 -#: collections.abc.MutableMapping.clear:1::1 of -msgid "Retrieve the corresponding layout by the string key." -msgstr "" - #: collections.abc.MutableMapping.clear:1::1 of msgid ":py:obj:`items `\\ \\(\\)" msgstr ":py:obj:`items `\\ \\(\\)" @@ -10635,22 +10889,6 @@ msgstr ":py:obj:`values `\\ \\(\\)" msgid "This function counts booleans as occupying 1 Byte." msgstr "이 함수는 booleans을 1바이트를 차지하는 것으로 계산합니다." -#: collections.abc.Mapping.get:3 of -msgid "" -"When there isn't an exact match, all the existing keys in the layout map " -"will be treated as a regex and map against the input key again. The first" -" match will be returned, based on the key insertion order. Return None if" -" there isn't any match found." -msgstr "" - -#: collections.abc.Mapping.get:8 of -msgid "the string key as the query for the layout." -msgstr "" - -#: collections.abc.Mapping.get:10 of -msgid "Corresponding layout based on the query." -msgstr "" - #: ../../source/ref-api/flwr.common.Context.rst:2 msgid "Context" msgstr "컨텍스트" @@ -11446,7 +11684,7 @@ msgstr "인코딩" msgid "The encoding in which to encode the string." msgstr "문자열을 인코딩합니다." -#: flwr.common.EventType.encode:5 of +#: flwr.common.EventType.encode:9 of msgid "errors" msgstr "오류" @@ -11640,7 +11878,7 @@ msgstr "" "문자열이 접미사 문자열로 끝나고 해당 접미사가 비어 있지 않으면 문자열[:-len(suffix)]을 반환합니다. 그렇지 않으면 원본" " 문자열의 복사본을 반환합니다." -#: flwr.common.EventType.replace:3 of +#: flwr.common.EventType.replace:5 of msgid "count" msgstr "카운트" @@ -11680,7 +11918,7 @@ msgid "" "strings and the original string." msgstr "구분 기호를 찾을 수 없는 경우 빈 문자열 2개와 원래 문자열을 포함하는 3-tuple을 반환합니다." -#: flwr.common.EventType.rsplit:3 flwr.common.EventType.split:3 of +#: flwr.common.EventType.rsplit:7 flwr.common.EventType.split:7 of msgid "sep" msgstr "sep" @@ -11697,7 +11935,7 @@ msgstr "" "None(기본값)으로 설정하면 모든 공백 문자(\\\\n \\\\r \\\\t \\\\f 및 공백 포함)를 분할하고 결과에서 빈 " "문자열을 삭제합니다." -#: flwr.common.EventType.rsplit:9 flwr.common.EventType.split:9 of +#: flwr.common.EventType.rsplit:11 flwr.common.EventType.split:11 of msgid "maxsplit" msgstr "maxsplit" @@ -11743,7 +11981,7 @@ msgid "" "remaining cased characters have lower case." msgstr "보다 구체적으로, 단어는 대문자로 시작하고 나머지 모든 대소문자는 소문자로 표기합니다." -#: flwr.common.EventType.translate:3 of +#: flwr.common.EventType.translate:5 of msgid "table" msgstr "table" @@ -12178,7 +12416,7 @@ msgstr ":py:obj:`count_bytes `\\ \\(\\)" #: collections.abc.MutableMapping.clear:1::1 of #, fuzzy -msgid ":py:obj:`get `\\ \\(key\\[\\, default\\]\\)" +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" #: collections.abc.MutableMapping.clear:1::1 of @@ -12325,9 +12563,7 @@ msgstr ":py:obj:`count_bytes `\\ \\(\\ #: collections.abc.MutableMapping.clear:1::1 of #, fuzzy -msgid "" -":py:obj:`get `\\ \\(key\\[\\, " -"default\\]\\)" +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" #: collections.abc.MutableMapping.clear:1::1 of @@ -12669,7 +12905,7 @@ msgstr "사용 가능한 클라이언트 그룹 제공." #: ../../source/ref-api/flwr.server.rst:56::1 #, fuzzy -msgid ":py:obj:`strategy `\\" +msgid ":py:obj:`flwr.server.strategy `\\" msgstr ":py:obj:`state `\\" #: ../../source/ref-api/flwr.server.rst:56::1 @@ -12679,7 +12915,7 @@ msgstr "" #: ../../source/ref-api/flwr.server.rst:56::1 #, fuzzy -msgid ":py:obj:`workflow `\\" +msgid ":py:obj:`flwr.server.workflow `\\" msgstr ":py:obj:`flwr.server `\\" #: ../../source/ref-api/flwr.server.rst:56::1 @@ -13161,8 +13397,7 @@ msgid "" msgstr "" #: flwr.server.app.start_server:9 -#: flwr.server.serverapp_components.ServerAppComponents:6 -#: flwr.simulation.app.start_simulation:29 of +#: flwr.server.serverapp_components.ServerAppComponents:6 of msgid "" "Currently supported values are `num_rounds` (int, default: 1) and " "`round_timeout` in seconds (float, default: None)." @@ -13284,14 +13519,6 @@ msgstr "" msgid "**success**" msgstr "" -#: ../../source/ref-api/flwr.server.run_server_app.rst:2 -msgid "run\\_server\\_app" -msgstr "" - -#: ../../source/ref-api/flwr.server.run_superlink.rst:2 -msgid "run\\_superlink" -msgstr "" - #: ../../source/ref-api/flwr.server.start_server.rst:2 msgid "start\\_server" msgstr "" @@ -16319,15 +16546,15 @@ msgstr "" #: ../../source/ref-api/flwr.simulation.rst:18::1 #, fuzzy msgid "" -":py:obj:`start_simulation `\\ \\(\\*\\," -" client\\_fn\\, num\\_clients\\)" +":py:obj:`start_simulation `\\ " +"\\(\\*args\\, \\*\\*kwargs\\)" msgstr "" ":py:obj:`start_client `\\ \\(\\*\\, " "server\\_address\\[\\, client\\_fn\\, ...\\]\\)" #: ../../source/ref-api/flwr.simulation.rst:18::1 -#: flwr.simulation.app.start_simulation:1 of -msgid "Start a Ray-based Flower simulation server." +#: flwr.simulation.start_simulation:1 of +msgid "Log error stating that module `ray` could not be imported." msgstr "" #: ../../source/ref-api/flwr.simulation.run_simulation.rst:2 @@ -16388,170 +16615,56 @@ msgstr "" msgid "start\\_simulation" msgstr "" -#: flwr.simulation.app.start_simulation:3 of -msgid "" -"A function creating `Client` instances. The function must have the " -"signature `client_fn(context: Context). It should return a single client " -"instance of type `Client`. Note that the created client instances are " -"ephemeral and will often be destroyed after a single method invocation. " -"Since client instances are not long-lived, they should not attempt to " -"carry state over method invocations. Any state required by the instance " -"(model, dataset, hyperparameters, ...) should be (re-)created in either " -"the call to `client_fn` or the call to any of the client methods (e.g., " -"load evaluation data in the `evaluate` method itself)." +#: ../../source/ref-changelog.md:1 +msgid "Changelog" msgstr "" -#: flwr.simulation.app.start_simulation:13 of -msgid "The total number of clients in this simulation." +#: ../../source/ref-changelog.md:3 +msgid "v1.11.1 (2024-09-11)" msgstr "" -#: flwr.simulation.app.start_simulation:15 of -msgid "" -"UNSUPPORTED, WILL BE REMOVED. USE `num_clients` INSTEAD. List " -"`client_id`s for each client. This is only required if `num_clients` is " -"not set. Setting both `num_clients` and `clients_ids` with " -"`len(clients_ids)` not equal to `num_clients` generates an error. Using " -"this argument will raise an error." +#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:37 +#: ../../source/ref-changelog.md:141 ../../source/ref-changelog.md:239 +#: ../../source/ref-changelog.md:339 ../../source/ref-changelog.md:403 +#: ../../source/ref-changelog.md:496 ../../source/ref-changelog.md:596 +#: ../../source/ref-changelog.md:680 ../../source/ref-changelog.md:744 +#: ../../source/ref-changelog.md:802 ../../source/ref-changelog.md:871 +#: ../../source/ref-changelog.md:940 +msgid "Thanks to our contributors" msgstr "" -#: flwr.simulation.app.start_simulation:21 of +#: ../../source/ref-changelog.md:7 ../../source/ref-changelog.md:39 +#: ../../source/ref-changelog.md:143 ../../source/ref-changelog.md:241 +#: ../../source/ref-changelog.md:341 ../../source/ref-changelog.md:405 +#: ../../source/ref-changelog.md:498 ../../source/ref-changelog.md:598 +#: ../../source/ref-changelog.md:682 ../../source/ref-changelog.md:746 +#: ../../source/ref-changelog.md:804 msgid "" -"CPU and GPU resources for a single client. Supported keys are `num_cpus` " -"and `num_gpus`. To understand the GPU utilization caused by `num_gpus`, " -"as well as using custom resources, please consult the Ray documentation." +"We would like to give our special thanks to all the contributors who made" +" the new version of Flower possible (in `git shortlog` order):" msgstr "" -#: flwr.simulation.app.start_simulation:26 of +#: ../../source/ref-changelog.md:9 msgid "" -"An implementation of the abstract base class `flwr.server.Server`. If no " -"instance is provided, then `start_server` will create one." +"`Charles Beauville`, `Chong Shen Ng`, `Daniel J. Beutel`, `Heng Pan`, " +"`Javier`, `Robert Steiner`, `Yan Gao` " msgstr "" -#: flwr.simulation.app.start_simulation:32 of +#: ../../source/ref-changelog.md:11 +#, fuzzy +msgid "Improvements" +msgstr "선택적 개선 사항" + +#: ../../source/ref-changelog.md:13 msgid "" -"An implementation of the abstract base class `flwr.server.Strategy`. If " -"no strategy is provided, then `start_server` will use " -"`flwr.server.strategy.FedAvg`." +"**Implement** `keys/values/items` **methods for** `TypedDict` " +"([#4146](https://github.com/adap/flower/pull/4146))" msgstr "" -#: flwr.simulation.app.start_simulation:36 of +#: ../../source/ref-changelog.md:15 msgid "" -"An implementation of the abstract base class `flwr.server.ClientManager`." -" If no implementation is provided, then `start_simulation` will use " -"`flwr.server.client_manager.SimpleClientManager`." -msgstr "" - -#: flwr.simulation.app.start_simulation:40 of -msgid "" -"Optional dictionary containing arguments for the call to `ray.init`. If " -"ray_init_args is None (the default), Ray will be initialized with the " -"following default args: { \"ignore_reinit_error\": True, " -"\"include_dashboard\": False } An empty dictionary can be used " -"(ray_init_args={}) to prevent any arguments from being passed to " -"ray.init." -msgstr "" - -#: flwr.simulation.app.start_simulation:40 of -msgid "" -"Optional dictionary containing arguments for the call to `ray.init`. If " -"ray_init_args is None (the default), Ray will be initialized with the " -"following default args:" -msgstr "" - -#: flwr.simulation.app.start_simulation:44 of -msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" -msgstr "" - -#: flwr.simulation.app.start_simulation:46 of -msgid "" -"An empty dictionary can be used (ray_init_args={}) to prevent any " -"arguments from being passed to ray.init." -msgstr "" - -#: flwr.simulation.app.start_simulation:49 of -msgid "" -"Set to True to prevent `ray.shutdown()` in case " -"`ray.is_initialized()=True`." -msgstr "" - -#: flwr.simulation.app.start_simulation:51 of -msgid "" -"Optionally specify the type of actor to use. The actor object, which " -"persists throughout the simulation, will be the process in charge of " -"executing a ClientApp wrapping input argument `client_fn`." -msgstr "" - -#: flwr.simulation.app.start_simulation:55 of -msgid "" -"If you want to create your own Actor classes, you might need to pass some" -" input argument. You can use this dictionary for such purpose." -msgstr "" - -#: flwr.simulation.app.start_simulation:58 of -msgid "" -"(default: \"DEFAULT\") Optional string (\"DEFAULT\" or \"SPREAD\") for " -"the VCE to choose in which node the actor is placed. If you are an " -"advanced user needed more control you can use lower-level scheduling " -"strategies to pin actors to specific compute nodes (e.g. via " -"NodeAffinitySchedulingStrategy). Please note this is an advanced feature." -" For all details, please refer to the Ray documentation: " -"https://docs.ray.io/en/latest/ray-core/scheduling/index.html" -msgstr "" - -#: flwr.simulation.app.start_simulation:67 of -msgid "**hist** -- Object containing metrics from training." -msgstr "" - -#: ../../source/ref-changelog.md:1 -msgid "Changelog" -msgstr "" - -#: ../../source/ref-changelog.md:3 -msgid "v1.11.1 (2024-09-11)" -msgstr "" - -#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:37 -#: ../../source/ref-changelog.md:141 ../../source/ref-changelog.md:239 -#: ../../source/ref-changelog.md:339 ../../source/ref-changelog.md:403 -#: ../../source/ref-changelog.md:496 ../../source/ref-changelog.md:596 -#: ../../source/ref-changelog.md:680 ../../source/ref-changelog.md:744 -#: ../../source/ref-changelog.md:802 ../../source/ref-changelog.md:871 -#: ../../source/ref-changelog.md:940 -msgid "Thanks to our contributors" -msgstr "" - -#: ../../source/ref-changelog.md:7 ../../source/ref-changelog.md:39 -#: ../../source/ref-changelog.md:143 ../../source/ref-changelog.md:241 -#: ../../source/ref-changelog.md:341 ../../source/ref-changelog.md:405 -#: ../../source/ref-changelog.md:498 ../../source/ref-changelog.md:598 -#: ../../source/ref-changelog.md:682 ../../source/ref-changelog.md:746 -#: ../../source/ref-changelog.md:804 -msgid "" -"We would like to give our special thanks to all the contributors who made" -" the new version of Flower possible (in `git shortlog` order):" -msgstr "" - -#: ../../source/ref-changelog.md:9 -msgid "" -"`Charles Beauville`, `Chong Shen Ng`, `Daniel J. Beutel`, `Heng Pan`, " -"`Javier`, `Robert Steiner`, `Yan Gao` " -msgstr "" - -#: ../../source/ref-changelog.md:11 -#, fuzzy -msgid "Improvements" -msgstr "선택적 개선 사항" - -#: ../../source/ref-changelog.md:13 -msgid "" -"**Implement** `keys/values/items` **methods for** `TypedDict` " -"([#4146](https://github.com/adap/flower/pull/4146))" -msgstr "" - -#: ../../source/ref-changelog.md:15 -msgid "" -"**Fix parsing of** `--executor-config` **if present** " -"([#4125](https://github.com/adap/flower/pull/4125))" +"**Fix parsing of** `--executor-config` **if present** " +"([#4125](https://github.com/adap/flower/pull/4125))" msgstr "" #: ../../source/ref-changelog.md:17 @@ -16608,13 +16721,6 @@ msgstr "" msgid "Incompatible changes" msgstr "" -#: ../../source/ref-changelog.md:33 ../../source/ref-changelog.md:399 -#: ../../source/ref-changelog.md:676 ../../source/ref-changelog.md:740 -#: ../../source/ref-changelog.md:798 ../../source/ref-changelog.md:867 -#: ../../source/ref-changelog.md:929 -msgid "None" -msgstr "" - #: ../../source/ref-changelog.md:35 msgid "v1.11.0 (2024-08-30)" msgstr "" @@ -21742,32 +21848,44 @@ msgid "" "blockchain environment is available here:" msgstr "" -#: ../../source/ref-faq.rst:28 +#: ../../source/ref-faq.rst:29 +msgid "`FLock: A Decentralised AI Training Platform `_." +msgstr "" + +#: ../../source/ref-faq.rst:29 +msgid "Contribute to on-chain training the model and earn rewards." +msgstr "" + +#: ../../source/ref-faq.rst:30 +msgid "Local blockchain with federated learning simulation." +msgstr "" + +#: ../../source/ref-faq.rst:31 msgid "" "`Flower meets Nevermined GitHub Repository `_." msgstr "" -#: ../../source/ref-faq.rst:29 +#: ../../source/ref-faq.rst:32 msgid "" "`Flower meets Nevermined YouTube video " "`_." msgstr "" -#: ../../source/ref-faq.rst:30 +#: ../../source/ref-faq.rst:33 msgid "" "`Flower meets KOSMoS `_." msgstr "" -#: ../../source/ref-faq.rst:31 +#: ../../source/ref-faq.rst:34 msgid "" "`Flower meets Talan blog post `_ ." msgstr "" -#: ../../source/ref-faq.rst:32 +#: ../../source/ref-faq.rst:35 msgid "" "`Flower meets Talan GitHub Repository " "`_ ." @@ -21990,178 +22108,298 @@ msgid "" "more." msgstr "" -#: ../../source/tutorial-quickstart-fastai.rst:-1 -msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with FastAI to train a vision model on CIFAR-10." -msgstr "" - #: ../../source/tutorial-quickstart-fastai.rst:5 msgid "Quickstart fastai" msgstr "" -#: ../../source/tutorial-quickstart-fastai.rst:10 -msgid "Let's build a federated learning system using fastai and Flower!" +#: ../../source/tutorial-quickstart-fastai.rst:7 +msgid "" +"In this federated learning tutorial we will learn how to train a " +"SqueezeNet model on MNIST using Flower and fastai. It is recommended to " +"create a virtual environment and run everything within a :doc:`virtualenv" +" `." msgstr "" #: ../../source/tutorial-quickstart-fastai.rst:12 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 +msgid "Then, clone the code example directly from GitHub:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:20 msgid "" -"Please refer to the `full code example " -"`_ " -"to learn more." +"This will create a new directory called `quickstart-fastai` containing " +"the following files:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:33 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:33 +#, fuzzy +msgid "Next, activate your environment, then run:" +msgstr "그 후 가상 환경을 활성화합니다:" + +#: ../../source/tutorial-quickstart-fastai.rst:43 +msgid "" +"This example by default runs the Flower Simulation Engine, creating a " +"federation of 10 nodes using `FedAvg `_ " +"as the aggregation strategy. The dataset will be partitioned using Flower" +" Dataset's `IidPartitioner `_." +" Let's run the project:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:56 +#: ../../source/tutorial-quickstart-huggingface.rst:65 +#: ../../source/tutorial-quickstart-mlx.rst:64 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:56 +#: ../../source/tutorial-quickstart-pytorch.rst:64 +#: ../../source/tutorial-quickstart-tensorflow.rst:65 +msgid "With default arguments you will see an output like this one:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:100 +#: ../../source/tutorial-quickstart-huggingface.rst:116 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:106 +#: ../../source/tutorial-quickstart-pytorch.rst:105 +#: ../../source/tutorial-quickstart-tensorflow.rst:106 +msgid "" +"You can also override the parameters defined in the " +"``[tool.flwr.app.config]`` section in ``pyproject.toml`` like this:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:110 +msgid "" +"Check the `source code `_ of this tutorial in ``examples/quickstart-fasai`` " +"in the Flower GitHub repository." msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:-1 msgid "" "Check out this Federating Learning quickstart tutorial for using Flower " -"with HuggingFace Transformers in order to fine-tune an LLM." +"with 🤗 HuggingFace Transformers in order to fine-tune an LLM." msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:5 msgid "Quickstart 🤗 Transformers" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:10 +#: ../../source/tutorial-quickstart-huggingface.rst:7 msgid "" -"Let's build a federated learning system using Hugging Face Transformers " -"and Flower!" +"In this federated learning tutorial we will learn how to train a large " +"language model (LLM) on the `IMDB " +"`_ dataset using Flower" +" and the 🤗 Hugging Face Transformers library. It is recommended to create" +" a virtual environment and run everything within a :doc:`virtualenv " +"`." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:12 +#: ../../source/tutorial-quickstart-huggingface.rst:14 msgid "" -"We will leverage Hugging Face to federate the training of language models" -" over multiple clients using Flower. More specifically, we will fine-tune" -" a pre-trained Transformer model (distilBERT) for sequence classification" -" over a dataset of IMDB ratings. The end goal is to detect if a movie " -"rating is positive or negative." -msgstr "" - -#: ../../source/tutorial-quickstart-huggingface.rst:18 -msgid "Dependencies" +"Let's use ``flwr new`` to create a complete Flower+🤗 Hugging Face " +"project. It will generate all the files needed to run, by default with " +"the Flower Simulation Engine, a federation of 10 nodes using |fedavg|_ " +"The dataset will be partitioned using |flowerdatasets|_'s " +"|iidpartitioner|_." msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:20 +#: ../../source/tutorial-quickstart-mlx.rst:19 +#: ../../source/tutorial-quickstart-pytorch.rst:19 +#: ../../source/tutorial-quickstart-tensorflow.rst:20 msgid "" -"To follow along this tutorial you will need to install the following " -"packages: :code:`datasets`, :code:`evaluate`, :code:`flwr`, " -":code:`torch`, and :code:`transformers`. This can be done using " -":code:`pip`:" +"Now that we have a rough idea of what this example is about, let's get " +"started. First, install Flower in your new environment:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:30 -msgid "Standard Hugging Face workflow" +#: ../../source/tutorial-quickstart-huggingface.rst:28 +msgid "" +"Then, run the command below. You will be prompted to select one of the " +"available templates (choose ``HuggingFace``), give a name to your " +"project, and type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:33 -msgid "Handling the data" +#: ../../source/tutorial-quickstart-huggingface.rst:36 +#: ../../source/tutorial-quickstart-mlx.rst:35 +#: ../../source/tutorial-quickstart-pytorch.rst:35 +#: ../../source/tutorial-quickstart-tensorflow.rst:36 +msgid "" +"After running it you'll notice a new directory with your project name has" +" been created. It should have the following structure:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:35 +#: ../../source/tutorial-quickstart-huggingface.rst:50 +#: ../../source/tutorial-quickstart-mlx.rst:49 +#: ../../source/tutorial-quickstart-pytorch.rst:49 +#: ../../source/tutorial-quickstart-tensorflow.rst:50 msgid "" -"To fetch the IMDB dataset, we will use Hugging Face's :code:`datasets` " -"library. We then need to tokenize the data and create :code:`PyTorch` " -"dataloaders, this is all done in the :code:`load_data` function:" +"If you haven't yet installed the project and its dependencies, you can do" +" so by:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:81 -msgid "Training and testing the model" +#: ../../source/tutorial-quickstart-huggingface.rst:58 +#: ../../source/tutorial-quickstart-pytorch.rst:57 +#: ../../source/tutorial-quickstart-tensorflow.rst:58 +msgid "To run the project, do:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:83 -msgid "" -"Once we have a way of creating our trainloader and testloader, we can " -"take care of the training and testing. This is very similar to any " -":code:`PyTorch` training or testing loop:" +#: ../../source/tutorial-quickstart-huggingface.rst:106 +msgid "You can also run the project with GPU as follows:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:121 -msgid "Creating the model itself" +#: ../../source/tutorial-quickstart-huggingface.rst:113 +msgid "" +"This will use the default arguments where each ``ClientApp`` will use 2 " +"CPUs and at most 4 ``ClientApp``\\s will run in a given GPU." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:123 +#: ../../source/tutorial-quickstart-huggingface.rst:124 +#: ../../source/tutorial-quickstart-mlx.rst:114 +#: ../../source/tutorial-quickstart-pytorch.rst:113 msgid "" -"To create the model itself, we will just load the pre-trained distillBERT" -" model using Hugging Face’s :code:`AutoModelForSequenceClassification` :" +"What follows is an explanation of each component in the project you just " +"created: dataset partition, the model, defining the ``ClientApp`` and " +"defining the ``ServerApp``." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:136 -msgid "Federating the example" -msgstr "" +#: ../../source/tutorial-quickstart-huggingface.rst:130 +#: ../../source/tutorial-quickstart-mlx.rst:120 +#: ../../source/tutorial-quickstart-pytorch.rst:119 +#: ../../source/tutorial-quickstart-tensorflow.rst:116 +#, fuzzy +msgid "The Data" +msgstr "Metadata" -#: ../../source/tutorial-quickstart-huggingface.rst:139 -msgid "Creating the IMDBClient" +#: ../../source/tutorial-quickstart-huggingface.rst:132 +msgid "" +"This tutorial uses |flowerdatasets|_ to easily download and partition the" +" `IMDB `_ dataset. In " +"this example you'll make use of the |iidpartitioner|_ to generate " +"``num_partitions`` partitions. You can choose |otherpartitioners|_ " +"available in Flower Datasets. To tokenize the text, we will also load the" +" tokenizer from the pre-trained Transformer model that we'll use during " +"training - more on that in the next section. Each ``ClientApp`` will call" +" this function to create dataloaders with the data that correspond to " +"their data partition." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:141 -msgid "" -"To federate our example to multiple clients, we first need to write our " -"Flower client class (inheriting from :code:`flwr.client.NumPyClient`). " -"This is very easy, as our model is a standard :code:`PyTorch` model:" +#: ../../source/tutorial-quickstart-huggingface.rst:178 +#: ../../source/tutorial-quickstart-mlx.rst:164 +#: ../../source/tutorial-quickstart-pytorch.rst:157 +#: ../../source/tutorial-quickstart-tensorflow.rst:145 +msgid "The Model" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:169 +#: ../../source/tutorial-quickstart-huggingface.rst:180 msgid "" -"The :code:`get_parameters` function lets the server get the client's " -"parameters. Inversely, the :code:`set_parameters` function allows the " -"server to send its parameters to the client. Finally, the :code:`fit` " -"function trains the model locally for the client, and the " -":code:`evaluate` function tests the model locally and returns the " -"relevant metrics." +"We will leverage 🤗 Hugging Face to federate the training of language " +"models over multiple clients using Flower. More specifically, we will " +"fine-tune a pre-trained Transformer model (|berttiny|_) for sequence " +"classification over the dataset of IMDB ratings. The end goal is to " +"detect if a movie rating is positive or negative. If you have access to " +"larger GPUs, feel free to use larger models!" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:175 -msgid "Starting the server" +#: ../../source/tutorial-quickstart-huggingface.rst:193 +msgid "" +"Note that here, ``model_name`` is a string that will be loaded from the " +"``Context`` in the ClientApp and ServerApp." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:177 +#: ../../source/tutorial-quickstart-huggingface.rst:196 msgid "" -"Now that we have a way to instantiate clients, we need to create our " -"server in order to aggregate the results. Using Flower, this can be done " -"very easily by first choosing a strategy (here, we are using " -":code:`FedAvg`, which will define the global weights as the average of " -"all the clients' weights at each round) and then using the " -":code:`flwr.server.start_server` function:" +"In addition to loading the pretrained model weights and architecture, we " +"also include two utility functions to perform both training (i.e. " +"``train()``) and evaluation (i.e. ``test()``) using the above model. " +"These functions should look fairly familiar if you have some prior " +"experience with PyTorch. Note these functions do not have anything " +"specific to Flower. That being said, the training function will normally " +"be called, as we'll see later, from a Flower client passing its own data." +" In summary, your clients can use standard training/testing functions to " +"perform local training or evaluation:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:205 +#: ../../source/tutorial-quickstart-huggingface.rst:239 +#: ../../source/tutorial-quickstart-mlx.rst:210 +#: ../../source/tutorial-quickstart-pytorch.rst:234 +#: ../../source/tutorial-quickstart-tensorflow.rst:176 +#, fuzzy +msgid "The ClientApp" +msgstr "클라이언트앱" + +#: ../../source/tutorial-quickstart-huggingface.rst:241 msgid "" -"The :code:`weighted_average` function is there to provide a way to " -"aggregate the metrics distributed amongst the clients (basically this " -"allows us to display a nice average accuracy and loss for every round)." +"The main changes we have to make to use 🤗 Hugging Face with Flower will " +"be found in the ``get_weights()`` and ``set_weights()`` functions. Under " +"the hood, the ``transformers`` library uses PyTorch, which means we can " +"reuse the ``get_weights()`` and ``set_weights()`` code that we defined in" +" the :doc:`Quickstart PyTorch ` tutorial. As" +" a reminder, in ``get_weights()``, PyTorch model parameters are extracted" +" and represented as a list of NumPy arrays. The ``set_weights()`` " +"function that's the opposite: given a list of NumPy arrays it applies " +"them to an existing PyTorch model. Doing this in fairly easy in PyTorch." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:209 -msgid "Putting everything together" +#: ../../source/tutorial-quickstart-huggingface.rst:254 +#: ../../source/tutorial-quickstart-pytorch.rst:245 +msgid "" +"The specific implementation of ``get_weights()`` and ``set_weights()`` " +"depends on the type of models you use. The ones shown below work for a " +"wide range of PyTorch models but you might need to adjust them if you " +"have more exotic model architectures." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:211 -msgid "We can now start client instances using:" +#: ../../source/tutorial-quickstart-huggingface.rst:269 +#: ../../source/tutorial-quickstart-pytorch.rst:261 +msgid "" +"The rest of the functionality is directly inspired by the centralized " +"case. The ``fit()`` method in the client trains the model using the local" +" dataset. Similarly, the ``evaluate()`` method is used to evaluate the " +"model received on a held-out validation set that the client might have:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:221 +#: ../../source/tutorial-quickstart-huggingface.rst:296 msgid "" -"And they will be able to connect to the server and start the federated " -"training." +"Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " +"defined above by means of a ``client_fn()`` callback. Note that the " +"`context` enables you to get access to hyperparemeters defined in your " +"``pyproject.toml`` to configure the run. In this tutorial we access the " +"``local-epochs`` setting to control the number of epochs a ``ClientApp`` " +"will perform when running the ``fit()`` method. You could define " +"additional hyperparameters in ``pyproject.toml`` and access them here." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:223 +#: ../../source/tutorial-quickstart-huggingface.rst:330 +#: ../../source/tutorial-quickstart-mlx.rst:376 +#: ../../source/tutorial-quickstart-pytorch.rst:321 +#: ../../source/tutorial-quickstart-tensorflow.rst:245 +#, fuzzy +msgid "The ServerApp" +msgstr "Flower 서버앱" + +#: ../../source/tutorial-quickstart-huggingface.rst:332 msgid "" -"If you want to check out everything put together, you should check out " -"the `full code example `_ ." +"To construct a ``ServerApp`` we define a ``server_fn()`` callback with an" +" identical signature to that of ``client_fn()`` but the return type is " +"|serverappcomponents|_ as opposed to a |client|_ In this example we use " +"the `FedAvg` strategy. To it we pass a randomly initialized model that " +"will server as the global model to federated. Note that the value of " +"``fraction_fit`` is read from the run config. You can find the default " +"value defined in the ``pyproject.toml``." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:226 +#: ../../source/tutorial-quickstart-huggingface.rst:371 msgid "" -"Of course, this is a very basic example, and a lot can be added or " -"modified, it was just to showcase how simply we could federate a Hugging " -"Face workflow using Flower." +"Congratulations! You've successfully built and run your first federated " +"learning system for an LLM." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:229 +#: ../../source/tutorial-quickstart-huggingface.rst:376 msgid "" -"Note that in this example we used :code:`PyTorch`, but we could have very" -" well used :code:`TensorFlow`." +"Check the source code of the extended version of this tutorial in " +"|quickstart_hf_link|_ in the Flower GitHub repository. For a " +"comprehensive example of a federated fine-tuning of an LLM with Flower, " +"refer to the |flowertune|_ example in the Flower GitHub repository." msgstr "" #: ../../source/tutorial-quickstart-ios.rst:-1 @@ -22216,7 +22454,6 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:34 #: ../../source/tutorial-quickstart-scikitlearn.rst:40 -#: ../../source/tutorial-quickstart-tensorflow.rst:29 #: ../../source/tutorial-quickstart-xgboost.rst:55 msgid "Flower Client" msgstr "" @@ -22290,13 +22527,11 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:129 #: ../../source/tutorial-quickstart-scikitlearn.rst:167 -#: ../../source/tutorial-quickstart-tensorflow.rst:98 #: ../../source/tutorial-quickstart-xgboost.rst:341 msgid "Flower Server" msgstr "" #: ../../source/tutorial-quickstart-ios.rst:131 -#: ../../source/tutorial-quickstart-tensorflow.rst:100 msgid "" "For simple workloads we can start a Flower server and leave all the " "configuration possibilities at their default values. In a file named " @@ -22305,12 +22540,10 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:142 #: ../../source/tutorial-quickstart-scikitlearn.rst:230 -#: ../../source/tutorial-quickstart-tensorflow.rst:112 msgid "Train the model, federated!" msgstr "" #: ../../source/tutorial-quickstart-ios.rst:144 -#: ../../source/tutorial-quickstart-tensorflow.rst:114 #: ../../source/tutorial-quickstart-xgboost.rst:567 msgid "" "With both client and server ready, we can now run everything and see " @@ -22515,7 +22748,7 @@ msgstr "" "code:`FlowerClient`는 모델 매개변수를 가져오거나 설정하는 메서드 2개, 모델 학습을 위한 메서드 1개, 모델 " "테스트를 위한 메서드 1개 등 총 4개의 메서드를 구현해야 합니다:" -#: ../../source/tutorial-quickstart-jax.rst:165 +#: ../../source/tutorial-quickstart-jax.rst:167 msgid ":code:`set_parameters (optional)`" msgstr ":code:`set_parameters (선택사항)`" @@ -22614,13 +22847,6 @@ msgid "" "api/flwr_datasets.partitioner.IidPartitioner.html#flwr_datasets.partitioner.IidPartitioner>`_." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:19 -#: ../../source/tutorial-quickstart-pytorch.rst:19 -msgid "" -"Now that we have a rough idea of what this example is about, let's get " -"started. First, install Flower in your new environment:" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:27 msgid "" "Then, run the command below. You will be prompted to select of the " @@ -22628,49 +22854,16 @@ msgid "" "type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:35 -#: ../../source/tutorial-quickstart-pytorch.rst:35 -msgid "" -"After running it you'll notice a new directory with your project name has" -" been created. It should have the following structure:" -msgstr "" - -#: ../../source/tutorial-quickstart-mlx.rst:49 -#: ../../source/tutorial-quickstart-pytorch.rst:49 -msgid "" -"If you haven't yet installed the project and its dependencies, you can do" -" so by:" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:57 msgid "To run the project do:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:64 -#: ../../source/tutorial-quickstart-pytorch.rst:64 -msgid "With default arguments you will see an output like this one:" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:106 msgid "" "You can also override the parameters defined in " "``[tool.flwr.app.config]`` section in the ``pyproject.toml`` like this:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:114 -#: ../../source/tutorial-quickstart-pytorch.rst:113 -msgid "" -"What follows is an explanation of each component in the project you just " -"created: dataset partition, the model, defining the ``ClientApp`` and " -"defining the ``ServerApp``." -msgstr "" - -#: ../../source/tutorial-quickstart-mlx.rst:120 -#: ../../source/tutorial-quickstart-pytorch.rst:119 -#, fuzzy -msgid "The Data" -msgstr "Metadata" - #: ../../source/tutorial-quickstart-mlx.rst:122 msgid "" "We will use `Flower Datasets `_ to " @@ -22682,11 +22875,6 @@ msgid "" "api/flwr_datasets.partitioner.html>`_ available in Flower Datasets:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:164 -#: ../../source/tutorial-quickstart-pytorch.rst:157 -msgid "The Model" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:166 msgid "" "We define the model as in the `centralized MLX example " @@ -22700,12 +22888,6 @@ msgid "" "over batches." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:210 -#: ../../source/tutorial-quickstart-pytorch.rst:234 -#, fuzzy -msgid "The ClientApp" -msgstr "클라이언트앱" - #: ../../source/tutorial-quickstart-mlx.rst:212 msgid "" "The main changes we have to make to use `MLX` with `Flower` will be found" @@ -22773,12 +22955,6 @@ msgid "" "method." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:376 -#: ../../source/tutorial-quickstart-pytorch.rst:321 -#, fuzzy -msgid "The ServerApp" -msgstr "Flower 서버앱" - #: ../../source/tutorial-quickstart-mlx.rst:378 msgid "" "To construct a ``ServerApp``, we define a ``server_fn()`` callback with " @@ -22792,6 +22968,7 @@ msgstr "" #: ../../source/tutorial-quickstart-mlx.rst:402 #: ../../source/tutorial-quickstart-pytorch.rst:360 +#: ../../source/tutorial-quickstart-tensorflow.rst:279 msgid "" "Congratulations! You've successfully built and run your first federated " "learning system." @@ -22858,16 +23035,6 @@ msgid "" "and type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:57 -msgid "To run the project, do:" -msgstr "" - -#: ../../source/tutorial-quickstart-pytorch.rst:105 -msgid "" -"You can also override the parameters defined in the " -"``[tool.flwr.app.config]`` section in ``pyproject.toml`` like this:" -msgstr "" - #: ../../source/tutorial-quickstart-pytorch.rst:121 msgid "" "This tutorial uses `Flower Datasets `_ " @@ -22911,22 +23078,6 @@ msgid "" "PyTorch model. Doing this in fairly easy in PyTorch." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:245 -msgid "" -"The specific implementation of ``get_weights()`` and ``set_weights()`` " -"depends on the type of models you use. The ones shown below work for a " -"wide range of PyTorch models but you might need to adjust them if you " -"have more exotic model architectures." -msgstr "" - -#: ../../source/tutorial-quickstart-pytorch.rst:261 -msgid "" -"The rest of the functionality is directly inspired by the centralized " -"case. The ``fit()`` method in the client trains the model using the local" -" dataset. Similarly, the ``evaluate()`` method is used to evaluate the " -"model received on a held-out validation set that the client might have:" -msgstr "" - #: ../../source/tutorial-quickstart-pytorch.rst:294 msgid "" "Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " @@ -22960,6 +23111,7 @@ msgid "" msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:372 +#: ../../source/tutorial-quickstart-tensorflow.rst:295 #, fuzzy msgid "Video tutorial" msgstr "튜토리얼" @@ -22971,27 +23123,46 @@ msgid "" "that shows the new APIs (as the content above does)" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:-1 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 +msgid "Quickstart PyTorch Lightning" +msgstr "" + +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:7 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with PyTorch Lightning to train an Auto Encoder model on MNIST." +"In this federated learning tutorial we will learn how to train an " +"AutoEncoder model on MNIST using Flower and PyTorch Lightning. It is " +"recommended to create a virtual environment and run everything within a " +":doc:`virtualenv `." msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 -msgid "Quickstart PyTorch Lightning" +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:20 +msgid "" +"This will create a new directory called `quickstart-pytorch-lightning` " +"containing the following files:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:10 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:43 msgid "" -"Let's build a horizontal federated learning system using PyTorch " -"Lightning and Flower!" +"By default, Flower Simulation Engine will be started and it will create a" +" federation of 4 nodes using `FedAvg `_ " +"as the aggregation strategy. The dataset will be partitioned using Flower" +" Dataset's `IidPartitioner `_." +" To run the project, do:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:94 msgid "" -"Please refer to the `full code example " -"`_ to learn more." +"Each simulated `ClientApp` (two per round) will also log a summary of " +"their local training process. Expect this output to be similar to:" +msgstr "" + +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:116 +msgid "" +"Check the `source code `_ of this tutorial in ``examples" +"/quickstart-pytorch-lightning`` in the Flower GitHub repository." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:-1 @@ -23072,7 +23243,7 @@ msgstr "" msgid "Sets the parameters of a :code:`sklearn` LogisticRegression model" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:49 +#: ../../source/tutorial-quickstart-scikitlearn.rst:50 msgid ":code:`set_initial_params()`" msgstr "" @@ -23128,7 +23299,7 @@ msgstr "" msgid "return the model weight as a list of NumPy ndarrays" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:120 +#: ../../source/tutorial-quickstart-scikitlearn.rst:121 msgid ":code:`set_parameters` (optional)" msgstr "" @@ -23223,7 +23394,6 @@ msgid "" msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:239 -#: ../../source/tutorial-quickstart-tensorflow.rst:122 #: ../../source/tutorial-quickstart-xgboost.rst:575 msgid "" "Once the server is running we can start the clients in different " @@ -23231,7 +23401,6 @@ msgid "" msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:246 -#: ../../source/tutorial-quickstart-tensorflow.rst:129 #: ../../source/tutorial-quickstart-xgboost.rst:582 msgid "Open another terminal and start the second client:" msgstr "" @@ -23256,99 +23425,107 @@ msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:-1 msgid "" "Check out this Federated Learning quickstart tutorial for using Flower " -"with TensorFlow to train a MobilNetV2 model on CIFAR-10." +"with TensorFlow to train a CNN model on CIFAR-10." msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:5 msgid "Quickstart TensorFlow" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:13 -msgid "Let's build a federated learning system in less than 20 lines of code!" -msgstr "" - -#: ../../source/tutorial-quickstart-tensorflow.rst:15 -msgid "Before Flower can be imported we have to install it:" -msgstr "" - -#: ../../source/tutorial-quickstart-tensorflow.rst:21 +#: ../../source/tutorial-quickstart-tensorflow.rst:7 msgid "" -"Since we want to use the Keras API of TensorFlow (TF), we have to install" -" TF as well:" -msgstr "" - -#: ../../source/tutorial-quickstart-tensorflow.rst:31 -msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:" +"In this tutorial we will learn how to train a Convolutional Neural " +"Network on CIFAR-10 using the Flower framework and TensorFlow. First of " +"all, it is recommended to create a virtual environment and run everything" +" within a :doc:`virtualenv `." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:38 +#: ../../source/tutorial-quickstart-tensorflow.rst:13 msgid "" -"We use the Keras utilities of TF to load CIFAR10, a popular colored image" -" classification dataset for machine learning. The call to " -":code:`tf.keras.datasets.cifar10.load_data()` downloads CIFAR10, caches " -"it locally, and then returns the entire training and test set as NumPy " -"ndarrays." +"Let's use `flwr new` to create a complete Flower+TensorFlow project. It " +"will generate all the files needed to run, by default with the Flower " +"Simulation Engine, a federation of 10 nodes using `FedAvg " +"`_. The " +"dataset will be partitioned using Flower Dataset's `IidPartitioner " +"`_." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:47 +#: ../../source/tutorial-quickstart-tensorflow.rst:28 msgid "" -"Next, we need a model. For the purpose of this tutorial, we use " -"MobilNetV2 with 10 output classes:" +"Then, run the command below. You will be prompted to select one of the " +"available templates (choose ``TensorFlow``), give a name to your project," +" and type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:54 +#: ../../source/tutorial-quickstart-tensorflow.rst:118 msgid "" -"The Flower server interacts with clients through an interface called " -":code:`Client`. When the server selects a particular client for training," -" it sends training instructions over the network. The client receives " -"those instructions and calls one of the :code:`Client` methods to run " -"your code (i.e., to train the neural network we defined earlier)." +"This tutorial uses `Flower Datasets `_ " +"to easily download and partition the `CIFAR-10` dataset. In this example " +"you'll make use of the `IidPartitioner `_" +" to generate `num_partitions` partitions. You can choose `other " +"partitioners `_ available in Flower Datasets. Each " +"``ClientApp`` will call this function to create the ``NumPy`` arrays that" +" correspond to their data partition." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:60 +#: ../../source/tutorial-quickstart-tensorflow.rst:147 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses Keras. The :code:`NumPyClient` interface defines three " -"methods which can be implemented in the following way:" +"Next, we need a model. We defined a simple Convolutional Neural Network " +"(CNN), but feel free to replace it with a more sophisticated model if " +"you'd like:" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:82 +#: ../../source/tutorial-quickstart-tensorflow.rst:178 msgid "" -"We can now create an instance of our class :code:`CifarClient` and add " -"one line to actually run this client:" +"With `TensorFlow`, we can use the built-in ``get_weights()`` and " +"``set_weights()`` functions, which simplifies the implementation with " +"`Flower`. The rest of the functionality in the ClientApp is directly " +"inspired by the centralized case. The ``fit()`` method in the client " +"trains the model using the local dataset. Similarly, the ``evaluate()`` " +"method is used to evaluate the model received on a held-out validation " +"set that the client might have:" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:90 +#: ../../source/tutorial-quickstart-tensorflow.rst:212 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " -"implement a client of type :code:`NumPyClient` you'll need to first call " -"its :code:`to_client()` method. The string :code:`\"[::]:8080\"` tells " -"the client which server to connect to. In our case we can run the server " -"and the client on the same machine, therefore we use " -":code:`\"[::]:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we point the client at." +"Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " +"defined above by means of a ``client_fn()`` callback. Note that the " +"`context` enables you to get access to hyperparameters defined in your " +"``pyproject.toml`` to configure the run. For example, in this tutorial we" +" access the `local-epochs` setting to control the number of epochs a " +"``ClientApp`` will perform when running the ``fit()`` method, in addition" +" to `batch-size`. You could define additional hyperparameters in " +"``pyproject.toml`` and access them here." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:135 -msgid "Each client will have its own dataset." +#: ../../source/tutorial-quickstart-tensorflow.rst:247 +msgid "" +"To construct a ``ServerApp`` we define a ``server_fn()`` callback with an" +" identical signature to that of ``client_fn()`` but the return type is " +"`ServerAppComponents `_ as " +"opposed to a `Client `_. In this example we use the " +"`FedAvg`. To it we pass a randomly initialized model that will serve as " +"the global model to federate." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:137 +#: ../../source/tutorial-quickstart-tensorflow.rst:284 msgid "" -"You should now see how the training does in the very first terminal (the " -"one that started the server):" +"Check the source code of the extended version of this tutorial in " +"|quickstart_tf_link|_ in the Flower GitHub repository." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:169 +#: ../../source/tutorial-quickstart-tensorflow.rst:299 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this can be found in :code:`examples" -"/quickstart-tensorflow/client.py`." +"The video shown below shows how to setup a TensorFlow + Flower project " +"using our previously recommended APIs. A new video tutorial will be " +"released that shows the new APIs (as the content above does)" msgstr "" #: ../../source/tutorial-quickstart-xgboost.rst:-1 @@ -25217,7 +25394,7 @@ msgstr "" " 수도 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 -msgid "|e5918c1c06a4434bbe4bf49235e40059|" +msgid "|e87b69b2ada74ea49412df16f4a0b9cc|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 @@ -25234,7 +25411,7 @@ msgstr "" " 바둑과 같은 게임을 하는 것일 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 -msgid "|c0165741bd1944f09ec55ce49032377d|" +msgid "|33cacb7d985c4906b348515c1a5cd993|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 @@ -25257,7 +25434,7 @@ msgstr "" "부르리는 것을 듣는 스마트 스피커에서 비롯됩니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 -msgid "|0a0ac9427ac7487b8e52d75ed514f04e|" +msgid "|cc080a555947492fa66131dc3a967603|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 @@ -25275,7 +25452,7 @@ msgstr "" "있습니다. 하지만 여러 조직이 모두 같은 작업을 위해 데이터를 생성하는 것일 수도 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 -msgid "|5defee3ea4ca40d99fcd3e4ea045be25|" +msgid "|085c3e0fb8664c6aa06246636524b20b|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 @@ -25293,7 +25470,7 @@ msgstr "" "서버는 데이터 센터 어딘가에 있을 수도 있고 클라우드 어딘가에 있을 수도 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 -msgid "|74f26ca701254d3db57d7899bd91eb55|" +msgid "|bfe69c74e48c45d49b50251c38c2a019|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 @@ -25310,7 +25487,7 @@ msgstr "" " 우리가 기본적으로 사용해 온 머신러닝 방법입니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 -msgid "|bda79f21f8154258a40e5766b2634ad7|" +msgid "|ebbecd651f0348d99c6511ea859bf4ca|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 @@ -25332,7 +25509,7 @@ msgstr "" "트래픽을 분석하는 것이 있습니다. 이러한 사례에서 모든 데이터는 자연스럽게 중앙 서버에 존재합니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 -msgid "|89d30862e62e4f9989e193483a08680a|" +msgid "|163117eb654a4273babba413cf8065f5|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 @@ -25349,7 +25526,7 @@ msgstr "" "좋은 모델을 훈련하기에 충분하지 않을 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 -msgid "|77e9918671c54b4f86e01369c0785ce8|" +msgid "|452ac3ba453b4cd1be27be1ba7560d64|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 @@ -25516,7 +25693,7 @@ msgstr "" "체크포인트에서 모델 매개변수를 초기화합니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 -msgid "|7e4ccef37cc94148a067107b34eb7447|" +msgid "|f403fcd69e4e44409627e748b404c086|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 @@ -25543,7 +25720,7 @@ msgstr "" "개의 연결 노드만 사용합니다. 그 이유는 점점 더 많은 클라이언트 노드를 선택하면 학습의 효율성이 감소하기 때문입니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 -msgid "|28e47e4cded14479a0846c8e5f22c872|" +msgid "|4b00fe63870145968f8443619a792a42|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 @@ -25570,7 +25747,7 @@ msgstr "" "데이터에서 한 단계 정도로 짧거나 몇 단계(mini-batches)에 불과할 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 -msgid "|4b8c5d1afa144294b76ffc76e4658a38|" +msgid "|368378731066486fa4397e89bc6b870c|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 @@ -25596,7 +25773,7 @@ msgstr "" "보냅니다. 보내는 모델 업데이트는 전체 모델 파라미터거나 로컬 교육 중에 누적된 그레디언트(gradient)일 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 -msgid "|9dbdb3a0f6cb4a129fac863eaa414c17|" +msgid "|a66aa83d85bf4ffba7ed660b718066da|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 @@ -25645,7 +25822,7 @@ msgstr "" "많은 영향을 미칩니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 -msgid "|81749d0ac0834c36a83bd38f433fea31|" +msgid "|82324b9af72a4582a81839d55caab767|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 @@ -25764,7 +25941,7 @@ msgstr "" "사용자는 모든 워크로드, 머신러닝 프레임워크 및 모든 프로그래밍 언어를 통합할 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 -msgid "|ed9aae51da70428eab7eef32f21e819e|" +msgid "|fbf2da0da3cc4f8ab3b3eff852d80c41|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 @@ -27424,3 +27601,523 @@ msgstr "" #~ msgid "|c00bf2750bc24d229737a0fe1395f0fc|" #~ msgstr "|c00bf2750bc24d229737a0fe1395f0fc|" +#~ msgid "run\\_client\\_app" +#~ msgstr "run\\_client\\_app" + +#~ msgid "run\\_supernode" +#~ msgstr "run\\_supernode" + +#~ msgid "Retrieve the corresponding layout by the string key." +#~ msgstr "" + +#~ msgid "" +#~ "When there isn't an exact match, " +#~ "all the existing keys in the " +#~ "layout map will be treated as a" +#~ " regex and map against the input " +#~ "key again. The first match will be" +#~ " returned, based on the key insertion" +#~ " order. Return None if there isn't" +#~ " any match found." +#~ msgstr "" + +#~ msgid "the string key as the query for the layout." +#~ msgstr "" + +#~ msgid "Corresponding layout based on the query." +#~ msgstr "" + +#~ msgid "run\\_server\\_app" +#~ msgstr "" + +#~ msgid "run\\_superlink" +#~ msgstr "" + +#~ msgid "Start a Ray-based Flower simulation server." +#~ msgstr "" + +#~ msgid "" +#~ "A function creating `Client` instances. " +#~ "The function must have the signature " +#~ "`client_fn(context: Context). It should return" +#~ " a single client instance of type " +#~ "`Client`. Note that the created client" +#~ " instances are ephemeral and will " +#~ "often be destroyed after a single " +#~ "method invocation. Since client instances " +#~ "are not long-lived, they should " +#~ "not attempt to carry state over " +#~ "method invocations. Any state required " +#~ "by the instance (model, dataset, " +#~ "hyperparameters, ...) should be (re-)created" +#~ " in either the call to `client_fn`" +#~ " or the call to any of the " +#~ "client methods (e.g., load evaluation " +#~ "data in the `evaluate` method itself)." +#~ msgstr "" + +#~ msgid "The total number of clients in this simulation." +#~ msgstr "" + +#~ msgid "" +#~ "UNSUPPORTED, WILL BE REMOVED. USE " +#~ "`num_clients` INSTEAD. List `client_id`s for" +#~ " each client. This is only required" +#~ " if `num_clients` is not set. Setting" +#~ " both `num_clients` and `clients_ids` with" +#~ " `len(clients_ids)` not equal to " +#~ "`num_clients` generates an error. Using " +#~ "this argument will raise an error." +#~ msgstr "" + +#~ msgid "" +#~ "CPU and GPU resources for a single" +#~ " client. Supported keys are `num_cpus` " +#~ "and `num_gpus`. To understand the GPU" +#~ " utilization caused by `num_gpus`, as " +#~ "well as using custom resources, please" +#~ " consult the Ray documentation." +#~ msgstr "" + +#~ msgid "" +#~ "An implementation of the abstract base" +#~ " class `flwr.server.Server`. If no instance" +#~ " is provided, then `start_server` will " +#~ "create one." +#~ msgstr "" + +#~ msgid "" +#~ "An implementation of the abstract base" +#~ " class `flwr.server.Strategy`. If no " +#~ "strategy is provided, then `start_server` " +#~ "will use `flwr.server.strategy.FedAvg`." +#~ msgstr "" + +#~ msgid "" +#~ "An implementation of the abstract base" +#~ " class `flwr.server.ClientManager`. If no " +#~ "implementation is provided, then " +#~ "`start_simulation` will use " +#~ "`flwr.server.client_manager.SimpleClientManager`." +#~ msgstr "" + +#~ msgid "" +#~ "Optional dictionary containing arguments for" +#~ " the call to `ray.init`. If " +#~ "ray_init_args is None (the default), Ray" +#~ " will be initialized with the " +#~ "following default args: { " +#~ "\"ignore_reinit_error\": True, \"include_dashboard\": " +#~ "False } An empty dictionary can " +#~ "be used (ray_init_args={}) to prevent " +#~ "any arguments from being passed to " +#~ "ray.init." +#~ msgstr "" + +#~ msgid "" +#~ "Optional dictionary containing arguments for" +#~ " the call to `ray.init`. If " +#~ "ray_init_args is None (the default), Ray" +#~ " will be initialized with the " +#~ "following default args:" +#~ msgstr "" + +#~ msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" +#~ msgstr "" + +#~ msgid "" +#~ "An empty dictionary can be used " +#~ "(ray_init_args={}) to prevent any arguments" +#~ " from being passed to ray.init." +#~ msgstr "" + +#~ msgid "" +#~ "Set to True to prevent `ray.shutdown()`" +#~ " in case `ray.is_initialized()=True`." +#~ msgstr "" + +#~ msgid "" +#~ "Optionally specify the type of actor " +#~ "to use. The actor object, which " +#~ "persists throughout the simulation, will " +#~ "be the process in charge of " +#~ "executing a ClientApp wrapping input " +#~ "argument `client_fn`." +#~ msgstr "" + +#~ msgid "" +#~ "If you want to create your own " +#~ "Actor classes, you might need to " +#~ "pass some input argument. You can " +#~ "use this dictionary for such purpose." +#~ msgstr "" + +#~ msgid "" +#~ "(default: \"DEFAULT\") Optional string " +#~ "(\"DEFAULT\" or \"SPREAD\") for the VCE" +#~ " to choose in which node the " +#~ "actor is placed. If you are an " +#~ "advanced user needed more control you" +#~ " can use lower-level scheduling " +#~ "strategies to pin actors to specific " +#~ "compute nodes (e.g. via " +#~ "NodeAffinitySchedulingStrategy). Please note this" +#~ " is an advanced feature. For all " +#~ "details, please refer to the Ray " +#~ "documentation: https://docs.ray.io/en/latest/ray-" +#~ "core/scheduling/index.html" +#~ msgstr "" + +#~ msgid "**hist** -- Object containing metrics from training." +#~ msgstr "" + +#~ msgid "" +#~ "Check out this Federated Learning " +#~ "quickstart tutorial for using Flower " +#~ "with FastAI to train a vision " +#~ "model on CIFAR-10." +#~ msgstr "" + +#~ msgid "Let's build a federated learning system using fastai and Flower!" +#~ msgstr "" + +#~ msgid "" +#~ "Please refer to the `full code " +#~ "example `_ to learn more." +#~ msgstr "" + +#~ msgid "" +#~ "Check out this Federating Learning " +#~ "quickstart tutorial for using Flower " +#~ "with HuggingFace Transformers in order " +#~ "to fine-tune an LLM." +#~ msgstr "" + +#~ msgid "" +#~ "Let's build a federated learning system" +#~ " using Hugging Face Transformers and " +#~ "Flower!" +#~ msgstr "" + +#~ msgid "" +#~ "We will leverage Hugging Face to " +#~ "federate the training of language models" +#~ " over multiple clients using Flower. " +#~ "More specifically, we will fine-tune " +#~ "a pre-trained Transformer model " +#~ "(distilBERT) for sequence classification over" +#~ " a dataset of IMDB ratings. The " +#~ "end goal is to detect if a " +#~ "movie rating is positive or negative." +#~ msgstr "" + +#~ msgid "Dependencies" +#~ msgstr "" + +#~ msgid "" +#~ "To follow along this tutorial you " +#~ "will need to install the following " +#~ "packages: :code:`datasets`, :code:`evaluate`, " +#~ ":code:`flwr`, :code:`torch`, and " +#~ ":code:`transformers`. This can be done " +#~ "using :code:`pip`:" +#~ msgstr "" + +#~ msgid "Standard Hugging Face workflow" +#~ msgstr "" + +#~ msgid "Handling the data" +#~ msgstr "" + +#~ msgid "" +#~ "To fetch the IMDB dataset, we will" +#~ " use Hugging Face's :code:`datasets` " +#~ "library. We then need to tokenize " +#~ "the data and create :code:`PyTorch` " +#~ "dataloaders, this is all done in " +#~ "the :code:`load_data` function:" +#~ msgstr "" + +#~ msgid "Training and testing the model" +#~ msgstr "" + +#~ msgid "" +#~ "Once we have a way of creating " +#~ "our trainloader and testloader, we can" +#~ " take care of the training and " +#~ "testing. This is very similar to " +#~ "any :code:`PyTorch` training or testing " +#~ "loop:" +#~ msgstr "" + +#~ msgid "Creating the model itself" +#~ msgstr "" + +#~ msgid "" +#~ "To create the model itself, we " +#~ "will just load the pre-trained " +#~ "distillBERT model using Hugging Face’s " +#~ ":code:`AutoModelForSequenceClassification` :" +#~ msgstr "" + +#~ msgid "Federating the example" +#~ msgstr "" + +#~ msgid "Creating the IMDBClient" +#~ msgstr "" + +#~ msgid "" +#~ "To federate our example to multiple " +#~ "clients, we first need to write " +#~ "our Flower client class (inheriting from" +#~ " :code:`flwr.client.NumPyClient`). This is very" +#~ " easy, as our model is a " +#~ "standard :code:`PyTorch` model:" +#~ msgstr "" + +#~ msgid "" +#~ "The :code:`get_parameters` function lets the" +#~ " server get the client's parameters. " +#~ "Inversely, the :code:`set_parameters` function " +#~ "allows the server to send its " +#~ "parameters to the client. Finally, the" +#~ " :code:`fit` function trains the model " +#~ "locally for the client, and the " +#~ ":code:`evaluate` function tests the model " +#~ "locally and returns the relevant " +#~ "metrics." +#~ msgstr "" + +#~ msgid "Starting the server" +#~ msgstr "" + +#~ msgid "" +#~ "Now that we have a way to " +#~ "instantiate clients, we need to create" +#~ " our server in order to aggregate " +#~ "the results. Using Flower, this can " +#~ "be done very easily by first " +#~ "choosing a strategy (here, we are " +#~ "using :code:`FedAvg`, which will define " +#~ "the global weights as the average " +#~ "of all the clients' weights at " +#~ "each round) and then using the " +#~ ":code:`flwr.server.start_server` function:" +#~ msgstr "" + +#~ msgid "" +#~ "The :code:`weighted_average` function is there" +#~ " to provide a way to aggregate " +#~ "the metrics distributed amongst the " +#~ "clients (basically this allows us to " +#~ "display a nice average accuracy and " +#~ "loss for every round)." +#~ msgstr "" + +#~ msgid "Putting everything together" +#~ msgstr "" + +#~ msgid "We can now start client instances using:" +#~ msgstr "" + +#~ msgid "" +#~ "And they will be able to connect" +#~ " to the server and start the " +#~ "federated training." +#~ msgstr "" + +#~ msgid "" +#~ "If you want to check out " +#~ "everything put together, you should " +#~ "check out the `full code example " +#~ "`_ ." +#~ msgstr "" + +#~ msgid "" +#~ "Of course, this is a very basic" +#~ " example, and a lot can be " +#~ "added or modified, it was just to" +#~ " showcase how simply we could " +#~ "federate a Hugging Face workflow using" +#~ " Flower." +#~ msgstr "" + +#~ msgid "" +#~ "Note that in this example we used" +#~ " :code:`PyTorch`, but we could have " +#~ "very well used :code:`TensorFlow`." +#~ msgstr "" + +#~ msgid "" +#~ "Check out this Federated Learning " +#~ "quickstart tutorial for using Flower " +#~ "with PyTorch Lightning to train an " +#~ "Auto Encoder model on MNIST." +#~ msgstr "" + +#~ msgid "" +#~ "Let's build a horizontal federated " +#~ "learning system using PyTorch Lightning " +#~ "and Flower!" +#~ msgstr "" + +#~ msgid "" +#~ "Please refer to the `full code " +#~ "example `_ to learn " +#~ "more." +#~ msgstr "" + +#~ msgid "" +#~ "Check out this Federated Learning " +#~ "quickstart tutorial for using Flower " +#~ "with TensorFlow to train a MobilNetV2" +#~ " model on CIFAR-10." +#~ msgstr "" + +#~ msgid "Let's build a federated learning system in less than 20 lines of code!" +#~ msgstr "" + +#~ msgid "Before Flower can be imported we have to install it:" +#~ msgstr "" + +#~ msgid "" +#~ "Since we want to use the Keras " +#~ "API of TensorFlow (TF), we have to" +#~ " install TF as well:" +#~ msgstr "" + +#~ msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:" +#~ msgstr "" + +#~ msgid "" +#~ "We use the Keras utilities of TF" +#~ " to load CIFAR10, a popular colored" +#~ " image classification dataset for machine" +#~ " learning. The call to " +#~ ":code:`tf.keras.datasets.cifar10.load_data()` downloads " +#~ "CIFAR10, caches it locally, and then " +#~ "returns the entire training and test " +#~ "set as NumPy ndarrays." +#~ msgstr "" + +#~ msgid "" +#~ "Next, we need a model. For the " +#~ "purpose of this tutorial, we use " +#~ "MobilNetV2 with 10 output classes:" +#~ msgstr "" + +#~ msgid "" +#~ "The Flower server interacts with clients" +#~ " through an interface called " +#~ ":code:`Client`. When the server selects " +#~ "a particular client for training, it " +#~ "sends training instructions over the " +#~ "network. The client receives those " +#~ "instructions and calls one of the " +#~ ":code:`Client` methods to run your code" +#~ " (i.e., to train the neural network" +#~ " we defined earlier)." +#~ msgstr "" + +#~ msgid "" +#~ "Flower provides a convenience class " +#~ "called :code:`NumPyClient` which makes it " +#~ "easier to implement the :code:`Client` " +#~ "interface when your workload uses Keras." +#~ " The :code:`NumPyClient` interface defines " +#~ "three methods which can be implemented" +#~ " in the following way:" +#~ msgstr "" + +#~ msgid "" +#~ "We can now create an instance of" +#~ " our class :code:`CifarClient` and add " +#~ "one line to actually run this " +#~ "client:" +#~ msgstr "" + +#~ msgid "" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` or " +#~ ":code:`NumPyClient` and call " +#~ ":code:`fl.client.start_client()`. If you implement" +#~ " a client of type :code:`NumPyClient` " +#~ "you'll need to first call its " +#~ ":code:`to_client()` method. The string " +#~ ":code:`\"[::]:8080\"` tells the client which" +#~ " server to connect to. In our " +#~ "case we can run the server and " +#~ "the client on the same machine, " +#~ "therefore we use :code:`\"[::]:8080\"`. If " +#~ "we run a truly federated workload " +#~ "with the server and clients running " +#~ "on different machines, all that needs" +#~ " to change is the :code:`server_address`" +#~ " we point the client at." +#~ msgstr "" + +#~ msgid "Each client will have its own dataset." +#~ msgstr "" + +#~ msgid "" +#~ "You should now see how the " +#~ "training does in the very first " +#~ "terminal (the one that started the " +#~ "server):" +#~ msgstr "" + +#~ msgid "" +#~ "Congratulations! You've successfully built and" +#~ " run your first federated learning " +#~ "system. The full `source code " +#~ "`_ for this can be " +#~ "found in :code:`examples/quickstart-" +#~ "tensorflow/client.py`." +#~ msgstr "" + +#~ msgid "|e5918c1c06a4434bbe4bf49235e40059|" +#~ msgstr "" + +#~ msgid "|c0165741bd1944f09ec55ce49032377d|" +#~ msgstr "" + +#~ msgid "|0a0ac9427ac7487b8e52d75ed514f04e|" +#~ msgstr "" + +#~ msgid "|5defee3ea4ca40d99fcd3e4ea045be25|" +#~ msgstr "" + +#~ msgid "|74f26ca701254d3db57d7899bd91eb55|" +#~ msgstr "" + +#~ msgid "|bda79f21f8154258a40e5766b2634ad7|" +#~ msgstr "" + +#~ msgid "|89d30862e62e4f9989e193483a08680a|" +#~ msgstr "" + +#~ msgid "|77e9918671c54b4f86e01369c0785ce8|" +#~ msgstr "" + +#~ msgid "|7e4ccef37cc94148a067107b34eb7447|" +#~ msgstr "" + +#~ msgid "|28e47e4cded14479a0846c8e5f22c872|" +#~ msgstr "" + +#~ msgid "|4b8c5d1afa144294b76ffc76e4658a38|" +#~ msgstr "" + +#~ msgid "|9dbdb3a0f6cb4a129fac863eaa414c17|" +#~ msgstr "" + +#~ msgid "|81749d0ac0834c36a83bd38f433fea31|" +#~ msgstr "" + +#~ msgid "|ed9aae51da70428eab7eef32f21e819e|" +#~ msgstr "" + diff --git a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po index d5f52b193e87..44223940cdce 100644 --- a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po +++ b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po @@ -7,7 +7,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower main\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-09-15 09:09+0200\n" +"POT-Creation-Date: 2024-09-24 00:29+0000\n" "PO-Revision-Date: 2024-05-25 11:09+0000\n" "Last-Translator: Gustavo Bertoli \n" "Language: pt_BR\n" @@ -17,7 +17,7 @@ msgstr "" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" -"Generated-By: Babel 2.15.0\n" +"Generated-By: Babel 2.16.0\n" #: ../../source/contributor-explanation-public-and-private-apis.rst:2 msgid "Public and private APIs" @@ -1401,7 +1401,7 @@ msgstr "" msgid "Setting up the repository" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:12 +#: ../../source/contributor-tutorial-contribute-on-github.rst:21 msgid "**Create a GitHub account and setup Git**" msgstr "" @@ -1435,7 +1435,7 @@ msgid "" "history back to GitHub." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:23 +#: ../../source/contributor-tutorial-contribute-on-github.rst:32 msgid "**Forking the Flower repository**" msgstr "" @@ -1455,7 +1455,7 @@ msgid "" " the top left corner that you are looking at your own version of Flower." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:34 +#: ../../source/contributor-tutorial-contribute-on-github.rst:47 msgid "**Cloning your forked repository**" msgstr "" @@ -1479,7 +1479,7 @@ msgid "" "it) folder in the current working directory." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:49 +#: ../../source/contributor-tutorial-contribute-on-github.rst:66 msgid "**Add origin**" msgstr "" @@ -1501,7 +1501,7 @@ msgid "" "terminal:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:68 +#: ../../source/contributor-tutorial-contribute-on-github.rst:90 msgid "**Add upstream**" msgstr "" @@ -1556,7 +1556,7 @@ msgstr "" msgid "And with Flower's repository:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:114 +#: ../../source/contributor-tutorial-contribute-on-github.rst:122 msgid "**Create a new branch**" msgstr "" @@ -1573,7 +1573,7 @@ msgid "" "directory:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:124 +#: ../../source/contributor-tutorial-contribute-on-github.rst:125 msgid "**Make changes**" msgstr "" @@ -1581,7 +1581,7 @@ msgstr "" msgid "Write great code and create wonderful changes using your favorite editor!" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:127 +#: ../../source/contributor-tutorial-contribute-on-github.rst:138 msgid "**Test and format your code**" msgstr "" @@ -1596,7 +1596,7 @@ msgstr "" msgid "To do so, we have written a few scripts that you can execute:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:140 +#: ../../source/contributor-tutorial-contribute-on-github.rst:150 msgid "**Stage changes**" msgstr "" @@ -1617,7 +1617,7 @@ msgid "" "the :code:`git status` command." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:152 +#: ../../source/contributor-tutorial-contribute-on-github.rst:160 msgid "**Commit changes**" msgstr "" @@ -1634,7 +1634,7 @@ msgid "" "example would be :code:`git commit -m \"Add images to README\"`." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:162 +#: ../../source/contributor-tutorial-contribute-on-github.rst:171 msgid "**Push the changes to the fork**" msgstr "" @@ -1655,7 +1655,7 @@ msgstr "" msgid "Creating and merging a pull request (PR)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:176 +#: ../../source/contributor-tutorial-contribute-on-github.rst:206 msgid "**Create the PR**" msgstr "" @@ -1718,7 +1718,7 @@ msgid "" "anyone, you have the option to create a draft pull request:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:209 msgid "**Making new changes**" msgstr "" @@ -1729,7 +1729,7 @@ msgid "" " associated with the PR." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:211 +#: ../../source/contributor-tutorial-contribute-on-github.rst:231 msgid "**Review the PR**" msgstr "" @@ -1765,7 +1765,7 @@ msgid "" "review." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:233 +#: ../../source/contributor-tutorial-contribute-on-github.rst:251 msgid "**Once the PR is merged**" msgstr "" @@ -2033,6 +2033,7 @@ msgstr "" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 #: ../../source/docker/run-as-subprocess.rst:11 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:15 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:12 #: ../../source/docker/tutorial-quickstart-docker.rst:11 msgid "Prerequisites" @@ -2737,6 +2738,221 @@ msgid "" " the SuperNode to execute the ClientApp as a subprocess:" msgstr "" +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:2 +msgid "Run Flower Quickstart Examples with Docker Compose" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:4 +msgid "" +"Flower provides a set of `quickstart examples " +"`_ to help you get " +"started with the framework. These examples are designed to demonstrate " +"the capabilities of Flower and by default run using the Simulation " +"Engine. This guide demonstrates how to run them using Flower's Deployment" +" Engine via Docker Compose." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:11 +msgid "" +"Some quickstart examples may have limitations or requirements that " +"prevent them from running on every environment. For more information, " +"please see `Limitations`_." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:17 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:14 +#: ../../source/docker/tutorial-quickstart-docker.rst:13 +msgid "Before you start, make sure that:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:19 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 +#: ../../source/docker/tutorial-quickstart-docker.rst:15 +msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:20 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:17 +#: ../../source/docker/tutorial-quickstart-docker.rst:16 +#, fuzzy +msgid "The Docker daemon is running." +msgstr "Verifique que o serviço Docker está rodando." + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:21 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:18 +msgid "Docker Compose is `installed `_." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:24 +msgid "Run the Quickstart Example" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:26 +msgid "" +"Clone the quickstart example you like to run. For example, ``quickstart-" +"pytorch``:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:34 +msgid "" +"Download the `compose.yml " +"`_" +" file into the example directory:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:41 +msgid "Build and start the services using the following command:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:47 +msgid "" +"Append the following lines to the end of the ``pyproject.toml`` file and " +"save it:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:49 +#: ../../source/docker/tutorial-quickstart-docker.rst:319 +msgid "pyproject.toml" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:58 +msgid "" +"You can customize the string that follows ``tool.flwr.federations.`` to " +"fit your needs. However, please note that the string cannot contain a dot" +" (``.``)." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:61 +msgid "" +"In this example, ``local-deployment`` has been used. Just remember to " +"replace ``local-deployment`` with your chosen name in both the " +"``tool.flwr.federations.`` string and the corresponding ``flwr run .`` " +"command." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:65 +#, fuzzy +msgid "Run the example:" +msgstr "Exemplo" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:71 +msgid "Follow the logs of the SuperExec service:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:77 +msgid "" +"That is all it takes! You can monitor the progress of the run through the" +" logs of the SuperExec." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:80 +msgid "Run a Different Quickstart Example" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:82 +msgid "" +"To run a different quickstart example, such as ``quickstart-tensorflow``," +" first, shut down the Docker Compose services of the current example:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:89 +msgid "After that, you can repeat the steps above." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:92 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:98 +msgid "Limitations" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:97 +msgid "Quickstart Example" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:99 +msgid "quickstart-fastai" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:100 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:102 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:110 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:112 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:116 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:118 +#: ../../source/ref-changelog.md:33 ../../source/ref-changelog.md:399 +#: ../../source/ref-changelog.md:676 ../../source/ref-changelog.md:740 +#: ../../source/ref-changelog.md:798 ../../source/ref-changelog.md:867 +#: ../../source/ref-changelog.md:929 +msgid "None" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:101 +msgid "quickstart-huggingface" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:103 +msgid "quickstart-jax" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:104 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:106 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:120 +msgid "" +"The example has not yet been updated to work with the latest ``flwr`` " +"version." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:105 +msgid "quickstart-mlcube" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:107 +msgid "quickstart-mlx" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:108 +msgid "" +"`Requires to run on macOS with Apple Silicon `_." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:109 +msgid "quickstart-monai" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:111 +msgid "quickstart-pandas" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:113 +msgid "quickstart-pytorch-lightning" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:114 +msgid "" +"Requires an older pip version that is not supported by the Flower Docker " +"images." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:115 +msgid "quickstart-pytorch" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:117 +msgid "quickstart-sklearn-tabular" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:119 +msgid "quickstart-tabnet" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:121 +msgid "quickstart-tensorflow" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:122 +msgid "Only runs on AMD64." +msgstr "" + #: ../../source/docker/set-environment-variables.rst:2 msgid "Set Environment Variables" msgstr "" @@ -2765,22 +2981,6 @@ msgid "" " understanding the basic workflow that uses the minimum configurations." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:14 -#: ../../source/docker/tutorial-quickstart-docker.rst:13 -msgid "Before you start, make sure that:" -msgstr "" - -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 -#: ../../source/docker/tutorial-quickstart-docker.rst:15 -msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." -msgstr "" - -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:17 -#: ../../source/docker/tutorial-quickstart-docker.rst:16 -#, fuzzy -msgid "The Docker daemon is running." -msgstr "Verifique que o serviço Docker está rodando." - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 #: ../../source/docker/tutorial-quickstart-docker.rst:19 msgid "Step 1: Set Up" @@ -3197,10 +3397,6 @@ msgstr "" msgid "Add the following lines to the ``pyproject.toml``:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:319 -msgid "pyproject.toml" -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker.rst:326 msgid "Run the ``quickstart-docker`` project by executing the command:" msgstr "" @@ -3248,6 +3444,7 @@ msgstr "" msgid "Remove the containers and the bridge network:" msgstr "" +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:401 #: ../../source/docker/tutorial-quickstart-docker.rst:399 msgid "Where to Go Next" msgstr "" @@ -3282,10 +3479,6 @@ msgid "" "configuration that best suits your project's needs." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:18 -msgid "Docker Compose is `installed `_." -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:23 msgid "Clone the Docker Compose ``complete`` directory:" msgstr "" @@ -3478,7 +3671,7 @@ msgstr "" #: ../../source/docker/tutorial-quickstart-docker-compose.rst:188 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:241 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:362 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:369 msgid "Rerun the ``quickstart-compose`` project:" msgstr "" @@ -3542,74 +3735,78 @@ msgstr "" msgid "compose.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:303 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:310 msgid "" "If you also want to enable TLS for the new SuperNodes, duplicate the " "SuperNode definition for each new SuperNode service in the ``with-" "tls.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:306 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:313 msgid "" "Make sure that the names of the services match with the one in the " "``compose.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:308 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:315 msgid "In ``with-tls.yml``, add the following:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:310 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:317 msgid "with-tls.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:332 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:339 msgid "Step 8: Persisting the SuperLink State and Enabling TLS" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:334 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:341 msgid "" "To run Flower with persisted SuperLink state and enabled TLS, a slight " "change in the ``with-state.yml`` file is required:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:337 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:344 msgid "Comment out the lines 2-4 and uncomment the lines 5-9:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:339 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:346 msgid "with-state.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:356 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:363 msgid "Restart the services:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:370 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:377 msgid "Step 9: Merge Multiple Compose Files" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:372 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:379 msgid "" "You can merge multiple Compose files into a single file. For instance, if" " you wish to combine the basic configuration with the TLS configuration, " "execute the following command:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:380 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:387 msgid "" "This will merge the contents of ``compose.yml`` and ``with-tls.yml`` into" " a new file called ``my_compose.yml``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:384 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:391 msgid "Step 10: Clean Up" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:386 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:393 msgid "Remove all services and volumes:" msgstr "" +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:403 +msgid ":doc:`run-quickstart-examples-docker-compose`" +msgstr "" + #: ../../source/docker/use-a-different-version.rst:2 msgid "Use a Different Flower Version" msgstr "" @@ -3873,7 +4070,7 @@ msgid "" "one method for testing the model:" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:218 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:219 msgid ":code:`set_parameters`" msgstr "" @@ -3904,9 +4101,9 @@ msgid "" ":code:`ndarray`'s (which is what :code:`flwr.client.NumPyClient` expects)" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:223 -#: ../../source/tutorial-quickstart-jax.rst:171 -#: ../../source/tutorial-quickstart-scikitlearn.rst:123 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:225 +#: ../../source/tutorial-quickstart-jax.rst:173 +#: ../../source/tutorial-quickstart-scikitlearn.rst:125 msgid ":code:`fit`" msgstr "" @@ -3928,9 +4125,9 @@ msgstr "" msgid "get the updated local model weights and return them to the server" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:227 -#: ../../source/tutorial-quickstart-jax.rst:175 -#: ../../source/tutorial-quickstart-scikitlearn.rst:127 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 +#: ../../source/tutorial-quickstart-jax.rst:178 +#: ../../source/tutorial-quickstart-scikitlearn.rst:128 msgid ":code:`evaluate`" msgstr "" @@ -4019,7 +4216,7 @@ msgid "" " individual's information remains hidden in the crowd." msgstr "" -#: ../../source/explanation-differential-privacy.rst:16 +#: ../../source/explanation-differential-privacy.rst:-1 msgid "DP Intro" msgstr "" @@ -4129,8 +4326,8 @@ msgid "" "the client's data." msgstr "" +#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:68 -#: ../../source/explanation-differential-privacy.rst:71 #: ../../source/how-to-use-differential-privacy.rst:11 msgid "Central Differential Privacy" msgstr "" @@ -4157,7 +4354,7 @@ msgid "" "that larger updates are scaled down to fit within the norm `S`." msgstr "" -#: ../../source/explanation-differential-privacy.rst:84 +#: ../../source/explanation-differential-privacy.rst:-1 msgid "clipping" msgstr "" @@ -4202,8 +4399,8 @@ msgid "" "others." msgstr "" +#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:105 -#: ../../source/explanation-differential-privacy.rst:110 #: ../../source/how-to-use-differential-privacy.rst:96 msgid "Local Differential Privacy" msgstr "" @@ -4434,7 +4631,7 @@ msgstr "" msgid "This is sometimes called a hub-and-spoke topology:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:18 +#: ../../source/explanation-flower-architecture.rst:24 msgid "Hub-and-spoke topology in federated learning" msgstr "" @@ -4506,7 +4703,7 @@ msgid "" "`missing link` between all those SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:65 +#: ../../source/explanation-flower-architecture.rst:71 #, fuzzy msgid "Basic Flower architecture" msgstr "Arquitetura do Flower" @@ -4543,7 +4740,7 @@ msgid "" "SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:91 +#: ../../source/explanation-flower-architecture.rst:97 msgid "Multi-tenancy federated learning architecture" msgstr "" @@ -4565,7 +4762,7 @@ msgid "" "their corresponding ``ClientApp``\\s:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:107 +#: ../../source/explanation-flower-architecture.rst:113 msgid "Multi-tenancy federated learning architecture - Run 1" msgstr "" @@ -4581,7 +4778,7 @@ msgid "" " to participate in the training:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:119 +#: ../../source/explanation-flower-architecture.rst:125 msgid "Multi-tenancy federated learning architecture - Run 2" msgstr "" @@ -4617,7 +4814,7 @@ msgid "" "developer machine." msgstr "" -#: ../../source/explanation-flower-architecture.rst:145 +#: ../../source/explanation-flower-architecture.rst:151 msgid "Flower Deployment Engine with SuperExec" msgstr "" @@ -7286,7 +7483,7 @@ msgid "" "adaptive clipping." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:25 +#: ../../source/how-to-use-differential-privacy.rst:-1 msgid "server side clipping" msgstr "" @@ -7315,7 +7512,7 @@ msgid "" ":code:`DifferentialPrivacyClientSideAdaptiveClipping`." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:57 +#: ../../source/how-to-use-differential-privacy.rst:-1 msgid "client side clipping" msgstr "" @@ -7342,7 +7539,7 @@ msgid "" "clipping norm value, sensitivity, epsilon, and delta." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:99 +#: ../../source/how-to-use-differential-privacy.rst:-1 msgid "local DP mod" msgstr "" @@ -7725,11 +7922,32 @@ msgstr "" msgid "Arguments" msgstr "Argumento de compilação" -#: ../../flwr install:1 new:1 run:1 +#: ../../flwr install:1 log:1 new:1 run:1 #, fuzzy msgid "Optional argument" msgstr "Argumento de compilação" +#: ../../flwr log:1 +msgid "Get logs from a Flower project run." +msgstr "" + +#: ../../flwr log:1 +msgid "Flag to stream or print logs from the Flower run" +msgstr "" + +#: ../../flwr log +msgid "default" +msgstr "" + +#: ../../flwr log:1 +msgid "``True``" +msgstr "" + +#: ../../flwr log:1 +#, fuzzy +msgid "Required argument" +msgstr "Argumento de compilação" + #: ../../flwr new:1 msgid "Create new Flower App." msgstr "" @@ -7812,7 +8030,7 @@ msgid "Modules" msgstr "" #: ../../source/ref-api/flwr.rst:35::1 -msgid ":py:obj:`client `\\" +msgid ":py:obj:`flwr.client `\\" msgstr "" #: ../../source/ref-api/flwr.rst:35::1 flwr.client:1 of @@ -7820,7 +8038,7 @@ msgid "Flower client." msgstr "" #: ../../source/ref-api/flwr.rst:35::1 -msgid ":py:obj:`common `\\" +msgid ":py:obj:`flwr.common `\\" msgstr "" #: ../../source/ref-api/flwr.rst:35::1 flwr.common:1 of @@ -7828,7 +8046,7 @@ msgid "Common components shared between server and client." msgstr "" #: ../../source/ref-api/flwr.rst:35::1 -msgid ":py:obj:`server `\\" +msgid ":py:obj:`flwr.server `\\" msgstr "" #: ../../source/ref-api/flwr.rst:35::1 @@ -7838,7 +8056,7 @@ msgid "Flower server." msgstr "" #: ../../source/ref-api/flwr.rst:35::1 -msgid ":py:obj:`simulation `\\" +msgid ":py:obj:`flwr.simulation `\\" msgstr "" #: ../../source/ref-api/flwr.rst:35::1 flwr.simulation:1 of @@ -7918,7 +8136,7 @@ msgid "Abstract base class for Flower clients using NumPy." msgstr "" #: ../../source/ref-api/flwr.client.rst:50::1 -msgid ":py:obj:`mod `\\" +msgid ":py:obj:`flwr.client.mod `\\" msgstr "" #: ../../source/ref-api/flwr.client.rst:50::1 flwr.client.mod:1 of @@ -8115,48 +8333,57 @@ msgstr "" msgid "Getter for `Context` client attribute." msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst -#: ../../source/ref-api/flwr.client.NumPyClient.rst -#: ../../source/ref-api/flwr.client.mod.LocalDpMod.rst -#: ../../source/ref-api/flwr.common.Array.rst -#: ../../source/ref-api/flwr.common.ConfigsRecord.rst -#: ../../source/ref-api/flwr.common.Context.rst -#: ../../source/ref-api/flwr.common.Error.rst -#: ../../source/ref-api/flwr.common.Message.rst -#: ../../source/ref-api/flwr.common.Metadata.rst -#: ../../source/ref-api/flwr.common.MetricsRecord.rst #: ../../source/ref-api/flwr.common.Parameters.rst:2 -#: ../../source/ref-api/flwr.common.ParametersRecord.rst -#: ../../source/ref-api/flwr.common.RecordSet.rst -#: ../../source/ref-api/flwr.server.ClientManager.rst -#: ../../source/ref-api/flwr.server.Driver.rst -#: ../../source/ref-api/flwr.server.ServerAppComponents.rst -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst -#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideAdaptiveClipping.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideFixedClipping.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideAdaptiveClipping.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideFixedClipping.rst -#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst -#: ../../source/ref-api/flwr.server.strategy.FedAdam.rst -#: ../../source/ref-api/flwr.server.strategy.FedAvg.rst -#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst -#: ../../source/ref-api/flwr.server.strategy.FedAvgM.rst -#: ../../source/ref-api/flwr.server.strategy.FedOpt.rst -#: ../../source/ref-api/flwr.server.strategy.FedProx.rst -#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst -#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst -#: ../../source/ref-api/flwr.server.strategy.Krum.rst -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst -#: ../../source/ref-api/flwr.server.workflow.SecAggPlusWorkflow.rst -#: ../../source/ref-api/flwr.server.workflow.SecAggWorkflow.rst -#: ../../source/ref-api/flwr.simulation.run_simulation.rst -#: ../../source/ref-api/flwr.simulation.start_simulation.rst #: flwr.client.app.start_client flwr.client.app.start_numpy_client -#: flwr.server.app.start_server -#: flwr.server.driver.driver.Driver.send_and_receive of +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.mod.localdp_mod.LocalDpMod +#: flwr.client.numpy_client.NumPyClient.evaluate +#: flwr.client.numpy_client.NumPyClient.fit +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.context.Context flwr.common.message.Error +#: flwr.common.message.Message flwr.common.message.Message.create_error_reply +#: flwr.common.message.Message.create_reply flwr.common.message.Metadata +#: flwr.common.record.configsrecord.ConfigsRecord +#: flwr.common.record.metricsrecord.MetricsRecord +#: flwr.common.record.parametersrecord.Array +#: flwr.common.record.parametersrecord.ParametersRecord +#: flwr.common.record.recordset.RecordSet flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.ClientManager.unregister +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.unregister +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.serverapp_components.ServerAppComponents +#: flwr.server.strategy.bulyan.Bulyan +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.fedadagrad.FedAdagrad +#: flwr.server.strategy.fedadam.FedAdam flwr.server.strategy.fedavg.FedAvg +#: flwr.server.strategy.fedavg_android.FedAvgAndroid +#: flwr.server.strategy.fedavgm.FedAvgM flwr.server.strategy.fedopt.FedOpt +#: flwr.server.strategy.fedprox.FedProx +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg +#: flwr.server.strategy.fedyogi.FedYogi flwr.server.strategy.krum.Krum +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow +#: flwr.simulation.run_simulation.run_simulation of msgid "Parameters" msgstr "" @@ -8167,21 +8394,31 @@ msgid "" "customize the local evaluation process." msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst -#: ../../source/ref-api/flwr.client.NumPyClient.rst -#: ../../source/ref-api/flwr.common.ConfigsRecord.rst -#: ../../source/ref-api/flwr.common.Message.rst -#: ../../source/ref-api/flwr.common.MetricsRecord.rst -#: ../../source/ref-api/flwr.common.ParametersRecord.rst -#: ../../source/ref-api/flwr.server.ClientManager.rst -#: ../../source/ref-api/flwr.server.Driver.rst -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst -#: ../../source/ref-api/flwr.simulation.start_simulation.rst -#: flwr.server.app.start_server -#: flwr.server.driver.driver.Driver.send_and_receive of +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.numpy_client.NumPyClient.evaluate +#: flwr.client.numpy_client.NumPyClient.fit +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.message.Message.create_reply flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.num_available +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.SimpleClientManager.num_available +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters of msgid "Returns" msgstr "" @@ -8191,18 +8428,29 @@ msgid "" "details such as the number of local data examples used for evaluation." msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst -#: ../../source/ref-api/flwr.client.NumPyClient.rst -#: ../../source/ref-api/flwr.common.Message.rst -#: ../../source/ref-api/flwr.server.ClientManager.rst -#: ../../source/ref-api/flwr.server.Driver.rst -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst -#: ../../source/ref-api/flwr.simulation.start_simulation.rst -#: flwr.server.app.start_server -#: flwr.server.driver.driver.Driver.send_and_receive of +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.message.Message.create_reply flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.num_available +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.SimpleClientManager.num_available +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters of msgid "Return type" msgstr "" @@ -8521,6 +8769,11 @@ msgstr "" msgid ":py:obj:`make_ffn `\\ \\(ffn\\, mods\\)" msgstr "" +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.utils.make_ffn:1 of +msgid "." +msgstr "" + #: ../../source/ref-api/flwr.client.mod.rst:28::1 msgid "" ":py:obj:`message_size_mod `\\ \\(msg\\," @@ -8685,10 +8938,6 @@ msgstr "" msgid "make\\_ffn" msgstr "" -#: flwr.client.mod.utils.make_ffn:1 of -msgid "." -msgstr "" - #: ../../source/ref-api/flwr.client.mod.message_size_mod.rst:2 msgid "message\\_size\\_mod" msgstr "" @@ -8715,14 +8964,6 @@ msgstr "" msgid "secaggplus\\_mod" msgstr "" -#: ../../source/ref-api/flwr.client.run_client_app.rst:2 -msgid "run\\_client\\_app" -msgstr "" - -#: ../../source/ref-api/flwr.client.run_supernode.rst:2 -msgid "run\\_supernode" -msgstr "" - #: ../../source/ref-api/flwr.client.start_client.rst:2 msgid "start\\_client" msgstr "" @@ -9428,12 +9669,7 @@ msgid "Return number of Bytes stored in this object." msgstr "" #: collections.abc.MutableMapping.clear:1::1 of -msgid ":py:obj:`get `\\ \\(key\\[\\, default\\]\\)" -msgstr "" - -#: collections.abc.Mapping.get:1 -#: collections.abc.MutableMapping.clear:1::1 of -msgid "Retrieve the corresponding layout by the string key." +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr "" #: collections.abc.MutableMapping.clear:1::1 of @@ -9490,22 +9726,6 @@ msgstr "" msgid "This function counts booleans as occupying 1 Byte." msgstr "" -#: collections.abc.Mapping.get:3 of -msgid "" -"When there isn't an exact match, all the existing keys in the layout map " -"will be treated as a regex and map against the input key again. The first" -" match will be returned, based on the key insertion order. Return None if" -" there isn't any match found." -msgstr "" - -#: collections.abc.Mapping.get:8 of -msgid "the string key as the query for the layout." -msgstr "" - -#: collections.abc.Mapping.get:10 of -msgid "Corresponding layout based on the query." -msgstr "" - #: ../../source/ref-api/flwr.common.Context.rst:2 msgid "Context" msgstr "" @@ -10218,7 +10438,7 @@ msgstr "" msgid "The encoding in which to encode the string." msgstr "" -#: flwr.common.EventType.encode:5 of +#: flwr.common.EventType.encode:9 of msgid "errors" msgstr "" @@ -10394,7 +10614,7 @@ msgid "" "string." msgstr "" -#: flwr.common.EventType.replace:3 of +#: flwr.common.EventType.replace:5 of msgid "count" msgstr "" @@ -10430,7 +10650,7 @@ msgid "" "strings and the original string." msgstr "" -#: flwr.common.EventType.rsplit:3 flwr.common.EventType.split:3 of +#: flwr.common.EventType.rsplit:7 flwr.common.EventType.split:7 of msgid "sep" msgstr "" @@ -10445,7 +10665,7 @@ msgid "" " empty strings from the result." msgstr "" -#: flwr.common.EventType.rsplit:9 flwr.common.EventType.split:9 of +#: flwr.common.EventType.rsplit:11 flwr.common.EventType.split:11 of msgid "maxsplit" msgstr "" @@ -10486,7 +10706,7 @@ msgid "" "remaining cased characters have lower case." msgstr "" -#: flwr.common.EventType.translate:3 of +#: flwr.common.EventType.translate:5 of msgid "table" msgstr "" @@ -10901,7 +11121,7 @@ msgid ":py:obj:`count_bytes `\\ \\(\\)" msgstr "" #: collections.abc.MutableMapping.clear:1::1 of -msgid ":py:obj:`get `\\ \\(key\\[\\, default\\]\\)" +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr "" #: collections.abc.MutableMapping.clear:1::1 of @@ -11033,9 +11253,7 @@ msgid ":py:obj:`count_bytes `\\ \\(\\) msgstr "" #: collections.abc.MutableMapping.clear:1::1 of -msgid "" -":py:obj:`get `\\ \\(key\\[\\, " -"default\\]\\)" +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr "" #: collections.abc.MutableMapping.clear:1::1 of @@ -11362,7 +11580,7 @@ msgid "Provides a pool of available clients." msgstr "" #: ../../source/ref-api/flwr.server.rst:56::1 -msgid ":py:obj:`strategy `\\" +msgid ":py:obj:`flwr.server.strategy `\\" msgstr "" #: ../../source/ref-api/flwr.server.rst:56::1 @@ -11371,7 +11589,7 @@ msgid "Contains the strategy abstraction and different implementations." msgstr "" #: ../../source/ref-api/flwr.server.rst:56::1 -msgid ":py:obj:`workflow `\\" +msgid ":py:obj:`flwr.server.workflow `\\" msgstr "" #: ../../source/ref-api/flwr.server.rst:56::1 @@ -11848,8 +12066,7 @@ msgid "" msgstr "" #: flwr.server.app.start_server:9 -#: flwr.server.serverapp_components.ServerAppComponents:6 -#: flwr.simulation.app.start_simulation:29 of +#: flwr.server.serverapp_components.ServerAppComponents:6 of msgid "" "Currently supported values are `num_rounds` (int, default: 1) and " "`round_timeout` in seconds (float, default: None)." @@ -11965,14 +12182,6 @@ msgstr "" msgid "**success**" msgstr "" -#: ../../source/ref-api/flwr.server.run_server_app.rst:2 -msgid "run\\_server\\_app" -msgstr "" - -#: ../../source/ref-api/flwr.server.run_superlink.rst:2 -msgid "run\\_superlink" -msgstr "" - #: ../../source/ref-api/flwr.server.start_server.rst:2 msgid "start\\_server" msgstr "" @@ -14999,13 +15208,13 @@ msgstr "" #: ../../source/ref-api/flwr.simulation.rst:18::1 msgid "" -":py:obj:`start_simulation `\\ \\(\\*\\," -" client\\_fn\\, num\\_clients\\)" +":py:obj:`start_simulation `\\ " +"\\(\\*args\\, \\*\\*kwargs\\)" msgstr "" #: ../../source/ref-api/flwr.simulation.rst:18::1 -#: flwr.simulation.app.start_simulation:1 of -msgid "Start a Ray-based Flower simulation server." +#: flwr.simulation.start_simulation:1 of +msgid "Log error stating that module `ray` could not be imported." msgstr "" #: ../../source/ref-api/flwr.simulation.run_simulation.rst:2 @@ -15066,120 +15275,6 @@ msgstr "" msgid "start\\_simulation" msgstr "" -#: flwr.simulation.app.start_simulation:3 of -msgid "" -"A function creating `Client` instances. The function must have the " -"signature `client_fn(context: Context). It should return a single client " -"instance of type `Client`. Note that the created client instances are " -"ephemeral and will often be destroyed after a single method invocation. " -"Since client instances are not long-lived, they should not attempt to " -"carry state over method invocations. Any state required by the instance " -"(model, dataset, hyperparameters, ...) should be (re-)created in either " -"the call to `client_fn` or the call to any of the client methods (e.g., " -"load evaluation data in the `evaluate` method itself)." -msgstr "" - -#: flwr.simulation.app.start_simulation:13 of -msgid "The total number of clients in this simulation." -msgstr "" - -#: flwr.simulation.app.start_simulation:15 of -msgid "" -"UNSUPPORTED, WILL BE REMOVED. USE `num_clients` INSTEAD. List " -"`client_id`s for each client. This is only required if `num_clients` is " -"not set. Setting both `num_clients` and `clients_ids` with " -"`len(clients_ids)` not equal to `num_clients` generates an error. Using " -"this argument will raise an error." -msgstr "" - -#: flwr.simulation.app.start_simulation:21 of -msgid "" -"CPU and GPU resources for a single client. Supported keys are `num_cpus` " -"and `num_gpus`. To understand the GPU utilization caused by `num_gpus`, " -"as well as using custom resources, please consult the Ray documentation." -msgstr "" - -#: flwr.simulation.app.start_simulation:26 of -msgid "" -"An implementation of the abstract base class `flwr.server.Server`. If no " -"instance is provided, then `start_server` will create one." -msgstr "" - -#: flwr.simulation.app.start_simulation:32 of -msgid "" -"An implementation of the abstract base class `flwr.server.Strategy`. If " -"no strategy is provided, then `start_server` will use " -"`flwr.server.strategy.FedAvg`." -msgstr "" - -#: flwr.simulation.app.start_simulation:36 of -msgid "" -"An implementation of the abstract base class `flwr.server.ClientManager`." -" If no implementation is provided, then `start_simulation` will use " -"`flwr.server.client_manager.SimpleClientManager`." -msgstr "" - -#: flwr.simulation.app.start_simulation:40 of -msgid "" -"Optional dictionary containing arguments for the call to `ray.init`. If " -"ray_init_args is None (the default), Ray will be initialized with the " -"following default args: { \"ignore_reinit_error\": True, " -"\"include_dashboard\": False } An empty dictionary can be used " -"(ray_init_args={}) to prevent any arguments from being passed to " -"ray.init." -msgstr "" - -#: flwr.simulation.app.start_simulation:40 of -msgid "" -"Optional dictionary containing arguments for the call to `ray.init`. If " -"ray_init_args is None (the default), Ray will be initialized with the " -"following default args:" -msgstr "" - -#: flwr.simulation.app.start_simulation:44 of -msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" -msgstr "" - -#: flwr.simulation.app.start_simulation:46 of -msgid "" -"An empty dictionary can be used (ray_init_args={}) to prevent any " -"arguments from being passed to ray.init." -msgstr "" - -#: flwr.simulation.app.start_simulation:49 of -msgid "" -"Set to True to prevent `ray.shutdown()` in case " -"`ray.is_initialized()=True`." -msgstr "" - -#: flwr.simulation.app.start_simulation:51 of -msgid "" -"Optionally specify the type of actor to use. The actor object, which " -"persists throughout the simulation, will be the process in charge of " -"executing a ClientApp wrapping input argument `client_fn`." -msgstr "" - -#: flwr.simulation.app.start_simulation:55 of -msgid "" -"If you want to create your own Actor classes, you might need to pass some" -" input argument. You can use this dictionary for such purpose." -msgstr "" - -#: flwr.simulation.app.start_simulation:58 of -msgid "" -"(default: \"DEFAULT\") Optional string (\"DEFAULT\" or \"SPREAD\") for " -"the VCE to choose in which node the actor is placed. If you are an " -"advanced user needed more control you can use lower-level scheduling " -"strategies to pin actors to specific compute nodes (e.g. via " -"NodeAffinitySchedulingStrategy). Please note this is an advanced feature." -" For all details, please refer to the Ray documentation: " -"https://docs.ray.io/en/latest/ray-core/scheduling/index.html" -msgstr "" - -#: flwr.simulation.app.start_simulation:67 of -msgid "**hist** -- Object containing metrics from training." -msgstr "" - #: ../../source/ref-changelog.md:1 msgid "Changelog" msgstr "" @@ -15285,13 +15380,6 @@ msgstr "" msgid "Incompatible changes" msgstr "" -#: ../../source/ref-changelog.md:33 ../../source/ref-changelog.md:399 -#: ../../source/ref-changelog.md:676 ../../source/ref-changelog.md:740 -#: ../../source/ref-changelog.md:798 ../../source/ref-changelog.md:867 -#: ../../source/ref-changelog.md:929 -msgid "None" -msgstr "" - #: ../../source/ref-changelog.md:35 msgid "v1.11.0 (2024-08-30)" msgstr "" @@ -20418,32 +20506,44 @@ msgid "" "blockchain environment is available here:" msgstr "" -#: ../../source/ref-faq.rst:28 -msgid "" -"`Flower meets Nevermined GitHub Repository `_." +#: ../../source/ref-faq.rst:29 +msgid "`FLock: A Decentralised AI Training Platform `_." msgstr "" #: ../../source/ref-faq.rst:29 -msgid "" +msgid "Contribute to on-chain training the model and earn rewards." +msgstr "" + +#: ../../source/ref-faq.rst:30 +msgid "Local blockchain with federated learning simulation." +msgstr "" + +#: ../../source/ref-faq.rst:31 +msgid "" +"`Flower meets Nevermined GitHub Repository `_." +msgstr "" + +#: ../../source/ref-faq.rst:32 +msgid "" "`Flower meets Nevermined YouTube video " "`_." msgstr "" -#: ../../source/ref-faq.rst:30 +#: ../../source/ref-faq.rst:33 msgid "" "`Flower meets KOSMoS `_." msgstr "" -#: ../../source/ref-faq.rst:31 +#: ../../source/ref-faq.rst:34 msgid "" "`Flower meets Talan blog post `_ ." msgstr "" -#: ../../source/ref-faq.rst:32 +#: ../../source/ref-faq.rst:35 msgid "" "`Flower meets Talan GitHub Repository " "`_ ." @@ -20666,178 +20766,294 @@ msgid "" "more." msgstr "" -#: ../../source/tutorial-quickstart-fastai.rst:-1 -msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with FastAI to train a vision model on CIFAR-10." -msgstr "" - #: ../../source/tutorial-quickstart-fastai.rst:5 msgid "Quickstart fastai" msgstr "" -#: ../../source/tutorial-quickstart-fastai.rst:10 -msgid "Let's build a federated learning system using fastai and Flower!" +#: ../../source/tutorial-quickstart-fastai.rst:7 +msgid "" +"In this federated learning tutorial we will learn how to train a " +"SqueezeNet model on MNIST using Flower and fastai. It is recommended to " +"create a virtual environment and run everything within a :doc:`virtualenv" +" `." msgstr "" #: ../../source/tutorial-quickstart-fastai.rst:12 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 +msgid "Then, clone the code example directly from GitHub:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:20 msgid "" -"Please refer to the `full code example " -"`_ " -"to learn more." +"This will create a new directory called `quickstart-fastai` containing " +"the following files:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:33 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:33 +msgid "Next, activate your environment, then run:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:43 +msgid "" +"This example by default runs the Flower Simulation Engine, creating a " +"federation of 10 nodes using `FedAvg `_ " +"as the aggregation strategy. The dataset will be partitioned using Flower" +" Dataset's `IidPartitioner `_." +" Let's run the project:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:56 +#: ../../source/tutorial-quickstart-huggingface.rst:65 +#: ../../source/tutorial-quickstart-mlx.rst:64 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:56 +#: ../../source/tutorial-quickstart-pytorch.rst:64 +#: ../../source/tutorial-quickstart-tensorflow.rst:65 +msgid "With default arguments you will see an output like this one:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:100 +#: ../../source/tutorial-quickstart-huggingface.rst:116 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:106 +#: ../../source/tutorial-quickstart-pytorch.rst:105 +#: ../../source/tutorial-quickstart-tensorflow.rst:106 +msgid "" +"You can also override the parameters defined in the " +"``[tool.flwr.app.config]`` section in ``pyproject.toml`` like this:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:110 +msgid "" +"Check the `source code `_ of this tutorial in ``examples/quickstart-fasai`` " +"in the Flower GitHub repository." msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:-1 msgid "" "Check out this Federating Learning quickstart tutorial for using Flower " -"with HuggingFace Transformers in order to fine-tune an LLM." +"with 🤗 HuggingFace Transformers in order to fine-tune an LLM." msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:5 msgid "Quickstart 🤗 Transformers" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:10 +#: ../../source/tutorial-quickstart-huggingface.rst:7 msgid "" -"Let's build a federated learning system using Hugging Face Transformers " -"and Flower!" +"In this federated learning tutorial we will learn how to train a large " +"language model (LLM) on the `IMDB " +"`_ dataset using Flower" +" and the 🤗 Hugging Face Transformers library. It is recommended to create" +" a virtual environment and run everything within a :doc:`virtualenv " +"`." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:12 +#: ../../source/tutorial-quickstart-huggingface.rst:14 msgid "" -"We will leverage Hugging Face to federate the training of language models" -" over multiple clients using Flower. More specifically, we will fine-tune" -" a pre-trained Transformer model (distilBERT) for sequence classification" -" over a dataset of IMDB ratings. The end goal is to detect if a movie " -"rating is positive or negative." -msgstr "" - -#: ../../source/tutorial-quickstart-huggingface.rst:18 -msgid "Dependencies" +"Let's use ``flwr new`` to create a complete Flower+🤗 Hugging Face " +"project. It will generate all the files needed to run, by default with " +"the Flower Simulation Engine, a federation of 10 nodes using |fedavg|_ " +"The dataset will be partitioned using |flowerdatasets|_'s " +"|iidpartitioner|_." msgstr "" #: ../../source/tutorial-quickstart-huggingface.rst:20 +#: ../../source/tutorial-quickstart-mlx.rst:19 +#: ../../source/tutorial-quickstart-pytorch.rst:19 +#: ../../source/tutorial-quickstart-tensorflow.rst:20 msgid "" -"To follow along this tutorial you will need to install the following " -"packages: :code:`datasets`, :code:`evaluate`, :code:`flwr`, " -":code:`torch`, and :code:`transformers`. This can be done using " -":code:`pip`:" +"Now that we have a rough idea of what this example is about, let's get " +"started. First, install Flower in your new environment:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:30 -msgid "Standard Hugging Face workflow" +#: ../../source/tutorial-quickstart-huggingface.rst:28 +msgid "" +"Then, run the command below. You will be prompted to select one of the " +"available templates (choose ``HuggingFace``), give a name to your " +"project, and type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:33 -msgid "Handling the data" +#: ../../source/tutorial-quickstart-huggingface.rst:36 +#: ../../source/tutorial-quickstart-mlx.rst:35 +#: ../../source/tutorial-quickstart-pytorch.rst:35 +#: ../../source/tutorial-quickstart-tensorflow.rst:36 +msgid "" +"After running it you'll notice a new directory with your project name has" +" been created. It should have the following structure:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:35 +#: ../../source/tutorial-quickstart-huggingface.rst:50 +#: ../../source/tutorial-quickstart-mlx.rst:49 +#: ../../source/tutorial-quickstart-pytorch.rst:49 +#: ../../source/tutorial-quickstart-tensorflow.rst:50 msgid "" -"To fetch the IMDB dataset, we will use Hugging Face's :code:`datasets` " -"library. We then need to tokenize the data and create :code:`PyTorch` " -"dataloaders, this is all done in the :code:`load_data` function:" +"If you haven't yet installed the project and its dependencies, you can do" +" so by:" +msgstr "" + +#: ../../source/tutorial-quickstart-huggingface.rst:58 +#: ../../source/tutorial-quickstart-pytorch.rst:57 +#: ../../source/tutorial-quickstart-tensorflow.rst:58 +msgid "To run the project, do:" +msgstr "" + +#: ../../source/tutorial-quickstart-huggingface.rst:106 +msgid "You can also run the project with GPU as follows:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:81 -msgid "Training and testing the model" +#: ../../source/tutorial-quickstart-huggingface.rst:113 +msgid "" +"This will use the default arguments where each ``ClientApp`` will use 2 " +"CPUs and at most 4 ``ClientApp``\\s will run in a given GPU." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:83 +#: ../../source/tutorial-quickstart-huggingface.rst:124 +#: ../../source/tutorial-quickstart-mlx.rst:114 +#: ../../source/tutorial-quickstart-pytorch.rst:113 msgid "" -"Once we have a way of creating our trainloader and testloader, we can " -"take care of the training and testing. This is very similar to any " -":code:`PyTorch` training or testing loop:" +"What follows is an explanation of each component in the project you just " +"created: dataset partition, the model, defining the ``ClientApp`` and " +"defining the ``ServerApp``." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:121 -msgid "Creating the model itself" +#: ../../source/tutorial-quickstart-huggingface.rst:130 +#: ../../source/tutorial-quickstart-mlx.rst:120 +#: ../../source/tutorial-quickstart-pytorch.rst:119 +#: ../../source/tutorial-quickstart-tensorflow.rst:116 +msgid "The Data" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:123 +#: ../../source/tutorial-quickstart-huggingface.rst:132 msgid "" -"To create the model itself, we will just load the pre-trained distillBERT" -" model using Hugging Face’s :code:`AutoModelForSequenceClassification` :" +"This tutorial uses |flowerdatasets|_ to easily download and partition the" +" `IMDB `_ dataset. In " +"this example you'll make use of the |iidpartitioner|_ to generate " +"``num_partitions`` partitions. You can choose |otherpartitioners|_ " +"available in Flower Datasets. To tokenize the text, we will also load the" +" tokenizer from the pre-trained Transformer model that we'll use during " +"training - more on that in the next section. Each ``ClientApp`` will call" +" this function to create dataloaders with the data that correspond to " +"their data partition." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:136 -msgid "Federating the example" +#: ../../source/tutorial-quickstart-huggingface.rst:178 +#: ../../source/tutorial-quickstart-mlx.rst:164 +#: ../../source/tutorial-quickstart-pytorch.rst:157 +#: ../../source/tutorial-quickstart-tensorflow.rst:145 +msgid "The Model" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:139 -msgid "Creating the IMDBClient" +#: ../../source/tutorial-quickstart-huggingface.rst:180 +msgid "" +"We will leverage 🤗 Hugging Face to federate the training of language " +"models over multiple clients using Flower. More specifically, we will " +"fine-tune a pre-trained Transformer model (|berttiny|_) for sequence " +"classification over the dataset of IMDB ratings. The end goal is to " +"detect if a movie rating is positive or negative. If you have access to " +"larger GPUs, feel free to use larger models!" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:141 +#: ../../source/tutorial-quickstart-huggingface.rst:193 msgid "" -"To federate our example to multiple clients, we first need to write our " -"Flower client class (inheriting from :code:`flwr.client.NumPyClient`). " -"This is very easy, as our model is a standard :code:`PyTorch` model:" +"Note that here, ``model_name`` is a string that will be loaded from the " +"``Context`` in the ClientApp and ServerApp." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:169 +#: ../../source/tutorial-quickstart-huggingface.rst:196 msgid "" -"The :code:`get_parameters` function lets the server get the client's " -"parameters. Inversely, the :code:`set_parameters` function allows the " -"server to send its parameters to the client. Finally, the :code:`fit` " -"function trains the model locally for the client, and the " -":code:`evaluate` function tests the model locally and returns the " -"relevant metrics." +"In addition to loading the pretrained model weights and architecture, we " +"also include two utility functions to perform both training (i.e. " +"``train()``) and evaluation (i.e. ``test()``) using the above model. " +"These functions should look fairly familiar if you have some prior " +"experience with PyTorch. Note these functions do not have anything " +"specific to Flower. That being said, the training function will normally " +"be called, as we'll see later, from a Flower client passing its own data." +" In summary, your clients can use standard training/testing functions to " +"perform local training or evaluation:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:175 -msgid "Starting the server" +#: ../../source/tutorial-quickstart-huggingface.rst:239 +#: ../../source/tutorial-quickstart-mlx.rst:210 +#: ../../source/tutorial-quickstart-pytorch.rst:234 +#: ../../source/tutorial-quickstart-tensorflow.rst:176 +msgid "The ClientApp" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:177 +#: ../../source/tutorial-quickstart-huggingface.rst:241 msgid "" -"Now that we have a way to instantiate clients, we need to create our " -"server in order to aggregate the results. Using Flower, this can be done " -"very easily by first choosing a strategy (here, we are using " -":code:`FedAvg`, which will define the global weights as the average of " -"all the clients' weights at each round) and then using the " -":code:`flwr.server.start_server` function:" +"The main changes we have to make to use 🤗 Hugging Face with Flower will " +"be found in the ``get_weights()`` and ``set_weights()`` functions. Under " +"the hood, the ``transformers`` library uses PyTorch, which means we can " +"reuse the ``get_weights()`` and ``set_weights()`` code that we defined in" +" the :doc:`Quickstart PyTorch ` tutorial. As" +" a reminder, in ``get_weights()``, PyTorch model parameters are extracted" +" and represented as a list of NumPy arrays. The ``set_weights()`` " +"function that's the opposite: given a list of NumPy arrays it applies " +"them to an existing PyTorch model. Doing this in fairly easy in PyTorch." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:205 +#: ../../source/tutorial-quickstart-huggingface.rst:254 +#: ../../source/tutorial-quickstart-pytorch.rst:245 msgid "" -"The :code:`weighted_average` function is there to provide a way to " -"aggregate the metrics distributed amongst the clients (basically this " -"allows us to display a nice average accuracy and loss for every round)." +"The specific implementation of ``get_weights()`` and ``set_weights()`` " +"depends on the type of models you use. The ones shown below work for a " +"wide range of PyTorch models but you might need to adjust them if you " +"have more exotic model architectures." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:209 -msgid "Putting everything together" +#: ../../source/tutorial-quickstart-huggingface.rst:269 +#: ../../source/tutorial-quickstart-pytorch.rst:261 +msgid "" +"The rest of the functionality is directly inspired by the centralized " +"case. The ``fit()`` method in the client trains the model using the local" +" dataset. Similarly, the ``evaluate()`` method is used to evaluate the " +"model received on a held-out validation set that the client might have:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:211 -msgid "We can now start client instances using:" +#: ../../source/tutorial-quickstart-huggingface.rst:296 +msgid "" +"Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " +"defined above by means of a ``client_fn()`` callback. Note that the " +"`context` enables you to get access to hyperparemeters defined in your " +"``pyproject.toml`` to configure the run. In this tutorial we access the " +"``local-epochs`` setting to control the number of epochs a ``ClientApp`` " +"will perform when running the ``fit()`` method. You could define " +"additional hyperparameters in ``pyproject.toml`` and access them here." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:221 -msgid "" -"And they will be able to connect to the server and start the federated " -"training." +#: ../../source/tutorial-quickstart-huggingface.rst:330 +#: ../../source/tutorial-quickstart-mlx.rst:376 +#: ../../source/tutorial-quickstart-pytorch.rst:321 +#: ../../source/tutorial-quickstart-tensorflow.rst:245 +msgid "The ServerApp" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:223 +#: ../../source/tutorial-quickstart-huggingface.rst:332 msgid "" -"If you want to check out everything put together, you should check out " -"the `full code example `_ ." +"To construct a ``ServerApp`` we define a ``server_fn()`` callback with an" +" identical signature to that of ``client_fn()`` but the return type is " +"|serverappcomponents|_ as opposed to a |client|_ In this example we use " +"the `FedAvg` strategy. To it we pass a randomly initialized model that " +"will server as the global model to federated. Note that the value of " +"``fraction_fit`` is read from the run config. You can find the default " +"value defined in the ``pyproject.toml``." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:226 +#: ../../source/tutorial-quickstart-huggingface.rst:371 msgid "" -"Of course, this is a very basic example, and a lot can be added or " -"modified, it was just to showcase how simply we could federate a Hugging " -"Face workflow using Flower." +"Congratulations! You've successfully built and run your first federated " +"learning system for an LLM." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:229 +#: ../../source/tutorial-quickstart-huggingface.rst:376 msgid "" -"Note that in this example we used :code:`PyTorch`, but we could have very" -" well used :code:`TensorFlow`." +"Check the source code of the extended version of this tutorial in " +"|quickstart_hf_link|_ in the Flower GitHub repository. For a " +"comprehensive example of a federated fine-tuning of an LLM with Flower, " +"refer to the |flowertune|_ example in the Flower GitHub repository." msgstr "" #: ../../source/tutorial-quickstart-ios.rst:-1 @@ -20892,7 +21108,6 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:34 #: ../../source/tutorial-quickstart-scikitlearn.rst:40 -#: ../../source/tutorial-quickstart-tensorflow.rst:29 #: ../../source/tutorial-quickstart-xgboost.rst:55 msgid "Flower Client" msgstr "" @@ -20966,13 +21181,11 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:129 #: ../../source/tutorial-quickstart-scikitlearn.rst:167 -#: ../../source/tutorial-quickstart-tensorflow.rst:98 #: ../../source/tutorial-quickstart-xgboost.rst:341 msgid "Flower Server" msgstr "" #: ../../source/tutorial-quickstart-ios.rst:131 -#: ../../source/tutorial-quickstart-tensorflow.rst:100 msgid "" "For simple workloads we can start a Flower server and leave all the " "configuration possibilities at their default values. In a file named " @@ -20981,12 +21194,10 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:142 #: ../../source/tutorial-quickstart-scikitlearn.rst:230 -#: ../../source/tutorial-quickstart-tensorflow.rst:112 msgid "Train the model, federated!" msgstr "" #: ../../source/tutorial-quickstart-ios.rst:144 -#: ../../source/tutorial-quickstart-tensorflow.rst:114 #: ../../source/tutorial-quickstart-xgboost.rst:567 msgid "" "With both client and server ready, we can now run everything and see " @@ -21151,7 +21362,7 @@ msgid "" " the model:" msgstr "" -#: ../../source/tutorial-quickstart-jax.rst:165 +#: ../../source/tutorial-quickstart-jax.rst:167 msgid ":code:`set_parameters (optional)`" msgstr "" @@ -21235,13 +21446,6 @@ msgid "" "api/flwr_datasets.partitioner.IidPartitioner.html#flwr_datasets.partitioner.IidPartitioner>`_." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:19 -#: ../../source/tutorial-quickstart-pytorch.rst:19 -msgid "" -"Now that we have a rough idea of what this example is about, let's get " -"started. First, install Flower in your new environment:" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:27 msgid "" "Then, run the command below. You will be prompted to select of the " @@ -21249,48 +21453,16 @@ msgid "" "type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:35 -#: ../../source/tutorial-quickstart-pytorch.rst:35 -msgid "" -"After running it you'll notice a new directory with your project name has" -" been created. It should have the following structure:" -msgstr "" - -#: ../../source/tutorial-quickstart-mlx.rst:49 -#: ../../source/tutorial-quickstart-pytorch.rst:49 -msgid "" -"If you haven't yet installed the project and its dependencies, you can do" -" so by:" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:57 msgid "To run the project do:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:64 -#: ../../source/tutorial-quickstart-pytorch.rst:64 -msgid "With default arguments you will see an output like this one:" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:106 msgid "" "You can also override the parameters defined in " "``[tool.flwr.app.config]`` section in the ``pyproject.toml`` like this:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:114 -#: ../../source/tutorial-quickstart-pytorch.rst:113 -msgid "" -"What follows is an explanation of each component in the project you just " -"created: dataset partition, the model, defining the ``ClientApp`` and " -"defining the ``ServerApp``." -msgstr "" - -#: ../../source/tutorial-quickstart-mlx.rst:120 -#: ../../source/tutorial-quickstart-pytorch.rst:119 -msgid "The Data" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:122 msgid "" "We will use `Flower Datasets `_ to " @@ -21302,11 +21474,6 @@ msgid "" "api/flwr_datasets.partitioner.html>`_ available in Flower Datasets:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:164 -#: ../../source/tutorial-quickstart-pytorch.rst:157 -msgid "The Model" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:166 msgid "" "We define the model as in the `centralized MLX example " @@ -21320,11 +21487,6 @@ msgid "" "over batches." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:210 -#: ../../source/tutorial-quickstart-pytorch.rst:234 -msgid "The ClientApp" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:212 msgid "" "The main changes we have to make to use `MLX` with `Flower` will be found" @@ -21392,11 +21554,6 @@ msgid "" "method." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:376 -#: ../../source/tutorial-quickstart-pytorch.rst:321 -msgid "The ServerApp" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:378 msgid "" "To construct a ``ServerApp``, we define a ``server_fn()`` callback with " @@ -21410,6 +21567,7 @@ msgstr "" #: ../../source/tutorial-quickstart-mlx.rst:402 #: ../../source/tutorial-quickstart-pytorch.rst:360 +#: ../../source/tutorial-quickstart-tensorflow.rst:279 msgid "" "Congratulations! You've successfully built and run your first federated " "learning system." @@ -21476,16 +21634,6 @@ msgid "" "and type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:57 -msgid "To run the project, do:" -msgstr "" - -#: ../../source/tutorial-quickstart-pytorch.rst:105 -msgid "" -"You can also override the parameters defined in the " -"``[tool.flwr.app.config]`` section in ``pyproject.toml`` like this:" -msgstr "" - #: ../../source/tutorial-quickstart-pytorch.rst:121 msgid "" "This tutorial uses `Flower Datasets `_ " @@ -21529,22 +21677,6 @@ msgid "" "PyTorch model. Doing this in fairly easy in PyTorch." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:245 -msgid "" -"The specific implementation of ``get_weights()`` and ``set_weights()`` " -"depends on the type of models you use. The ones shown below work for a " -"wide range of PyTorch models but you might need to adjust them if you " -"have more exotic model architectures." -msgstr "" - -#: ../../source/tutorial-quickstart-pytorch.rst:261 -msgid "" -"The rest of the functionality is directly inspired by the centralized " -"case. The ``fit()`` method in the client trains the model using the local" -" dataset. Similarly, the ``evaluate()`` method is used to evaluate the " -"model received on a held-out validation set that the client might have:" -msgstr "" - #: ../../source/tutorial-quickstart-pytorch.rst:294 msgid "" "Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " @@ -21578,6 +21710,7 @@ msgid "" msgstr "" #: ../../source/tutorial-quickstart-pytorch.rst:372 +#: ../../source/tutorial-quickstart-tensorflow.rst:295 msgid "Video tutorial" msgstr "" @@ -21588,27 +21721,46 @@ msgid "" "that shows the new APIs (as the content above does)" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:-1 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 +msgid "Quickstart PyTorch Lightning" +msgstr "" + +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:7 msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with PyTorch Lightning to train an Auto Encoder model on MNIST." +"In this federated learning tutorial we will learn how to train an " +"AutoEncoder model on MNIST using Flower and PyTorch Lightning. It is " +"recommended to create a virtual environment and run everything within a " +":doc:`virtualenv `." msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 -msgid "Quickstart PyTorch Lightning" +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:20 +msgid "" +"This will create a new directory called `quickstart-pytorch-lightning` " +"containing the following files:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:10 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:43 msgid "" -"Let's build a horizontal federated learning system using PyTorch " -"Lightning and Flower!" +"By default, Flower Simulation Engine will be started and it will create a" +" federation of 4 nodes using `FedAvg `_ " +"as the aggregation strategy. The dataset will be partitioned using Flower" +" Dataset's `IidPartitioner `_." +" To run the project, do:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:94 msgid "" -"Please refer to the `full code example " -"`_ to learn more." +"Each simulated `ClientApp` (two per round) will also log a summary of " +"their local training process. Expect this output to be similar to:" +msgstr "" + +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:116 +msgid "" +"Check the `source code `_ of this tutorial in ``examples" +"/quickstart-pytorch-lightning`` in the Flower GitHub repository." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:-1 @@ -21689,7 +21841,7 @@ msgstr "" msgid "Sets the parameters of a :code:`sklearn` LogisticRegression model" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:49 +#: ../../source/tutorial-quickstart-scikitlearn.rst:50 msgid ":code:`set_initial_params()`" msgstr "" @@ -21745,7 +21897,7 @@ msgstr "" msgid "return the model weight as a list of NumPy ndarrays" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:120 +#: ../../source/tutorial-quickstart-scikitlearn.rst:121 msgid ":code:`set_parameters` (optional)" msgstr "" @@ -21839,7 +21991,6 @@ msgid "" msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:239 -#: ../../source/tutorial-quickstart-tensorflow.rst:122 #: ../../source/tutorial-quickstart-xgboost.rst:575 msgid "" "Once the server is running we can start the clients in different " @@ -21847,7 +21998,6 @@ msgid "" msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:246 -#: ../../source/tutorial-quickstart-tensorflow.rst:129 #: ../../source/tutorial-quickstart-xgboost.rst:582 msgid "Open another terminal and start the second client:" msgstr "" @@ -21872,102 +22022,110 @@ msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:-1 msgid "" "Check out this Federated Learning quickstart tutorial for using Flower " -"with TensorFlow to train a MobilNetV2 model on CIFAR-10." +"with TensorFlow to train a CNN model on CIFAR-10." msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:5 msgid "Quickstart TensorFlow" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:13 -msgid "Let's build a federated learning system in less than 20 lines of code!" -msgstr "" - -#: ../../source/tutorial-quickstart-tensorflow.rst:15 -msgid "Before Flower can be imported we have to install it:" -msgstr "" - -#: ../../source/tutorial-quickstart-tensorflow.rst:21 +#: ../../source/tutorial-quickstart-tensorflow.rst:7 msgid "" -"Since we want to use the Keras API of TensorFlow (TF), we have to install" -" TF as well:" +"In this tutorial we will learn how to train a Convolutional Neural " +"Network on CIFAR-10 using the Flower framework and TensorFlow. First of " +"all, it is recommended to create a virtual environment and run everything" +" within a :doc:`virtualenv `." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:31 -msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:" +#: ../../source/tutorial-quickstart-tensorflow.rst:13 +msgid "" +"Let's use `flwr new` to create a complete Flower+TensorFlow project. It " +"will generate all the files needed to run, by default with the Flower " +"Simulation Engine, a federation of 10 nodes using `FedAvg " +"`_. The " +"dataset will be partitioned using Flower Dataset's `IidPartitioner " +"`_." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:38 +#: ../../source/tutorial-quickstart-tensorflow.rst:28 msgid "" -"We use the Keras utilities of TF to load CIFAR10, a popular colored image" -" classification dataset for machine learning. The call to " -":code:`tf.keras.datasets.cifar10.load_data()` downloads CIFAR10, caches " -"it locally, and then returns the entire training and test set as NumPy " -"ndarrays." +"Then, run the command below. You will be prompted to select one of the " +"available templates (choose ``TensorFlow``), give a name to your project," +" and type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:47 +#: ../../source/tutorial-quickstart-tensorflow.rst:118 msgid "" -"Next, we need a model. For the purpose of this tutorial, we use " -"MobilNetV2 with 10 output classes:" +"This tutorial uses `Flower Datasets `_ " +"to easily download and partition the `CIFAR-10` dataset. In this example " +"you'll make use of the `IidPartitioner `_" +" to generate `num_partitions` partitions. You can choose `other " +"partitioners `_ available in Flower Datasets. Each " +"``ClientApp`` will call this function to create the ``NumPy`` arrays that" +" correspond to their data partition." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:54 +#: ../../source/tutorial-quickstart-tensorflow.rst:147 msgid "" -"The Flower server interacts with clients through an interface called " -":code:`Client`. When the server selects a particular client for training," -" it sends training instructions over the network. The client receives " -"those instructions and calls one of the :code:`Client` methods to run " -"your code (i.e., to train the neural network we defined earlier)." +"Next, we need a model. We defined a simple Convolutional Neural Network " +"(CNN), but feel free to replace it with a more sophisticated model if " +"you'd like:" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:60 +#: ../../source/tutorial-quickstart-tensorflow.rst:178 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses Keras. The :code:`NumPyClient` interface defines three " -"methods which can be implemented in the following way:" +"With `TensorFlow`, we can use the built-in ``get_weights()`` and " +"``set_weights()`` functions, which simplifies the implementation with " +"`Flower`. The rest of the functionality in the ClientApp is directly " +"inspired by the centralized case. The ``fit()`` method in the client " +"trains the model using the local dataset. Similarly, the ``evaluate()`` " +"method is used to evaluate the model received on a held-out validation " +"set that the client might have:" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:82 +#: ../../source/tutorial-quickstart-tensorflow.rst:212 msgid "" -"We can now create an instance of our class :code:`CifarClient` and add " -"one line to actually run this client:" +"Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " +"defined above by means of a ``client_fn()`` callback. Note that the " +"`context` enables you to get access to hyperparameters defined in your " +"``pyproject.toml`` to configure the run. For example, in this tutorial we" +" access the `local-epochs` setting to control the number of epochs a " +"``ClientApp`` will perform when running the ``fit()`` method, in addition" +" to `batch-size`. You could define additional hyperparameters in " +"``pyproject.toml`` and access them here." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:90 +#: ../../source/tutorial-quickstart-tensorflow.rst:247 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " -"implement a client of type :code:`NumPyClient` you'll need to first call " -"its :code:`to_client()` method. The string :code:`\"[::]:8080\"` tells " -"the client which server to connect to. In our case we can run the server " -"and the client on the same machine, therefore we use " -":code:`\"[::]:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we point the client at." +"To construct a ``ServerApp`` we define a ``server_fn()`` callback with an" +" identical signature to that of ``client_fn()`` but the return type is " +"`ServerAppComponents `_ as " +"opposed to a `Client `_. In this example we use the " +"`FedAvg`. To it we pass a randomly initialized model that will serve as " +"the global model to federate." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:135 -msgid "Each client will have its own dataset." +#: ../../source/tutorial-quickstart-tensorflow.rst:284 +msgid "" +"Check the source code of the extended version of this tutorial in " +"|quickstart_tf_link|_ in the Flower GitHub repository." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:137 +#: ../../source/tutorial-quickstart-tensorflow.rst:299 msgid "" -"You should now see how the training does in the very first terminal (the " -"one that started the server):" +"The video shown below shows how to setup a TensorFlow + Flower project " +"using our previously recommended APIs. A new video tutorial will be " +"released that shows the new APIs (as the content above does)" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:169 -msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this can be found in :code:`examples" -"/quickstart-tensorflow/client.py`." -msgstr "" - -#: ../../source/tutorial-quickstart-xgboost.rst:-1 +#: ../../source/tutorial-quickstart-xgboost.rst:-1 msgid "" "Check out this Federated Learning quickstart tutorial for using Flower " "with XGBoost to train classification models on trees." @@ -23811,7 +23969,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 -msgid "|e5918c1c06a4434bbe4bf49235e40059|" +msgid "|e87b69b2ada74ea49412df16f4a0b9cc|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 @@ -23826,7 +23984,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 -msgid "|c0165741bd1944f09ec55ce49032377d|" +msgid "|33cacb7d985c4906b348515c1a5cd993|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 @@ -23847,7 +24005,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 -msgid "|0a0ac9427ac7487b8e52d75ed514f04e|" +msgid "|cc080a555947492fa66131dc3a967603|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 @@ -23863,7 +24021,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 -msgid "|5defee3ea4ca40d99fcd3e4ea045be25|" +msgid "|085c3e0fb8664c6aa06246636524b20b|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 @@ -23879,7 +24037,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 -msgid "|74f26ca701254d3db57d7899bd91eb55|" +msgid "|bfe69c74e48c45d49b50251c38c2a019|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 @@ -23894,7 +24052,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 -msgid "|bda79f21f8154258a40e5766b2634ad7|" +msgid "|ebbecd651f0348d99c6511ea859bf4ca|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 @@ -23914,7 +24072,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 -msgid "|89d30862e62e4f9989e193483a08680a|" +msgid "|163117eb654a4273babba413cf8065f5|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 @@ -23929,7 +24087,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 -msgid "|77e9918671c54b4f86e01369c0785ce8|" +msgid "|452ac3ba453b4cd1be27be1ba7560d64|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 @@ -24069,7 +24227,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 -msgid "|7e4ccef37cc94148a067107b34eb7447|" +msgid "|f403fcd69e4e44409627e748b404c086|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 @@ -24093,7 +24251,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 -msgid "|28e47e4cded14479a0846c8e5f22c872|" +msgid "|4b00fe63870145968f8443619a792a42|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 @@ -24117,7 +24275,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 -msgid "|4b8c5d1afa144294b76ffc76e4658a38|" +msgid "|368378731066486fa4397e89bc6b870c|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 @@ -24140,7 +24298,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 -msgid "|9dbdb3a0f6cb4a129fac863eaa414c17|" +msgid "|a66aa83d85bf4ffba7ed660b718066da|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 @@ -24178,7 +24336,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 -msgid "|81749d0ac0834c36a83bd38f433fea31|" +msgid "|82324b9af72a4582a81839d55caab767|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 @@ -24272,7 +24430,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 -msgid "|ed9aae51da70428eab7eef32f21e819e|" +msgid "|fbf2da0da3cc4f8ab3b3eff852d80c41|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 @@ -29467,3 +29625,564 @@ msgstr "" #~ msgid "|c00bf2750bc24d229737a0fe1395f0fc|" #~ msgstr "" +#~ msgid ":py:obj:`client `\\" +#~ msgstr "" + +#~ msgid ":py:obj:`common `\\" +#~ msgstr "" + +#~ msgid ":py:obj:`server `\\" +#~ msgstr "" + +#~ msgid ":py:obj:`simulation `\\" +#~ msgstr "" + +#~ msgid ":py:obj:`mod `\\" +#~ msgstr "" + +#~ msgid "run\\_client\\_app" +#~ msgstr "" + +#~ msgid "run\\_supernode" +#~ msgstr "" + +#~ msgid "" +#~ ":py:obj:`get `\\ " +#~ "\\(key\\[\\, default\\]\\)" +#~ msgstr "" + +#~ msgid "Retrieve the corresponding layout by the string key." +#~ msgstr "" + +#~ msgid "" +#~ "When there isn't an exact match, " +#~ "all the existing keys in the " +#~ "layout map will be treated as a" +#~ " regex and map against the input " +#~ "key again. The first match will be" +#~ " returned, based on the key insertion" +#~ " order. Return None if there isn't" +#~ " any match found." +#~ msgstr "" + +#~ msgid "the string key as the query for the layout." +#~ msgstr "" + +#~ msgid "Corresponding layout based on the query." +#~ msgstr "" + +#~ msgid "" +#~ ":py:obj:`get `\\ " +#~ "\\(key\\[\\, default\\]\\)" +#~ msgstr "" + +#~ msgid "" +#~ ":py:obj:`get `\\ " +#~ "\\(key\\[\\, default\\]\\)" +#~ msgstr "" + +#~ msgid ":py:obj:`strategy `\\" +#~ msgstr "" + +#~ msgid ":py:obj:`workflow `\\" +#~ msgstr "" + +#~ msgid "run\\_server\\_app" +#~ msgstr "" + +#~ msgid "run\\_superlink" +#~ msgstr "" + +#~ msgid "" +#~ ":py:obj:`start_simulation `\\" +#~ " \\(\\*\\, client\\_fn\\, num\\_clients\\)" +#~ msgstr "" + +#~ msgid "Start a Ray-based Flower simulation server." +#~ msgstr "" + +#~ msgid "" +#~ "A function creating `Client` instances. " +#~ "The function must have the signature " +#~ "`client_fn(context: Context). It should return" +#~ " a single client instance of type " +#~ "`Client`. Note that the created client" +#~ " instances are ephemeral and will " +#~ "often be destroyed after a single " +#~ "method invocation. Since client instances " +#~ "are not long-lived, they should " +#~ "not attempt to carry state over " +#~ "method invocations. Any state required " +#~ "by the instance (model, dataset, " +#~ "hyperparameters, ...) should be (re-)created" +#~ " in either the call to `client_fn`" +#~ " or the call to any of the " +#~ "client methods (e.g., load evaluation " +#~ "data in the `evaluate` method itself)." +#~ msgstr "" + +#~ msgid "The total number of clients in this simulation." +#~ msgstr "" + +#~ msgid "" +#~ "UNSUPPORTED, WILL BE REMOVED. USE " +#~ "`num_clients` INSTEAD. List `client_id`s for" +#~ " each client. This is only required" +#~ " if `num_clients` is not set. Setting" +#~ " both `num_clients` and `clients_ids` with" +#~ " `len(clients_ids)` not equal to " +#~ "`num_clients` generates an error. Using " +#~ "this argument will raise an error." +#~ msgstr "" + +#~ msgid "" +#~ "CPU and GPU resources for a single" +#~ " client. Supported keys are `num_cpus` " +#~ "and `num_gpus`. To understand the GPU" +#~ " utilization caused by `num_gpus`, as " +#~ "well as using custom resources, please" +#~ " consult the Ray documentation." +#~ msgstr "" + +#~ msgid "" +#~ "An implementation of the abstract base" +#~ " class `flwr.server.Server`. If no instance" +#~ " is provided, then `start_server` will " +#~ "create one." +#~ msgstr "" + +#~ msgid "" +#~ "An implementation of the abstract base" +#~ " class `flwr.server.Strategy`. If no " +#~ "strategy is provided, then `start_server` " +#~ "will use `flwr.server.strategy.FedAvg`." +#~ msgstr "" + +#~ msgid "" +#~ "An implementation of the abstract base" +#~ " class `flwr.server.ClientManager`. If no " +#~ "implementation is provided, then " +#~ "`start_simulation` will use " +#~ "`flwr.server.client_manager.SimpleClientManager`." +#~ msgstr "" + +#~ msgid "" +#~ "Optional dictionary containing arguments for" +#~ " the call to `ray.init`. If " +#~ "ray_init_args is None (the default), Ray" +#~ " will be initialized with the " +#~ "following default args: { " +#~ "\"ignore_reinit_error\": True, \"include_dashboard\": " +#~ "False } An empty dictionary can " +#~ "be used (ray_init_args={}) to prevent " +#~ "any arguments from being passed to " +#~ "ray.init." +#~ msgstr "" + +#~ msgid "" +#~ "Optional dictionary containing arguments for" +#~ " the call to `ray.init`. If " +#~ "ray_init_args is None (the default), Ray" +#~ " will be initialized with the " +#~ "following default args:" +#~ msgstr "" + +#~ msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" +#~ msgstr "" + +#~ msgid "" +#~ "An empty dictionary can be used " +#~ "(ray_init_args={}) to prevent any arguments" +#~ " from being passed to ray.init." +#~ msgstr "" + +#~ msgid "" +#~ "Set to True to prevent `ray.shutdown()`" +#~ " in case `ray.is_initialized()=True`." +#~ msgstr "" + +#~ msgid "" +#~ "Optionally specify the type of actor " +#~ "to use. The actor object, which " +#~ "persists throughout the simulation, will " +#~ "be the process in charge of " +#~ "executing a ClientApp wrapping input " +#~ "argument `client_fn`." +#~ msgstr "" + +#~ msgid "" +#~ "If you want to create your own " +#~ "Actor classes, you might need to " +#~ "pass some input argument. You can " +#~ "use this dictionary for such purpose." +#~ msgstr "" + +#~ msgid "" +#~ "(default: \"DEFAULT\") Optional string " +#~ "(\"DEFAULT\" or \"SPREAD\") for the VCE" +#~ " to choose in which node the " +#~ "actor is placed. If you are an " +#~ "advanced user needed more control you" +#~ " can use lower-level scheduling " +#~ "strategies to pin actors to specific " +#~ "compute nodes (e.g. via " +#~ "NodeAffinitySchedulingStrategy). Please note this" +#~ " is an advanced feature. For all " +#~ "details, please refer to the Ray " +#~ "documentation: https://docs.ray.io/en/latest/ray-" +#~ "core/scheduling/index.html" +#~ msgstr "" + +#~ msgid "**hist** -- Object containing metrics from training." +#~ msgstr "" + +#~ msgid "" +#~ "Check out this Federated Learning " +#~ "quickstart tutorial for using Flower " +#~ "with FastAI to train a vision " +#~ "model on CIFAR-10." +#~ msgstr "" + +#~ msgid "Let's build a federated learning system using fastai and Flower!" +#~ msgstr "" + +#~ msgid "" +#~ "Please refer to the `full code " +#~ "example `_ to learn more." +#~ msgstr "" + +#~ msgid "" +#~ "Check out this Federating Learning " +#~ "quickstart tutorial for using Flower " +#~ "with HuggingFace Transformers in order " +#~ "to fine-tune an LLM." +#~ msgstr "" + +#~ msgid "" +#~ "Let's build a federated learning system" +#~ " using Hugging Face Transformers and " +#~ "Flower!" +#~ msgstr "" + +#~ msgid "" +#~ "We will leverage Hugging Face to " +#~ "federate the training of language models" +#~ " over multiple clients using Flower. " +#~ "More specifically, we will fine-tune " +#~ "a pre-trained Transformer model " +#~ "(distilBERT) for sequence classification over" +#~ " a dataset of IMDB ratings. The " +#~ "end goal is to detect if a " +#~ "movie rating is positive or negative." +#~ msgstr "" + +#~ msgid "Dependencies" +#~ msgstr "" + +#~ msgid "" +#~ "To follow along this tutorial you " +#~ "will need to install the following " +#~ "packages: :code:`datasets`, :code:`evaluate`, " +#~ ":code:`flwr`, :code:`torch`, and " +#~ ":code:`transformers`. This can be done " +#~ "using :code:`pip`:" +#~ msgstr "" + +#~ msgid "Standard Hugging Face workflow" +#~ msgstr "" + +#~ msgid "Handling the data" +#~ msgstr "" + +#~ msgid "" +#~ "To fetch the IMDB dataset, we will" +#~ " use Hugging Face's :code:`datasets` " +#~ "library. We then need to tokenize " +#~ "the data and create :code:`PyTorch` " +#~ "dataloaders, this is all done in " +#~ "the :code:`load_data` function:" +#~ msgstr "" + +#~ msgid "Training and testing the model" +#~ msgstr "" + +#~ msgid "" +#~ "Once we have a way of creating " +#~ "our trainloader and testloader, we can" +#~ " take care of the training and " +#~ "testing. This is very similar to " +#~ "any :code:`PyTorch` training or testing " +#~ "loop:" +#~ msgstr "" + +#~ msgid "Creating the model itself" +#~ msgstr "" + +#~ msgid "" +#~ "To create the model itself, we " +#~ "will just load the pre-trained " +#~ "distillBERT model using Hugging Face’s " +#~ ":code:`AutoModelForSequenceClassification` :" +#~ msgstr "" + +#~ msgid "Federating the example" +#~ msgstr "" + +#~ msgid "Creating the IMDBClient" +#~ msgstr "" + +#~ msgid "" +#~ "To federate our example to multiple " +#~ "clients, we first need to write " +#~ "our Flower client class (inheriting from" +#~ " :code:`flwr.client.NumPyClient`). This is very" +#~ " easy, as our model is a " +#~ "standard :code:`PyTorch` model:" +#~ msgstr "" + +#~ msgid "" +#~ "The :code:`get_parameters` function lets the" +#~ " server get the client's parameters. " +#~ "Inversely, the :code:`set_parameters` function " +#~ "allows the server to send its " +#~ "parameters to the client. Finally, the" +#~ " :code:`fit` function trains the model " +#~ "locally for the client, and the " +#~ ":code:`evaluate` function tests the model " +#~ "locally and returns the relevant " +#~ "metrics." +#~ msgstr "" + +#~ msgid "Starting the server" +#~ msgstr "" + +#~ msgid "" +#~ "Now that we have a way to " +#~ "instantiate clients, we need to create" +#~ " our server in order to aggregate " +#~ "the results. Using Flower, this can " +#~ "be done very easily by first " +#~ "choosing a strategy (here, we are " +#~ "using :code:`FedAvg`, which will define " +#~ "the global weights as the average " +#~ "of all the clients' weights at " +#~ "each round) and then using the " +#~ ":code:`flwr.server.start_server` function:" +#~ msgstr "" + +#~ msgid "" +#~ "The :code:`weighted_average` function is there" +#~ " to provide a way to aggregate " +#~ "the metrics distributed amongst the " +#~ "clients (basically this allows us to " +#~ "display a nice average accuracy and " +#~ "loss for every round)." +#~ msgstr "" + +#~ msgid "Putting everything together" +#~ msgstr "" + +#~ msgid "We can now start client instances using:" +#~ msgstr "" + +#~ msgid "" +#~ "And they will be able to connect" +#~ " to the server and start the " +#~ "federated training." +#~ msgstr "" + +#~ msgid "" +#~ "If you want to check out " +#~ "everything put together, you should " +#~ "check out the `full code example " +#~ "`_ ." +#~ msgstr "" + +#~ msgid "" +#~ "Of course, this is a very basic" +#~ " example, and a lot can be " +#~ "added or modified, it was just to" +#~ " showcase how simply we could " +#~ "federate a Hugging Face workflow using" +#~ " Flower." +#~ msgstr "" + +#~ msgid "" +#~ "Note that in this example we used" +#~ " :code:`PyTorch`, but we could have " +#~ "very well used :code:`TensorFlow`." +#~ msgstr "" + +#~ msgid "" +#~ "Check out this Federated Learning " +#~ "quickstart tutorial for using Flower " +#~ "with PyTorch Lightning to train an " +#~ "Auto Encoder model on MNIST." +#~ msgstr "" + +#~ msgid "" +#~ "Let's build a horizontal federated " +#~ "learning system using PyTorch Lightning " +#~ "and Flower!" +#~ msgstr "" + +#~ msgid "" +#~ "Please refer to the `full code " +#~ "example `_ to learn " +#~ "more." +#~ msgstr "" + +#~ msgid "" +#~ "Check out this Federated Learning " +#~ "quickstart tutorial for using Flower " +#~ "with TensorFlow to train a MobilNetV2" +#~ " model on CIFAR-10." +#~ msgstr "" + +#~ msgid "Let's build a federated learning system in less than 20 lines of code!" +#~ msgstr "" + +#~ msgid "Before Flower can be imported we have to install it:" +#~ msgstr "" + +#~ msgid "" +#~ "Since we want to use the Keras " +#~ "API of TensorFlow (TF), we have to" +#~ " install TF as well:" +#~ msgstr "" + +#~ msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:" +#~ msgstr "" + +#~ msgid "" +#~ "We use the Keras utilities of TF" +#~ " to load CIFAR10, a popular colored" +#~ " image classification dataset for machine" +#~ " learning. The call to " +#~ ":code:`tf.keras.datasets.cifar10.load_data()` downloads " +#~ "CIFAR10, caches it locally, and then " +#~ "returns the entire training and test " +#~ "set as NumPy ndarrays." +#~ msgstr "" + +#~ msgid "" +#~ "Next, we need a model. For the " +#~ "purpose of this tutorial, we use " +#~ "MobilNetV2 with 10 output classes:" +#~ msgstr "" + +#~ msgid "" +#~ "The Flower server interacts with clients" +#~ " through an interface called " +#~ ":code:`Client`. When the server selects " +#~ "a particular client for training, it " +#~ "sends training instructions over the " +#~ "network. The client receives those " +#~ "instructions and calls one of the " +#~ ":code:`Client` methods to run your code" +#~ " (i.e., to train the neural network" +#~ " we defined earlier)." +#~ msgstr "" + +#~ msgid "" +#~ "Flower provides a convenience class " +#~ "called :code:`NumPyClient` which makes it " +#~ "easier to implement the :code:`Client` " +#~ "interface when your workload uses Keras." +#~ " The :code:`NumPyClient` interface defines " +#~ "three methods which can be implemented" +#~ " in the following way:" +#~ msgstr "" + +#~ msgid "" +#~ "We can now create an instance of" +#~ " our class :code:`CifarClient` and add " +#~ "one line to actually run this " +#~ "client:" +#~ msgstr "" + +#~ msgid "" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` or " +#~ ":code:`NumPyClient` and call " +#~ ":code:`fl.client.start_client()`. If you implement" +#~ " a client of type :code:`NumPyClient` " +#~ "you'll need to first call its " +#~ ":code:`to_client()` method. The string " +#~ ":code:`\"[::]:8080\"` tells the client which" +#~ " server to connect to. In our " +#~ "case we can run the server and " +#~ "the client on the same machine, " +#~ "therefore we use :code:`\"[::]:8080\"`. If " +#~ "we run a truly federated workload " +#~ "with the server and clients running " +#~ "on different machines, all that needs" +#~ " to change is the :code:`server_address`" +#~ " we point the client at." +#~ msgstr "" + +#~ msgid "Each client will have its own dataset." +#~ msgstr "" + +#~ msgid "" +#~ "You should now see how the " +#~ "training does in the very first " +#~ "terminal (the one that started the " +#~ "server):" +#~ msgstr "" + +#~ msgid "" +#~ "Congratulations! You've successfully built and" +#~ " run your first federated learning " +#~ "system. The full `source code " +#~ "`_ for this can be " +#~ "found in :code:`examples/quickstart-" +#~ "tensorflow/client.py`." +#~ msgstr "" + +#~ msgid "|e5918c1c06a4434bbe4bf49235e40059|" +#~ msgstr "" + +#~ msgid "|c0165741bd1944f09ec55ce49032377d|" +#~ msgstr "" + +#~ msgid "|0a0ac9427ac7487b8e52d75ed514f04e|" +#~ msgstr "" + +#~ msgid "|5defee3ea4ca40d99fcd3e4ea045be25|" +#~ msgstr "" + +#~ msgid "|74f26ca701254d3db57d7899bd91eb55|" +#~ msgstr "" + +#~ msgid "|bda79f21f8154258a40e5766b2634ad7|" +#~ msgstr "" + +#~ msgid "|89d30862e62e4f9989e193483a08680a|" +#~ msgstr "" + +#~ msgid "|77e9918671c54b4f86e01369c0785ce8|" +#~ msgstr "" + +#~ msgid "|7e4ccef37cc94148a067107b34eb7447|" +#~ msgstr "" + +#~ msgid "|28e47e4cded14479a0846c8e5f22c872|" +#~ msgstr "" + +#~ msgid "|4b8c5d1afa144294b76ffc76e4658a38|" +#~ msgstr "" + +#~ msgid "|9dbdb3a0f6cb4a129fac863eaa414c17|" +#~ msgstr "" + +#~ msgid "|81749d0ac0834c36a83bd38f433fea31|" +#~ msgstr "" + +#~ msgid "|ed9aae51da70428eab7eef32f21e819e|" +#~ msgstr "" + diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po index 674417e69791..9af452fb0be2 100644 --- a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po +++ b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po @@ -7,7 +7,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower main\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-09-15 09:09+0200\n" +"POT-Creation-Date: 2024-09-24 00:29+0000\n" "PO-Revision-Date: 2024-06-12 10:09+0000\n" "Last-Translator: Yan Gao \n" "Language: zh_Hans\n" @@ -17,7 +17,7 @@ msgstr "" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" -"Generated-By: Babel 2.15.0\n" +"Generated-By: Babel 2.16.0\n" #: ../../source/contributor-explanation-public-and-private-apis.rst:2 msgid "Public and private APIs" @@ -1476,7 +1476,7 @@ msgstr "" msgid "Setting up the repository" msgstr "建立资源库" -#: ../../source/contributor-tutorial-contribute-on-github.rst:12 +#: ../../source/contributor-tutorial-contribute-on-github.rst:21 msgid "**Create a GitHub account and setup Git**" msgstr "**创建 GitHub 账户并设置 Git**" @@ -1515,7 +1515,7 @@ msgstr "" "通用的 Git 和 GitHub 工作流程背后的理念可以归结为：从 GitHub 上的远程仓库下载代码，在本地进行修改并使用 Git " "进行跟踪，然后将新的历史记录上传回 GitHub。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:23 +#: ../../source/contributor-tutorial-contribute-on-github.rst:32 msgid "**Forking the Flower repository**" msgstr "**叉花仓库**" @@ -1540,7 +1540,7 @@ msgstr "" "您可以更改名称，但没有必要，因为这个版本的 Flower " "将是您自己的，并位于您自己的账户中（即，在您自己的版本库列表中）。创建完成后，您会在左上角看到自己的 Flower 版本。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:34 +#: ../../source/contributor-tutorial-contribute-on-github.rst:47 msgid "**Cloning your forked repository**" msgstr "**克隆你的分叉仓库**" @@ -1567,7 +1567,7 @@ msgid "" "it) folder in the current working directory." msgstr "这将在当前工作目录下创建一个 `flower/`（如果重命名了，则使用 fork 的名称）文件夹。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:49 +#: ../../source/contributor-tutorial-contribute-on-github.rst:66 msgid "**Add origin**" msgstr "**添加原产地**" @@ -1591,7 +1591,7 @@ msgid "" "terminal:" msgstr "一旦复制了 \\ ，我们就可以在终端中键入以下命令：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:68 +#: ../../source/contributor-tutorial-contribute-on-github.rst:90 msgid "**Add upstream**" msgstr "**增加上游**" @@ -1650,7 +1650,7 @@ msgstr "在进行任何更改之前，请确保您的版本库是最新的：" msgid "And with Flower's repository:" msgstr "还有Flower的存储库：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:114 +#: ../../source/contributor-tutorial-contribute-on-github.rst:122 msgid "**Create a new branch**" msgstr "**创建一个新分支**" @@ -1667,7 +1667,7 @@ msgid "" "directory:" msgstr "为此，只需在版本库目录下运行以下命令即可：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:124 +#: ../../source/contributor-tutorial-contribute-on-github.rst:125 msgid "**Make changes**" msgstr "**进行修改**" @@ -1675,7 +1675,7 @@ msgstr "**进行修改**" msgid "Write great code and create wonderful changes using your favorite editor!" msgstr "使用您最喜欢的编辑器编写优秀的代码并创建精彩的更改！" -#: ../../source/contributor-tutorial-contribute-on-github.rst:127 +#: ../../source/contributor-tutorial-contribute-on-github.rst:138 msgid "**Test and format your code**" msgstr "**测试并格式化您的代码**" @@ -1690,7 +1690,7 @@ msgstr "不要忘记测试和格式化您的代码！否则您的代码将无法 msgid "To do so, we have written a few scripts that you can execute:" msgstr "为此，我们编写了一些脚本供您执行：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:140 +#: ../../source/contributor-tutorial-contribute-on-github.rst:150 msgid "**Stage changes**" msgstr "**舞台变化**" @@ -1711,7 +1711,7 @@ msgid "" "the :code:`git status` command." msgstr "要查看与上一版本（上次提交）相比哪些文件已被修改，以及哪些文件处于提交阶段，可以使用 :code:`git status` 命令。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:152 +#: ../../source/contributor-tutorial-contribute-on-github.rst:160 msgid "**Commit changes**" msgstr "**提交更改**" @@ -1730,7 +1730,7 @@ msgstr "" " 用于向他人解释提交的作用。它应该以命令式风格书写，并且简明扼要。例如 :code:`git commit " "-m \"Add images to README\"`。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:162 +#: ../../source/contributor-tutorial-contribute-on-github.rst:171 msgid "**Push the changes to the fork**" msgstr "**将更改推送到分叉**" @@ -1751,7 +1751,7 @@ msgstr "完成此操作后，您将在 GitHub 上看到您的分叉仓库已根 msgid "Creating and merging a pull request (PR)" msgstr "创建和合并拉取请求 (PR)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:176 +#: ../../source/contributor-tutorial-contribute-on-github.rst:206 msgid "**Create the PR**" msgstr "**创建 PR**" @@ -1820,7 +1820,7 @@ msgid "" "anyone, you have the option to create a draft pull request:" msgstr "如果您的 PR 尚未准备好接受审核，而且您不想通知任何人，您可以选择创建一个草案拉取请求：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:209 msgid "**Making new changes**" msgstr "**作出新的改变**" @@ -1831,7 +1831,7 @@ msgid "" " associated with the PR." msgstr "一旦 PR 被打开（无论是否作为草案），你仍然可以像以前一样，通过修改与 PR 关联的分支来推送新的提交。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:211 +#: ../../source/contributor-tutorial-contribute-on-github.rst:231 msgid "**Review the PR**" msgstr "**审查 PR**" @@ -1867,7 +1867,7 @@ msgid "" "review." msgstr "一旦所有对话都得到解决，您就可以重新申请审核。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:233 +#: ../../source/contributor-tutorial-contribute-on-github.rst:251 msgid "**Once the PR is merged**" msgstr "**一旦 PR 被合并**" @@ -2179,6 +2179,7 @@ msgstr "成为贡献者" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 #: ../../source/docker/run-as-subprocess.rst:11 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:15 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:12 #: ../../source/docker/tutorial-quickstart-docker.rst:11 msgid "Prerequisites" @@ -2960,6 +2961,242 @@ msgid "" " the SuperNode to execute the ClientApp as a subprocess:" msgstr "" +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:2 +#, fuzzy +msgid "Run Flower Quickstart Examples with Docker Compose" +msgstr "快速入门 iOS" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:4 +msgid "" +"Flower provides a set of `quickstart examples " +"`_ to help you get " +"started with the framework. These examples are designed to demonstrate " +"the capabilities of Flower and by default run using the Simulation " +"Engine. This guide demonstrates how to run them using Flower's Deployment" +" Engine via Docker Compose." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:11 +msgid "" +"Some quickstart examples may have limitations or requirements that " +"prevent them from running on every environment. For more information, " +"please see `Limitations`_." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:17 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:14 +#: ../../source/docker/tutorial-quickstart-docker.rst:13 +#, fuzzy +msgid "Before you start, make sure that:" +msgstr "开始之前，请确保 Docker 守护进程正在运行：" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:19 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 +#: ../../source/docker/tutorial-quickstart-docker.rst:15 +msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:20 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:17 +#: ../../source/docker/tutorial-quickstart-docker.rst:16 +#, fuzzy +msgid "The Docker daemon is running." +msgstr "验证 Docker 守护进程是否正在运行。" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:21 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:18 +msgid "Docker Compose is `installed `_." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:24 +#, fuzzy +msgid "Run the Quickstart Example" +msgstr "示例请求" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:26 +msgid "" +"Clone the quickstart example you like to run. For example, ``quickstart-" +"pytorch``:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:34 +msgid "" +"Download the `compose.yml " +"`_" +" file into the example directory:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:41 +#, fuzzy +msgid "Build and start the services using the following command:" +msgstr "运行以下命令激活 virtualenv：" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:47 +#, fuzzy +msgid "" +"Append the following lines to the end of the ``pyproject.toml`` file and " +"save it:" +msgstr "将 ``pyproject.toml`` 中的次要版本增加一个。" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:49 +#: ../../source/docker/tutorial-quickstart-docker.rst:319 +#, fuzzy +msgid "pyproject.toml" +msgstr "或 ``pyproject.toml```：" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:58 +msgid "" +"You can customize the string that follows ``tool.flwr.federations.`` to " +"fit your needs. However, please note that the string cannot contain a dot" +" (``.``)." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:61 +msgid "" +"In this example, ``local-deployment`` has been used. Just remember to " +"replace ``local-deployment`` with your chosen name in both the " +"``tool.flwr.federations.`` string and the corresponding ``flwr run .`` " +"command." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:65 +#, fuzzy +msgid "Run the example:" +msgstr "将示例联邦化" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:71 +msgid "Follow the logs of the SuperExec service:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:77 +msgid "" +"That is all it takes! You can monitor the progress of the run through the" +" logs of the SuperExec." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:80 +msgid "Run a Different Quickstart Example" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:82 +msgid "" +"To run a different quickstart example, such as ``quickstart-tensorflow``," +" first, shut down the Docker Compose services of the current example:" +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:89 +msgid "After that, you can repeat the steps above." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:92 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:98 +#, fuzzy +msgid "Limitations" +msgstr "运行模拟" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:97 +#, fuzzy +msgid "Quickstart Example" +msgstr "快速入门 JAX" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:99 +#, fuzzy +msgid "quickstart-fastai" +msgstr "快速入门 fastai" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:100 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:102 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:110 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:112 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:116 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:118 +#: ../../source/ref-changelog.md:33 ../../source/ref-changelog.md:399 +#: ../../source/ref-changelog.md:676 ../../source/ref-changelog.md:740 +#: ../../source/ref-changelog.md:798 ../../source/ref-changelog.md:867 +#: ../../source/ref-changelog.md:929 +msgid "None" +msgstr "无" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:101 +#, fuzzy +msgid "quickstart-huggingface" +msgstr "快速入门教程" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:103 +#, fuzzy +msgid "quickstart-jax" +msgstr "快速入门 JAX" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:104 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:106 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:120 +#, fuzzy +msgid "" +"The example has not yet been updated to work with the latest ``flwr`` " +"version." +msgstr "涵盖 scikit-learn 和 PyTorch Lightning 的代码示例已更新，以便与最新版本的 Flower 配合使用。" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:105 +#, fuzzy +msgid "quickstart-mlcube" +msgstr "快速入门 JAX" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:107 +#, fuzzy +msgid "quickstart-mlx" +msgstr "快速入门 JAX" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:108 +msgid "" +"`Requires to run on macOS with Apple Silicon `_." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:109 +#, fuzzy +msgid "quickstart-monai" +msgstr "快速入门 JAX" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:111 +#, fuzzy +msgid "quickstart-pandas" +msgstr "快速入门Pandas" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:113 +#, fuzzy +msgid "quickstart-pytorch-lightning" +msgstr "快速入门 PyTorch Lightning" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:114 +msgid "" +"Requires an older pip version that is not supported by the Flower Docker " +"images." +msgstr "" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:115 +#, fuzzy +msgid "quickstart-pytorch" +msgstr "PyTorch快速入门" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:117 +#, fuzzy +msgid "quickstart-sklearn-tabular" +msgstr "scikit-learn快速入门" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:119 +#, fuzzy +msgid "quickstart-tabnet" +msgstr "快速入门 JAX" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:121 +#, fuzzy +msgid "quickstart-tensorflow" +msgstr "快速入门 TensorFlow" + +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:122 +msgid "Only runs on AMD64." +msgstr "" + #: ../../source/docker/set-environment-variables.rst:2 #, fuzzy msgid "Set Environment Variables" @@ -2991,23 +3228,6 @@ msgid "" " understanding the basic workflow that uses the minimum configurations." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:14 -#: ../../source/docker/tutorial-quickstart-docker.rst:13 -#, fuzzy -msgid "Before you start, make sure that:" -msgstr "开始之前，请确保 Docker 守护进程正在运行：" - -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 -#: ../../source/docker/tutorial-quickstart-docker.rst:15 -msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." -msgstr "" - -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:17 -#: ../../source/docker/tutorial-quickstart-docker.rst:16 -#, fuzzy -msgid "The Docker daemon is running." -msgstr "验证 Docker 守护进程是否正在运行。" - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 #: ../../source/docker/tutorial-quickstart-docker.rst:19 msgid "Step 1: Set Up" @@ -3440,11 +3660,6 @@ msgstr "" msgid "Add the following lines to the ``pyproject.toml``:" msgstr "将 ``pyproject.toml`` 中的次要版本增加一个。" -#: ../../source/docker/tutorial-quickstart-docker.rst:319 -#, fuzzy -msgid "pyproject.toml" -msgstr "或 ``pyproject.toml```：" - #: ../../source/docker/tutorial-quickstart-docker.rst:326 msgid "Run the ``quickstart-docker`` project by executing the command:" msgstr "" @@ -3495,6 +3710,7 @@ msgstr "" msgid "Remove the containers and the bridge network:" msgstr "" +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:401 #: ../../source/docker/tutorial-quickstart-docker.rst:399 #, fuzzy msgid "Where to Go Next" @@ -3531,10 +3747,6 @@ msgid "" "configuration that best suits your project's needs." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:18 -msgid "Docker Compose is `installed `_." -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:23 msgid "Clone the Docker Compose ``complete`` directory:" msgstr "" @@ -3731,7 +3943,7 @@ msgstr "" #: ../../source/docker/tutorial-quickstart-docker-compose.rst:188 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:241 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:362 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:369 msgid "Rerun the ``quickstart-compose`` project:" msgstr "" @@ -3795,76 +4007,81 @@ msgstr "" msgid "compose.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:303 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:310 msgid "" "If you also want to enable TLS for the new SuperNodes, duplicate the " "SuperNode definition for each new SuperNode service in the ``with-" "tls.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:306 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:313 msgid "" "Make sure that the names of the services match with the one in the " "``compose.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:308 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:315 msgid "In ``with-tls.yml``, add the following:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:310 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:317 msgid "with-tls.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:332 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:339 msgid "Step 8: Persisting the SuperLink State and Enabling TLS" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:334 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:341 msgid "" "To run Flower with persisted SuperLink state and enabled TLS, a slight " "change in the ``with-state.yml`` file is required:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:337 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:344 msgid "Comment out the lines 2-4 and uncomment the lines 5-9:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:339 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:346 msgid "with-state.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:356 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:363 #, fuzzy msgid "Restart the services:" msgstr "启动服务器" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:370 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:377 msgid "Step 9: Merge Multiple Compose Files" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:372 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:379 msgid "" "You can merge multiple Compose files into a single file. For instance, if" " you wish to combine the basic configuration with the TLS configuration, " "execute the following command:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:380 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:387 msgid "" "This will merge the contents of ``compose.yml`` and ``with-tls.yml`` into" " a new file called ``my_compose.yml``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:384 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:391 msgid "Step 10: Clean Up" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:386 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:393 #, fuzzy msgid "Remove all services and volumes:" msgstr "从 R 中删除所有项目。" +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:403 +#, fuzzy +msgid ":doc:`run-quickstart-examples-docker-compose`" +msgstr "快速入门 iOS" + #: ../../source/docker/use-a-different-version.rst:2 #, fuzzy msgid "Use a Different Flower Version" @@ -4189,7 +4406,7 @@ msgstr "" ":code:`Client`略微容易一些，因为它避免了一些不必要的操作。:code:`CifarClient` " "需要实现四个方法，两个用于获取/设置模型参数，一个用于训练模型，一个用于测试模型：" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:218 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:219 msgid ":code:`set_parameters`" msgstr ":code:`set_parameters`" @@ -4222,9 +4439,9 @@ msgstr "" "获取模型参数，并以 NumPy :code:`ndarray`的列表形式返回（这正是 " ":code:`flwr.client.NumPyClient`所匹配的格式）" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:223 -#: ../../source/tutorial-quickstart-jax.rst:171 -#: ../../source/tutorial-quickstart-scikitlearn.rst:123 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:225 +#: ../../source/tutorial-quickstart-jax.rst:173 +#: ../../source/tutorial-quickstart-scikitlearn.rst:125 msgid ":code:`fit`" msgstr ":code:`fit`" @@ -4246,9 +4463,9 @@ msgstr "在本地训练集上训练模型" msgid "get the updated local model weights and return them to the server" msgstr "获取更新后的本地模型参数并发送回服务器" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:227 -#: ../../source/tutorial-quickstart-jax.rst:175 -#: ../../source/tutorial-quickstart-scikitlearn.rst:127 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 +#: ../../source/tutorial-quickstart-jax.rst:178 +#: ../../source/tutorial-quickstart-scikitlearn.rst:128 msgid ":code:`evaluate`" msgstr ":code:`evaluate`" @@ -4352,7 +4569,7 @@ msgstr "" "的数据）之外完全相同。差分隐私（DP）可以保证任何分析（M），比如计算平均收入，对两个数据集都会产生几乎相同的结果（O 和 O' " "将是相似的）。这既保留了群体模式，又掩盖了个人细节，确保个人的信息隐藏在人群中。" -#: ../../source/explanation-differential-privacy.rst:16 +#: ../../source/explanation-differential-privacy.rst:-1 #, fuzzy msgid "DP Intro" msgstr "DP 介绍" @@ -4490,8 +4707,8 @@ msgid "" "the client's data." msgstr "**本地差分隐私**： 在向服务器发送任何信息之前，在客户端应用 DP，目的是防止向服务器发送的更新泄露任何有关客户端数据的信息。" +#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:68 -#: ../../source/explanation-differential-privacy.rst:71 #: ../../source/how-to-use-differential-privacy.rst:11 #, fuzzy msgid "Central Differential Privacy" @@ -4523,7 +4740,7 @@ msgstr "" "虽然在联合学习中实现中央数据处理的方法有很多种，但我们将重点放在[2]和[3]提出的算法上。总体方法是剪辑客户端发送的模型更新，并在聚合模型中添加一定量的噪声。在每次迭代中，以特定概率随机选择一组客户端进行训练。每个客户端对自己的数据进行局部训练。然后，每个客户端的更新会被某个值`S`（灵敏度`S`）剪切。这将限制任何单个客户端的影响，这对隐私至关重要，通常也有利于稳健性。实现这一点的常用方法是限制客户机模型更新的" " `L2` 准则，确保较大的更新被缩减以适应 `S` 准则。" -#: ../../source/explanation-differential-privacy.rst:84 +#: ../../source/explanation-differential-privacy.rst:-1 #, fuzzy msgid "clipping" msgstr "剪贴" @@ -4577,8 +4794,8 @@ msgid "" "others." msgstr "在固定剪切和自适应剪切之间做出选择取决于各种因素，如隐私要求、数据分布、模型复杂性等。" +#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:105 -#: ../../source/explanation-differential-privacy.rst:110 #: ../../source/how-to-use-differential-privacy.rst:96 #, fuzzy msgid "Local Differential Privacy" @@ -4849,7 +5066,7 @@ msgstr "" msgid "This is sometimes called a hub-and-spoke topology:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:18 +#: ../../source/explanation-flower-architecture.rst:24 #, fuzzy msgid "Hub-and-spoke topology in federated learning" msgstr "什么是联邦学习？" @@ -4922,7 +5139,7 @@ msgid "" "`missing link` between all those SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:65 +#: ../../source/explanation-flower-architecture.rst:71 #, fuzzy msgid "Basic Flower architecture" msgstr "Flower的架构" @@ -4960,7 +5177,7 @@ msgid "" "SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:91 +#: ../../source/explanation-flower-architecture.rst:97 #, fuzzy msgid "Multi-tenancy federated learning architecture" msgstr "使用联邦学习策略" @@ -4984,7 +5201,7 @@ msgid "" "their corresponding ``ClientApp``\\s:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:107 +#: ../../source/explanation-flower-architecture.rst:113 #, fuzzy msgid "Multi-tenancy federated learning architecture - Run 1" msgstr "使用联邦学习策略" @@ -5001,7 +5218,7 @@ msgid "" " to participate in the training:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:119 +#: ../../source/explanation-flower-architecture.rst:125 #, fuzzy msgid "Multi-tenancy federated learning architecture - Run 2" msgstr "使用联邦学习策略" @@ -5038,7 +5255,7 @@ msgid "" "developer machine." msgstr "" -#: ../../source/explanation-flower-architecture.rst:145 +#: ../../source/explanation-flower-architecture.rst:151 msgid "Flower Deployment Engine with SuperExec" msgstr "" @@ -8153,7 +8370,7 @@ msgstr "" ":code:`DifferentialPrivacyServerSideFixedClipping` 和 " ":code:`DifferentialPrivacyServerSideAdaptiveClipping` ，用于固定剪辑和自适应剪辑。" -#: ../../source/how-to-use-differential-privacy.rst:25 +#: ../../source/how-to-use-differential-privacy.rst:-1 #, fuzzy msgid "server side clipping" msgstr "服务器端逻辑" @@ -8194,7 +8411,7 @@ msgstr "" ":code:`DifferentialPrivacyClientSideFixedClipping` 和 " ":code:`DifferentialPrivacyClientSideAdaptiveClipping`。" -#: ../../source/how-to-use-differential-privacy.rst:57 +#: ../../source/how-to-use-differential-privacy.rst:-1 #, fuzzy msgid "client side clipping" msgstr "客户端逻辑" @@ -8231,7 +8448,7 @@ msgstr "" "要利用本地差分隐私（DP）并在将客户端模型参数传输到 Flower 服务器之前为其添加噪声，可以使用 " "`LocalDpMod`。需要设置以下超参数：剪切规范值、灵敏度、ε 和 delta。" -#: ../../source/how-to-use-differential-privacy.rst:99 +#: ../../source/how-to-use-differential-privacy.rst:-1 #, fuzzy msgid "local DP mod" msgstr "本地 DP 模式" @@ -8653,11 +8870,33 @@ msgstr "" msgid "Arguments" msgstr "参数解析器" -#: ../../flwr install:1 new:1 run:1 +#: ../../flwr install:1 log:1 new:1 run:1 #, fuzzy msgid "Optional argument" msgstr "可选的改进措施" +#: ../../flwr log:1 +msgid "Get logs from a Flower project run." +msgstr "" + +#: ../../flwr log:1 +msgid "Flag to stream or print logs from the Flower run" +msgstr "" + +#: ../../flwr log +#, fuzzy +msgid "default" +msgstr "工作流程" + +#: ../../flwr log:1 +msgid "``True``" +msgstr "" + +#: ../../flwr log:1 +#, fuzzy +msgid "Required argument" +msgstr "构建文档" + #: ../../flwr new:1 #, fuzzy msgid "Create new Flower App." @@ -8750,7 +8989,7 @@ msgstr "模块" #: ../../source/ref-api/flwr.rst:35::1 #, fuzzy -msgid ":py:obj:`client `\\" +msgid ":py:obj:`flwr.client `\\" msgstr ":py:obj:`flwr.client `\\" #: ../../source/ref-api/flwr.rst:35::1 flwr.client:1 of @@ -8759,7 +8998,7 @@ msgstr "Flower 客户端。" #: ../../source/ref-api/flwr.rst:35::1 #, fuzzy -msgid ":py:obj:`common `\\" +msgid ":py:obj:`flwr.common `\\" msgstr ":py:obj:`flwr.common `\\" #: ../../source/ref-api/flwr.rst:35::1 flwr.common:1 of @@ -8768,7 +9007,7 @@ msgstr "服务器和客户端共享的通用组件。" #: ../../source/ref-api/flwr.rst:35::1 #, fuzzy -msgid ":py:obj:`server `\\" +msgid ":py:obj:`flwr.server `\\" msgstr ":py:obj:`flwr.server `\\" #: ../../source/ref-api/flwr.rst:35::1 @@ -8779,7 +9018,7 @@ msgstr "Flower 服务器。" #: ../../source/ref-api/flwr.rst:35::1 #, fuzzy -msgid ":py:obj:`simulation `\\" +msgid ":py:obj:`flwr.simulation `\\" msgstr ":py:obj:`flwr.simulation `\\" #: ../../source/ref-api/flwr.rst:35::1 flwr.simulation:1 of @@ -8875,7 +9114,7 @@ msgstr "使用 NumPy 的 Flower 客户端的抽象基类。" #: ../../source/ref-api/flwr.client.rst:50::1 #, fuzzy -msgid ":py:obj:`mod `\\" +msgid ":py:obj:`flwr.client.mod `\\" msgstr ":py:obj:`flwr.client `\\" #: ../../source/ref-api/flwr.client.rst:50::1 flwr.client.mod:1 of @@ -9086,48 +9325,57 @@ msgstr ":py:obj:`context `\\" msgid "Getter for `Context` client attribute." msgstr "" -#: ../../source/ref-api/flwr.client.Client.rst -#: ../../source/ref-api/flwr.client.NumPyClient.rst -#: ../../source/ref-api/flwr.client.mod.LocalDpMod.rst -#: ../../source/ref-api/flwr.common.Array.rst -#: ../../source/ref-api/flwr.common.ConfigsRecord.rst -#: ../../source/ref-api/flwr.common.Context.rst -#: ../../source/ref-api/flwr.common.Error.rst -#: ../../source/ref-api/flwr.common.Message.rst -#: ../../source/ref-api/flwr.common.Metadata.rst -#: ../../source/ref-api/flwr.common.MetricsRecord.rst #: ../../source/ref-api/flwr.common.Parameters.rst:2 -#: ../../source/ref-api/flwr.common.ParametersRecord.rst -#: ../../source/ref-api/flwr.common.RecordSet.rst -#: ../../source/ref-api/flwr.server.ClientManager.rst -#: ../../source/ref-api/flwr.server.Driver.rst -#: ../../source/ref-api/flwr.server.ServerAppComponents.rst -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst -#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideAdaptiveClipping.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyClientSideFixedClipping.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideAdaptiveClipping.rst -#: ../../source/ref-api/flwr.server.strategy.DifferentialPrivacyServerSideFixedClipping.rst -#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst -#: ../../source/ref-api/flwr.server.strategy.FedAdam.rst -#: ../../source/ref-api/flwr.server.strategy.FedAvg.rst -#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst -#: ../../source/ref-api/flwr.server.strategy.FedAvgM.rst -#: ../../source/ref-api/flwr.server.strategy.FedOpt.rst -#: ../../source/ref-api/flwr.server.strategy.FedProx.rst -#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst -#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst -#: ../../source/ref-api/flwr.server.strategy.Krum.rst -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst -#: ../../source/ref-api/flwr.server.workflow.SecAggPlusWorkflow.rst -#: ../../source/ref-api/flwr.server.workflow.SecAggWorkflow.rst -#: ../../source/ref-api/flwr.simulation.run_simulation.rst -#: ../../source/ref-api/flwr.simulation.start_simulation.rst #: flwr.client.app.start_client flwr.client.app.start_numpy_client -#: flwr.server.app.start_server -#: flwr.server.driver.driver.Driver.send_and_receive of +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.mod.localdp_mod.LocalDpMod +#: flwr.client.numpy_client.NumPyClient.evaluate +#: flwr.client.numpy_client.NumPyClient.fit +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.context.Context flwr.common.message.Error +#: flwr.common.message.Message flwr.common.message.Message.create_error_reply +#: flwr.common.message.Message.create_reply flwr.common.message.Metadata +#: flwr.common.record.configsrecord.ConfigsRecord +#: flwr.common.record.metricsrecord.MetricsRecord +#: flwr.common.record.parametersrecord.Array +#: flwr.common.record.parametersrecord.ParametersRecord +#: flwr.common.record.recordset.RecordSet flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.ClientManager.unregister +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.unregister +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.serverapp_components.ServerAppComponents +#: flwr.server.strategy.bulyan.Bulyan +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyClientSideAdaptiveClipping +#: flwr.server.strategy.dp_adaptive_clipping.DifferentialPrivacyServerSideAdaptiveClipping +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyClientSideFixedClipping +#: flwr.server.strategy.dp_fixed_clipping.DifferentialPrivacyServerSideFixedClipping +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.fedadagrad.FedAdagrad +#: flwr.server.strategy.fedadam.FedAdam flwr.server.strategy.fedavg.FedAvg +#: flwr.server.strategy.fedavg_android.FedAvgAndroid +#: flwr.server.strategy.fedavgm.FedAvgM flwr.server.strategy.fedopt.FedOpt +#: flwr.server.strategy.fedprox.FedProx +#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg +#: flwr.server.strategy.fedyogi.FedYogi flwr.server.strategy.krum.Krum +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters +#: flwr.server.workflow.secure_aggregation.secagg_workflow.SecAggWorkflow +#: flwr.server.workflow.secure_aggregation.secaggplus_workflow.SecAggPlusWorkflow +#: flwr.simulation.run_simulation.run_simulation of msgid "Parameters" msgstr "参数" @@ -9138,21 +9386,31 @@ msgid "" "customize the local evaluation process." msgstr "评估指令包含从服务器接收的（全局）模型参数，以及用于定制本地评估流程的配置值字典。" -#: ../../source/ref-api/flwr.client.Client.rst -#: ../../source/ref-api/flwr.client.NumPyClient.rst -#: ../../source/ref-api/flwr.common.ConfigsRecord.rst -#: ../../source/ref-api/flwr.common.Message.rst -#: ../../source/ref-api/flwr.common.MetricsRecord.rst -#: ../../source/ref-api/flwr.common.ParametersRecord.rst -#: ../../source/ref-api/flwr.server.ClientManager.rst -#: ../../source/ref-api/flwr.server.Driver.rst -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst -#: ../../source/ref-api/flwr.simulation.start_simulation.rst -#: flwr.server.app.start_server -#: flwr.server.driver.driver.Driver.send_and_receive of +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.numpy_client.NumPyClient.evaluate +#: flwr.client.numpy_client.NumPyClient.fit +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.message.Message.create_reply flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.num_available +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.SimpleClientManager.num_available +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters of msgid "Returns" msgstr "返回" @@ -9162,18 +9420,29 @@ msgid "" "details such as the number of local data examples used for evaluation." msgstr "评估结果包含本地数据集上的损失值和其他详细信息，如用于评估的本地数据的数量。" -#: ../../source/ref-api/flwr.client.Client.rst -#: ../../source/ref-api/flwr.client.NumPyClient.rst -#: ../../source/ref-api/flwr.common.Message.rst -#: ../../source/ref-api/flwr.server.ClientManager.rst -#: ../../source/ref-api/flwr.server.Driver.rst -#: ../../source/ref-api/flwr.server.SimpleClientManager.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst -#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst -#: ../../source/ref-api/flwr.server.strategy.Strategy.rst -#: ../../source/ref-api/flwr.simulation.start_simulation.rst -#: flwr.server.app.start_server -#: flwr.server.driver.driver.Driver.send_and_receive of +#: flwr.client.client.Client.evaluate flwr.client.client.Client.fit +#: flwr.client.client.Client.get_parameters +#: flwr.client.client.Client.get_properties +#: flwr.client.numpy_client.NumPyClient.get_parameters +#: flwr.client.numpy_client.NumPyClient.get_properties +#: flwr.common.message.Message.create_reply flwr.server.app.start_server +#: flwr.server.client_manager.ClientManager.num_available +#: flwr.server.client_manager.ClientManager.register +#: flwr.server.client_manager.SimpleClientManager.num_available +#: flwr.server.client_manager.SimpleClientManager.register +#: flwr.server.client_manager.SimpleClientManager.wait_for +#: flwr.server.driver.driver.Driver.create_message +#: flwr.server.driver.driver.Driver.pull_messages +#: flwr.server.driver.driver.Driver.push_messages +#: flwr.server.driver.driver.Driver.send_and_receive +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate +#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit +#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate +#: flwr.server.strategy.strategy.Strategy.aggregate_fit +#: flwr.server.strategy.strategy.Strategy.configure_evaluate +#: flwr.server.strategy.strategy.Strategy.configure_fit +#: flwr.server.strategy.strategy.Strategy.evaluate +#: flwr.server.strategy.strategy.Strategy.initialize_parameters of msgid "Return type" msgstr "返回类型" @@ -9537,6 +9806,11 @@ msgstr "客户端逻辑" msgid ":py:obj:`make_ffn `\\ \\(ffn\\, mods\\)" msgstr ":py:obj:`Client `\\ \\(\\)" +#: ../../source/ref-api/flwr.client.mod.rst:28::1 +#: flwr.client.mod.utils.make_ffn:1 of +msgid "." +msgstr "" + #: ../../source/ref-api/flwr.client.mod.rst:28::1 #, fuzzy msgid "" @@ -9724,10 +9998,6 @@ msgstr "" msgid "make\\_ffn" msgstr "" -#: flwr.client.mod.utils.make_ffn:1 of -msgid "." -msgstr "" - #: ../../source/ref-api/flwr.client.mod.message_size_mod.rst:2 msgid "message\\_size\\_mod" msgstr "" @@ -9756,16 +10026,6 @@ msgstr "" msgid "secaggplus\\_mod" msgstr "工作流程" -#: ../../source/ref-api/flwr.client.run_client_app.rst:2 -#, fuzzy -msgid "run\\_client\\_app" -msgstr "run\\_client\\_app" - -#: ../../source/ref-api/flwr.client.run_supernode.rst:2 -#, fuzzy -msgid "run\\_supernode" -msgstr "flower-superlink" - #: ../../source/ref-api/flwr.client.start_client.rst:2 #, fuzzy msgid "start\\_client" @@ -10643,14 +10903,9 @@ msgstr "返回存储在此对象中的字节数。" #: collections.abc.MutableMapping.clear:1::1 of #, fuzzy -msgid ":py:obj:`get `\\ \\(key\\[\\, default\\]\\)" +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" -#: collections.abc.Mapping.get:1 -#: collections.abc.MutableMapping.clear:1::1 of -msgid "Retrieve the corresponding layout by the string key." -msgstr "" - #: collections.abc.MutableMapping.clear:1::1 of #, fuzzy msgid ":py:obj:`items `\\ \\(\\)" @@ -10716,22 +10971,6 @@ msgstr ":py:obj:`values `\\ \\(\\)" msgid "This function counts booleans as occupying 1 Byte." msgstr "该函数将布尔值计算为占用 1 个字节。" -#: collections.abc.Mapping.get:3 of -msgid "" -"When there isn't an exact match, all the existing keys in the layout map " -"will be treated as a regex and map against the input key again. The first" -" match will be returned, based on the key insertion order. Return None if" -" there isn't any match found." -msgstr "" - -#: collections.abc.Mapping.get:8 of -msgid "the string key as the query for the layout." -msgstr "" - -#: collections.abc.Mapping.get:10 of -msgid "Corresponding layout based on the query." -msgstr "" - #: ../../source/ref-api/flwr.common.Context.rst:2 #, fuzzy msgid "Context" @@ -11620,7 +11859,7 @@ msgstr "编码" msgid "The encoding in which to encode the string." msgstr "字符串的编码。" -#: flwr.common.EventType.encode:5 of +#: flwr.common.EventType.encode:9 of #, fuzzy msgid "errors" msgstr "错误" @@ -11832,7 +12071,7 @@ msgid "" "string." msgstr "如果字符串以后缀字符串结尾，且后缀不为空，则返回 string[:-len(suffix)]。否则，返回原始字符串的副本。" -#: flwr.common.EventType.replace:3 of +#: flwr.common.EventType.replace:5 of #, fuzzy msgid "count" msgstr "背景" @@ -11874,7 +12113,7 @@ msgid "" "strings and the original string." msgstr "如果找不到分隔符，则返回一个包含两个空字符串和原始字符串的 3 元组。" -#: flwr.common.EventType.rsplit:3 flwr.common.EventType.split:3 of +#: flwr.common.EventType.rsplit:7 flwr.common.EventType.split:7 of #, fuzzy msgid "sep" msgstr "sep" @@ -11892,7 +12131,7 @@ msgid "" " empty strings from the result." msgstr "当设置为 \"无\"（默认值）时，将对任何空白字符（包括 \\n \\r \\t \\f 和空格）进行分割，并从结果中剔除空字符串。" -#: flwr.common.EventType.rsplit:9 flwr.common.EventType.split:9 of +#: flwr.common.EventType.rsplit:11 flwr.common.EventType.split:11 of #, fuzzy msgid "maxsplit" msgstr "最大分割" @@ -11942,7 +12181,7 @@ msgid "" "remaining cased characters have lower case." msgstr "更具体地说，单词以大写字母开头，其余所有大小写字符均为小写。" -#: flwr.common.EventType.translate:3 of +#: flwr.common.EventType.translate:5 of #, fuzzy msgid "table" msgstr "数据库" @@ -12451,7 +12690,7 @@ msgstr ":py:obj:`count_bytes `\\ \\(\\)" #: collections.abc.MutableMapping.clear:1::1 of #, fuzzy -msgid ":py:obj:`get `\\ \\(key\\[\\, default\\]\\)" +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" #: collections.abc.MutableMapping.clear:1::1 of @@ -12607,9 +12846,7 @@ msgstr ":py:obj:`count_bytes `\\ \\(\\ #: collections.abc.MutableMapping.clear:1::1 of #, fuzzy -msgid "" -":py:obj:`get `\\ \\(key\\[\\, " -"default\\]\\)" +msgid ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" msgstr ":py:obj:`get `\\ \\(k\\[\\,d\\]\\)" #: collections.abc.MutableMapping.clear:1::1 of @@ -13005,7 +13242,7 @@ msgstr "使用部分可用客户进行评估。" #: ../../source/ref-api/flwr.server.rst:56::1 #, fuzzy -msgid ":py:obj:`strategy `\\" +msgid ":py:obj:`flwr.server.strategy `\\" msgstr "server.strategy.Strategy" #: ../../source/ref-api/flwr.server.rst:56::1 @@ -13015,7 +13252,7 @@ msgstr "包含策略抽象和不同的实现方法。" #: ../../source/ref-api/flwr.server.rst:56::1 #, fuzzy -msgid ":py:obj:`workflow `\\" +msgid ":py:obj:`flwr.server.workflow `\\" msgstr "server.strategy.Strategy" #: ../../source/ref-api/flwr.server.rst:56::1 @@ -13619,8 +13856,7 @@ msgid "" msgstr "服务器实现，可以是 `flwr.server.Server` 或其子类。如果没有提供实例，`start_server` 将创建一个。" #: flwr.server.app.start_server:9 -#: flwr.server.serverapp_components.ServerAppComponents:6 -#: flwr.simulation.app.start_simulation:29 of +#: flwr.server.serverapp_components.ServerAppComponents:6 of msgid "" "Currently supported values are `num_rounds` (int, default: 1) and " "`round_timeout` in seconds (float, default: None)." @@ -13772,16 +14008,6 @@ msgstr "以秒为单位的等待时间，默认为 86400（24 小时）。" msgid "**success**" msgstr "**success**" -#: ../../source/ref-api/flwr.server.run_server_app.rst:2 -#, fuzzy -msgid "run\\_server\\_app" -msgstr "run\\_server\\_app" - -#: ../../source/ref-api/flwr.server.run_superlink.rst:2 -#, fuzzy -msgid "run\\_superlink" -msgstr "flower-superlink" - #: ../../source/ref-api/flwr.server.start_server.rst:2 #, fuzzy msgid "start\\_server" @@ -17774,16 +18000,16 @@ msgstr "使用模拟引擎运行花朵应用程序。" #: ../../source/ref-api/flwr.simulation.rst:18::1 #, fuzzy msgid "" -":py:obj:`start_simulation `\\ \\(\\*\\," -" client\\_fn\\, num\\_clients\\)" +":py:obj:`start_simulation `\\ " +"\\(\\*args\\, \\*\\*kwargs\\)" msgstr "" ":py:obj:`start_simulation `\\ \\(\\*\\," " client\\_fn\\[\\, ...\\]\\)" #: ../../source/ref-api/flwr.simulation.rst:18::1 -#: flwr.simulation.app.start_simulation:1 of -msgid "Start a Ray-based Flower simulation server." -msgstr "启动基于 Ray 的Flower模拟服务器。" +#: flwr.simulation.start_simulation:1 of +msgid "Log error stating that module `ray` could not be imported." +msgstr "" #: ../../source/ref-api/flwr.simulation.run_simulation.rst:2 #, fuzzy @@ -17855,168 +18081,31 @@ msgstr "启用后，将只显示 INFO、WARNING 和 ERROR 日志信息。启用 msgid "start\\_simulation" msgstr "start_simulation" -#: flwr.simulation.app.start_simulation:3 of -#, fuzzy -msgid "" -"A function creating `Client` instances. The function must have the " -"signature `client_fn(context: Context). It should return a single client " -"instance of type `Client`. Note that the created client instances are " -"ephemeral and will often be destroyed after a single method invocation. " -"Since client instances are not long-lived, they should not attempt to " -"carry state over method invocations. Any state required by the instance " -"(model, dataset, hyperparameters, ...) should be (re-)created in either " -"the call to `client_fn` or the call to any of the client methods (e.g., " -"load evaluation data in the `evaluate` method itself)." -msgstr "" -"创建客户端实例的函数。该函数必须接受一个名为 `cid` 的 `str` 参数。它应返回一个 Client " -"类型的客户端实例。请注意，创建的客户端实例是短暂的，通常在调用一个方法后就会被销毁。由于客户机实例不是长期存在的，它们不应试图在方法调用时携带状态数据。实例所需的任何状态数据（模型、数据集、超参数......）都应在调用" -" `client_fn` 或任何客户端方法（例如，在 `evaluate` 方法中加载评估数据）时（重新）创建。" +#: ../../source/ref-changelog.md:1 +msgid "Changelog" +msgstr "更新日志" -#: flwr.simulation.app.start_simulation:13 of +#: ../../source/ref-changelog.md:3 #, fuzzy -msgid "The total number of clients in this simulation." -msgstr "需要等待的客户数量。" +msgid "v1.11.1 (2024-09-11)" +msgstr "v1.3.0 (2023-02-06)" -#: flwr.simulation.app.start_simulation:15 of -#, fuzzy -msgid "" -"UNSUPPORTED, WILL BE REMOVED. USE `num_clients` INSTEAD. List " -"`client_id`s for each client. This is only required if `num_clients` is " -"not set. Setting both `num_clients` and `clients_ids` with " -"`len(clients_ids)` not equal to `num_clients` generates an error. Using " -"this argument will raise an error." -msgstr "" -"列出每个客户的 `client_id`。只有在未设置 `num_clients` " -"时才需要这样做。同时设置`num_clients`和`clients_ids`，且`len(clients_ids)`不等于`num_clients`，会产生错误。" +#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:37 +#: ../../source/ref-changelog.md:141 ../../source/ref-changelog.md:239 +#: ../../source/ref-changelog.md:339 ../../source/ref-changelog.md:403 +#: ../../source/ref-changelog.md:496 ../../source/ref-changelog.md:596 +#: ../../source/ref-changelog.md:680 ../../source/ref-changelog.md:744 +#: ../../source/ref-changelog.md:802 ../../source/ref-changelog.md:871 +#: ../../source/ref-changelog.md:940 +msgid "Thanks to our contributors" +msgstr "感谢我们的贡献者" -#: flwr.simulation.app.start_simulation:21 of -#, fuzzy -msgid "" -"CPU and GPU resources for a single client. Supported keys are `num_cpus` " -"and `num_gpus`. To understand the GPU utilization caused by `num_gpus`, " -"as well as using custom resources, please consult the Ray documentation." -msgstr "" -"\"num_gpus\"： 0.0` 单个客户端的 CPU 和 GPU 资源。支持的键值为 `num_cpus` 和 `num_gpus`。要了解" -" `num_gpus` 所导致的 GPU 利用率，以及使用自定义资源的情况，请查阅 Ray 文档。" - -#: flwr.simulation.app.start_simulation:26 of -msgid "" -"An implementation of the abstract base class `flwr.server.Server`. If no " -"instance is provided, then `start_server` will create one." -msgstr "抽象基类 `flwr.server.Server`的实现。如果没有提供实例，`start_server` 将创建一个。" - -#: flwr.simulation.app.start_simulation:32 of -msgid "" -"An implementation of the abstract base class `flwr.server.Strategy`. If " -"no strategy is provided, then `start_server` will use " -"`flwr.server.strategy.FedAvg`." -msgstr "" -"抽象基类 `flwr.server.strategy` 的实现。如果没有提供策略，`start_server` 将使用 " -"`flwr.server.strategy.FedAvg`。" - -#: flwr.simulation.app.start_simulation:36 of -msgid "" -"An implementation of the abstract base class `flwr.server.ClientManager`." -" If no implementation is provided, then `start_simulation` will use " -"`flwr.server.client_manager.SimpleClientManager`." -msgstr "" -"抽象基类 `flwr.server.ClientManager` 的实现。如果没有提供实现，`start_simulation` 将使用 " -"`flwr.server.client_manager.SimpleClientManager`。" - -#: flwr.simulation.app.start_simulation:40 of -msgid "" -"Optional dictionary containing arguments for the call to `ray.init`. If " -"ray_init_args is None (the default), Ray will be initialized with the " -"following default args: { \"ignore_reinit_error\": True, " -"\"include_dashboard\": False } An empty dictionary can be used " -"(ray_init_args={}) to prevent any arguments from being passed to " -"ray.init." -msgstr "" -"可选字典，包含调用 `ray.init` 时的参数。如果 ray_init_args 为 None（默认值），则将使用以下默认参数初始化 Ray：" -" { \"ignore_reinit_error\"： True, \"include_dashboard\"： False } " -"可以使用空字典（ray_init_args={}）来防止向 ray.init 传递任何参数。" - -#: flwr.simulation.app.start_simulation:40 of -msgid "" -"Optional dictionary containing arguments for the call to `ray.init`. If " -"ray_init_args is None (the default), Ray will be initialized with the " -"following default args:" -msgstr "可选字典，包含调用 `ray.init` 时的参数。如果 ray_init_args 为 None（默认值），则将使用以下默认参数初始化 Ray：" - -#: flwr.simulation.app.start_simulation:44 of -msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" -msgstr "{ \"ignore_reinit_error\"： True, \"include_dashboard\"： False }" - -#: flwr.simulation.app.start_simulation:46 of -msgid "" -"An empty dictionary can be used (ray_init_args={}) to prevent any " -"arguments from being passed to ray.init." -msgstr "可以使用空字典 (ray_init_args={}) 来防止向 ray.init 传递任何参数。" - -#: flwr.simulation.app.start_simulation:49 of -msgid "" -"Set to True to prevent `ray.shutdown()` in case " -"`ray.is_initialized()=True`." -msgstr "设为 True 可在 `ray.is_initialized()=True` 情况下阻止 `ray.shutdown()` 。" - -#: flwr.simulation.app.start_simulation:51 of -#, fuzzy -msgid "" -"Optionally specify the type of actor to use. The actor object, which " -"persists throughout the simulation, will be the process in charge of " -"executing a ClientApp wrapping input argument `client_fn`." -msgstr "可选择指定要使用的actor类型。actor对象将在整个模拟过程中持续存在，它将是负责运行客户端作业（即其 `fit()`方法）的进程。" - -#: flwr.simulation.app.start_simulation:55 of -msgid "" -"If you want to create your own Actor classes, you might need to pass some" -" input argument. You can use this dictionary for such purpose." -msgstr "如果您想创建自己的 Actor 类，可能需要传递一些输入参数。为此，您可以使用本字典。" - -#: flwr.simulation.app.start_simulation:58 of -msgid "" -"(default: \"DEFAULT\") Optional string (\"DEFAULT\" or \"SPREAD\") for " -"the VCE to choose in which node the actor is placed. If you are an " -"advanced user needed more control you can use lower-level scheduling " -"strategies to pin actors to specific compute nodes (e.g. via " -"NodeAffinitySchedulingStrategy). Please note this is an advanced feature." -" For all details, please refer to the Ray documentation: " -"https://docs.ray.io/en/latest/ray-core/scheduling/index.html" -msgstr "" -"(默认：\"DEFAULT\"）可选字符串（\"DEFAULT \"或 \"SPREAD\"），供 VCE " -"选择将行为体放置在哪个节点上。如果你是需要更多控制权的高级用户，可以使用低级调度策略将actor固定到特定计算节点（例如，通过 " -"NodeAffinitySchedulingStrategy）。请注意，这是一项高级功能。有关详细信息，请参阅 Ray " -"文档：https://docs.ray.io/en/latest/ray-core/scheduling/index.html" - -#: flwr.simulation.app.start_simulation:67 of -msgid "**hist** -- Object containing metrics from training." -msgstr "**hist** -- 包含训练指标的对象。" - -#: ../../source/ref-changelog.md:1 -msgid "Changelog" -msgstr "更新日志" - -#: ../../source/ref-changelog.md:3 -#, fuzzy -msgid "v1.11.1 (2024-09-11)" -msgstr "v1.3.0 (2023-02-06)" - -#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:37 -#: ../../source/ref-changelog.md:141 ../../source/ref-changelog.md:239 -#: ../../source/ref-changelog.md:339 ../../source/ref-changelog.md:403 -#: ../../source/ref-changelog.md:496 ../../source/ref-changelog.md:596 -#: ../../source/ref-changelog.md:680 ../../source/ref-changelog.md:744 -#: ../../source/ref-changelog.md:802 ../../source/ref-changelog.md:871 -#: ../../source/ref-changelog.md:940 -msgid "Thanks to our contributors" -msgstr "感谢我们的贡献者" - -#: ../../source/ref-changelog.md:7 ../../source/ref-changelog.md:39 -#: ../../source/ref-changelog.md:143 ../../source/ref-changelog.md:241 -#: ../../source/ref-changelog.md:341 ../../source/ref-changelog.md:405 -#: ../../source/ref-changelog.md:498 ../../source/ref-changelog.md:598 -#: ../../source/ref-changelog.md:682 ../../source/ref-changelog.md:746 -#: ../../source/ref-changelog.md:804 +#: ../../source/ref-changelog.md:7 ../../source/ref-changelog.md:39 +#: ../../source/ref-changelog.md:143 ../../source/ref-changelog.md:241 +#: ../../source/ref-changelog.md:341 ../../source/ref-changelog.md:405 +#: ../../source/ref-changelog.md:498 ../../source/ref-changelog.md:598 +#: ../../source/ref-changelog.md:682 ../../source/ref-changelog.md:746 +#: ../../source/ref-changelog.md:804 msgid "" "We would like to give our special thanks to all the contributors who made" " the new version of Flower possible (in `git shortlog` order):" @@ -18122,13 +18211,6 @@ msgstr "** 添加一个新的 gRPC 选项**（[#2197](https://github.com/adap/fl msgid "Incompatible changes" msgstr "不兼容的更改" -#: ../../source/ref-changelog.md:33 ../../source/ref-changelog.md:399 -#: ../../source/ref-changelog.md:676 ../../source/ref-changelog.md:740 -#: ../../source/ref-changelog.md:798 ../../source/ref-changelog.md:867 -#: ../../source/ref-changelog.md:929 -msgid "None" -msgstr "无" - #: ../../source/ref-changelog.md:35 #, fuzzy msgid "v1.11.0 (2024-08-30)" @@ -24877,7 +24959,20 @@ msgid "" "blockchain environment is available here:" msgstr "当然可以。有关在区块链环境中使用 Flower 的可用示例列表，请点击此处：" -#: ../../source/ref-faq.rst:28 +#: ../../source/ref-faq.rst:29 +msgid "`FLock: A Decentralised AI Training Platform `_." +msgstr "" + +#: ../../source/ref-faq.rst:29 +msgid "Contribute to on-chain training the model and earn rewards." +msgstr "" + +#: ../../source/ref-faq.rst:30 +#, fuzzy +msgid "Local blockchain with federated learning simulation." +msgstr "扩大联邦学习的规模" + +#: ../../source/ref-faq.rst:31 msgid "" "`Flower meets Nevermined GitHub Repository `_." @@ -24885,7 +24980,7 @@ msgstr "" "`Flower meets Nevermined GitHub Repository `_." -#: ../../source/ref-faq.rst:29 +#: ../../source/ref-faq.rst:32 msgid "" "`Flower meets Nevermined YouTube video " "`_." @@ -24893,7 +24988,7 @@ msgstr "" "`Flower meets Nevermined YouTube 视频 " "`_." -#: ../../source/ref-faq.rst:30 +#: ../../source/ref-faq.rst:33 #, fuzzy msgid "" "`Flower meets KOSMoS `_." -#: ../../source/ref-faq.rst:31 +#: ../../source/ref-faq.rst:34 msgid "" "`Flower meets Talan blog post `_ 。" -#: ../../source/ref-faq.rst:32 +#: ../../source/ref-faq.rst:35 msgid "" "`Flower meets Talan GitHub Repository " "`_ ." @@ -25163,205 +25258,314 @@ msgstr "" "请参阅`完整代码示例 " "`_了解更多信息。" -#: ../../source/tutorial-quickstart-fastai.rst:-1 -msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with FastAI to train a vision model on CIFAR-10." -msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 FastAI 在 CIFAR-10 上训练视觉模型。" - #: ../../source/tutorial-quickstart-fastai.rst:5 msgid "Quickstart fastai" msgstr "快速入门 fastai" -#: ../../source/tutorial-quickstart-fastai.rst:10 -msgid "Let's build a federated learning system using fastai and Flower!" -msgstr "让我们用 fastai 和 Flower 建立一个联邦学习系统！" +#: ../../source/tutorial-quickstart-fastai.rst:7 +#, fuzzy +msgid "" +"In this federated learning tutorial we will learn how to train a " +"SqueezeNet model on MNIST using Flower and fastai. It is recommended to " +"create a virtual environment and run everything within a :doc:`virtualenv" +" `." +msgstr "" +"首先，建议创建一个虚拟环境，并在 `virtualenv `_ 中运行一切。" #: ../../source/tutorial-quickstart-fastai.rst:12 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 +msgid "Then, clone the code example directly from GitHub:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:20 msgid "" -"Please refer to the `full code example " -"`_ " -"to learn more." +"This will create a new directory called `quickstart-fastai` containing " +"the following files:" msgstr "" -"请参阅 `完整代码示例 `_了解更多信息。" + +#: ../../source/tutorial-quickstart-fastai.rst:33 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:33 +#, fuzzy +msgid "Next, activate your environment, then run:" +msgstr "并激活虚拟环境：" + +#: ../../source/tutorial-quickstart-fastai.rst:43 +msgid "" +"This example by default runs the Flower Simulation Engine, creating a " +"federation of 10 nodes using `FedAvg `_ " +"as the aggregation strategy. The dataset will be partitioned using Flower" +" Dataset's `IidPartitioner `_." +" Let's run the project:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:56 +#: ../../source/tutorial-quickstart-huggingface.rst:65 +#: ../../source/tutorial-quickstart-mlx.rst:64 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:56 +#: ../../source/tutorial-quickstart-pytorch.rst:64 +#: ../../source/tutorial-quickstart-tensorflow.rst:65 +msgid "With default arguments you will see an output like this one:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:100 +#: ../../source/tutorial-quickstart-huggingface.rst:116 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:106 +#: ../../source/tutorial-quickstart-pytorch.rst:105 +#: ../../source/tutorial-quickstart-tensorflow.rst:106 +msgid "" +"You can also override the parameters defined in the " +"``[tool.flwr.app.config]`` section in ``pyproject.toml`` like this:" +msgstr "" + +#: ../../source/tutorial-quickstart-fastai.rst:110 +#, fuzzy +msgid "" +"Check the `source code `_ of this tutorial in ``examples/quickstart-fasai`` " +"in the Flower GitHub repository." +msgstr "" +"此示例的`完整源代码 `_ 可在 :code:`examples/xgboost-quickstart` 中找到。" #: ../../source/tutorial-quickstart-huggingface.rst:-1 +#, fuzzy msgid "" "Check out this Federating Learning quickstart tutorial for using Flower " -"with HuggingFace Transformers in order to fine-tune an LLM." +"with 🤗 HuggingFace Transformers in order to fine-tune an LLM." msgstr "查看此联邦学习 快速入门教程，了解如何使用 Flower 和 HuggingFace Transformers 来微调 LLM。" #: ../../source/tutorial-quickstart-huggingface.rst:5 msgid "Quickstart 🤗 Transformers" msgstr "🤗 Transformers快速入门" -#: ../../source/tutorial-quickstart-huggingface.rst:10 +#: ../../source/tutorial-quickstart-huggingface.rst:7 +#, fuzzy msgid "" -"Let's build a federated learning system using Hugging Face Transformers " -"and Flower!" -msgstr "让我们用Hugging Face Transformers和Flower来构建一个联邦学习系统！" +"In this federated learning tutorial we will learn how to train a large " +"language model (LLM) on the `IMDB " +"`_ dataset using Flower" +" and the 🤗 Hugging Face Transformers library. It is recommended to create" +" a virtual environment and run everything within a :doc:`virtualenv " +"`." +msgstr "" +"首先，建议创建一个虚拟环境，并在 `virtualenv `_ 中运行一切。" -#: ../../source/tutorial-quickstart-huggingface.rst:12 +#: ../../source/tutorial-quickstart-huggingface.rst:14 msgid "" -"We will leverage Hugging Face to federate the training of language models" -" over multiple clients using Flower. More specifically, we will fine-tune" -" a pre-trained Transformer model (distilBERT) for sequence classification" -" over a dataset of IMDB ratings. The end goal is to detect if a movie " -"rating is positive or negative." +"Let's use ``flwr new`` to create a complete Flower+🤗 Hugging Face " +"project. It will generate all the files needed to run, by default with " +"the Flower Simulation Engine, a federation of 10 nodes using |fedavg|_ " +"The dataset will be partitioned using |flowerdatasets|_'s " +"|iidpartitioner|_." msgstr "" -"我们将利用Hugging Face技术，使用 Flower 在多个客户端上联邦训练语言模型。更具体地说，我们将对预先训练好的 " -"Transformer 模型（distilBERT）进行微调，以便在 IMDB 评分数据集上进行序列分类。最终目标是检测电影评分是正面还是负面。" - -#: ../../source/tutorial-quickstart-huggingface.rst:18 -msgid "Dependencies" -msgstr "依赖关系" #: ../../source/tutorial-quickstart-huggingface.rst:20 +#: ../../source/tutorial-quickstart-mlx.rst:19 +#: ../../source/tutorial-quickstart-pytorch.rst:19 +#: ../../source/tutorial-quickstart-tensorflow.rst:20 +#, fuzzy msgid "" -"To follow along this tutorial you will need to install the following " -"packages: :code:`datasets`, :code:`evaluate`, :code:`flwr`, " -":code:`torch`, and :code:`transformers`. This can be done using " -":code:`pip`:" -msgstr "" -"要学习本教程，您需要安装以下软件包： :code:`datasets`、 :code:`evaluate`、 :code:`flwr`、 " -":code:`torch`和 :code:`transformers`。这可以通过 :code:`pip` 来完成：" +"Now that we have a rough idea of what this example is about, let's get " +"started. First, install Flower in your new environment:" +msgstr "现在，我们已经有了一个大致的概念，让我们开始吧。首先，我们需要安装 Flower。运行：" -#: ../../source/tutorial-quickstart-huggingface.rst:30 -msgid "Standard Hugging Face workflow" -msgstr "标准Hugging Face工作流程" +#: ../../source/tutorial-quickstart-huggingface.rst:28 +msgid "" +"Then, run the command below. You will be prompted to select one of the " +"available templates (choose ``HuggingFace``), give a name to your " +"project, and type in your developer name:" +msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:33 -msgid "Handling the data" -msgstr "处理数据" +#: ../../source/tutorial-quickstart-huggingface.rst:36 +#: ../../source/tutorial-quickstart-mlx.rst:35 +#: ../../source/tutorial-quickstart-pytorch.rst:35 +#: ../../source/tutorial-quickstart-tensorflow.rst:36 +msgid "" +"After running it you'll notice a new directory with your project name has" +" been created. It should have the following structure:" +msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:35 +#: ../../source/tutorial-quickstart-huggingface.rst:50 +#: ../../source/tutorial-quickstart-mlx.rst:49 +#: ../../source/tutorial-quickstart-pytorch.rst:49 +#: ../../source/tutorial-quickstart-tensorflow.rst:50 msgid "" -"To fetch the IMDB dataset, we will use Hugging Face's :code:`datasets` " -"library. We then need to tokenize the data and create :code:`PyTorch` " -"dataloaders, this is all done in the :code:`load_data` function:" +"If you haven't yet installed the project and its dependencies, you can do" +" so by:" +msgstr "" + +#: ../../source/tutorial-quickstart-huggingface.rst:58 +#: ../../source/tutorial-quickstart-pytorch.rst:57 +#: ../../source/tutorial-quickstart-tensorflow.rst:58 +msgid "To run the project, do:" +msgstr "" + +#: ../../source/tutorial-quickstart-huggingface.rst:106 +msgid "You can also run the project with GPU as follows:" msgstr "" -"为了获取 IMDB 数据集，我们将使用 Hugging Face 的 :code:`datasets` 库。然后，我们需要对数据进行标记化，并创建" -" :code:`PyTorch` 数据加载器，这些都将在 :code:`load_data` 函数中完成：" -#: ../../source/tutorial-quickstart-huggingface.rst:81 -msgid "Training and testing the model" -msgstr "训练和测试模型" +#: ../../source/tutorial-quickstart-huggingface.rst:113 +msgid "" +"This will use the default arguments where each ``ClientApp`` will use 2 " +"CPUs and at most 4 ``ClientApp``\\s will run in a given GPU." +msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:83 +#: ../../source/tutorial-quickstart-huggingface.rst:124 +#: ../../source/tutorial-quickstart-mlx.rst:114 +#: ../../source/tutorial-quickstart-pytorch.rst:113 msgid "" -"Once we have a way of creating our trainloader and testloader, we can " -"take care of the training and testing. This is very similar to any " -":code:`PyTorch` training or testing loop:" +"What follows is an explanation of each component in the project you just " +"created: dataset partition, the model, defining the ``ClientApp`` and " +"defining the ``ServerApp``." msgstr "" -"有了创建 trainloader 和 testloader 的方法后，我们就可以进行训练和测试了。这与任何 :code:`PyTorch` " -"训练或测试循环都非常相似：" -#: ../../source/tutorial-quickstart-huggingface.rst:121 -msgid "Creating the model itself" -msgstr "创建模型本身" +#: ../../source/tutorial-quickstart-huggingface.rst:130 +#: ../../source/tutorial-quickstart-mlx.rst:120 +#: ../../source/tutorial-quickstart-pytorch.rst:119 +#: ../../source/tutorial-quickstart-tensorflow.rst:116 +#, fuzzy +msgid "The Data" +msgstr "加载数据" -#: ../../source/tutorial-quickstart-huggingface.rst:123 +#: ../../source/tutorial-quickstart-huggingface.rst:132 msgid "" -"To create the model itself, we will just load the pre-trained distillBERT" -" model using Hugging Face’s :code:`AutoModelForSequenceClassification` :" +"This tutorial uses |flowerdatasets|_ to easily download and partition the" +" `IMDB `_ dataset. In " +"this example you'll make use of the |iidpartitioner|_ to generate " +"``num_partitions`` partitions. You can choose |otherpartitioners|_ " +"available in Flower Datasets. To tokenize the text, we will also load the" +" tokenizer from the pre-trained Transformer model that we'll use during " +"training - more on that in the next section. Each ``ClientApp`` will call" +" this function to create dataloaders with the data that correspond to " +"their data partition." msgstr "" -"要创建模型本身，我们只需使用 Hugging Face 的 :code:`AutoModelForSequenceClassification` " -"加载预训练的 distillBERT 模型：" -#: ../../source/tutorial-quickstart-huggingface.rst:136 -msgid "Federating the example" -msgstr "将示例联邦化" +#: ../../source/tutorial-quickstart-huggingface.rst:178 +#: ../../source/tutorial-quickstart-mlx.rst:164 +#: ../../source/tutorial-quickstart-pytorch.rst:157 +#: ../../source/tutorial-quickstart-tensorflow.rst:145 +#, fuzzy +msgid "The Model" +msgstr "训练模型" -#: ../../source/tutorial-quickstart-huggingface.rst:139 -msgid "Creating the IMDBClient" -msgstr "创建 IMDBClient" +#: ../../source/tutorial-quickstart-huggingface.rst:180 +#, fuzzy +msgid "" +"We will leverage 🤗 Hugging Face to federate the training of language " +"models over multiple clients using Flower. More specifically, we will " +"fine-tune a pre-trained Transformer model (|berttiny|_) for sequence " +"classification over the dataset of IMDB ratings. The end goal is to " +"detect if a movie rating is positive or negative. If you have access to " +"larger GPUs, feel free to use larger models!" +msgstr "" +"我们将利用Hugging Face技术，使用 Flower 在多个客户端上联邦训练语言模型。更具体地说，我们将对预先训练好的 " +"Transformer 模型（distilBERT）进行微调，以便在 IMDB 评分数据集上进行序列分类。最终目标是检测电影评分是正面还是负面。" -#: ../../source/tutorial-quickstart-huggingface.rst:141 +#: ../../source/tutorial-quickstart-huggingface.rst:193 msgid "" -"To federate our example to multiple clients, we first need to write our " -"Flower client class (inheriting from :code:`flwr.client.NumPyClient`). " -"This is very easy, as our model is a standard :code:`PyTorch` model:" +"Note that here, ``model_name`` is a string that will be loaded from the " +"``Context`` in the ClientApp and ServerApp." msgstr "" -"要将我们的示例联邦到多个客户端，我们首先需要编写 Flower 客户端类（继承自 " -":code:`flwr.client.NumPyClient`）。这很容易，因为我们的模型是一个标准的 :code:`PyTorch` 模型：" -#: ../../source/tutorial-quickstart-huggingface.rst:169 +#: ../../source/tutorial-quickstart-huggingface.rst:196 msgid "" -"The :code:`get_parameters` function lets the server get the client's " -"parameters. Inversely, the :code:`set_parameters` function allows the " -"server to send its parameters to the client. Finally, the :code:`fit` " -"function trains the model locally for the client, and the " -":code:`evaluate` function tests the model locally and returns the " -"relevant metrics." +"In addition to loading the pretrained model weights and architecture, we " +"also include two utility functions to perform both training (i.e. " +"``train()``) and evaluation (i.e. ``test()``) using the above model. " +"These functions should look fairly familiar if you have some prior " +"experience with PyTorch. Note these functions do not have anything " +"specific to Flower. That being said, the training function will normally " +"be called, as we'll see later, from a Flower client passing its own data." +" In summary, your clients can use standard training/testing functions to " +"perform local training or evaluation:" msgstr "" -":code:`get_parameters` " -"函数允许服务器获取客户端的参数。相反，:code:`set_parameters`函数允许服务器将其参数发送给客户端。最后，:code:`fit`函数在本地为客户端训练模型，:code:`evaluate`函数在本地测试模型并返回相关指标。" -#: ../../source/tutorial-quickstart-huggingface.rst:175 -msgid "Starting the server" -msgstr "启动服务器" +#: ../../source/tutorial-quickstart-huggingface.rst:239 +#: ../../source/tutorial-quickstart-mlx.rst:210 +#: ../../source/tutorial-quickstart-pytorch.rst:234 +#: ../../source/tutorial-quickstart-tensorflow.rst:176 +#, fuzzy +msgid "The ClientApp" +msgstr "客户端" -#: ../../source/tutorial-quickstart-huggingface.rst:177 +#: ../../source/tutorial-quickstart-huggingface.rst:241 msgid "" -"Now that we have a way to instantiate clients, we need to create our " -"server in order to aggregate the results. Using Flower, this can be done " -"very easily by first choosing a strategy (here, we are using " -":code:`FedAvg`, which will define the global weights as the average of " -"all the clients' weights at each round) and then using the " -":code:`flwr.server.start_server` function:" +"The main changes we have to make to use 🤗 Hugging Face with Flower will " +"be found in the ``get_weights()`` and ``set_weights()`` functions. Under " +"the hood, the ``transformers`` library uses PyTorch, which means we can " +"reuse the ``get_weights()`` and ``set_weights()`` code that we defined in" +" the :doc:`Quickstart PyTorch ` tutorial. As" +" a reminder, in ``get_weights()``, PyTorch model parameters are extracted" +" and represented as a list of NumPy arrays. The ``set_weights()`` " +"function that's the opposite: given a list of NumPy arrays it applies " +"them to an existing PyTorch model. Doing this in fairly easy in PyTorch." msgstr "" -"现在我们有了实例化客户端的方法，我们需要创建服务器，以便汇总结果。使用 Flower，首先选择一个策略（这里我们使用 " -":code:`FedAvg`，它将把全局模型参数定义为每轮所有客户端模型参数的平均值），然后使用 " -":code:`flwr.server.start_server`函数，就可以非常轻松地完成这项工作：" -#: ../../source/tutorial-quickstart-huggingface.rst:205 +#: ../../source/tutorial-quickstart-huggingface.rst:254 +#: ../../source/tutorial-quickstart-pytorch.rst:245 msgid "" -"The :code:`weighted_average` function is there to provide a way to " -"aggregate the metrics distributed amongst the clients (basically this " -"allows us to display a nice average accuracy and loss for every round)." +"The specific implementation of ``get_weights()`` and ``set_weights()`` " +"depends on the type of models you use. The ones shown below work for a " +"wide range of PyTorch models but you might need to adjust them if you " +"have more exotic model architectures." msgstr "" -"使用 :code:`weighted_average` " -"函数是为了提供一种方法来汇总分布在客户端的指标（基本上，这可以让我们显示每一轮的平均精度和损失值）。" - -#: ../../source/tutorial-quickstart-huggingface.rst:209 -msgid "Putting everything together" -msgstr "把所有东西放在一起" -#: ../../source/tutorial-quickstart-huggingface.rst:211 -msgid "We can now start client instances using:" -msgstr "现在我们可以使用：" +#: ../../source/tutorial-quickstart-huggingface.rst:269 +#: ../../source/tutorial-quickstart-pytorch.rst:261 +msgid "" +"The rest of the functionality is directly inspired by the centralized " +"case. The ``fit()`` method in the client trains the model using the local" +" dataset. Similarly, the ``evaluate()`` method is used to evaluate the " +"model received on a held-out validation set that the client might have:" +msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:221 +#: ../../source/tutorial-quickstart-huggingface.rst:296 msgid "" -"And they will be able to connect to the server and start the federated " -"training." -msgstr "他们就能连接到服务器，开始联邦训练。" +"Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " +"defined above by means of a ``client_fn()`` callback. Note that the " +"`context` enables you to get access to hyperparemeters defined in your " +"``pyproject.toml`` to configure the run. In this tutorial we access the " +"``local-epochs`` setting to control the number of epochs a ``ClientApp`` " +"will perform when running the ``fit()`` method. You could define " +"additional hyperparameters in ``pyproject.toml`` and access them here." +msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:223 +#: ../../source/tutorial-quickstart-huggingface.rst:330 +#: ../../source/tutorial-quickstart-mlx.rst:376 +#: ../../source/tutorial-quickstart-pytorch.rst:321 +#: ../../source/tutorial-quickstart-tensorflow.rst:245 #, fuzzy +msgid "The ServerApp" +msgstr "服务器" + +#: ../../source/tutorial-quickstart-huggingface.rst:332 msgid "" -"If you want to check out everything put together, you should check out " -"the `full code example `_ ." +"To construct a ``ServerApp`` we define a ``server_fn()`` callback with an" +" identical signature to that of ``client_fn()`` but the return type is " +"|serverappcomponents|_ as opposed to a |client|_ In this example we use " +"the `FedAvg` strategy. To it we pass a randomly initialized model that " +"will server as the global model to federated. Note that the value of " +"``fraction_fit`` is read from the run config. You can find the default " +"value defined in the ``pyproject.toml``." msgstr "" -"如果您想查看所有内容，请查看完整的代码示例： [https://github.com/adap/flower/tree/main/examples" -"/quickstart-" -"huggingface](https://github.com/adap/flower/tree/main/examples" -"/quickstart-huggingface)." -#: ../../source/tutorial-quickstart-huggingface.rst:226 +#: ../../source/tutorial-quickstart-huggingface.rst:371 msgid "" -"Of course, this is a very basic example, and a lot can be added or " -"modified, it was just to showcase how simply we could federate a Hugging " -"Face workflow using Flower." -msgstr "当然，这只是一个非常基本的示例，还可以添加或修改很多内容，只是为了展示我们可以如何简单地使用 Flower 联合Hugging Face的工作流程。" +"Congratulations! You've successfully built and run your first federated " +"learning system for an LLM." +msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:229 +#: ../../source/tutorial-quickstart-huggingface.rst:376 msgid "" -"Note that in this example we used :code:`PyTorch`, but we could have very" -" well used :code:`TensorFlow`." -msgstr "请注意，在本例中我们使用了 :code:`PyTorch`，但也完全可以使用 :code:`TensorFlow`。" +"Check the source code of the extended version of this tutorial in " +"|quickstart_hf_link|_ in the Flower GitHub repository. For a " +"comprehensive example of a federated fine-tuning of an LLM with Flower, " +"refer to the |flowertune|_ example in the Flower GitHub repository." +msgstr "" #: ../../source/tutorial-quickstart-ios.rst:-1 msgid "" @@ -25419,7 +25623,6 @@ msgstr "或者Poetry：" #: ../../source/tutorial-quickstart-ios.rst:34 #: ../../source/tutorial-quickstart-scikitlearn.rst:40 -#: ../../source/tutorial-quickstart-tensorflow.rst:29 #: ../../source/tutorial-quickstart-xgboost.rst:55 msgid "Flower Client" msgstr "Flower 客户端" @@ -25511,13 +25714,11 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:129 #: ../../source/tutorial-quickstart-scikitlearn.rst:167 -#: ../../source/tutorial-quickstart-tensorflow.rst:98 #: ../../source/tutorial-quickstart-xgboost.rst:341 msgid "Flower Server" msgstr "Flower 服务器" #: ../../source/tutorial-quickstart-ios.rst:131 -#: ../../source/tutorial-quickstart-tensorflow.rst:100 msgid "" "For simple workloads we can start a Flower server and leave all the " "configuration possibilities at their default values. In a file named " @@ -25528,12 +25729,10 @@ msgstr "" #: ../../source/tutorial-quickstart-ios.rst:142 #: ../../source/tutorial-quickstart-scikitlearn.rst:230 -#: ../../source/tutorial-quickstart-tensorflow.rst:112 msgid "Train the model, federated!" msgstr "联邦训练模型！" #: ../../source/tutorial-quickstart-ios.rst:144 -#: ../../source/tutorial-quickstart-tensorflow.rst:114 #: ../../source/tutorial-quickstart-xgboost.rst:567 msgid "" "With both client and server ready, we can now run everything and see " @@ -25736,7 +25935,7 @@ msgstr "" ":code:`Client`更容易实现，因为它避免了一些不必要的操作。:code:`FlowerClient` " "需要实现四个方法，两个用于获取/设置模型参数，一个用于训练模型，一个用于测试模型：" -#: ../../source/tutorial-quickstart-jax.rst:165 +#: ../../source/tutorial-quickstart-jax.rst:167 msgid ":code:`set_parameters (optional)`" msgstr ":code:`set_parameters (可选)`" @@ -25833,14 +26032,6 @@ msgid "" "api/flwr_datasets.partitioner.IidPartitioner.html#flwr_datasets.partitioner.IidPartitioner>`_." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:19 -#: ../../source/tutorial-quickstart-pytorch.rst:19 -#, fuzzy -msgid "" -"Now that we have a rough idea of what this example is about, let's get " -"started. First, install Flower in your new environment:" -msgstr "现在，我们已经有了一个大致的概念，让我们开始吧。首先，我们需要安装 Flower。运行：" - #: ../../source/tutorial-quickstart-mlx.rst:27 msgid "" "Then, run the command below. You will be prompted to select of the " @@ -25848,66 +26039,27 @@ msgid "" "type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:35 -#: ../../source/tutorial-quickstart-pytorch.rst:35 -msgid "" -"After running it you'll notice a new directory with your project name has" -" been created. It should have the following structure:" -msgstr "" - -#: ../../source/tutorial-quickstart-mlx.rst:49 -#: ../../source/tutorial-quickstart-pytorch.rst:49 -msgid "" -"If you haven't yet installed the project and its dependencies, you can do" -" so by:" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:57 msgid "To run the project do:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:64 -#: ../../source/tutorial-quickstart-pytorch.rst:64 -msgid "With default arguments you will see an output like this one:" -msgstr "" - #: ../../source/tutorial-quickstart-mlx.rst:106 msgid "" "You can also override the parameters defined in " "``[tool.flwr.app.config]`` section in the ``pyproject.toml`` like this:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:114 -#: ../../source/tutorial-quickstart-pytorch.rst:113 +#: ../../source/tutorial-quickstart-mlx.rst:122 msgid "" -"What follows is an explanation of each component in the project you just " -"created: dataset partition, the model, defining the ``ClientApp`` and " -"defining the ``ServerApp``." +"We will use `Flower Datasets `_ to " +"easily download and partition the `MNIST` dataset. In this example you'll" +" make use of the `IidPartitioner `_" +" to generate `num_partitions` partitions. You can choose `other " +"partitioners `_ available in Flower Datasets:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:120 -#: ../../source/tutorial-quickstart-pytorch.rst:119 -#, fuzzy -msgid "The Data" -msgstr "加载数据" - -#: ../../source/tutorial-quickstart-mlx.rst:122 -msgid "" -"We will use `Flower Datasets `_ to " -"easily download and partition the `MNIST` dataset. In this example you'll" -" make use of the `IidPartitioner `_" -" to generate `num_partitions` partitions. You can choose `other " -"partitioners `_ available in Flower Datasets:" -msgstr "" - -#: ../../source/tutorial-quickstart-mlx.rst:164 -#: ../../source/tutorial-quickstart-pytorch.rst:157 -#, fuzzy -msgid "The Model" -msgstr "训练模型" - #: ../../source/tutorial-quickstart-mlx.rst:166 msgid "" "We define the model as in the `centralized MLX example " @@ -25921,12 +26073,6 @@ msgid "" "over batches." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:210 -#: ../../source/tutorial-quickstart-pytorch.rst:234 -#, fuzzy -msgid "The ClientApp" -msgstr "客户端" - #: ../../source/tutorial-quickstart-mlx.rst:212 msgid "" "The main changes we have to make to use `MLX` with `Flower` will be found" @@ -25995,12 +26141,6 @@ msgid "" "method." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:376 -#: ../../source/tutorial-quickstart-pytorch.rst:321 -#, fuzzy -msgid "The ServerApp" -msgstr "服务器" - #: ../../source/tutorial-quickstart-mlx.rst:378 msgid "" "To construct a ``ServerApp``, we define a ``server_fn()`` callback with " @@ -26014,6 +26154,7 @@ msgstr "" #: ../../source/tutorial-quickstart-mlx.rst:402 #: ../../source/tutorial-quickstart-pytorch.rst:360 +#: ../../source/tutorial-quickstart-tensorflow.rst:279 msgid "" "Congratulations! You've successfully built and run your first federated " "learning system." @@ -26088,16 +26229,6 @@ msgid "" "and type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:57 -msgid "To run the project, do:" -msgstr "" - -#: ../../source/tutorial-quickstart-pytorch.rst:105 -msgid "" -"You can also override the parameters defined in the " -"``[tool.flwr.app.config]`` section in ``pyproject.toml`` like this:" -msgstr "" - #: ../../source/tutorial-quickstart-pytorch.rst:121 msgid "" "This tutorial uses `Flower Datasets `_ " @@ -26141,22 +26272,6 @@ msgid "" "PyTorch model. Doing this in fairly easy in PyTorch." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:245 -msgid "" -"The specific implementation of ``get_weights()`` and ``set_weights()`` " -"depends on the type of models you use. The ones shown below work for a " -"wide range of PyTorch models but you might need to adjust them if you " -"have more exotic model architectures." -msgstr "" - -#: ../../source/tutorial-quickstart-pytorch.rst:261 -msgid "" -"The rest of the functionality is directly inspired by the centralized " -"case. The ``fit()`` method in the client trains the model using the local" -" dataset. Similarly, the ``evaluate()`` method is used to evaluate the " -"model received on a held-out validation set that the client might have:" -msgstr "" - #: ../../source/tutorial-quickstart-pytorch.rst:294 msgid "" "Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " @@ -26193,6 +26308,7 @@ msgstr "" "quickstart/>`_ 可在 :code:`examples/xgboost-quickstart` 中找到。" #: ../../source/tutorial-quickstart-pytorch.rst:372 +#: ../../source/tutorial-quickstart-tensorflow.rst:295 #, fuzzy msgid "Video tutorial" msgstr "教程" @@ -26204,30 +26320,53 @@ msgid "" "that shows the new APIs (as the content above does)" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:-1 -msgid "" -"Check out this Federated Learning quickstart tutorial for using Flower " -"with PyTorch Lightning to train an Auto Encoder model on MNIST." -msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 PyTorch Lightning 在 MNIST 上训练自动编码器模型。" - #: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 msgid "Quickstart PyTorch Lightning" msgstr "快速入门 PyTorch Lightning" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:10 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:7 +#, fuzzy +msgid "" +"In this federated learning tutorial we will learn how to train an " +"AutoEncoder model on MNIST using Flower and PyTorch Lightning. It is " +"recommended to create a virtual environment and run everything within a " +":doc:`virtualenv `." +msgstr "" +"首先，建议创建一个虚拟环境，并在 `virtualenv `_ 中运行一切。" + +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:20 msgid "" -"Let's build a horizontal federated learning system using PyTorch " -"Lightning and Flower!" -msgstr "让我们使用 PyTorch Lightning 和 Flower 构建一个水平联邦学习系统！" +"This will create a new directory called `quickstart-pytorch-lightning` " +"containing the following files:" +msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:43 msgid "" -"Please refer to the `full code example " -"`_ to learn more." +"By default, Flower Simulation Engine will be started and it will create a" +" federation of 4 nodes using `FedAvg `_ " +"as the aggregation strategy. The dataset will be partitioned using Flower" +" Dataset's `IidPartitioner `_." +" To run the project, do:" +msgstr "" + +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:94 +msgid "" +"Each simulated `ClientApp` (two per round) will also log a summary of " +"their local training process. Expect this output to be similar to:" msgstr "" -"请参阅 `完整代码示例 `_ 了解更多信息。" + +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:116 +#, fuzzy +msgid "" +"Check the `source code `_ of this tutorial in ``examples" +"/quickstart-pytorch-lightning`` in the Flower GitHub repository." +msgstr "" +"此示例的`完整源代码 `_ 可在 :code:`examples/xgboost-quickstart` 中找到。" #: ../../source/tutorial-quickstart-scikitlearn.rst:-1 msgid "" @@ -26316,7 +26455,7 @@ msgstr ":code:`set_model_params()`" msgid "Sets the parameters of a :code:`sklearn` LogisticRegression model" msgstr "设置:code:`sklean`的LogisticRegression模型的参数" -#: ../../source/tutorial-quickstart-scikitlearn.rst:49 +#: ../../source/tutorial-quickstart-scikitlearn.rst:50 msgid ":code:`set_initial_params()`" msgstr ":code:`set_initial_params()`" @@ -26387,7 +26526,7 @@ msgstr "" msgid "return the model weight as a list of NumPy ndarrays" msgstr "以 NumPy ndarrays 列表形式返回模型参数" -#: ../../source/tutorial-quickstart-scikitlearn.rst:120 +#: ../../source/tutorial-quickstart-scikitlearn.rst:121 msgid ":code:`set_parameters` (optional)" msgstr ":code:`set_parameters` （可选）" @@ -26499,7 +26638,6 @@ msgid "" msgstr "客户端和服务器都准备就绪后，我们现在就可以运行一切，看看联邦学习的运行情况。联邦学习系统通常有一个服务器和多个客户端。因此，我们必须先启动服务器：" #: ../../source/tutorial-quickstart-scikitlearn.rst:239 -#: ../../source/tutorial-quickstart-tensorflow.rst:122 #: ../../source/tutorial-quickstart-xgboost.rst:575 msgid "" "Once the server is running we can start the clients in different " @@ -26507,7 +26645,6 @@ msgid "" msgstr "服务器运行后，我们就可以在不同终端启动客户端了。打开一个新终端，启动第一个客户端：" #: ../../source/tutorial-quickstart-scikitlearn.rst:246 -#: ../../source/tutorial-quickstart-tensorflow.rst:129 #: ../../source/tutorial-quickstart-xgboost.rst:582 msgid "Open another terminal and start the second client:" msgstr "打开另一台终端，启动第二个客户端：" @@ -26533,121 +26670,117 @@ msgstr "" "mnist>`_ 可以在 :code:`examples/sklearn-logreg-mnist` 中找到。" #: ../../source/tutorial-quickstart-tensorflow.rst:-1 +#, fuzzy msgid "" "Check out this Federated Learning quickstart tutorial for using Flower " -"with TensorFlow to train a MobilNetV2 model on CIFAR-10." +"with TensorFlow to train a CNN model on CIFAR-10." msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 TensorFlow 在 CIFAR-10 上训练 MobilNetV2 模型。" #: ../../source/tutorial-quickstart-tensorflow.rst:5 msgid "Quickstart TensorFlow" msgstr "快速入门 TensorFlow" -#: ../../source/tutorial-quickstart-tensorflow.rst:13 -msgid "Let's build a federated learning system in less than 20 lines of code!" -msgstr "让我们用不到 20 行代码构建一个联邦学习系统！" - -#: ../../source/tutorial-quickstart-tensorflow.rst:15 -msgid "Before Flower can be imported we have to install it:" -msgstr "在导入 Flower 之前，我们必须先安装它：" - -#: ../../source/tutorial-quickstart-tensorflow.rst:21 +#: ../../source/tutorial-quickstart-tensorflow.rst:7 +#, fuzzy msgid "" -"Since we want to use the Keras API of TensorFlow (TF), we have to install" -" TF as well:" -msgstr "由于我们要使用 TensorFlow (TF) 的 Keras API，因此还必须安装 TF：" - -#: ../../source/tutorial-quickstart-tensorflow.rst:31 -msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:" -msgstr "接下来，在名为 :code:`client.py` 的文件中导入 Flower 和 TensorFlow：" +"In this tutorial we will learn how to train a Convolutional Neural " +"Network on CIFAR-10 using the Flower framework and TensorFlow. First of " +"all, it is recommended to create a virtual environment and run everything" +" within a :doc:`virtualenv `." +msgstr "" +"首先，建议创建一个虚拟环境，并在 `virtualenv `_ 中运行一切。" -#: ../../source/tutorial-quickstart-tensorflow.rst:38 +#: ../../source/tutorial-quickstart-tensorflow.rst:13 msgid "" -"We use the Keras utilities of TF to load CIFAR10, a popular colored image" -" classification dataset for machine learning. The call to " -":code:`tf.keras.datasets.cifar10.load_data()` downloads CIFAR10, caches " -"it locally, and then returns the entire training and test set as NumPy " -"ndarrays." +"Let's use `flwr new` to create a complete Flower+TensorFlow project. It " +"will generate all the files needed to run, by default with the Flower " +"Simulation Engine, a federation of 10 nodes using `FedAvg " +"`_. The " +"dataset will be partitioned using Flower Dataset's `IidPartitioner " +"`_." msgstr "" -"我们使用 TF 的 Keras 实用程序加载 CIFAR10，这是一个用于机器学习的流行彩色图像分类数据集。调用 " -":code:`tf.keras.datasets.cifar10.load_data()` 会下载 CIFAR10，将其缓存到本地，然后以 " -"NumPy ndarrays 的形式返回整个训练集和测试集。" -#: ../../source/tutorial-quickstart-tensorflow.rst:47 +#: ../../source/tutorial-quickstart-tensorflow.rst:28 msgid "" -"Next, we need a model. For the purpose of this tutorial, we use " -"MobilNetV2 with 10 output classes:" -msgstr "接下来，我们需要一个模型。在本教程中，我们使用带有 10 个输出类的 MobilNetV2：" +"Then, run the command below. You will be prompted to select one of the " +"available templates (choose ``TensorFlow``), give a name to your project," +" and type in your developer name:" +msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:54 +#: ../../source/tutorial-quickstart-tensorflow.rst:118 msgid "" -"The Flower server interacts with clients through an interface called " -":code:`Client`. When the server selects a particular client for training," -" it sends training instructions over the network. The client receives " -"those instructions and calls one of the :code:`Client` methods to run " -"your code (i.e., to train the neural network we defined earlier)." +"This tutorial uses `Flower Datasets `_ " +"to easily download and partition the `CIFAR-10` dataset. In this example " +"you'll make use of the `IidPartitioner `_" +" to generate `num_partitions` partitions. You can choose `other " +"partitioners `_ available in Flower Datasets. Each " +"``ClientApp`` will call this function to create the ``NumPy`` arrays that" +" correspond to their data partition." msgstr "" -"Flower 服务器通过一个名为 :code:`Client` " -"的接口与客户端交互。当服务器选择一个特定的客户端进行训练时，它会通过网络发送训练指令。客户端接收到这些指令后，会调用 :code:`Client`" -" 方法之一来运行您的代码（即训练我们之前定义的神经网络）。" -#: ../../source/tutorial-quickstart-tensorflow.rst:60 +#: ../../source/tutorial-quickstart-tensorflow.rst:147 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses Keras. The :code:`NumPyClient` interface defines three " -"methods which can be implemented in the following way:" +"Next, we need a model. We defined a simple Convolutional Neural Network " +"(CNN), but feel free to replace it with a more sophisticated model if " +"you'd like:" msgstr "" -"Flower 提供了一个名为 :code:`NumPyClient` 的便捷类，当您的工作负载使用 Keras 时，该类可以更轻松地实现 " -":code:`Client` 接口。:code:`NumPyClient` 接口定义了三个方法，可以通过以下方式实现：" -#: ../../source/tutorial-quickstart-tensorflow.rst:82 +#: ../../source/tutorial-quickstart-tensorflow.rst:178 msgid "" -"We can now create an instance of our class :code:`CifarClient` and add " -"one line to actually run this client:" -msgstr "现在我们可以创建一个 :code:`CifarClient` 类的实例，并添加一行来实际运行该客户端：" +"With `TensorFlow`, we can use the built-in ``get_weights()`` and " +"``set_weights()`` functions, which simplifies the implementation with " +"`Flower`. The rest of the functionality in the ClientApp is directly " +"inspired by the centralized case. The ``fit()`` method in the client " +"trains the model using the local dataset. Similarly, the ``evaluate()`` " +"method is used to evaluate the model received on a held-out validation " +"set that the client might have:" +msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:90 -#, fuzzy +#: ../../source/tutorial-quickstart-tensorflow.rst:212 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " -"implement a client of type :code:`NumPyClient` you'll need to first call " -"its :code:`to_client()` method. The string :code:`\"[::]:8080\"` tells " -"the client which server to connect to. In our case we can run the server " -"and the client on the same machine, therefore we use " -":code:`\"[::]:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we point the client at." +"Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " +"defined above by means of a ``client_fn()`` callback. Note that the " +"`context` enables you to get access to hyperparameters defined in your " +"``pyproject.toml`` to configure the run. For example, in this tutorial we" +" access the `local-epochs` setting to control the number of epochs a " +"``ClientApp`` will perform when running the ``fit()`` method, in addition" +" to `batch-size`. You could define additional hyperparameters in " +"``pyproject.toml`` and access them here." msgstr "" -"这就是客户端。我们只需实现 :code:`Client` 或 :code:`NumPyClient` 并调用 " -":code:`fl.client.start_client()` 或 " -":code:`fl.client.start_numpy_client()`。字符串 " -":code:`\"[::]:8080\"`会告诉客户端要连接的服务器。在本例中，我们可以在同一台机器上运行服务器和客户端，因此使用 " -":code:`\"[::]:8080\"。如果我们运行的是真正的联邦工作负载，服务器和客户端运行在不同的机器上，那么需要改变的只是客户端指向的 " -":code:`server_address`。" -#: ../../source/tutorial-quickstart-tensorflow.rst:135 -msgid "Each client will have its own dataset." -msgstr "每个客户都有自己的数据集。" +#: ../../source/tutorial-quickstart-tensorflow.rst:247 +msgid "" +"To construct a ``ServerApp`` we define a ``server_fn()`` callback with an" +" identical signature to that of ``client_fn()`` but the return type is " +"`ServerAppComponents `_ as " +"opposed to a `Client `_. In this example we use the " +"`FedAvg`. To it we pass a randomly initialized model that will serve as " +"the global model to federate." +msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:137 +#: ../../source/tutorial-quickstart-tensorflow.rst:284 +#, fuzzy msgid "" -"You should now see how the training does in the very first terminal (the " -"one that started the server):" -msgstr "现在你应该能在第一个终端（启动服务器的终端）看到训练的效果了：" +"Check the source code of the extended version of this tutorial in " +"|quickstart_tf_link|_ in the Flower GitHub repository." +msgstr "" +"此示例的`完整源代码 `_ 可在 :code:`examples/xgboost-quickstart` 中找到。" -#: ../../source/tutorial-quickstart-tensorflow.rst:169 +#: ../../source/tutorial-quickstart-tensorflow.rst:299 msgid "" -"Congratulations! You've successfully built and run your first federated " -"learning system. The full `source code " -"`_ for this can be found in :code:`examples" -"/quickstart-tensorflow/client.py`." +"The video shown below shows how to setup a TensorFlow + Flower project " +"using our previously recommended APIs. A new video tutorial will be " +"released that shows the new APIs (as the content above does)" msgstr "" -"恭喜您！您已经成功构建并运行了第一个联邦学习系统。`完整的源代码 " -"`_ 可以在 :code:`examples/quickstart-" -"tensorflow/client.py` 中找到。" #: ../../source/tutorial-quickstart-xgboost.rst:-1 msgid "" @@ -28861,7 +28994,7 @@ msgid "" msgstr "在机器学习中，我们有一个模型和数据。模型可以是一个神经网络（如图所示），也可以是其他东西，比如经典的线性回归。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 -msgid "|e5918c1c06a4434bbe4bf49235e40059|" +msgid "|e87b69b2ada74ea49412df16f4a0b9cc|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 @@ -28876,7 +29009,7 @@ msgid "" msgstr "我们使用数据来训练模型，以完成一项有用的任务。任务可以是检测图像中的物体、转录音频或玩围棋等游戏。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 -msgid "|c0165741bd1944f09ec55ce49032377d|" +msgid "|33cacb7d985c4906b348515c1a5cd993|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 @@ -28897,7 +29030,7 @@ msgid "" msgstr "它源于智能手机上用户与应用程序的交互、汽车上传感器数据的收集、笔记本电脑上键盘输入的接收，或者智能扬声器上某人试着唱的歌。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 -msgid "|0a0ac9427ac7487b8e52d75ed514f04e|" +msgid "|cc080a555947492fa66131dc3a967603|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 @@ -28915,7 +29048,7 @@ msgstr "" "\"通常不只是一个地方，而是很多地方。它可能是多个运行同一应用程序的设备。但也可能是多个组织，都在为同一任务生成数据。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 -msgid "|5defee3ea4ca40d99fcd3e4ea045be25|" +msgid "|085c3e0fb8664c6aa06246636524b20b|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 @@ -28931,7 +29064,7 @@ msgid "" msgstr "因此，要使用机器学习或任何类型的数据分析，过去使用的方法是在中央服务器上收集所有数据。这个服务器可以在数据中心的某个地方，也可以在云端的某个地方。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 -msgid "|74f26ca701254d3db57d7899bd91eb55|" +msgid "|bfe69c74e48c45d49b50251c38c2a019|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 @@ -28946,7 +29079,7 @@ msgid "" msgstr "一旦所有数据都收集到一处，我们最终就可以使用机器学习算法在数据上训练我们的模型。这就是我们基本上一直依赖的机器学习方法。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 -msgid "|bda79f21f8154258a40e5766b2634ad7|" +msgid "|ebbecd651f0348d99c6511ea859bf4ca|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 @@ -28966,7 +29099,7 @@ msgid "" msgstr "我们刚刚看到的经典机器学习方法可以在某些情况下使用。很好的例子包括对假日照片进行分类或分析网络流量。在这些案例中，所有数据自然都可以在中央服务器上获得。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 -msgid "|89d30862e62e4f9989e193483a08680a|" +msgid "|163117eb654a4273babba413cf8065f5|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 @@ -28981,7 +29114,7 @@ msgid "" msgstr "但这种方法并不适用于许多其他情况。例如，集中服务器上没有数据，或者一台服务器上的数据不足以训练出一个好的模型。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 -msgid "|77e9918671c54b4f86e01369c0785ce8|" +msgid "|452ac3ba453b4cd1be27be1ba7560d64|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 @@ -29129,7 +29262,7 @@ msgid "" msgstr "我们首先在服务器上初始化模型。这与经典的集中式学习完全相同：我们随机或从先前保存的检查点初始化模型参数。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 -msgid "|7e4ccef37cc94148a067107b34eb7447|" +msgid "|f403fcd69e4e44409627e748b404c086|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 @@ -29153,7 +29286,7 @@ msgid "" msgstr "接下来，我们会将全局模型的参数发送到连接的客户端节点（如智能手机等边缘设备或企业的服务器）。这是为了确保每个参与节点都使用相同的模型参数开始本地训练。我们通常只使用几个连接节点，而不是所有节点。这样做的原因是，选择越来越多的客户端节点会导致收益递减。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 -msgid "|28e47e4cded14479a0846c8e5f22c872|" +msgid "|4b00fe63870145968f8443619a792a42|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 @@ -29179,7 +29312,7 @@ msgstr "" "（mini-batches）。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 -msgid "|4b8c5d1afa144294b76ffc76e4658a38|" +msgid "|368378731066486fa4397e89bc6b870c|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 @@ -29202,7 +29335,7 @@ msgid "" msgstr "经过本地训练后，每个客户节点最初收到的模型参数都会略有不同。参数之所以不同，是因为每个客户端节点的本地数据集中都有不同的数据。然后，客户端节点将这些模型更新发回服务器。它们发送的模型更新既可以是完整的模型参数，也可以只是本地训练过程中积累的梯度。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 -msgid "|9dbdb3a0f6cb4a129fac863eaa414c17|" +msgid "|a66aa83d85bf4ffba7ed660b718066da|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 @@ -29248,7 +29381,7 @@ msgstr "" " 100 个示例的 10 倍。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 -msgid "|81749d0ac0834c36a83bd38f433fea31|" +msgid "|82324b9af72a4582a81839d55caab767|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 @@ -29355,7 +29488,7 @@ msgstr "" "为联邦学习、分析和评估提供了一种统一的方法。它允许用户联邦化任何工作负载、任何 ML 框架和任何编程语言。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 -msgid "|ed9aae51da70428eab7eef32f21e819e|" +msgid "|fbf2da0da3cc4f8ab3b3eff852d80c41|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 @@ -32665,3 +32798,575 @@ msgstr "" #~ msgid "|c00bf2750bc24d229737a0fe1395f0fc|" #~ msgstr "" +#~ msgid "run\\_client\\_app" +#~ msgstr "run\\_client\\_app" + +#~ msgid "run\\_supernode" +#~ msgstr "flower-superlink" + +#~ msgid "Retrieve the corresponding layout by the string key." +#~ msgstr "" + +#~ msgid "" +#~ "When there isn't an exact match, " +#~ "all the existing keys in the " +#~ "layout map will be treated as a" +#~ " regex and map against the input " +#~ "key again. The first match will be" +#~ " returned, based on the key insertion" +#~ " order. Return None if there isn't" +#~ " any match found." +#~ msgstr "" + +#~ msgid "the string key as the query for the layout." +#~ msgstr "" + +#~ msgid "Corresponding layout based on the query." +#~ msgstr "" + +#~ msgid "run\\_server\\_app" +#~ msgstr "run\\_server\\_app" + +#~ msgid "run\\_superlink" +#~ msgstr "flower-superlink" + +#~ msgid "Start a Ray-based Flower simulation server." +#~ msgstr "启动基于 Ray 的Flower模拟服务器。" + +#~ msgid "" +#~ "A function creating `Client` instances. " +#~ "The function must have the signature " +#~ "`client_fn(context: Context). It should return" +#~ " a single client instance of type " +#~ "`Client`. Note that the created client" +#~ " instances are ephemeral and will " +#~ "often be destroyed after a single " +#~ "method invocation. Since client instances " +#~ "are not long-lived, they should " +#~ "not attempt to carry state over " +#~ "method invocations. Any state required " +#~ "by the instance (model, dataset, " +#~ "hyperparameters, ...) should be (re-)created" +#~ " in either the call to `client_fn`" +#~ " or the call to any of the " +#~ "client methods (e.g., load evaluation " +#~ "data in the `evaluate` method itself)." +#~ msgstr "" +#~ "创建客户端实例的函数。该函数必须接受一个名为 `cid` 的 `str` 参数。它应返回一个" +#~ " Client " +#~ "类型的客户端实例。请注意，创建的客户端实例是短暂的，通常在调用一个方法后就会被销毁。由于客户机实例不是长期存在的，它们不应试图在方法调用时携带状态数据。实例所需的任何状态数据（模型、数据集、超参数......）都应在调用" +#~ " `client_fn` 或任何客户端方法（例如，在 `evaluate` " +#~ "方法中加载评估数据）时（重新）创建。" + +#~ msgid "The total number of clients in this simulation." +#~ msgstr "需要等待的客户数量。" + +#~ msgid "" +#~ "UNSUPPORTED, WILL BE REMOVED. USE " +#~ "`num_clients` INSTEAD. List `client_id`s for" +#~ " each client. This is only required" +#~ " if `num_clients` is not set. Setting" +#~ " both `num_clients` and `clients_ids` with" +#~ " `len(clients_ids)` not equal to " +#~ "`num_clients` generates an error. Using " +#~ "this argument will raise an error." +#~ msgstr "" +#~ "列出每个客户的 `client_id`。只有在未设置 `num_clients` " +#~ "时才需要这样做。同时设置`num_clients`和`clients_ids`，且`len(clients_ids)`不等于`num_clients`，会产生错误。" + +#~ msgid "" +#~ "CPU and GPU resources for a single" +#~ " client. Supported keys are `num_cpus` " +#~ "and `num_gpus`. To understand the GPU" +#~ " utilization caused by `num_gpus`, as " +#~ "well as using custom resources, please" +#~ " consult the Ray documentation." +#~ msgstr "" +#~ "\"num_gpus\"： 0.0` 单个客户端的 CPU 和 GPU " +#~ "资源。支持的键值为 `num_cpus` 和 `num_gpus`。要了解 " +#~ "`num_gpus` 所导致的 GPU 利用率，以及使用自定义资源的情况，请查阅 Ray" +#~ " 文档。" + +#~ msgid "" +#~ "An implementation of the abstract base" +#~ " class `flwr.server.Server`. If no instance" +#~ " is provided, then `start_server` will " +#~ "create one." +#~ msgstr "抽象基类 `flwr.server.Server`的实现。如果没有提供实例，`start_server` 将创建一个。" + +#~ msgid "" +#~ "An implementation of the abstract base" +#~ " class `flwr.server.Strategy`. If no " +#~ "strategy is provided, then `start_server` " +#~ "will use `flwr.server.strategy.FedAvg`." +#~ msgstr "" +#~ "抽象基类 `flwr.server.strategy` 的实现。如果没有提供策略，`start_server`" +#~ " 将使用 `flwr.server.strategy.FedAvg`。" + +#~ msgid "" +#~ "An implementation of the abstract base" +#~ " class `flwr.server.ClientManager`. If no " +#~ "implementation is provided, then " +#~ "`start_simulation` will use " +#~ "`flwr.server.client_manager.SimpleClientManager`." +#~ msgstr "" +#~ "抽象基类 `flwr.server.ClientManager` " +#~ "的实现。如果没有提供实现，`start_simulation` 将使用 " +#~ "`flwr.server.client_manager.SimpleClientManager`。" + +#~ msgid "" +#~ "Optional dictionary containing arguments for" +#~ " the call to `ray.init`. If " +#~ "ray_init_args is None (the default), Ray" +#~ " will be initialized with the " +#~ "following default args: { " +#~ "\"ignore_reinit_error\": True, \"include_dashboard\": " +#~ "False } An empty dictionary can " +#~ "be used (ray_init_args={}) to prevent " +#~ "any arguments from being passed to " +#~ "ray.init." +#~ msgstr "" +#~ "可选字典，包含调用 `ray.init` 时的参数。如果 ray_init_args 为" +#~ " None（默认值），则将使用以下默认参数初始化 Ray： { " +#~ "\"ignore_reinit_error\"： True, \"include_dashboard\"： " +#~ "False } 可以使用空字典（ray_init_args={}）来防止向 ray.init " +#~ "传递任何参数。" + +#~ msgid "" +#~ "Optional dictionary containing arguments for" +#~ " the call to `ray.init`. If " +#~ "ray_init_args is None (the default), Ray" +#~ " will be initialized with the " +#~ "following default args:" +#~ msgstr "" +#~ "可选字典，包含调用 `ray.init` 时的参数。如果 ray_init_args 为" +#~ " None（默认值），则将使用以下默认参数初始化 Ray：" + +#~ msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }" +#~ msgstr "{ \"ignore_reinit_error\"： True, \"include_dashboard\"： False }" + +#~ msgid "" +#~ "An empty dictionary can be used " +#~ "(ray_init_args={}) to prevent any arguments" +#~ " from being passed to ray.init." +#~ msgstr "可以使用空字典 (ray_init_args={}) 来防止向 ray.init 传递任何参数。" + +#~ msgid "" +#~ "Set to True to prevent `ray.shutdown()`" +#~ " in case `ray.is_initialized()=True`." +#~ msgstr "设为 True 可在 `ray.is_initialized()=True` 情况下阻止 `ray.shutdown()` 。" + +#~ msgid "" +#~ "Optionally specify the type of actor " +#~ "to use. The actor object, which " +#~ "persists throughout the simulation, will " +#~ "be the process in charge of " +#~ "executing a ClientApp wrapping input " +#~ "argument `client_fn`." +#~ msgstr "可选择指定要使用的actor类型。actor对象将在整个模拟过程中持续存在，它将是负责运行客户端作业（即其 `fit()`方法）的进程。" + +#~ msgid "" +#~ "If you want to create your own " +#~ "Actor classes, you might need to " +#~ "pass some input argument. You can " +#~ "use this dictionary for such purpose." +#~ msgstr "如果您想创建自己的 Actor 类，可能需要传递一些输入参数。为此，您可以使用本字典。" + +#~ msgid "" +#~ "(default: \"DEFAULT\") Optional string " +#~ "(\"DEFAULT\" or \"SPREAD\") for the VCE" +#~ " to choose in which node the " +#~ "actor is placed. If you are an " +#~ "advanced user needed more control you" +#~ " can use lower-level scheduling " +#~ "strategies to pin actors to specific " +#~ "compute nodes (e.g. via " +#~ "NodeAffinitySchedulingStrategy). Please note this" +#~ " is an advanced feature. For all " +#~ "details, please refer to the Ray " +#~ "documentation: https://docs.ray.io/en/latest/ray-" +#~ "core/scheduling/index.html" +#~ msgstr "" +#~ "(默认：\"DEFAULT\"）可选字符串（\"DEFAULT \"或 \"SPREAD\"），供 " +#~ "VCE " +#~ "选择将行为体放置在哪个节点上。如果你是需要更多控制权的高级用户，可以使用低级调度策略将actor固定到特定计算节点（例如，通过 " +#~ "NodeAffinitySchedulingStrategy）。请注意，这是一项高级功能。有关详细信息，请参阅 Ray " +#~ "文档：https://docs.ray.io/en/latest/ray-core/scheduling/index.html" + +#~ msgid "**hist** -- Object containing metrics from training." +#~ msgstr "**hist** -- 包含训练指标的对象。" + +#~ msgid "" +#~ "Check out this Federated Learning " +#~ "quickstart tutorial for using Flower " +#~ "with FastAI to train a vision " +#~ "model on CIFAR-10." +#~ msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 FastAI 在 CIFAR-10 上训练视觉模型。" + +#~ msgid "Let's build a federated learning system using fastai and Flower!" +#~ msgstr "让我们用 fastai 和 Flower 建立一个联邦学习系统！" + +#~ msgid "" +#~ "Please refer to the `full code " +#~ "example `_ to learn more." +#~ msgstr "" +#~ "请参阅 `完整代码示例 " +#~ "`_了解更多信息。" + +#~ msgid "" +#~ "Let's build a federated learning system" +#~ " using Hugging Face Transformers and " +#~ "Flower!" +#~ msgstr "让我们用Hugging Face Transformers和Flower来构建一个联邦学习系统！" + +#~ msgid "Dependencies" +#~ msgstr "依赖关系" + +#~ msgid "" +#~ "To follow along this tutorial you " +#~ "will need to install the following " +#~ "packages: :code:`datasets`, :code:`evaluate`, " +#~ ":code:`flwr`, :code:`torch`, and " +#~ ":code:`transformers`. This can be done " +#~ "using :code:`pip`:" +#~ msgstr "" +#~ "要学习本教程，您需要安装以下软件包： :code:`datasets`、 :code:`evaluate`、 " +#~ ":code:`flwr`、 :code:`torch`和 :code:`transformers`。这可以通过" +#~ " :code:`pip` 来完成：" + +#~ msgid "Standard Hugging Face workflow" +#~ msgstr "标准Hugging Face工作流程" + +#~ msgid "Handling the data" +#~ msgstr "处理数据" + +#~ msgid "" +#~ "To fetch the IMDB dataset, we will" +#~ " use Hugging Face's :code:`datasets` " +#~ "library. We then need to tokenize " +#~ "the data and create :code:`PyTorch` " +#~ "dataloaders, this is all done in " +#~ "the :code:`load_data` function:" +#~ msgstr "" +#~ "为了获取 IMDB 数据集，我们将使用 Hugging Face 的 " +#~ ":code:`datasets` 库。然后，我们需要对数据进行标记化，并创建 :code:`PyTorch` " +#~ "数据加载器，这些都将在 :code:`load_data` 函数中完成：" + +#~ msgid "Training and testing the model" +#~ msgstr "训练和测试模型" + +#~ msgid "" +#~ "Once we have a way of creating " +#~ "our trainloader and testloader, we can" +#~ " take care of the training and " +#~ "testing. This is very similar to " +#~ "any :code:`PyTorch` training or testing " +#~ "loop:" +#~ msgstr "" +#~ "有了创建 trainloader 和 testloader " +#~ "的方法后，我们就可以进行训练和测试了。这与任何 :code:`PyTorch` 训练或测试循环都非常相似：" + +#~ msgid "Creating the model itself" +#~ msgstr "创建模型本身" + +#~ msgid "" +#~ "To create the model itself, we " +#~ "will just load the pre-trained " +#~ "distillBERT model using Hugging Face’s " +#~ ":code:`AutoModelForSequenceClassification` :" +#~ msgstr "" +#~ "要创建模型本身，我们只需使用 Hugging Face 的 " +#~ ":code:`AutoModelForSequenceClassification` 加载预训练的 " +#~ "distillBERT 模型：" + +#~ msgid "Creating the IMDBClient" +#~ msgstr "创建 IMDBClient" + +#~ msgid "" +#~ "To federate our example to multiple " +#~ "clients, we first need to write " +#~ "our Flower client class (inheriting from" +#~ " :code:`flwr.client.NumPyClient`). This is very" +#~ " easy, as our model is a " +#~ "standard :code:`PyTorch` model:" +#~ msgstr "" +#~ "要将我们的示例联邦到多个客户端，我们首先需要编写 Flower 客户端类（继承自 " +#~ ":code:`flwr.client.NumPyClient`）。这很容易，因为我们的模型是一个标准的 " +#~ ":code:`PyTorch` 模型：" + +#~ msgid "" +#~ "The :code:`get_parameters` function lets the" +#~ " server get the client's parameters. " +#~ "Inversely, the :code:`set_parameters` function " +#~ "allows the server to send its " +#~ "parameters to the client. Finally, the" +#~ " :code:`fit` function trains the model " +#~ "locally for the client, and the " +#~ ":code:`evaluate` function tests the model " +#~ "locally and returns the relevant " +#~ "metrics." +#~ msgstr "" +#~ ":code:`get_parameters` " +#~ "函数允许服务器获取客户端的参数。相反，:code:`set_parameters`函数允许服务器将其参数发送给客户端。最后，:code:`fit`函数在本地为客户端训练模型，:code:`evaluate`函数在本地测试模型并返回相关指标。" + +#~ msgid "Starting the server" +#~ msgstr "启动服务器" + +#~ msgid "" +#~ "Now that we have a way to " +#~ "instantiate clients, we need to create" +#~ " our server in order to aggregate " +#~ "the results. Using Flower, this can " +#~ "be done very easily by first " +#~ "choosing a strategy (here, we are " +#~ "using :code:`FedAvg`, which will define " +#~ "the global weights as the average " +#~ "of all the clients' weights at " +#~ "each round) and then using the " +#~ ":code:`flwr.server.start_server` function:" +#~ msgstr "" +#~ "现在我们有了实例化客户端的方法，我们需要创建服务器，以便汇总结果。使用 Flower，首先选择一个策略（这里我们使用 " +#~ ":code:`FedAvg`，它将把全局模型参数定义为每轮所有客户端模型参数的平均值），然后使用 " +#~ ":code:`flwr.server.start_server`函数，就可以非常轻松地完成这项工作：" + +#~ msgid "" +#~ "The :code:`weighted_average` function is there" +#~ " to provide a way to aggregate " +#~ "the metrics distributed amongst the " +#~ "clients (basically this allows us to " +#~ "display a nice average accuracy and " +#~ "loss for every round)." +#~ msgstr "" +#~ "使用 :code:`weighted_average` " +#~ "函数是为了提供一种方法来汇总分布在客户端的指标（基本上，这可以让我们显示每一轮的平均精度和损失值）。" + +#~ msgid "Putting everything together" +#~ msgstr "把所有东西放在一起" + +#~ msgid "We can now start client instances using:" +#~ msgstr "现在我们可以使用：" + +#~ msgid "" +#~ "And they will be able to connect" +#~ " to the server and start the " +#~ "federated training." +#~ msgstr "他们就能连接到服务器，开始联邦训练。" + +#~ msgid "" +#~ "If you want to check out " +#~ "everything put together, you should " +#~ "check out the `full code example " +#~ "`_ ." +#~ msgstr "" +#~ "如果您想查看所有内容，请查看完整的代码示例： " +#~ "[https://github.com/adap/flower/tree/main/examples/quickstart-" +#~ "huggingface](https://github.com/adap/flower/tree/main/examples" +#~ "/quickstart-huggingface)." + +#~ msgid "" +#~ "Of course, this is a very basic" +#~ " example, and a lot can be " +#~ "added or modified, it was just to" +#~ " showcase how simply we could " +#~ "federate a Hugging Face workflow using" +#~ " Flower." +#~ msgstr "" +#~ "当然，这只是一个非常基本的示例，还可以添加或修改很多内容，只是为了展示我们可以如何简单地使用 Flower " +#~ "联合Hugging Face的工作流程。" + +#~ msgid "" +#~ "Note that in this example we used" +#~ " :code:`PyTorch`, but we could have " +#~ "very well used :code:`TensorFlow`." +#~ msgstr "请注意，在本例中我们使用了 :code:`PyTorch`，但也完全可以使用 :code:`TensorFlow`。" + +#~ msgid "" +#~ "Check out this Federated Learning " +#~ "quickstart tutorial for using Flower " +#~ "with PyTorch Lightning to train an " +#~ "Auto Encoder model on MNIST." +#~ msgstr "查看此联邦学习快速入门教程，了解如何使用 Flower 和 PyTorch Lightning 在 MNIST 上训练自动编码器模型。" + +#~ msgid "" +#~ "Let's build a horizontal federated " +#~ "learning system using PyTorch Lightning " +#~ "and Flower!" +#~ msgstr "让我们使用 PyTorch Lightning 和 Flower 构建一个水平联邦学习系统！" + +#~ msgid "" +#~ "Please refer to the `full code " +#~ "example `_ to learn " +#~ "more." +#~ msgstr "" +#~ "请参阅 `完整代码示例 " +#~ "`_ 了解更多信息。" + +#~ msgid "Let's build a federated learning system in less than 20 lines of code!" +#~ msgstr "让我们用不到 20 行代码构建一个联邦学习系统！" + +#~ msgid "Before Flower can be imported we have to install it:" +#~ msgstr "在导入 Flower 之前，我们必须先安装它：" + +#~ msgid "" +#~ "Since we want to use the Keras " +#~ "API of TensorFlow (TF), we have to" +#~ " install TF as well:" +#~ msgstr "由于我们要使用 TensorFlow (TF) 的 Keras API，因此还必须安装 TF：" + +#~ msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:" +#~ msgstr "接下来，在名为 :code:`client.py` 的文件中导入 Flower 和 TensorFlow：" + +#~ msgid "" +#~ "We use the Keras utilities of TF" +#~ " to load CIFAR10, a popular colored" +#~ " image classification dataset for machine" +#~ " learning. The call to " +#~ ":code:`tf.keras.datasets.cifar10.load_data()` downloads " +#~ "CIFAR10, caches it locally, and then " +#~ "returns the entire training and test " +#~ "set as NumPy ndarrays." +#~ msgstr "" +#~ "我们使用 TF 的 Keras 实用程序加载 " +#~ "CIFAR10，这是一个用于机器学习的流行彩色图像分类数据集。调用 " +#~ ":code:`tf.keras.datasets.cifar10.load_data()` 会下载 " +#~ "CIFAR10，将其缓存到本地，然后以 NumPy ndarrays 的形式返回整个训练集和测试集。" + +#~ msgid "" +#~ "Next, we need a model. For the " +#~ "purpose of this tutorial, we use " +#~ "MobilNetV2 with 10 output classes:" +#~ msgstr "接下来，我们需要一个模型。在本教程中，我们使用带有 10 个输出类的 MobilNetV2：" + +#~ msgid "" +#~ "The Flower server interacts with clients" +#~ " through an interface called " +#~ ":code:`Client`. When the server selects " +#~ "a particular client for training, it " +#~ "sends training instructions over the " +#~ "network. The client receives those " +#~ "instructions and calls one of the " +#~ ":code:`Client` methods to run your code" +#~ " (i.e., to train the neural network" +#~ " we defined earlier)." +#~ msgstr "" +#~ "Flower 服务器通过一个名为 :code:`Client` " +#~ "的接口与客户端交互。当服务器选择一个特定的客户端进行训练时，它会通过网络发送训练指令。客户端接收到这些指令后，会调用 " +#~ ":code:`Client` 方法之一来运行您的代码（即训练我们之前定义的神经网络）。" + +#~ msgid "" +#~ "Flower provides a convenience class " +#~ "called :code:`NumPyClient` which makes it " +#~ "easier to implement the :code:`Client` " +#~ "interface when your workload uses Keras." +#~ " The :code:`NumPyClient` interface defines " +#~ "three methods which can be implemented" +#~ " in the following way:" +#~ msgstr "" +#~ "Flower 提供了一个名为 :code:`NumPyClient` 的便捷类，当您的工作负载使用" +#~ " Keras 时，该类可以更轻松地实现 :code:`Client` " +#~ "接口。:code:`NumPyClient` 接口定义了三个方法，可以通过以下方式实现：" + +#~ msgid "" +#~ "We can now create an instance of" +#~ " our class :code:`CifarClient` and add " +#~ "one line to actually run this " +#~ "client:" +#~ msgstr "现在我们可以创建一个 :code:`CifarClient` 类的实例，并添加一行来实际运行该客户端：" + +#~ msgid "" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` or " +#~ ":code:`NumPyClient` and call " +#~ ":code:`fl.client.start_client()`. If you implement" +#~ " a client of type :code:`NumPyClient` " +#~ "you'll need to first call its " +#~ ":code:`to_client()` method. The string " +#~ ":code:`\"[::]:8080\"` tells the client which" +#~ " server to connect to. In our " +#~ "case we can run the server and " +#~ "the client on the same machine, " +#~ "therefore we use :code:`\"[::]:8080\"`. If " +#~ "we run a truly federated workload " +#~ "with the server and clients running " +#~ "on different machines, all that needs" +#~ " to change is the :code:`server_address`" +#~ " we point the client at." +#~ msgstr "" +#~ "这就是客户端。我们只需实现 :code:`Client` 或 :code:`NumPyClient`" +#~ " 并调用 :code:`fl.client.start_client()` 或 " +#~ ":code:`fl.client.start_numpy_client()`。字符串 " +#~ ":code:`\"[::]:8080\"`会告诉客户端要连接的服务器。在本例中，我们可以在同一台机器上运行服务器和客户端，因此使用 " +#~ ":code:`\"[::]:8080\"。如果我们运行的是真正的联邦工作负载，服务器和客户端运行在不同的机器上，那么需要改变的只是客户端指向的" +#~ " :code:`server_address`。" + +#~ msgid "Each client will have its own dataset." +#~ msgstr "每个客户都有自己的数据集。" + +#~ msgid "" +#~ "You should now see how the " +#~ "training does in the very first " +#~ "terminal (the one that started the " +#~ "server):" +#~ msgstr "现在你应该能在第一个终端（启动服务器的终端）看到训练的效果了：" + +#~ msgid "" +#~ "Congratulations! You've successfully built and" +#~ " run your first federated learning " +#~ "system. The full `source code " +#~ "`_ for this can be " +#~ "found in :code:`examples/quickstart-" +#~ "tensorflow/client.py`." +#~ msgstr "" +#~ "恭喜您！您已经成功构建并运行了第一个联邦学习系统。`完整的源代码 " +#~ "`_ 可以在 :code:`examples/quickstart-" +#~ "tensorflow/client.py` 中找到。" + +#~ msgid "|e5918c1c06a4434bbe4bf49235e40059|" +#~ msgstr "" + +#~ msgid "|c0165741bd1944f09ec55ce49032377d|" +#~ msgstr "" + +#~ msgid "|0a0ac9427ac7487b8e52d75ed514f04e|" +#~ msgstr "" + +#~ msgid "|5defee3ea4ca40d99fcd3e4ea045be25|" +#~ msgstr "" + +#~ msgid "|74f26ca701254d3db57d7899bd91eb55|" +#~ msgstr "" + +#~ msgid "|bda79f21f8154258a40e5766b2634ad7|" +#~ msgstr "" + +#~ msgid "|89d30862e62e4f9989e193483a08680a|" +#~ msgstr "" + +#~ msgid "|77e9918671c54b4f86e01369c0785ce8|" +#~ msgstr "" + +#~ msgid "|7e4ccef37cc94148a067107b34eb7447|" +#~ msgstr "" + +#~ msgid "|28e47e4cded14479a0846c8e5f22c872|" +#~ msgstr "" + +#~ msgid "|4b8c5d1afa144294b76ffc76e4658a38|" +#~ msgstr "" + +#~ msgid "|9dbdb3a0f6cb4a129fac863eaa414c17|" +#~ msgstr "" + +#~ msgid "|81749d0ac0834c36a83bd38f433fea31|" +#~ msgstr "" + +#~ msgid "|ed9aae51da70428eab7eef32f21e819e|" +#~ msgstr "" + From 55a2a97da7b44631bcbefba24860beeda6a1fdfb Mon Sep 17 00:00:00 2001 From: Charles Beauville Date: Tue, 24 Sep 2024 12:35:54 +0200 Subject: [PATCH 08/23] docs(framework:skip) Update Sphinx and related dependencies to latest (#2938) --- pyproject.toml | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 536d0ddd20c4..f4555a6f1762 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -109,25 +109,24 @@ mypy-protobuf = "==3.2.0" jupyterlab = "==4.0.12" rope = "==1.11.0" semver = "==3.0.2" -sphinx = "==6.2.1" +sphinx = "==7.4.7" sphinx-intl = "==2.2.0" -sphinx-click = "==5.1.0" -myst-parser = "==1.0.0" -sphinx-design = "==0.5.0" +sphinx-click = "==6.0.0" +myst-parser = "==3.0.1" +sphinx-design = "==0.6.1" sphinx-copybutton = "==0.5.2" sphinxcontrib-mermaid = "==0.9.2" sphinxcontrib-youtube = "==1.4.1" -furo = "==2023.9.10" -sphinx-reredirects = "==0.1.3" -nbsphinx = "==0.9.4" +furo = "==2024.8.6" +sphinx-reredirects = "==0.1.5" +nbsphinx = "==0.9.5" nbstripout = "==0.6.1" ruff = "==0.1.9" sphinx-argparse = "==0.4.0" pipreqs = "==0.4.13" -mdformat-gfm = "==0.3.5" +mdformat-gfm = "==0.3.6" mdformat-frontmatter = "==2.0.1" mdformat-beautysh = "==0.1.1" -mdformat-myst = "==0.1.5" twine = "==5.1.1" pyroma = "==4.2" check-wheel-contents = "==0.4.0" From b911d8da34478055242ee42c9957b046cdde4e96 Mon Sep 17 00:00:00 2001 From: Robert Steiner Date: Tue, 24 Sep 2024 12:43:35 +0200 Subject: [PATCH 09/23] feat(framework) Upgrade Ubuntu base image to version 24.04 (#4226) Signed-off-by: Robert Steiner --- dev/build-docker-image-matrix.py | 2 +- doc/source/conf.py | 2 +- src/docker/base/ubuntu/Dockerfile | 9 ++++----- 3 files changed, 6 insertions(+), 7 deletions(-) diff --git a/dev/build-docker-image-matrix.py b/dev/build-docker-image-matrix.py index c19949e358b9..52c96e3cca7a 100644 --- a/dev/build-docker-image-matrix.py +++ b/dev/build-docker-image-matrix.py @@ -134,7 +134,7 @@ def tag_latest_ubuntu_with_flwr_version(image: BaseImage) -> List[str]: ubuntu_base_images = generate_base_images( flwr_version, SUPPORTED_PYTHON_VERSIONS, - [Distro(DistroName.UBUNTU, "22.04")], + [Distro(DistroName.UBUNTU, "24.04")], ) # alpine base images for the latest supported python version alpine_base_images = generate_base_images( diff --git a/doc/source/conf.py b/doc/source/conf.py index 033b345b60cc..d78aeda0d48e 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -95,7 +95,7 @@ rst_prolog = """ .. |stable_flwr_version| replace:: 1.11.1 .. |stable_flwr_superlink_docker_digest| replace:: 4b317d5b6030710b476f4dbfab2c3a33021ad40a0fcfa54d7edd45e0c51d889c -.. |ubuntu_version| replace:: 22.04 +.. |ubuntu_version| replace:: 24.04 .. |setuptools_version| replace:: 70.3.0 .. |pip_version| replace:: 24.1.2 """ diff --git a/src/docker/base/ubuntu/Dockerfile b/src/docker/base/ubuntu/Dockerfile index 47655b1a52a1..b52599a80784 100644 --- a/src/docker/base/ubuntu/Dockerfile +++ b/src/docker/base/ubuntu/Dockerfile @@ -15,7 +15,7 @@ # hadolint global ignore=DL3008 ARG DISTRO=ubuntu -ARG DISTRO_VERSION=22.04 +ARG DISTRO_VERSION=24.04 FROM $DISTRO:$DISTRO_VERSION AS python ENV DEBIAN_FRONTEND=noninteractive @@ -87,11 +87,10 @@ RUN apt-get update \ ca-certificates \ && rm -rf /var/lib/apt/lists/* \ # add non-root user - && adduser \ + && useradd \ --no-create-home \ - --home /app \ - --disabled-password \ - --gecos "" \ + --home-dir /app \ + -c "" \ --uid 49999 app \ && mkdir -p /app \ && chown -R app:app /app From 42e1460739df6f20aa09478715fe1eb5e0842e28 Mon Sep 17 00:00:00 2001 From: Robert Steiner Date: Tue, 24 Sep 2024 13:18:20 +0200 Subject: [PATCH 10/23] docs(framework:skip) Add docs for distributed Docker Compose guide (#3928) Signed-off-by: Robert Steiner Co-authored-by: Chong Shen Ng --- doc/source/docker/index.rst | 1 + .../tutorial-deploy-on-multiple-machines.rst | 191 ++++++++++++++++++ 2 files changed, 192 insertions(+) create mode 100644 doc/source/docker/tutorial-deploy-on-multiple-machines.rst diff --git a/doc/source/docker/index.rst b/doc/source/docker/index.rst index 968f01581b34..2c972b805930 100644 --- a/doc/source/docker/index.rst +++ b/doc/source/docker/index.rst @@ -45,3 +45,4 @@ Run Flower using Docker Compose tutorial-quickstart-docker-compose run-quickstart-examples-docker-compose + tutorial-deploy-on-multiple-machines diff --git a/doc/source/docker/tutorial-deploy-on-multiple-machines.rst b/doc/source/docker/tutorial-deploy-on-multiple-machines.rst new file mode 100644 index 000000000000..7b6cec8292eb --- /dev/null +++ b/doc/source/docker/tutorial-deploy-on-multiple-machines.rst @@ -0,0 +1,191 @@ +######################################################## + Deploy Flower on Multiple Machines with Docker Compose +######################################################## + +This guide will help you set up a Flower project on multiple machines +using Docker Compose. + +You will learn how to run the Flower client and server components on two +separate machines, with Flower configured to use TLS encryption and +persist SuperLink state across restarts. A server consists of a +SuperLink and ``SuperExec``. For more details about the Flower +architecture, refer to the :doc:`../explanation-flower-architecture` +explainer page. + +This guide assumes you have completed the +:doc:`tutorial-quickstart-docker-compose` tutorial. It is highly +recommended that you follow and understand the contents of that tutorial +before proceeding with this guide. + +*************** + Prerequisites +*************** + +Before you begin, make sure you have the following prerequisites: + +- The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` + locally. +- The Docker daemon is running on your local machine and the remote + machine. +- Docker Compose V2 is installed on both your local machine and the + remote machine. +- You can connect to the remote machine from your local machine. +- Ports ``9091`` and ``9093`` are accessible on the remote machine. + +.. note:: + + The guide uses the |quickstart_sklearn_tabular|_ example as an + example project. + + If your project has a different name or location, please remember to + adjust the commands/paths accordingly. + +**************** + Step 1: Set Up +**************** + +#. Clone the Flower repository and change to the ``distributed`` + directory: + + .. code:: bash + + $ git clone --depth=1 https://github.com/adap/flower.git + $ cd flower/src/docker/distributed + +#. Get the IP address from the remote machine and save it for later. + +#. Use the ``certs.yml`` Compose file to generate your own self-signed + certificates. If you have certificates, you can continue with Step 2. + + .. important:: + + These certificates should be used only for development purposes. + + For production environments, you may have to use dedicated + services to obtain your certificates. + + First, set the environment variables ``SUPERLINK_IP`` and + ``SUPEREXEC_IP`` with the IP address from the remote machine. For + example, if the IP is ``192.168.2.33``, execute: + + .. code:: bash + + $ export SUPERLINK_IP=192.168.2.33 + $ export SUPEREXEC_IP=192.168.2.33 + + Next, generate the self-signed certificates: + + .. code:: bash + + $ docker compose -f certs.yml -f ../complete/certs.yml up --build + +*************************************** + Step 2: Copy the Server Compose Files +*************************************** + +Use the method that works best for you to copy the ``server`` directory, +the certificates, and your Flower project to the remote machine. + +For example, you can use ``scp`` to copy the directories: + +.. code:: bash + + $ scp -r ./server \ + ./superexec-certificates \ + ./superlink-certificates \ + ../../../examples/quickstart-sklearn-tabular remote:~/distributed + +******************************************** + Step 3: Start the Flower Server Components +******************************************** + +Log into the remote machine using ``ssh`` and run the following command +to start the SuperLink and SuperExec services: + +.. code:: bash + + $ ssh + # In your remote machine + $ cd + $ export PROJECT_DIR=../quickstart-sklearn-tabular + $ docker compose -f server/compose.yml up --build -d + +.. note:: + + The Path of the ``PROJECT_DIR`` should be relative to the location of + the ``server`` Docker Compose files. + +Go back to your terminal on your local machine. + +******************************************** + Step 4: Start the Flower Client Components +******************************************** + +On your local machine, run the following command to start the client +components: + +.. code:: bash + + # In the `docker/distributed` directory + $ export PROJECT_DIR=../../../../examples/quickstart-sklearn-tabular + $ docker compose -f client/compose.yml up --build -d + +.. note:: + + The Path of the ``PROJECT_DIR`` should be relative to the location of + the ``client`` Docker Compose files. + +********************************* + Step 5: Run Your Flower Project +********************************* + +Specify the remote SuperExec IP addresses and the path to the root +certificate in the ``[tool.flwr.federations.remote-superexec]`` table in +the ``pyproject.toml`` file. Here, we have named our remote federation +``remote-superexec``: + +.. code:: toml + :caption: examples/quickstart-sklearn-tabular/pyproject.toml + + [tool.flwr.federations.remote-superexec] + address = "192.168.2.33:9093" + root-certificates = "../../src/docker/distributed/superexec-certificates/ca.crt" + +.. note:: + + The Path of the ``root-certificates`` should be relative to the + location of the ``pyproject.toml`` file. + +To run the project, execute: + +.. code:: bash + + $ flwr run ../../../examples/quickstart-sklearn-tabular remote-superexec + +That's it! With these steps, you've set up Flower on two separate +machines and are ready to start using it. + +****************** + Step 6: Clean Up +****************** + +Shut down the Flower client components: + +.. code:: bash + + # In the `docker/distributed` directory + $ docker compose -f client/compose.yml down + +Shut down the Flower server components and delete the SuperLink state: + +.. code:: bash + + $ ssh + $ cd + $ docker compose -f server/compose.yml down -v + +.. |quickstart_sklearn_tabular| replace:: + + ``examples/quickstart-sklearn-tabular`` + +.. _quickstart_sklearn_tabular: https://github.com/adap/flower/tree/main/examples/quickstart-sklearn-tabular From 70585837a5f9e544586a07f0fc482cf7b8bcd0bd Mon Sep 17 00:00:00 2001 From: Robert Steiner Date: Tue, 24 Sep 2024 20:10:12 +0200 Subject: [PATCH 11/23] feat(framework) Enable SBOM Docker images (#4136) Signed-off-by: Robert Steiner --- .github/workflows/_docker-build.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/_docker-build.yml b/.github/workflows/_docker-build.yml index 227b0d7482ae..b6a74d1b4677 100644 --- a/.github/workflows/_docker-build.yml +++ b/.github/workflows/_docker-build.yml @@ -101,6 +101,7 @@ jobs: attempt_delay: 9000 # 9 secs with: | pull: true + sbom: true platforms: ${{ matrix.platform.docker }} context: "{{defaultContext}}:${{ inputs.file-dir }}" outputs: type=image,name=${{ inputs.namespace-repository }},push-by-digest=true,name-canonical=true,push=true From 2efdd176ce32c0a5ccfb06f08bb7fba736b17fdf Mon Sep 17 00:00:00 2001 From: Charles Beauville Date: Wed, 25 Sep 2024 15:15:47 +0200 Subject: [PATCH 12/23] ci(framework:skip) Add rST formatting to docs (#4254) --- dev/format.sh | 3 + dev/test.sh | 8 + ...or-explanation-public-and-private-apis.rst | 162 ++- ...contributor-how-to-build-docker-images.rst | 222 +-- ...ributor-how-to-contribute-translations.rst | 81 +- ...ow-to-develop-in-vscode-dev-containers.rst | 57 +- ...or-how-to-install-development-versions.rst | 35 +- .../contributor-how-to-release-flower.rst | 46 +- ...ontributor-how-to-set-up-a-virtual-env.rst | 42 +- ...contributor-how-to-write-documentation.rst | 10 +- ...ntributor-ref-good-first-contributions.rst | 34 +- ...butor-ref-secure-aggregation-protocols.rst | 253 ++-- ...tributor-tutorial-contribute-on-github.rst | 362 ++--- ...-tutorial-get-started-as-a-contributor.rst | 156 ++- doc/source/docker/enable-tls.rst | 218 +-- doc/source/docker/index.rst | 40 +- doc/source/docker/persist-superlink-state.rst | 55 +- doc/source/docker/pin-version.rst | 27 +- doc/source/docker/run-as-root-user.rst | 39 +- doc/source/docker/run-as-subprocess.rst | 60 +- ...run-quickstart-examples-docker-compose.rst | 127 +- .../docker/set-environment-variables.rst | 6 +- .../tutorial-deploy-on-multiple-machines.rst | 218 ++- .../tutorial-quickstart-docker-compose.rst | 429 +++--- .../docker/tutorial-quickstart-docker.rst | 487 +++---- doc/source/docker/use-a-different-version.rst | 13 +- ...-pytorch-from-centralized-to-federated.rst | 64 +- ...-pytorch-from-centralized-to-federated.rst | 172 ++- .../explanation-differential-privacy.rst | 182 ++- .../explanation-federated-evaluation.rst | 69 +- .../explanation-flower-architecture.rst | 230 ++-- .../how-to-aggregate-evaluation-results.rst | 23 +- doc/source/how-to-authenticate-supernodes.rst | 95 +- doc/source/how-to-configure-clients.rst | 71 +- doc/source/how-to-configure-logging.rst | 86 +- doc/source/how-to-enable-ssl-connections.rst | 78 +- doc/source/how-to-implement-strategies.rst | 189 ++- doc/source/how-to-install-flower.rst | 75 +- doc/source/how-to-monitor-simulation.rst | 273 ++-- doc/source/how-to-run-simulations.rst | 261 ++-- ...how-to-save-and-load-model-checkpoints.rst | 57 +- doc/source/how-to-upgrade-to-flower-1.0.rst | 90 +- doc/source/how-to-upgrade-to-flower-next.rst | 137 +- doc/source/how-to-use-built-in-mods.rst | 34 +- .../how-to-use-differential-privacy.rst | 185 +-- doc/source/how-to-use-strategies.rst | 50 +- doc/source/index.rst | 229 ++-- doc/source/ref-api-cli.rst | 56 +- doc/source/ref-example-projects.rst | 46 +- doc/source/ref-faq.rst | 3 +- doc/source/tutorial-quickstart-android.rst | 6 +- doc/source/tutorial-quickstart-fastai.rst | 169 ++- .../tutorial-quickstart-huggingface.rst | 674 +++++---- doc/source/tutorial-quickstart-ios.rst | 196 +-- doc/source/tutorial-quickstart-jax.rst | 204 +-- doc/source/tutorial-quickstart-mlx.rst | 669 +++++---- doc/source/tutorial-quickstart-pandas.rst | 6 +- .../tutorial-quickstart-pytorch-lightning.rst | 183 ++- doc/source/tutorial-quickstart-pytorch.rst | 636 +++++---- .../tutorial-quickstart-scikitlearn.rst | 202 +-- doc/source/tutorial-quickstart-tensorflow.rst | 493 ++++--- doc/source/tutorial-quickstart-xgboost.rst | 1215 +++++++++-------- pyproject.toml | 9 + 63 files changed, 5678 insertions(+), 4929 deletions(-) diff --git a/dev/format.sh b/dev/format.sh index e1e2abc307f1..ada5a7f13abc 100755 --- a/dev/format.sh +++ b/dev/format.sh @@ -36,3 +36,6 @@ python -m nbstripout examples/*/*.ipynb --extra-keys "$KEYS" # Markdown python -m mdformat --number doc/source examples + +# RST +docstrfmt doc/source diff --git a/dev/test.sh b/dev/test.sh index 58ac0b3d24cd..170d9f4acd1e 100755 --- a/dev/test.sh +++ b/dev/test.sh @@ -56,6 +56,14 @@ echo "- mdformat: done" echo "- All Markdown checks passed" +echo "- Start rST checks" + +echo "- docstrfmt: start" +docstrfmt --check doc/source +echo "- docstrfmt: done" + +echo "- All rST checks passed" + echo "- Start license checks" echo "- copyright: start" diff --git a/doc/source/contributor-explanation-public-and-private-apis.rst b/doc/source/contributor-explanation-public-and-private-apis.rst index 1dfdf88f97d3..ac62ae341f14 100644 --- a/doc/source/contributor-explanation-public-and-private-apis.rst +++ b/doc/source/contributor-explanation-public-and-private-apis.rst @@ -1,22 +1,23 @@ Public and private APIs ======================= -In Python, everything is public. -To enable developers to understand which components can be relied upon, Flower declares a public API. -Components that are part of the public API can be relied upon. -Changes to the public API are announced in the release notes and are subject to deprecation policies. +In Python, everything is public. To enable developers to understand which components can +be relied upon, Flower declares a public API. Components that are part of the public API +can be relied upon. Changes to the public API are announced in the release notes and are +subject to deprecation policies. -Everything that is not part of the public API is part of the private API. -Even though Python allows accessing them, user code should never use those components. -Private APIs can change at any time, even in patch releases. +Everything that is not part of the public API is part of the private API. Even though +Python allows accessing them, user code should never use those components. Private APIs +can change at any time, even in patch releases. How can you determine whether a component is part of the public API or not? Easy: - `Use the Flower API reference documentation `_ - `Use the Flower CLI reference documentation `_ -Everything listed in the reference documentation is part of the public API. -This document explains how Flower maintainers define the public API and how you can determine whether a component is part of the public API or not by reading the Flower source code. +Everything listed in the reference documentation is part of the public API. This +document explains how Flower maintainers define the public API and how you can determine +whether a component is part of the public API or not by reading the Flower source code. Flower public API ----------------- @@ -25,94 +26,117 @@ Flower has a well-defined public API. Let's look at this in more detail. .. important:: - Every component that is reachable by recursively following ``__init__.__all__`` starting from the root package (``flwr``) is part of the public API. + Every component that is reachable by recursively following ``__init__.__all__`` + starting from the root package (``flwr``) is part of the public API. -If you want to determine whether a component (class/function/generator/...) is part of the public API or not, you need to start at the root of the ``flwr`` package. -Let's use ``tree -L 1 -d src/py/flwr`` to look at the Python sub-packages contained ``flwr``: +If you want to determine whether a component (class/function/generator/...) is part of +the public API or not, you need to start at the root of the ``flwr`` package. Let's use +``tree -L 1 -d src/py/flwr`` to look at the Python sub-packages contained ``flwr``: .. code-block:: bash - flwr - ├── cli - ├── client - ├── common - ├── proto - ├── server - └── simulation + flwr + ├── cli + ├── client + ├── common + ├── proto + ├── server + └── simulation -Contrast this with the definition of ``__all__`` in the root ``src/py/flwr/__init__.py``: +Contrast this with the definition of ``__all__`` in the root +``src/py/flwr/__init__.py``: .. code-block:: python - # From `flwr/__init__.py` - __all__ = [ - "client", - "common", - "server", - "simulation", - ] - -You can see that ``flwr`` has six subpackages (``cli``, ``client``, ``common``, ``proto``, ``server``, ``simulation``), but only four of them are "exported" via ``__all__`` (``client``, ``common``, ``server``, ``simulation``). - -What does this mean? It means that ``client``, ``common``, ``server`` and ``simulation`` are part of the public API, but ``cli`` and ``proto`` are not. -The ``flwr`` subpackages ``cli`` and ``proto`` are private APIs. -A private API can change completely from one release to the next (even in patch releases). -It can change in a breaking way, it can be renamed (for example, ``flwr.cli`` could be renamed to ``flwr.command``) and it can even be removed completely. + # From `flwr/__init__.py` + __all__ = [ + "client", + "common", + "server", + "simulation", + ] + +You can see that ``flwr`` has six subpackages (``cli``, ``client``, ``common``, +``proto``, ``server``, ``simulation``), but only four of them are "exported" via +``__all__`` (``client``, ``common``, ``server``, ``simulation``). + +What does this mean? It means that ``client``, ``common``, ``server`` and ``simulation`` +are part of the public API, but ``cli`` and ``proto`` are not. The ``flwr`` subpackages +``cli`` and ``proto`` are private APIs. A private API can change completely from one +release to the next (even in patch releases). It can change in a breaking way, it can be +renamed (for example, ``flwr.cli`` could be renamed to ``flwr.command``) and it can even +be removed completely. Therefore, as a Flower user: - ``from flwr import client`` ✅ Ok, you're importing a public API. - ``from flwr import proto`` ❌ Not recommended, you're importing a private API. -What about components that are nested deeper in the hierarchy? Let's look at Flower strategies to see another typical pattern. -Flower strategies like ``FedAvg`` are often imported using ``from flwr.server.strategy import FedAvg``. -Let's look at ``src/py/flwr/server/strategy/__init__.py``: +What about components that are nested deeper in the hierarchy? Let's look at Flower +strategies to see another typical pattern. Flower strategies like ``FedAvg`` are often +imported using ``from flwr.server.strategy import FedAvg``. Let's look at +``src/py/flwr/server/strategy/__init__.py``: .. code-block:: python - from .fedavg import FedAvg as FedAvg - # ... more imports + from .fedavg import FedAvg as FedAvg + + # ... more imports - __all__ = [ - "FedAvg", - # ... more exports - ] + __all__ = [ + "FedAvg", + # ... more exports + ] -What's notable here is that all strategies are implemented in dedicated modules (e.g., ``fedavg.py``). -In ``__init__.py``, we *import* the components we want to make part of the public API and then *export* them via ``__all__``. -Note that we export the component itself (for example, the ``FedAvg`` class), but not the module it is defined in (for example, ``fedavg.py``). -This allows us to move the definition of ``FedAvg`` into a different module (or even a module in a subpackage) without breaking the public API (as long as we update the import path in ``__init__.py``). +What's notable here is that all strategies are implemented in dedicated modules (e.g., +``fedavg.py``). In ``__init__.py``, we *import* the components we want to make part of +the public API and then *export* them via ``__all__``. Note that we export the component +itself (for example, the ``FedAvg`` class), but not the module it is defined in (for +example, ``fedavg.py``). This allows us to move the definition of ``FedAvg`` into a +different module (or even a module in a subpackage) without breaking the public API (as +long as we update the import path in ``__init__.py``). Therefore: -- ``from flwr.server.strategy import FedAvg`` ✅ Ok, you're importing a class that is part of the public API. -- ``from flwr.server.strategy import fedavg`` ❌ Not recommended, you're importing a private module. +- ``from flwr.server.strategy import FedAvg`` ✅ Ok, you're importing a class that is + part of the public API. +- ``from flwr.server.strategy import fedavg`` ❌ Not recommended, you're importing a + private module. -This approach is also implemented in the tooling that automatically builds API reference docs. +This approach is also implemented in the tooling that automatically builds API reference +docs. Flower public API of private packages ------------------------------------- -We also use this to define the public API of private subpackages. -Public, in this context, means the API that other ``flwr`` subpackages should use. -For example, ``flwr.server.driver`` is a private subpackage (it's not exported via ``src/py/flwr/server/__init__.py``'s ``__all__``). +We also use this to define the public API of private subpackages. Public, in this +context, means the API that other ``flwr`` subpackages should use. For example, +``flwr.server.driver`` is a private subpackage (it's not exported via +``src/py/flwr/server/__init__.py``'s ``__all__``). -Still, the private sub-package ``flwr.server.driver`` defines a "public" API using ``__all__`` in ``src/py/flwr/server/driver/__init__.py``: +Still, the private sub-package ``flwr.server.driver`` defines a "public" API using +``__all__`` in ``src/py/flwr/server/driver/__init__.py``: .. code-block:: python - from .driver import Driver - from .grpc_driver import GrpcDriver - from .inmemory_driver import InMemoryDriver - - __all__ = [ - "Driver", - "GrpcDriver", - "InMemoryDriver", - ] - -The interesting part is that both ``GrpcDriver`` and ``InMemoryDriver`` are never used by Flower framework users, only by other parts of the Flower framework codebase. -Those other parts of the codebase import, for example, ``InMemoryDriver`` using ``from flwr.server.driver import InMemoryDriver`` (i.e., the ``InMemoryDriver`` exported via ``__all__``), not ``from flwr.server.driver.in_memory_driver import InMemoryDriver`` (``in_memory_driver.py`` is the module containing the actual ``InMemoryDriver`` class definition). - -This is because ``flwr.server.driver`` defines a public interface for other ``flwr`` subpackages. -This allows codeowners of ``flwr.server.driver`` to refactor the package without breaking other ``flwr``-internal users. + from .driver import Driver + from .grpc_driver import GrpcDriver + from .inmemory_driver import InMemoryDriver + + __all__ = [ + "Driver", + "GrpcDriver", + "InMemoryDriver", + ] + +The interesting part is that both ``GrpcDriver`` and ``InMemoryDriver`` are never used +by Flower framework users, only by other parts of the Flower framework codebase. Those +other parts of the codebase import, for example, ``InMemoryDriver`` using ``from +flwr.server.driver import InMemoryDriver`` (i.e., the ``InMemoryDriver`` exported via +``__all__``), not ``from flwr.server.driver.in_memory_driver import InMemoryDriver`` +(``in_memory_driver.py`` is the module containing the actual ``InMemoryDriver`` class +definition). + +This is because ``flwr.server.driver`` defines a public interface for other ``flwr`` +subpackages. This allows codeowners of ``flwr.server.driver`` to refactor the package +without breaking other ``flwr``-internal users. diff --git a/doc/source/contributor-how-to-build-docker-images.rst b/doc/source/contributor-how-to-build-docker-images.rst index d6acad4afa03..0b3ce243ce50 100644 --- a/doc/source/contributor-how-to-build-docker-images.rst +++ b/doc/source/contributor-how-to-build-docker-images.rst @@ -2,153 +2,161 @@ How to Build Docker Flower Images Locally ========================================= Flower provides pre-made docker images on `Docker Hub `_ -that include all necessary dependencies for running the SuperLink, SuperNode or ServerApp. -You can also build your own custom docker images from scratch with a different version of Python -or Linux distribution (Ubuntu/Alpine) if that is what you need. In this guide, we will explain what -images exist and how to build them locally. +that include all necessary dependencies for running the SuperLink, SuperNode or +ServerApp. You can also build your own custom docker images from scratch with a +different version of Python or Linux distribution (Ubuntu/Alpine) if that is what you +need. In this guide, we will explain what images exist and how to build them locally. -Before we can start, we need to meet a few prerequisites in our local development environment. +Before we can start, we need to meet a few prerequisites in our local development +environment. -#. Clone the ``flower`` repository. +1. Clone the ``flower`` repository. - .. code-block:: bash + .. code-block:: bash - $ git clone --depth=1 https://github.com/adap/flower.git && cd flower + $ git clone --depth=1 https://github.com/adap/flower.git && cd flower -#. Verify the Docker daemon is running. +2. Verify the Docker daemon is running. - The build instructions that assemble the images are located in the respective Dockerfiles. You - can find them in the subdirectories of ``src/docker``. + The build instructions that assemble the images are located in the respective + Dockerfiles. You can find them in the subdirectories of ``src/docker``. - Flower Docker images are configured via build arguments. Through build arguments, we can make the - creation of images more flexible. For example, in the base image, we can specify the version of - Python to install using the ``PYTHON_VERSION`` build argument. Some of the build arguments have - default values, others must be specified when building the image. All available build arguments for - each image are listed in one of the tables below. + Flower Docker images are configured via build arguments. Through build arguments, we + can make the creation of images more flexible. For example, in the base image, we can + specify the version of Python to install using the ``PYTHON_VERSION`` build argument. + Some of the build arguments have default values, others must be specified when + building the image. All available build arguments for each image are listed in one of + the tables below. Building the Base Image ----------------------- .. list-table:: - :widths: 25 45 15 15 - :header-rows: 1 - - * - Build argument - - Description - - Required - - Example - * - ``DISTRO`` - - The Linux distribution to use as the base image. - - No - - ``ubuntu`` - * - ``DISTRO_VERSION`` - - Version of the Linux distribution. - - No - - :substitution-code:`|ubuntu_version|` - * - ``PYTHON_VERSION`` - - Version of ``python`` to be installed. - - No - - ``3.11`` or ``3.11.1`` - * - ``PIP_VERSION`` - - Version of ``pip`` to be installed. - - Yes - - :substitution-code:`|pip_version|` - * - ``SETUPTOOLS_VERSION`` - - Version of ``setuptools`` to be installed. - - Yes - - :substitution-code:`|setuptools_version|` - * - ``FLWR_VERSION`` - - Version of Flower to be installed. - - Yes - - :substitution-code:`|stable_flwr_version|` - * - ``FLWR_PACKAGE`` - - The Flower package to be installed. - - No - - ``flwr`` or ``flwr-nightly`` - * - ``FLWR_VERSION_REF`` - - A `direct reference `_ without the ``@`` specifier. If both ``FLWR_VERSION`` and ``FLWR_VERSION_REF`` are specified, the ``FLWR_VERSION_REF`` has precedence. - - No - - `Direct Reference Examples`_ - -The following example creates a base Ubuntu/Alpine image with Python ``3.11.0``, -pip :substitution-code:`|pip_version|`, setuptools :substitution-code:`|setuptools_version|` + :widths: 25 45 15 15 + :header-rows: 1 + + - - Build argument + - Description + - Required + - Example + - - ``DISTRO`` + - The Linux distribution to use as the base image. + - No + - ``ubuntu`` + - - ``DISTRO_VERSION`` + - Version of the Linux distribution. + - No + - :substitution-code:`|ubuntu_version|` + - - ``PYTHON_VERSION`` + - Version of ``python`` to be installed. + - No + - ``3.11`` or ``3.11.1`` + - - ``PIP_VERSION`` + - Version of ``pip`` to be installed. + - Yes + - :substitution-code:`|pip_version|` + - - ``SETUPTOOLS_VERSION`` + - Version of ``setuptools`` to be installed. + - Yes + - :substitution-code:`|setuptools_version|` + - - ``FLWR_VERSION`` + - Version of Flower to be installed. + - Yes + - :substitution-code:`|stable_flwr_version|` + - - ``FLWR_PACKAGE`` + - The Flower package to be installed. + - No + - ``flwr`` or ``flwr-nightly`` + - - ``FLWR_VERSION_REF`` + - A `direct reference + `_ + without the ``@`` specifier. If both ``FLWR_VERSION`` and ``FLWR_VERSION_REF`` + are specified, the ``FLWR_VERSION_REF`` has precedence. + - No + - `Direct Reference Examples`_ + +The following example creates a base Ubuntu/Alpine image with Python ``3.11.0``, pip +:substitution-code:`|pip_version|`, setuptools :substitution-code:`|setuptools_version|` and Flower :substitution-code:`|stable_flwr_version|`: .. code-block:: bash - :substitutions: + :substitutions: - $ cd src/docker/base/ - $ docker build \ - --build-arg PYTHON_VERSION=3.11.0 \ - --build-arg FLWR_VERSION=|stable_flwr_version| \ - --build-arg PIP_VERSION=|pip_version| \ - --build-arg SETUPTOOLS_VERSION=|setuptools_version| \ - -t flwr_base:0.1.0 . + $ cd src/docker/base/ + $ docker build \ + --build-arg PYTHON_VERSION=3.11.0 \ + --build-arg FLWR_VERSION=|stable_flwr_version| \ + --build-arg PIP_VERSION=|pip_version| \ + --build-arg SETUPTOOLS_VERSION=|setuptools_version| \ + -t flwr_base:0.1.0 . -In this example, we specify our image name as ``flwr_base`` and the tag as ``0.1.0``. Remember that the build arguments as well -as the name and tag can be adapted to your needs. These values serve as examples only. +In this example, we specify our image name as ``flwr_base`` and the tag as ``0.1.0``. +Remember that the build arguments as well as the name and tag can be adapted to your +needs. These values serve as examples only. Building a Flower Binary Image ------------------------------ .. list-table:: - :widths: 25 45 15 15 - :header-rows: 1 - - * - Build argument - - Description - - Required - - Example - * - ``BASE_REPOSITORY`` - - The repository name of the base image. - - No - - ``flwr/base`` - * - ``BASE_IMAGE`` - - The Tag of the Flower base image. - - Yes - - :substitution-code:`|stable_flwr_version|-py3.11-ubuntu|ubuntu_version|` - -For example, to build a SuperLink image with the latest Flower version, Python 3.11 and Ubuntu 22.04, run the following: + :widths: 25 45 15 15 + :header-rows: 1 + + - - Build argument + - Description + - Required + - Example + - - ``BASE_REPOSITORY`` + - The repository name of the base image. + - No + - ``flwr/base`` + - - ``BASE_IMAGE`` + - The Tag of the Flower base image. + - Yes + - :substitution-code:`|stable_flwr_version|-py3.11-ubuntu|ubuntu_version|` + +For example, to build a SuperLink image with the latest Flower version, Python 3.11 and +Ubuntu 22.04, run the following: .. code-block:: bash - :substitutions: + :substitutions: - $ cd src/docker/superlink - $ docker build \ - --build-arg BASE_IMAGE=|stable_flwr_version|-py3.11-ubuntu22.04 \ - -t flwr_superlink:0.1.0 . + $ cd src/docker/superlink + $ docker build \ + --build-arg BASE_IMAGE=|stable_flwr_version|-py3.11-ubuntu22.04 \ + -t flwr_superlink:0.1.0 . -If you want to use your own base image instead of the official Flower base image, all you need to do -is set the ``BASE_REPOSITORY`` build argument to ``flwr_base`` (as we've specified above). +If you want to use your own base image instead of the official Flower base image, all +you need to do is set the ``BASE_REPOSITORY`` build argument to ``flwr_base`` (as we've +specified above). .. code-block:: bash - $ cd src/docker/superlink/ - $ docker build \ - --build-arg BASE_REPOSITORY=flwr_base \ - --build-arg BASE_IMAGE=0.1.0 - -t flwr_superlink:0.1.0 . + $ cd src/docker/superlink/ + $ docker build \ + --build-arg BASE_REPOSITORY=flwr_base \ + --build-arg BASE_IMAGE=0.1.0 + -t flwr_superlink:0.1.0 . After creating the image, we can test whether the image is working: .. code-block:: bash - $ docker run --rm flwr_superlink:0.1.0 --help + $ docker run --rm flwr_superlink:0.1.0 --help Direct Reference Examples ------------------------- .. code-block:: bash - :substitutions: + :substitutions: - # main branch - git+https://github.com/adap/flower.git@main + # main branch + git+https://github.com/adap/flower.git@main - # commit hash - git+https://github.com/adap/flower.git@1187c707f1894924bfa693d99611cf6f93431835 + # commit hash + git+https://github.com/adap/flower.git@1187c707f1894924bfa693d99611cf6f93431835 - # tag - git+https://github.com/adap/flower.git@|stable_flwr_version| + # tag + git+https://github.com/adap/flower.git@|stable_flwr_version| - # artifact store - https://artifact.flower.ai/py/main/latest/flwr-|stable_flwr_version|-py3-none-any.whl + # artifact store + https://artifact.flower.ai/py/main/latest/flwr-|stable_flwr_version|-py3-none-any.whl diff --git a/doc/source/contributor-how-to-contribute-translations.rst b/doc/source/contributor-how-to-contribute-translations.rst index ba59901cf1c4..5fff62833b0e 100644 --- a/doc/source/contributor-how-to-contribute-translations.rst +++ b/doc/source/contributor-how-to-contribute-translations.rst @@ -2,70 +2,67 @@ Contribute translations ======================= Since `Flower 1.5 -`_ we -have introduced translations to our doc pages, but, as you might have noticed, -the translations are often imperfect. If you speak languages other than -English, you might be able to help us in our effort to make Federated Learning -accessible to as many people as possible by contributing to those translations! -This might also be a great opportunity for those wanting to become open source -contributors with little prerequisites. +`_ we have +introduced translations to our doc pages, but, as you might have noticed, the +translations are often imperfect. If you speak languages other than English, you might +be able to help us in our effort to make Federated Learning accessible to as many people +as possible by contributing to those translations! This might also be a great +opportunity for those wanting to become open source contributors with little +prerequisites. Our translation project is publicly available over on `Weblate -`_, this where most -of the work will happen. +`_, this where most of the +work will happen. Contribute to existing languages -------------------------------- .. youtube:: 10_Xfy5BOfQ - :width: 100% + :width: 100% -The first thing you will need to do in order to contribute is to create a -free Weblate account on this `page -`_. More information -about profile settings can be found `here +The first thing you will need to do in order to contribute is to create a free Weblate +account on this `page `_. More +information about profile settings can be found `here `_. -Once you are signed in to Weblate, you can navigate to the `Flower Framework -project `_. Here, -you should see the different existing languages that can be found on the -website. +Once you are signed in to Weblate, you can navigate to the `Flower Framework project +`_. Here, you should see the +different existing languages that can be found on the website. -Once you have selected the language you want to contribute to, you should see a -similar interface to this: +Once you have selected the language you want to contribute to, you should see a similar +interface to this: - .. image:: _static/weblate_status.png + .. image:: _static/weblate_status.png -The most straight forward option here is to click on the ``Translate`` button -on the top right (in the ``Translation status`` section). This will -automatically bring you to the translation interface for untranslated strings. +The most straight forward option here is to click on the ``Translate`` button on the top +right (in the ``Translation status`` section). This will automatically bring you to the +translation interface for untranslated strings. This is what the interface looks like: - .. image:: _static/weblate_interface.png + .. image:: _static/weblate_interface.png -You input your translation in the text box at the top and then, once you are -happy with it, you either press ``Save and continue`` (to save the translation -and go to the next untranslated string), ``Save and stay`` (to save the -translation and stay on the same page), ``Suggest`` (to add your translation to -suggestions for other users to view), or ``Skip`` (to go to the next -untranslated string without saving anything). +You input your translation in the text box at the top and then, once you are happy with +it, you either press ``Save and continue`` (to save the translation and go to the next +untranslated string), ``Save and stay`` (to save the translation and stay on the same +page), ``Suggest`` (to add your translation to suggestions for other users to view), or +``Skip`` (to go to the next untranslated string without saving anything). In order to help with the translations, you can see on the bottom the ``Nearby -strings``, the ``Comments`` (from other contributors), the ``Automatic -suggestions`` (from machine translation engines), the translations in ``Other -languages``, and the ``History`` of translations for this string. +strings``, the ``Comments`` (from other contributors), the ``Automatic suggestions`` +(from machine translation engines), the translations in ``Other languages``, and the +``History`` of translations for this string. -On the right, under the ``String information`` section, you can also click the -link under ``Source string location`` in order to view the source of the doc -file containing the string. +On the right, under the ``String information`` section, you can also click the link +under ``Source string location`` in order to view the source of the doc file containing +the string. -For more information about translating using Weblate, you can check out this -`in-depth guide `_. +For more information about translating using Weblate, you can check out this `in-depth +guide `_. Add new languages ----------------- -If you want to add a new language, you will first have to contact us, either on -`Slack `_, or by opening an issue on our `GitHub -repo `_. +If you want to add a new language, you will first have to contact us, either on `Slack +`_, or by opening an issue on our `GitHub repo +`_. diff --git a/doc/source/contributor-how-to-develop-in-vscode-dev-containers.rst b/doc/source/contributor-how-to-develop-in-vscode-dev-containers.rst index c861457b6edc..79f52f8d8f6f 100644 --- a/doc/source/contributor-how-to-develop-in-vscode-dev-containers.rst +++ b/doc/source/contributor-how-to-develop-in-vscode-dev-containers.rst @@ -1,24 +1,47 @@ Develop in VSCode Dev Containers ================================ -When working on the Flower framework we want to ensure that all contributors use the same developer environment to format code or run tests. For this purpose we are using the VSCode Remote Containers extension. What is it? Read the following quote: - - - The Visual Studio Code Remote - Containers extension lets you use a Docker container as a fully-featured development environment. It allows you to open any folder inside (or mounted into) a container and take advantage of Visual Studio Code's full feature set. A :code:`devcontainer.json` file in your project tells VS Code how to access (or create) a development container with a well-defined tool and runtime stack. This container can be used to run an application or to separate tools, libraries, or runtimes needed for working with a codebase. - - Workspace files are mounted from the local file system or copied or cloned into the container. Extensions are installed and run inside the container, where they have full access to the tools, platform, and file system. This means that you can seamlessly switch your entire development environment just by connecting to a different container. - -Source: `Official VSCode documentation `_ - +When working on the Flower framework we want to ensure that all contributors use the +same developer environment to format code or run tests. For this purpose we are using +the VSCode Remote Containers extension. What is it? Read the following quote: + + The Visual Studio Code Remote - Containers extension lets you use a Docker container + as a fully-featured development environment. It allows you to open any folder inside + (or mounted into) a container and take advantage of Visual Studio Code's full + feature set. A ``devcontainer.json`` file in your project tells VS Code how to + access (or create) a development container with a well-defined tool and runtime + stack. This container can be used to run an application or to separate tools, + libraries, or runtimes needed for working with a codebase. + + Workspace files are mounted from the local file system or copied or cloned into the + container. Extensions are installed and run inside the container, where they have + full access to the tools, platform, and file system. This means that you can + seamlessly switch your entire development environment just by connecting to a + different container. + +Source: `Official VSCode documentation +`_ Getting started --------------- -Configuring and setting up the :code:`Dockerfile` as well the configuration for the devcontainer can be a bit more involved. The good thing is you don't have to do it. Usually it should be enough to install `Docker `_ on your system and ensure its available on your command line. Additionally, install the `VSCode Containers Extension `_. - -Now you should be good to go. When starting VSCode, it will ask you to run in the container environment and - if you confirm - automatically build the container and use it. To manually instruct VSCode to use the devcontainer, you can, after installing the extension, click the green area in the bottom left corner of your VSCode window and select the option *(Re)Open Folder in Container*. - -In some cases your setup might be more involved. For those cases consult the following sources: - -* `Developing inside a Container `_ -* `Remote development in Containers `_ +Configuring and setting up the ``Dockerfile`` as well the configuration for the +devcontainer can be a bit more involved. The good thing is you don't have to do it. +Usually it should be enough to install `Docker +`_ on your system and ensure its available on +your command line. Additionally, install the `VSCode Containers Extension +`_. + +Now you should be good to go. When starting VSCode, it will ask you to run in the +container environment and - if you confirm - automatically build the container and use +it. To manually instruct VSCode to use the devcontainer, you can, after installing the +extension, click the green area in the bottom left corner of your VSCode window and +select the option *(Re)Open Folder in Container*. + +In some cases your setup might be more involved. For those cases consult the following +sources: + +- `Developing inside a Container + `_ +- `Remote development in Containers + `_ diff --git a/doc/source/contributor-how-to-install-development-versions.rst b/doc/source/contributor-how-to-install-development-versions.rst index 0f0773c85e73..61c123a24309 100644 --- a/doc/source/contributor-how-to-install-development-versions.rst +++ b/doc/source/contributor-how-to-install-development-versions.rst @@ -7,10 +7,13 @@ Install development versions of Flower Using Poetry (recommended) ~~~~~~~~~~~~~~~~~~~~~~~~~~ -Install a ``flwr`` pre-release from PyPI: update the ``flwr`` dependency in ``pyproject.toml`` and then reinstall (don't forget to delete ``poetry.lock`` (``rm poetry.lock``) before running ``poetry install``). +Install a ``flwr`` pre-release from PyPI: update the ``flwr`` dependency in +``pyproject.toml`` and then reinstall (don't forget to delete ``poetry.lock`` (``rm +poetry.lock``) before running ``poetry install``). - ``flwr = { version = "1.0.0a0", allow-prereleases = true }`` (without extras) -- ``flwr = { version = "1.0.0a0", allow-prereleases = true, extras = ["simulation"] }`` (with extras) +- ``flwr = { version = "1.0.0a0", allow-prereleases = true, extras = ["simulation"] }`` + (with extras) Install ``flwr`` from a local copy of the Flower source code via ``pyproject.toml``: @@ -20,9 +23,11 @@ Install ``flwr`` from a local copy of the Flower source code via ``pyproject.tom Install ``flwr`` from a local wheel file via ``pyproject.toml``: - ``flwr = { path = "../../dist/flwr-1.8.0-py3-none-any.whl" }`` (without extras) -- ``flwr = { path = "../../dist/flwr-1.8.0-py3-none-any.whl", extras = ["simulation"] }`` (with extras) +- ``flwr = { path = "../../dist/flwr-1.8.0-py3-none-any.whl", extras = ["simulation"] + }`` (with extras) -Please refer to the Poetry documentation for further details: `Poetry Dependency Specification `_ +Please refer to the Poetry documentation for further details: `Poetry Dependency +Specification `_ Using pip (recommended on Colab) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -32,18 +37,21 @@ Install a ``flwr`` pre-release from PyPI: - ``pip install -U --pre flwr`` (without extras) - ``pip install -U --pre 'flwr[simulation]'`` (with extras) -Python packages can be installed from git repositories. Use one of the following commands to install the Flower directly from GitHub. +Python packages can be installed from git repositories. Use one of the following +commands to install the Flower directly from GitHub. Install ``flwr`` from the default GitHub branch (``main``): - ``pip install flwr@git+https://github.com/adap/flower.git`` (without extras) -- ``pip install 'flwr[simulation]@git+https://github.com/adap/flower.git'`` (with extras) +- ``pip install 'flwr[simulation]@git+https://github.com/adap/flower.git'`` (with + extras) Install ``flwr`` from a specific GitHub branch (``branch-name``): -- ``pip install flwr@git+https://github.com/adap/flower.git@branch-name`` (without extras) -- ``pip install 'flwr[simulation]@git+https://github.com/adap/flower.git@branch-name'`` (with extras) - +- ``pip install flwr@git+https://github.com/adap/flower.git@branch-name`` (without + extras) +- ``pip install 'flwr[simulation]@git+https://github.com/adap/flower.git@branch-name'`` + (with extras) Open Jupyter Notebooks on Google Colab -------------------------------------- @@ -52,12 +60,15 @@ Open the notebook ``doc/source/tutorial-series-get-started-with-flower-pytorch.i - https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb -Open a development version of the same notebook from branch `branch-name` by changing ``main`` to ``branch-name`` (right after ``blob``): +Open a development version of the same notebook from branch `branch-name` by changing +``main`` to ``branch-name`` (right after ``blob``): - https://colab.research.google.com/github/adap/flower/blob/branch-name/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb Install a `whl` on Google Colab: -1. In the vertical icon grid on the left hand side, select ``Files`` > ``Upload to session storage`` +1. In the vertical icon grid on the left hand side, select ``Files`` > ``Upload to + session storage`` 2. Upload the whl (e.g., ``flwr-1.8.0-py3-none-any.whl``) -3. Change ``!pip install -q 'flwr[simulation]' torch torchvision matplotlib`` to ``!pip install -q 'flwr-1.8.0-py3-none-any.whl[simulation]' torch torchvision matplotlib`` +3. Change ``!pip install -q 'flwr[simulation]' torch torchvision matplotlib`` to ``!pip + install -q 'flwr-1.8.0-py3-none-any.whl[simulation]' torch torchvision matplotlib`` diff --git a/doc/source/contributor-how-to-release-flower.rst b/doc/source/contributor-how-to-release-flower.rst index 4853d87bc4c1..fafc02cab64c 100644 --- a/doc/source/contributor-how-to-release-flower.rst +++ b/doc/source/contributor-how-to-release-flower.rst @@ -1,16 +1,28 @@ Release Flower ============== -This document describes the current release process. It may or may not change in the future. +This document describes the current release process. It may or may not change in the +future. During the release ------------------ -The version number of a release is stated in ``pyproject.toml``. To release a new version of Flower, the following things need to happen (in that order): - -1. Run ``python3 src/py/flwr_tool/update_changelog.py `` in order to add every new change to the changelog (feel free to make manual changes to the changelog afterwards until it looks good). -2. Once the changelog has been updated with all the changes, run ``./dev/prepare-release-changelog.sh v``, where ```` is the version stated in ``pyproject.toml`` (notice the ``v`` added before it). This will replace the ``Unreleased`` header of the changelog by the version and current date, and it will add a thanking message for the contributors. Open a pull request with those changes. -3. Once the pull request is merged, tag the release commit with the version number as soon as the PR is merged: ``git tag v`` (notice the ``v`` added before the version number), then ``git push --tags``. This will create a draft release on GitHub containing the correct artifacts and the relevant part of the changelog. +The version number of a release is stated in ``pyproject.toml``. To release a new +version of Flower, the following things need to happen (in that order): + +1. Run ``python3 src/py/flwr_tool/update_changelog.py `` in order to add + every new change to the changelog (feel free to make manual changes to the changelog + afterwards until it looks good). +2. Once the changelog has been updated with all the changes, run + ``./dev/prepare-release-changelog.sh v``, where ```` is the + version stated in ``pyproject.toml`` (notice the ``v`` added before it). This will + replace the ``Unreleased`` header of the changelog by the version and current date, + and it will add a thanking message for the contributors. Open a pull request with + those changes. +3. Once the pull request is merged, tag the release commit with the version number as + soon as the PR is merged: ``git tag v`` (notice the ``v`` added before + the version number), then ``git push --tags``. This will create a draft release on + GitHub containing the correct artifacts and the relevant part of the changelog. 4. Check the draft release on GitHub, and if everything is good, publish it. After the release @@ -22,7 +34,8 @@ Create a pull request which contains the following changes: 2. Update all files which contain the current version number if necessary. 3. Add a new ``Unreleased`` section in ``changelog.md``. -Merge the pull request on the same day (i.e., before a new nightly release gets published to PyPI). +Merge the pull request on the same day (i.e., before a new nightly release gets +published to PyPI). Publishing a pre-release ------------------------ @@ -30,7 +43,8 @@ Publishing a pre-release Pre-release naming ~~~~~~~~~~~~~~~~~~ -PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases MUST use one of the following naming patterns: +PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases MUST use one +of the following naming patterns: - Alpha: ``MAJOR.MINOR.PATCHaN`` - Beta: ``MAJOR.MINOR.PATCHbN`` @@ -43,19 +57,25 @@ Examples include: - ``1.0.0rc0`` - ``1.0.0rc1`` -This is in line with PEP-440 and the recommendations from the Python Packaging -Authority (PyPA): +This is in line with PEP-440 and the recommendations from the Python Packaging Authority +(PyPA): - `PEP-440 `_ -- `PyPA Choosing a versioning scheme `_ +- `PyPA Choosing a versioning scheme + `_ -Note that the approach defined by PyPA is not compatible with SemVer 2.0.0 spec, for details consult the `Semantic Versioning Specification `_ (specifically item 11 on precedence). +Note that the approach defined by PyPA is not compatible with SemVer 2.0.0 spec, for +details consult the `Semantic Versioning Specification +`_ (specifically item 11 on +precedence). Pre-release classification ~~~~~~~~~~~~~~~~~~~~~~~~~~ Should the next pre-release be called alpha, beta, or release candidate? -- RC: feature complete, no known issues (apart from issues that are classified as "won't fix" for the next stable release) - if no issues surface this will become the next stable release +- RC: feature complete, no known issues (apart from issues that are classified as "won't + fix" for the next stable release) - if no issues surface this will become the next + stable release - Beta: feature complete, allowed to have known issues - Alpha: not feature complete, allowed to have known issues diff --git a/doc/source/contributor-how-to-set-up-a-virtual-env.rst b/doc/source/contributor-how-to-set-up-a-virtual-env.rst index a844298fdca9..7e54ed64c9c9 100644 --- a/doc/source/contributor-how-to-set-up-a-virtual-env.rst +++ b/doc/source/contributor-how-to-set-up-a-virtual-env.rst @@ -1,26 +1,33 @@ Set up a virtual env ==================== -It is recommended to run your Python setup within a virtual environment. -This guide shows three different examples how to create a virtual environment with pyenv virtualenv, poetry, or Anaconda. -You can follow the instructions or choose your preferred setup. +It is recommended to run your Python setup within a virtual environment. This guide +shows three different examples how to create a virtual environment with pyenv +virtualenv, poetry, or Anaconda. You can follow the instructions or choose your +preferred setup. Python Version -------------- -Flower requires at least `Python 3.9 `_, but `Python 3.10 `_ or above is recommended. +Flower requires at least `Python 3.9 `_, but `Python 3.10 +`_ or above is recommended. .. note:: - Due to a known incompatibility with `ray `_, - we currently recommend utilizing at most `Python 3.11 `_ for - running Flower simulations. + + Due to a known incompatibility with `ray `_, we + currently recommend utilizing at most `Python 3.11 `_ + for running Flower simulations. Virtualenv with Pyenv/Virtualenv -------------------------------- -One of the recommended virtual environment is `pyenv `_/`virtualenv `_. Please see `Flower examples `_ for details. +One of the recommended virtual environment is `pyenv +`_/`virtualenv +`_. Please see `Flower examples +`_ for details. -Once Pyenv is set up, you can use it to install `Python Version 3.10 `_ or above: +Once Pyenv is set up, you can use it to install `Python Version 3.10 +`_ or above: .. code-block:: shell @@ -32,34 +39,35 @@ Create the virtualenv with: pyenv virtualenv 3.10.12 flower-3.10.12 - Activate the virtualenv by running the following command: .. code-block:: shell echo flower-3.10.12 > .python-version - Virtualenv with Poetry ---------------------- -The Flower examples are based on `Poetry `_ to manage dependencies. After installing Poetry you simply create a virtual environment with: +The Flower examples are based on `Poetry `_ to manage +dependencies. After installing Poetry you simply create a virtual environment with: .. code-block:: shell poetry shell -If you open a new terminal you can activate the previously created virtual environment with the following command: +If you open a new terminal you can activate the previously created virtual environment +with the following command: .. code-block:: shell source $(poetry env info --path)/bin/activate - Virtualenv with Anaconda ------------------------ -If you prefer to use Anaconda for your virtual environment then install and setup the `conda `_ package. After setting it up you can create a virtual environment with: +If you prefer to use Anaconda for your virtual environment then install and setup the +`conda `_ +package. After setting it up you can create a virtual environment with: .. code-block:: shell @@ -71,8 +79,8 @@ and activate the virtual environment with: conda activate flower-3.10.12 - And then? --------- -As soon as you created your virtual environment you clone one of the `Flower examples `_. +As soon as you created your virtual environment you clone one of the `Flower examples +`_. diff --git a/doc/source/contributor-how-to-write-documentation.rst b/doc/source/contributor-how-to-write-documentation.rst index fcd8c5bb18c6..6209530b71e0 100644 --- a/doc/source/contributor-how-to-write-documentation.rst +++ b/doc/source/contributor-how-to-write-documentation.rst @@ -1,14 +1,15 @@ Write documentation =================== - Project layout -------------- -The Flower documentation lives in the ``doc`` directory. The Sphinx-based documentation system supports both reStructuredText (``.rst`` files) and Markdown (``.md`` files). - -Note that, in order to build the documentation locally (with ``poetry run make html``, like described below), `Pandoc `_ needs to be installed on the system. +The Flower documentation lives in the ``doc`` directory. The Sphinx-based documentation +system supports both reStructuredText (``.rst`` files) and Markdown (``.md`` files). +Note that, in order to build the documentation locally (with ``poetry run make html``, +like described below), `Pandoc `_ needs to be +installed on the system. Edit an existing page --------------------- @@ -17,7 +18,6 @@ Edit an existing page 2. Compile the docs: ``cd doc``, then ``poetry run make html`` 3. Open ``doc/build/html/index.html`` in the browser to check the result - Create a new page ----------------- diff --git a/doc/source/contributor-ref-good-first-contributions.rst b/doc/source/contributor-ref-good-first-contributions.rst index 2b8ce88413f5..a715e006f905 100644 --- a/doc/source/contributor-ref-good-first-contributions.rst +++ b/doc/source/contributor-ref-good-first-contributions.rst @@ -1,41 +1,41 @@ Good first contributions ======================== -We welcome contributions to Flower! However, it is not always easy to know -where to start. We therefore put together a few recommendations on where to -start to increase your chances of getting your PR accepted into the Flower -codebase. - +We welcome contributions to Flower! However, it is not always easy to know where to +start. We therefore put together a few recommendations on where to start to increase +your chances of getting your PR accepted into the Flower codebase. Where to start -------------- -Until the Flower core library matures it will be easier to get PR's accepted if -they only touch non-core areas of the codebase. Good candidates to get started -are: +Until the Flower core library matures it will be easier to get PR's accepted if they +only touch non-core areas of the codebase. Good candidates to get started are: - Documentation: What's missing? What could be expressed more clearly? - Baselines: See below. - Examples: See below. - Request for Flower Baselines ---------------------------- -If you are not familiar with Flower Baselines, you should probably check-out our `contributing guide for baselines `_. +If you are not familiar with Flower Baselines, you should probably check-out our +`contributing guide for baselines +`_. -You should then check out the open -`issues `_ for baseline requests. -If you find a baseline that you'd like to work on and that has no assignees, feel free to assign it to yourself and start working on it! +You should then check out the open `issues +`_ +for baseline requests. If you find a baseline that you'd like to work on and that has no +assignees, feel free to assign it to yourself and start working on it! -Otherwise, if you don't find a baseline you'd like to work on, be sure to open a new issue with the baseline request template! +Otherwise, if you don't find a baseline you'd like to work on, be sure to open a new +issue with the baseline request template! Request for examples -------------------- -We wish we had more time to write usage examples because we believe they help -users to get started with building what they want to build. Here are a few -ideas where we'd be happy to accept a PR: +We wish we had more time to write usage examples because we believe they help users to +get started with building what they want to build. Here are a few ideas where we'd be +happy to accept a PR: - Llama 2 fine-tuning, with Hugging Face Transformers and PyTorch - XGBoost diff --git a/doc/source/contributor-ref-secure-aggregation-protocols.rst b/doc/source/contributor-ref-secure-aggregation-protocols.rst index 7107d04b8cd0..347cb2724424 100644 --- a/doc/source/contributor-ref-secure-aggregation-protocols.rst +++ b/doc/source/contributor-ref-secure-aggregation-protocols.rst @@ -1,13 +1,16 @@ Secure Aggregation Protocols ============================ -Include SecAgg, SecAgg+, and LightSecAgg protocol. The LightSecAgg protocol has not been implemented yet, so its diagram and abstraction may not be accurate in practice. -The SecAgg protocol can be considered as a special case of the SecAgg+ protocol. +Include SecAgg, SecAgg+, and LightSecAgg protocol. The LightSecAgg protocol has not been +implemented yet, so its diagram and abstraction may not be accurate in practice. The +SecAgg protocol can be considered as a special case of the SecAgg+ protocol. -The :code:`SecAgg+` abstraction -------------------------------- +The ``SecAgg+`` abstraction +--------------------------- -In this implementation, each client will be assigned with a unique index (int) for secure aggregation, and thus many python dictionaries used have keys of int type rather than ClientProxy type. +In this implementation, each client will be assigned with a unique index (int) for +secure aggregation, and thus many python dictionaries used have keys of int type rather +than ClientProxy type. .. code-block:: python @@ -15,9 +18,7 @@ In this implementation, each client will be assigned with a unique index (int) f """Abstract base class for the SecAgg+ protocol implementations.""" @abstractmethod - def generate_graph( - self, clients: List[ClientProxy], k: int - ) -> ClientGraph: + def generate_graph(self, clients: List[ClientProxy], k: int) -> ClientGraph: """Build a k-degree undirected graph of clients. Each client will only generate pair-wise masks with its k neighbours. k is equal to the number of clients in SecAgg, i.e., a complete graph. @@ -31,16 +32,16 @@ In this implementation, each client will be assigned with a unique index (int) f @abstractmethod def ask_keys( - self, - clients: List[ClientProxy], ask_keys_ins_list: List[AskKeysIns] + self, clients: List[ClientProxy], ask_keys_ins_list: List[AskKeysIns] ) -> AskKeysResultsAndFailures: """Ask public keys. (AskKeysIns is an empty class, and hence ask_keys_ins_list can be omitted.)""" @abstractmethod def share_keys( self, - clients: List[ClientProxy], public_keys_dict: Dict[int, AskKeysRes], - graph: ClientGraph + clients: List[ClientProxy], + public_keys_dict: Dict[int, AskKeysRes], + graph: ClientGraph, ) -> ShareKeysResultsAndFailures: """Send public keys.""" @@ -48,17 +49,18 @@ In this implementation, each client will be assigned with a unique index (int) f def ask_vectors( clients: List[ClientProxy], forward_packet_list_dict: Dict[int, List[ShareKeysPacket]], - client_instructions=None: Dict[int, FitIns] + client_instructions: Dict[int, FitIns] = None, ) -> AskVectorsResultsAndFailures: """Ask vectors of local model parameters. (If client_instructions is not None, local models will be trained in the ask vectors stage, - rather than trained parallelly as the protocol goes through the previous stages.)""" + rather than trained parallelly as the protocol goes through the previous stages.) + """ @abstractmethod def unmask_vectors( clients: List[ClientProxy], dropout_clients: List[ClientProxy], - graph: ClientGraph + graph: ClientGraph, ) -> UnmaskVectorsResultsAndFailures: """Unmask and compute the aggregated model. UnmaskVectorRes contains shares of keys needed to generate masks.""" @@ -155,10 +157,12 @@ The Flower server will execute and process received results in the following ord deactivate P end -The :code:`LightSecAgg` abstraction ------------------------------------ +The ``LightSecAgg`` abstraction +------------------------------- -In this implementation, each client will be assigned with a unique index (int) for secure aggregation, and thus many python dictionaries used have keys of int type rather than ClientProxy type. +In this implementation, each client will be assigned with a unique index (int) for +secure aggregation, and thus many python dictionaries used have keys of int type rather +than ClientProxy type. .. code-block:: python @@ -174,7 +178,8 @@ In this implementation, each client will be assigned with a unique index (int) f @abstractmethod def ask_encrypted_encoded_masks( self, - clients: List[ClientProxy], public_keys_dict: Dict[int, LightSecAggSetupConfigRes] + clients: List[ClientProxy], + public_keys_dict: Dict[int, LightSecAggSetupConfigRes], ) -> AskEncryptedEncodedMasksResultsAndFailures: """Ask encrypted encoded masks. The protocol adopts Diffie-Hellman keys to build pair-wise secured channels to transfer encoded mask.""" @@ -183,15 +188,16 @@ In this implementation, each client will be assigned with a unique index (int) f self, clients: List[ClientProxy], forward_packet_list_dict: Dict[int, List[EncryptedEncodedMasksPacket]], - client_instructions=None: Dict[int, FitIns] + client_instructions: Dict[int, FitIns] = None, ) -> AskMaskedModelsResultsAndFailures: """Ask the masked local models. (If client_instructions is not None, local models will be trained in the ask vectors stage, - rather than trained parallelly as the protocol goes through the previous stages.)""" + rather than trained parallelly as the protocol goes through the previous stages.) + """ @abstractmethod def ask_aggregated_encoded_masks( - clients: List[ClientProxy] + clients: List[ClientProxy], ) -> AskAggregatedEncodedMasksResultsAndFailures: """Ask aggregated encoded masks""" @@ -272,158 +278,157 @@ Types .. code-block:: python - # the SecAgg+ protocol + # the SecAgg+ protocol + + ClientGraph = Dict[int, List[int]] - ClientGraph = Dict[int, List[int]] + SetupConfigResultsAndFailures = Tuple[ + List[Tuple[ClientProxy, SetupConfigRes]], List[BaseException] + ] - SetupConfigResultsAndFailures = Tuple[ - List[Tuple[ClientProxy, SetupConfigRes]], List[BaseException] - ] + AskKeysResultsAndFailures = Tuple[ + List[Tuple[ClientProxy, AskKeysRes]], List[BaseException] + ] - AskKeysResultsAndFailures = Tuple[ - List[Tuple[ClientProxy, AskKeysRes]], List[BaseException] - ] + ShareKeysResultsAndFailures = Tuple[ + List[Tuple[ClientProxy, ShareKeysRes]], List[BaseException] + ] - ShareKeysResultsAndFailures = Tuple[ - List[Tuple[ClientProxy, ShareKeysRes]], List[BaseException] - ] + AskVectorsResultsAndFailures = Tuple[ + List[Tuple[ClientProxy, AskVectorsRes]], List[BaseException] + ] - AskVectorsResultsAndFailures = Tuple[ - List[Tuple[ClientProxy, AskVectorsRes]], List[BaseException] - ] + UnmaskVectorsResultsAndFailures = Tuple[ + List[Tuple[ClientProxy, UnmaskVectorsRes]], List[BaseException] + ] - UnmaskVectorsResultsAndFailures = Tuple[ - List[Tuple[ClientProxy, UnmaskVectorsRes]], List[BaseException] - ] + FitResultsAndFailures = Tuple[List[Tuple[ClientProxy, FitRes]], List[BaseException]] - FitResultsAndFailures = Tuple[ - List[Tuple[ClientProxy, FitRes]], List[BaseException] - ] + @dataclass + class SetupConfigIns: + sec_agg_cfg_dict: Dict[str, Scalar] - @dataclass - class SetupConfigIns: - sec_agg_cfg_dict: Dict[str, Scalar] + @dataclass + class SetupConfigRes: + pass - @dataclass - class SetupConfigRes: - pass + @dataclass + class AskKeysIns: + pass - @dataclass - class AskKeysIns: - pass + @dataclass + class AskKeysRes: + """Ask Keys Stage Response from client to server""" - @dataclass - class AskKeysRes: - """Ask Keys Stage Response from client to server""" - pk1: bytes - pk2: bytes + pk1: bytes + pk2: bytes - @dataclass - class ShareKeysIns: - public_keys_dict: Dict[int, AskKeysRes] + @dataclass + class ShareKeysIns: + public_keys_dict: Dict[int, AskKeysRes] - @dataclass - class ShareKeysPacket: - source: int - destination: int - ciphertext: bytes + @dataclass + class ShareKeysPacket: + source: int + destination: int + ciphertext: bytes - @dataclass - class ShareKeysRes: - share_keys_res_list: List[ShareKeysPacket] + @dataclass + class ShareKeysRes: + share_keys_res_list: List[ShareKeysPacket] - @dataclass - class AskVectorsIns: - ask_vectors_in_list: List[ShareKeysPacket] - fit_ins: FitIns + @dataclass + class AskVectorsIns: + ask_vectors_in_list: List[ShareKeysPacket] + fit_ins: FitIns - @dataclass - class AskVectorsRes: - parameters: Parameters + @dataclass + class AskVectorsRes: + parameters: Parameters - @dataclass - class UnmaskVectorsIns: - available_clients: List[int] - dropout_clients: List[int] + @dataclass + class UnmaskVectorsIns: + available_clients: List[int] + dropout_clients: List[int] - @dataclass - class UnmaskVectorsRes: - share_dict: Dict[int, bytes] + @dataclass + class UnmaskVectorsRes: + share_dict: Dict[int, bytes] - # the LightSecAgg protocol + # the LightSecAgg protocol - LightSecAggSetupConfigResultsAndFailures = Tuple[ - List[Tuple[ClientProxy, LightSecAggSetupConfigRes]], List[BaseException] - ] + LightSecAggSetupConfigResultsAndFailures = Tuple[ + List[Tuple[ClientProxy, LightSecAggSetupConfigRes]], List[BaseException] + ] - AskEncryptedEncodedMasksResultsAndFailures = Tuple[ - List[Tuple[ClientProxy, AskEncryptedEncodedMasksRes]], List[BaseException] - ] + AskEncryptedEncodedMasksResultsAndFailures = Tuple[ + List[Tuple[ClientProxy, AskEncryptedEncodedMasksRes]], List[BaseException] + ] - AskMaskedModelsResultsAndFailures = Tuple[ - List[Tuple[ClientProxy, AskMaskedModelsRes]], List[BaseException] - ] + AskMaskedModelsResultsAndFailures = Tuple[ + List[Tuple[ClientProxy, AskMaskedModelsRes]], List[BaseException] + ] - AskAggregatedEncodedMasksResultsAndFailures = Tuple[ - List[Tuple[ClientProxy, AskAggregatedEncodedMasksRes]], List[BaseException] - ] + AskAggregatedEncodedMasksResultsAndFailures = Tuple[ + List[Tuple[ClientProxy, AskAggregatedEncodedMasksRes]], List[BaseException] + ] - @dataclass - class LightSecAggSetupConfigIns: - sec_agg_cfg_dict: Dict[str, Scalar] + @dataclass + class LightSecAggSetupConfigIns: + sec_agg_cfg_dict: Dict[str, Scalar] - @dataclass - class LightSecAggSetupConfigRes: - pk: bytes + @dataclass + class LightSecAggSetupConfigRes: + pk: bytes - @dataclass - class AskEncryptedEncodedMasksIns: - public_keys_dict: Dict[int, LightSecAggSetupConfigRes] + @dataclass + class AskEncryptedEncodedMasksIns: + public_keys_dict: Dict[int, LightSecAggSetupConfigRes] - @dataclass - class EncryptedEncodedMasksPacket: - source: int - destination: int - ciphertext: bytes + @dataclass + class EncryptedEncodedMasksPacket: + source: int + destination: int + ciphertext: bytes - @dataclass - class AskEncryptedEncodedMasksRes: - packet_list: List[EncryptedEncodedMasksPacket] + @dataclass + class AskEncryptedEncodedMasksRes: + packet_list: List[EncryptedEncodedMasksPacket] - @dataclass - class AskMaskedModelsIns: - packet_list: List[EncryptedEncodedMasksPacket] - fit_ins: FitIns + @dataclass + class AskMaskedModelsIns: + packet_list: List[EncryptedEncodedMasksPacket] + fit_ins: FitIns - @dataclass - class AskMaskedModelsRes: - parameters: Parameters + @dataclass + class AskMaskedModelsRes: + parameters: Parameters - @dataclass - class AskAggregatedEncodedMasksIns: - surviving_clients: List[int] + @dataclass + class AskAggregatedEncodedMasksIns: + surviving_clients: List[int] - @dataclass - class AskAggregatedEncodedMasksRes: - aggregated_encoded_mask: Parameters + @dataclass + class AskAggregatedEncodedMasksRes: + aggregated_encoded_mask: Parameters diff --git a/doc/source/contributor-tutorial-contribute-on-github.rst b/doc/source/contributor-tutorial-contribute-on-github.rst index 6970e7e8a580..22c6c6ef86b0 100644 --- a/doc/source/contributor-tutorial-contribute-on-github.rst +++ b/doc/source/contributor-tutorial-contribute-on-github.rst @@ -1,100 +1,113 @@ Contribute on GitHub ==================== -This guide is for people who want to get involved with Flower, but who are not used to contributing to GitHub projects. - -If you're familiar with how contributing on GitHub works, you can directly checkout our :doc:`getting started guide for contributors `. +This guide is for people who want to get involved with Flower, but who are not used to +contributing to GitHub projects. +If you're familiar with how contributing on GitHub works, you can directly checkout our +:doc:`getting started guide for contributors +`. Setting up the repository ------------------------- 1. **Create a GitHub account and setup Git** - Git is a distributed version control tool. This allows for an entire codebase's history to be stored and every developer's machine. - It is a software that will need to be installed on your local machine, you can follow this `guide `_ to set it up. - - GitHub, itself, is a code hosting platform for version control and collaboration. It allows for everyone to collaborate and work from anywhere on remote repositories. - - If you haven't already, you will need to create an account on `GitHub `_. - - The idea behind the generic Git and GitHub workflow boils down to this: - you download code from a remote repository on GitHub, make changes locally and keep track of them using Git and then you upload your new history back to GitHub. - + Git is a distributed version control tool. This allows for an entire codebase's + history to be stored and every developer's machine. It is a software that will + need to be installed on your local machine, you can follow this `guide + `_ to + set it up. + + GitHub, itself, is a code hosting platform for version control and collaboration. + It allows for everyone to collaborate and work from anywhere on remote + repositories. + + If you haven't already, you will need to create an account on `GitHub + `_. + + The idea behind the generic Git and GitHub workflow boils down to this: you + download code from a remote repository on GitHub, make changes locally and keep + track of them using Git and then you upload your new history back to GitHub. 2. **Forking the Flower repository** - A fork is a personal copy of a GitHub repository. To create one for Flower, you must navigate to ``_ (while connected to your GitHub account) - and click the ``Fork`` button situated on the top right of the page. - - .. image:: _static/fork_button.png + A fork is a personal copy of a GitHub repository. To create one for Flower, you + must navigate to https://github.com/adap/flower (while connected to your GitHub + account) and click the ``Fork`` button situated on the top right of the page. - You can change the name if you want, but this is not necessary as this version of Flower will be yours and will sit inside your own account (i.e., in your own list of repositories). - Once created, you should see on the top left corner that you are looking at your own version of Flower. + .. image:: _static/fork_button.png - .. image:: _static/fork_link.png + You can change the name if you want, but this is not necessary as this version of + Flower will be yours and will sit inside your own account (i.e., in your own list + of repositories). Once created, you should see on the top left corner that you + are looking at your own version of Flower. + .. image:: _static/fork_link.png 3. **Cloning your forked repository** - The next step is to download the forked repository on your machine to be able to make changes to it. - On your forked repository page, you should first click on the ``Code`` button on the right, - this will give you the ability to copy the HTTPS link of the repository. + The next step is to download the forked repository on your machine to be able to + make changes to it. On your forked repository page, you should first click on the + ``Code`` button on the right, this will give you the ability to copy the HTTPS + link of the repository. - .. image:: _static/cloning_fork.png + .. image:: _static/cloning_fork.png - Once you copied the \, you can open a terminal on your machine, navigate to the place you want to download the repository to and type: + Once you copied the \, you can open a terminal on your machine, navigate to + the place you want to download the repository to and type: - .. code-block:: shell + .. code-block:: shell - $ git clone - - This will create a ``flower/`` (or the name of your fork if you renamed it) folder in the current working directory. + $ git clone + This will create a ``flower/`` (or the name of your fork if you renamed it) + folder in the current working directory. 4. **Add origin** - You can then go into the repository folder: - - .. code-block:: shell - - $ cd flower + You can then go into the repository folder: - And here we will need to add an origin to our repository. The origin is the \ of the remote fork repository. - To obtain it, we can do as previously mentioned by going to our fork repository on our GitHub account and copying the link. + .. code-block:: shell - .. image:: _static/cloning_fork.png + $ cd flower - Once the \ is copied, we can type the following command in our terminal: + And here we will need to add an origin to our repository. The origin is the + \ of the remote fork repository. To obtain it, we can do as previously + mentioned by going to our fork repository on our GitHub account and copying the + link. - .. code-block:: shell + .. image:: _static/cloning_fork.png - $ git remote add origin + Once the \ is copied, we can type the following command in our terminal: + .. code-block:: shell + $ git remote add origin 5. **Add upstream** - Now we will add an upstream address to our repository. - Still in the same directory, we must run the following command: + Now we will add an upstream address to our repository. Still in the same + directory, we must run the following command: - .. code-block:: shell + .. code-block:: shell - $ git remote add upstream https://github.com/adap/flower.git + $ git remote add upstream https://github.com/adap/flower.git - The following diagram visually explains what we did in the previous steps: + The following diagram visually explains what we did in the previous steps: - .. image:: _static/github_schema.png + .. image:: _static/github_schema.png - The upstream is the GitHub remote address of the parent repository (in this case Flower), - i.e. the one we eventually want to contribute to and therefore need an up-to-date history of. - The origin is just the GitHub remote address of the forked repository we created, i.e. the copy (fork) in our own account. + The upstream is the GitHub remote address of the parent repository (in this case + Flower), i.e. the one we eventually want to contribute to and therefore need an + up-to-date history of. The origin is just the GitHub remote address of the forked + repository we created, i.e. the copy (fork) in our own account. - To make sure our local version of the fork is up-to-date with the latest changes from the Flower repository, - we can execute the following command: + To make sure our local version of the fork is up-to-date with the latest changes + from the Flower repository, we can execute the following command: - .. code-block:: shell - - $ git pull upstream main + .. code-block:: shell + $ git pull upstream main Setting up the coding environment --------------------------------- -This can be achieved by following this :doc:`getting started guide for contributors ` (note that you won't need to clone the repository). -Once you are able to write code and test it, you can finally start making changes! - +This can be achieved by following this :doc:`getting started guide for contributors +` (note that you won't need to clone +the repository). Once you are able to write code and test it, you can finally start +making changes! Making changes -------------- @@ -112,211 +125,233 @@ And with Flower's repository: $ git pull upstream main 1. **Create a new branch** - To make the history cleaner and easier to work with, it is good practice to - create a new branch for each feature/project that needs to be implemented. - - To do so, just run the following command inside the repository's directory: + To make the history cleaner and easier to work with, it is good practice to + create a new branch for each feature/project that needs to be implemented. - .. code-block:: shell + To do so, just run the following command inside the repository's directory: - $ git switch -c + .. code-block:: shell + $ git switch -c 2. **Make changes** - Write great code and create wonderful changes using your favorite editor! - + Write great code and create wonderful changes using your favorite editor! 3. **Test and format your code** - Don't forget to test and format your code! Otherwise your code won't be able to be merged into the Flower repository. - This is done so the codebase stays consistent and easy to understand. - - To do so, we have written a few scripts that you can execute: + Don't forget to test and format your code! Otherwise your code won't be able to + be merged into the Flower repository. This is done so the codebase stays + consistent and easy to understand. - .. code-block:: shell + To do so, we have written a few scripts that you can execute: - $ ./dev/format.sh # to format your code - $ ./dev/test.sh # to test that your code can be accepted - $ ./baselines/dev/format.sh # same as above but for code added to baselines - $ ./baselines/dev/test.sh # same as above but for code added to baselines + .. code-block:: shell + $ ./dev/format.sh # to format your code + $ ./dev/test.sh # to test that your code can be accepted + $ ./baselines/dev/format.sh # same as above but for code added to baselines + $ ./baselines/dev/test.sh # same as above but for code added to baselines 4. **Stage changes** - Before creating a commit that will update your history, you must specify to Git which files it needs to take into account. - - This can be done with: + Before creating a commit that will update your history, you must specify to Git + which files it needs to take into account. - .. code-block:: shell + This can be done with: - $ git add + .. code-block:: shell - To check which files have been modified compared to the last version (last commit) and to see which files are staged for commit, - you can use the :code:`git status` command. + $ git add + To check which files have been modified compared to the last version (last + commit) and to see which files are staged for commit, you can use the ``git + status`` command. 5. **Commit changes** - Once you have added all the files you wanted to commit using :code:`git add`, you can finally create your commit using this command: + Once you have added all the files you wanted to commit using ``git add``, you can + finally create your commit using this command: - .. code-block:: shell + .. code-block:: shell - $ git commit -m "" - - The \ is there to explain to others what the commit does. It should be written in an imperative style and be concise. - An example would be :code:`git commit -m "Add images to README"`. + $ git commit -m "" + The \ is there to explain to others what the commit does. It + should be written in an imperative style and be concise. An example would be + ``git commit -m "Add images to README"``. 6. **Push the changes to the fork** - Once we have committed our changes, we have effectively updated our local history, but GitHub has no way of knowing this unless we push - our changes to our origin's remote address: - - .. code-block:: shell + Once we have committed our changes, we have effectively updated our local + history, but GitHub has no way of knowing this unless we push our changes to our + origin's remote address: - $ git push -u origin + .. code-block:: shell - Once this is done, you will see on the GitHub that your forked repo was updated with the changes you have made. + $ git push -u origin + Once this is done, you will see on the GitHub that your forked repo was updated + with the changes you have made. Creating and merging a pull request (PR) ---------------------------------------- 1. **Create the PR** - Once you have pushed changes, on the GitHub webpage of your repository you should see the following message: - - .. image:: _static/compare_and_pr.png + Once you have pushed changes, on the GitHub webpage of your repository you should + see the following message: - Otherwise you can always find this option in the ``Branches`` page. + .. image:: _static/compare_and_pr.png - Once you click the ``Compare & pull request`` button, you should see something similar to this: + Otherwise you can always find this option in the ``Branches`` page. - .. image:: _static/creating_pr.png + Once you click the ``Compare & pull request`` button, you should see something + similar to this: - At the top you have an explanation of which branch will be merged where: + .. image:: _static/creating_pr.png - .. image:: _static/merging_branch.png + At the top you have an explanation of which branch will be merged where: - In this example you can see that the request is to merge the branch ``doc-fixes`` from my forked repository to branch ``main`` from the Flower repository. + .. image:: _static/merging_branch.png - The title should be changed to adhere to the :ref:`pr_title_format` guidelines, otherwise it won't be possible to merge the PR. So in this case, - a correct title might be ``docs(framework:skip) Fix typos``. + In this example you can see that the request is to merge the branch ``doc-fixes`` + from my forked repository to branch ``main`` from the Flower repository. - The input box in the middle is there for you to describe what your PR does and to link it to existing issues. - We have placed comments (that won't be rendered once the PR is opened) to guide you through the process. + The title should be changed to adhere to the :ref:`pr_title_format` guidelines, + otherwise it won't be possible to merge the PR. So in this case, a correct title + might be ``docs(framework:skip) Fix typos``. - It is important to follow the instructions described in comments. + The input box in the middle is there for you to describe what your PR does and to + link it to existing issues. We have placed comments (that won't be rendered once + the PR is opened) to guide you through the process. - At the bottom you will find the button to open the PR. This will notify reviewers that a new PR has been opened and - that they should look over it to merge or to request changes. + It is important to follow the instructions described in comments. - If your PR is not yet ready for review, and you don't want to notify anyone, you have the option to create a draft pull request: + At the bottom you will find the button to open the PR. This will notify reviewers + that a new PR has been opened and that they should look over it to merge or to + request changes. - .. image:: _static/draft_pr.png + If your PR is not yet ready for review, and you don't want to notify anyone, you + have the option to create a draft pull request: + .. image:: _static/draft_pr.png 2. **Making new changes** - Once the PR has been opened (as draft or not), you can still push new commits to it the same way we did before, by making changes to the branch associated with the PR. - + Once the PR has been opened (as draft or not), you can still push new commits to + it the same way we did before, by making changes to the branch associated with + the PR. 3. **Review the PR** - Once the PR has been opened or once the draft PR has been marked as ready, a review from code owners will be automatically requested: - - .. image:: _static/opened_pr.png - - Code owners will then look into the code, ask questions, request changes or validate the PR. + Once the PR has been opened or once the draft PR has been marked as ready, a + review from code owners will be automatically requested: - Merging will be blocked if there are ongoing requested changes. + .. image:: _static/opened_pr.png - .. image:: _static/changes_requested.png + Code owners will then look into the code, ask questions, request changes or + validate the PR. - To resolve them, just push the necessary changes to the branch associated with the PR: + Merging will be blocked if there are ongoing requested changes. - .. image:: _static/make_changes.png + .. image:: _static/changes_requested.png - And resolve the conversation: + To resolve them, just push the necessary changes to the branch associated with + the PR: - .. image:: _static/resolve_conv.png + .. image:: _static/make_changes.png - Once all the conversations have been resolved, you can re-request a review. + And resolve the conversation: + .. image:: _static/resolve_conv.png + Once all the conversations have been resolved, you can re-request a review. 4. **Once the PR is merged** - If all the automatic tests have passed and reviewers have no more changes to request, they can approve the PR and merge it. + If all the automatic tests have passed and reviewers have no more changes to + request, they can approve the PR and merge it. - .. image:: _static/merging_pr.png + .. image:: _static/merging_pr.png - Once it is merged, you can delete the branch on GitHub (a button should appear to do so) and also delete it locally by doing: + Once it is merged, you can delete the branch on GitHub (a button should appear to + do so) and also delete it locally by doing: - .. code-block:: shell + .. code-block:: shell - $ git switch main - $ git branch -D + $ git switch main + $ git branch -D - Then you should update your forked repository by doing: + Then you should update your forked repository by doing: - .. code-block:: shell - - $ git pull upstream main # to update the local repository - $ git push origin main # to push the changes to the remote repository + .. code-block:: shell + $ git pull upstream main # to update the local repository + $ git push origin main # to push the changes to the remote repository Example of first contribution ----------------------------- Problem -******* +~~~~~~~ -For our documentation, we've started to use the `Diàtaxis framework `_. +For our documentation, we've started to use the `Diàtaxis framework +`_. -Our "How to" guides should have titles that continue the sentence "How to …", for example, "How to upgrade to Flower 1.0". +Our "How to" guides should have titles that continue the sentence "How to …", for +example, "How to upgrade to Flower 1.0". -Most of our guides do not follow this new format yet, and changing their title is (unfortunately) more involved than one might think. +Most of our guides do not follow this new format yet, and changing their title is +(unfortunately) more involved than one might think. -This issue is about changing the title of a doc from present continuous to present simple. +This issue is about changing the title of a doc from present continuous to present +simple. -Let's take the example of "Saving Progress" which we changed to "Save Progress". Does this pass our check? +Let's take the example of "Saving Progress" which we changed to "Save Progress". Does +this pass our check? Before: "How to saving progress" ❌ After: "How to save progress" ✅ Solution -******** +~~~~~~~~ -This is a tiny change, but it'll allow us to test your end-to-end setup. After cloning and setting up the Flower repo, here's what you should do: +This is a tiny change, but it'll allow us to test your end-to-end setup. After cloning +and setting up the Flower repo, here's what you should do: - Find the source file in ``doc/source`` -- Make the change in the ``.rst`` file (beware, the dashes under the title should be the same length as the title itself) -- Build the docs and `check the result `_ +- Make the change in the ``.rst`` file (beware, the dashes under the title should be the + same length as the title itself) +- Build the docs and `check the result + `_ Rename file -::::::::::: ++++++++++++ -You might have noticed that the file name still reflects the old wording. -If we just change the file, then we break all existing links to it - it is **very important** to avoid that, breaking links can harm our search engine ranking. +You might have noticed that the file name still reflects the old wording. If we just +change the file, then we break all existing links to it - it is **very important** to +avoid that, breaking links can harm our search engine ranking. Here's how to change the file name: - Change the file name to ``save-progress.rst`` - Add a redirect rule to ``doc/source/conf.py`` -This will cause a redirect from ``saving-progress.html`` to ``save-progress.html``, old links will continue to work. +This will cause a redirect from ``saving-progress.html`` to ``save-progress.html``, old +links will continue to work. Apply changes in the index file -::::::::::::::::::::::::::::::: ++++++++++++++++++++++++++++++++ -For the lateral navigation bar to work properly, it is very important to update the ``index.rst`` file as well. -This is where we define the whole arborescence of the navbar. +For the lateral navigation bar to work properly, it is very important to update the +``index.rst`` file as well. This is where we define the whole arborescence of the +navbar. - Find and modify the file name in ``index.rst`` Open PR -::::::: ++++++++ -- Commit the changes (commit messages are always imperative: "Do something", in this case "Change …") +- Commit the changes (commit messages are always imperative: "Do something", in this + case "Change …") - Push the changes to your fork - Open a PR (as shown above) with title ``docs(framework) Update how-to guide title`` - Wait for it to be approved! - Congrats! 🥳 You're now officially a Flower contributor! - Next steps ---------- -Once you have made your first PR, and want to contribute more, be sure to check out the following : - -- :doc:`Good first contributions `, where you should particularly look into the :code:`baselines` contributions. +Once you have made your first PR, and want to contribute more, be sure to check out the +following : +- :doc:`Good first contributions `, where you + should particularly look into the ``baselines`` contributions. Appendix -------- @@ -324,7 +359,7 @@ Appendix .. _pr_title_format: PR title format -*************** +~~~~~~~~~~~~~~~ We enforce the following PR title format: @@ -334,9 +369,10 @@ We enforce the following PR title format: (or ``(:skip) `` to ignore the PR in the changelog) -Where ```` needs to be in ``{ci, fix, feat, docs, refactor, break}``, ```` -should be in ``{framework, baselines, datasets, examples, or '*' when modifying multiple projects which requires the ':skip' flag to be used}``, -and ```` starts with a capitalised verb in the imperative mood. +Where ```` needs to be in ``{ci, fix, feat, docs, refactor, break}``, +```` should be in ``{framework, baselines, datasets, examples, or '*' when +modifying multiple projects which requires the ':skip' flag to be used}``, and +```` starts with a capitalised verb in the imperative mood. Valid examples: diff --git a/doc/source/contributor-tutorial-get-started-as-a-contributor.rst b/doc/source/contributor-tutorial-get-started-as-a-contributor.rst index 3dac8647fa33..60b3ebdef743 100644 --- a/doc/source/contributor-tutorial-get-started-as-a-contributor.rst +++ b/doc/source/contributor-tutorial-get-started-as-a-contributor.rst @@ -9,163 +9,187 @@ Prerequisites - (Optional) `pyenv `_ - (Optional) `pyenv-virtualenv `_ -Flower uses :code:`pyproject.toml` to manage dependencies and configure -development tools (the ones which support it). Poetry is a build tool which -supports `PEP 517 `_. - +Flower uses ``pyproject.toml`` to manage dependencies and configure development tools +(the ones which support it). Poetry is a build tool which supports `PEP 517 +`_. Developer Machine Setup ----------------------- Preliminaries ~~~~~~~~~~~~~ + Some system-wide dependencies are needed. For macOS -^^^^^^^^^ ++++++++++ + +- Install `homebrew `_. Don't forget the post-installation actions to + add `brew` to your PATH. +- Install `xz` (to install different Python versions) and `pandoc` to build the docs: -* Install `homebrew `_. Don't forget the post-installation actions to add `brew` to your PATH. -* Install `xz` (to install different Python versions) and `pandoc` to build the - docs:: + :: - $ brew install xz pandoc + $ brew install xz pandoc For Ubuntu -^^^^^^^^^^ -Ensure you system (Ubuntu 22.04+) is up-to-date, and you have all necessary -packages:: +++++++++++ - $ apt update - $ apt install build-essential zlib1g-dev libssl-dev libsqlite3-dev \ - libreadline-dev libbz2-dev libffi-dev liblzma-dev pandoc +Ensure you system (Ubuntu 22.04+) is up-to-date, and you have all necessary packages: + +:: + $ apt update + $ apt install build-essential zlib1g-dev libssl-dev libsqlite3-dev \ + libreadline-dev libbz2-dev libffi-dev liblzma-dev pandoc Create Flower Dev Environment ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -1. Clone the `Flower repository `_ from -GitHub:: +1. Clone the `Flower repository `_ from GitHub: - $ git clone git@github.com:adap/flower.git - $ cd flower +:: + + $ git clone git@github.com:adap/flower.git + $ cd flower + +2. Let's create the Python environment for all-things Flower. If you wish to use + ``pyenv``, we provide two convenience scripts that you can use. If you prefer using + something else than ``pyenv``, create a new environment, activate and skip to the + last point where all packages are installed. -2. Let's create the Python environment for all-things Flower. If you wish to use :code:`pyenv`, we provide two convenience scripts that you can use. If you prefer using something else than :code:`pyenv`, create a new environment, activate and skip to the last point where all packages are installed. +- If you don't have ``pyenv`` installed, the following script that will install it, set + it up, and create the virtual environment (with ``Python 3.9.20`` by default): -* If you don't have :code:`pyenv` installed, the following script that will install it, set it up, and create the virtual environment (with :code:`Python 3.9.20` by default):: + :: - $ ./dev/setup-defaults.sh # once completed, run the bootstrap script + $ ./dev/setup-defaults.sh # once completed, run the bootstrap script -* If you already have :code:`pyenv` installed (along with the :code:`pyenv-virtualenv` plugin), you can use the following convenience script (with :code:`Python 3.9.20` by default):: +- If you already have ``pyenv`` installed (along with the ``pyenv-virtualenv`` plugin), + you can use the following convenience script (with ``Python 3.9.20`` by default): - $ ./dev/venv-create.sh # once completed, run the `bootstrap.sh` script + :: -3. Install the Flower package in development mode (think -:code:`pip install -e`) along with all necessary dependencies:: + $ ./dev/venv-create.sh # once completed, run the `bootstrap.sh` script - (flower-) $ ./dev/bootstrap.sh +3. Install the Flower package in development mode (think ``pip install -e``) along with +all necessary dependencies: + +:: + (flower-) $ ./dev/bootstrap.sh Convenience Scripts ------------------- -The Flower repository contains a number of convenience scripts to make -recurring development tasks easier and less error-prone. See the :code:`/dev` -subdirectory for a full list. The following scripts are amongst the most -important ones: +The Flower repository contains a number of convenience scripts to make recurring +development tasks easier and less error-prone. See the ``/dev`` subdirectory for a full +list. The following scripts are amongst the most important ones: Create/Delete Virtual Environment ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :: - $ ./dev/venv-create.sh # Default is 3.9.20 - $ ./dev/venv-delete.sh # Default is 3.9.20 + $ ./dev/venv-create.sh # Default is 3.9.20 + $ ./dev/venv-delete.sh # Default is 3.9.20 Compile ProtoBuf Definitions ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :: - $ python -m flwr_tool.protoc + $ python -m flwr_tool.protoc Auto-Format Code ~~~~~~~~~~~~~~~~ :: - $ ./dev/format.sh + $ ./dev/format.sh Run Linters and Tests ~~~~~~~~~~~~~~~~~~~~~ :: - $ ./dev/test.sh + $ ./dev/test.sh Add a pre-commit hook ~~~~~~~~~~~~~~~~~~~~~ -Developers may integrate a pre-commit hook into their workflow utilizing the `pre-commit `_ library. The pre-commit hook is configured to execute two primary operations: ``./dev/format.sh`` and ``./dev/test.sh`` scripts. +Developers may integrate a pre-commit hook into their workflow utilizing the `pre-commit +`_ library. The pre-commit hook is configured to +execute two primary operations: ``./dev/format.sh`` and ``./dev/test.sh`` scripts. There are multiple ways developers can use this: 1. Install the pre-commit hook to your local git directory by simply running: :: - - $ pre-commit install - - Each ``git commit`` will trigger the execution of formatting and linting/test scripts. - - If in a hurry, bypass the hook using ``--no-verify`` with the ``git commit`` command. + $ pre-commit install + + - Each ``git commit`` will trigger the execution of formatting and linting/test + scripts. + - If in a hurry, bypass the hook using ``--no-verify`` with the ``git commit`` + command. + :: - - $ git commit --no-verify -m "Add new feature" - -2. For developers who prefer not to install the hook permanently, it is possible to execute a one-time check prior to committing changes by using the following command: - + + $ git commit --no-verify -m "Add new feature" + +2. For developers who prefer not to install the hook permanently, it is possible to + execute a one-time check prior to committing changes by using the following command: + :: - $ pre-commit run --all-files - - This executes the formatting and linting checks/tests on all the files without modifying the default behavior of ``git commit``. + $ pre-commit run --all-files + + This executes the formatting and linting checks/tests on all the files without + modifying the default behavior of ``git commit``. Run Github Actions (CI) locally ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Developers could run the full set of Github Actions workflows under their local -environment by using `Act `_. Please refer to -the installation instructions under the linked repository and run the next -command under Flower main cloned repository folder:: +environment by using `Act `_. Please refer to the +installation instructions under the linked repository and run the next command under +Flower main cloned repository folder: - $ act +:: -The Flower default workflow would run by setting up the required Docker -machines underneath. + $ act +The Flower default workflow would run by setting up the required Docker machines +underneath. Build Release ------------- -Flower uses Poetry to build releases. The necessary command is wrapped in a -simple script:: +Flower uses Poetry to build releases. The necessary command is wrapped in a simple +script: - $ ./dev/build.sh +:: -The resulting :code:`.whl` and :code:`.tar.gz` releases will be stored in the -:code:`/dist` subdirectory. + $ ./dev/build.sh +The resulting ``.whl`` and ``.tar.gz`` releases will be stored in the ``/dist`` +subdirectory. Build Documentation ------------------- Flower's documentation uses `Sphinx `_. There's no -convenience script to re-build the documentation yet, but it's pretty easy:: +convenience script to re-build the documentation yet, but it's pretty easy: + +:: - $ cd doc - $ make html + $ cd doc + $ make html This will generate HTML documentation in ``doc/build/html``. -Note that, in order to build the documentation locally -(with ``poetry run make html``, like described below), -`Pandoc `_ needs to be installed on the system. +Note that, in order to build the documentation locally (with ``poetry run make html``, +like described below), `Pandoc `_ needs to be +installed on the system. diff --git a/doc/source/docker/enable-tls.rst b/doc/source/docker/enable-tls.rst index ac604b708f88..f50edb8c651d 100644 --- a/doc/source/docker/enable-tls.rst +++ b/doc/source/docker/enable-tls.rst @@ -1,152 +1,152 @@ Enable TLS for Secure Connections ================================= -When operating in a production environment, it is strongly recommended to enable Transport Layer -Security (TLS) for each Flower Component to ensure secure communication. +When operating in a production environment, it is strongly recommended to enable +Transport Layer Security (TLS) for each Flower Component to ensure secure communication. -To enable TLS, you will need a PEM-encoded root certificate, a PEM-encoded private key and a -PEM-encoded certificate chain. +To enable TLS, you will need a PEM-encoded root certificate, a PEM-encoded private key +and a PEM-encoded certificate chain. .. note:: - For testing purposes, you can generate your own self-signed certificates. The - `Enable SSL connections `__ - page contains a section that will guide you through the process. + For testing purposes, you can generate your own self-signed certificates. The + `Enable SSL connections + `__ + page contains a section that will guide you through the process. +Because Flower containers, by default, run with a non-root user ``app``, the mounted +files and directories must have the proper permissions for the user ID ``49999``. -Because Flower containers, by default, run with a non-root user ``app``, the mounted files and -directories must have the proper permissions for the user ID ``49999``. +For example, to change the user ID of all files in the ``certificates/`` directory, you +can run ``sudo chown -R 49999:49999 certificates/*``. -For example, to change the user ID of all files in the ``certificates/`` directory, you can run -``sudo chown -R 49999:49999 certificates/*``. - -If you later want to delete the directory, you can change the user ID back to the current user -ID by running ``sudo chown -R $USER:$(id -gn) state``. +If you later want to delete the directory, you can change the user ID back to the +current user ID by running ``sudo chown -R $USER:$(id -gn) state``. SuperLink --------- -Assuming all files we need are in the local ``certificates`` directory, we can use the flag -``--volume`` to mount the local directory into the ``/app/certificates/`` directory of the container: +Assuming all files we need are in the local ``certificates`` directory, we can use the +flag ``--volume`` to mount the local directory into the ``/app/certificates/`` directory +of the container: .. code-block:: bash - :substitutions: + :substitutions: - $ docker run --rm \ - --volume ./certificates/:/app/certificates/:ro \ - flwr/superlink:|stable_flwr_version| \ - --ssl-ca-certfile certificates/ca.crt \ - --ssl-certfile certificates/server.pem \ - --ssl-keyfile certificates/server.key + $ docker run --rm \ + --volume ./certificates/:/app/certificates/:ro \ + flwr/superlink:|stable_flwr_version| \ + --ssl-ca-certfile certificates/ca.crt \ + --ssl-certfile certificates/server.pem \ + --ssl-keyfile certificates/server.key .. dropdown:: Understanding the command - * ``docker run``: This tells Docker to run a container from an image. - * ``--rm``: Remove the container once it is stopped or the command exits. - * | ``--volume ./certificates/:/app/certificates/:ro``: Mount the ``certificates`` directory in - | the current working directory of the host machine as a read-only volume at the - | ``/app/certificates`` directory inside the container. - | - | This allows the container to access the TLS certificates that are stored in the certificates - | directory. - * | :substitution-code:`flwr/superlink:|stable_flwr_version|`: The name of the image to be run and the specific - | tag of the image. The tag :substitution-code:`|stable_flwr_version|` represents a specific version of the image. - * | ``--ssl-ca-certfile certificates/ca.crt``: Specify the location of the CA certificate file - | inside the container. - | - | The ``certificates/ca.crt`` file is a certificate that is used to verify the identity of the - | SuperLink. - * | ``--ssl-certfile certificates/server.pem``: Specify the location of the SuperLink's - | TLS certificate file inside the container. - | - | The ``certificates/server.pem`` file is used to identify the SuperLink and to encrypt the - | data that is transmitted over the network. - * | ``--ssl-keyfile certificates/server.key``: Specify the location of the SuperLink's - | TLS private key file inside the container. - | - | The ``certificates/server.key`` file is used to decrypt the data that is transmitted over - | the network. + * ``docker run``: This tells Docker to run a container from an image. + * ``--rm``: Remove the container once it is stopped or the command exits. + * | ``--volume ./certificates/:/app/certificates/:ro``: Mount the ``certificates`` directory in + | the current working directory of the host machine as a read-only volume at the + | ``/app/certificates`` directory inside the container. + | + | This allows the container to access the TLS certificates that are stored in the certificates + | directory. + * | :substitution-code:`flwr/superlink:|stable_flwr_version|`: The name of the image to be run and the specific + | tag of the image. The tag :substitution-code:`|stable_flwr_version|` represents a specific version of the image. + * | ``--ssl-ca-certfile certificates/ca.crt``: Specify the location of the CA certificate file + | inside the container. + | + | The ``certificates/ca.crt`` file is a certificate that is used to verify the identity of the + | SuperLink. + * | ``--ssl-certfile certificates/server.pem``: Specify the location of the SuperLink's + | TLS certificate file inside the container. + | + | The ``certificates/server.pem`` file is used to identify the SuperLink and to encrypt the + | data that is transmitted over the network. + * | ``--ssl-keyfile certificates/server.key``: Specify the location of the SuperLink's + | TLS private key file inside the container. + | + | The ``certificates/server.key`` file is used to decrypt the data that is transmitted over + | the network. SuperNode --------- -Assuming that the ``ca.crt`` certificate already exists locally, we can use the flag ``--volume`` to mount the local -certificate into the container's ``/app/`` directory. +Assuming that the ``ca.crt`` certificate already exists locally, we can use the flag +``--volume`` to mount the local certificate into the container's ``/app/`` directory. .. note:: - If you're generating self-signed certificates and the ``ca.crt`` certificate doesn't exist - on the SuperNode, you can copy it over after the generation step. + If you're generating self-signed certificates and the ``ca.crt`` certificate doesn't + exist on the SuperNode, you can copy it over after the generation step. .. code-block:: bash - :substitutions: + :substitutions: - $ docker run --rm \ - --volume ./ca.crt:/app/ca.crt/:ro \ - flwr/supernode:|stable_flwr_version| \ - --root-certificates ca.crt + $ docker run --rm \ + --volume ./ca.crt:/app/ca.crt/:ro \ + flwr/supernode:|stable_flwr_version| \ + --root-certificates ca.crt .. dropdown:: Understanding the command - * ``docker run``: This tells Docker to run a container from an image. - * ``--rm``: Remove the container once it is stopped or the command exits. - * | ``--volume ./ca.crt:/app/ca.crt/:ro``: Mount the ``ca.crt`` file from the - | current working directory of the host machine as a read-only volume at the ``/app/ca.crt`` - | directory inside the container. - * | :substitution-code:`flwr/supernode:|stable_flwr_version|`: The name of the image to be run and the specific - | tag of the image. The tag :substitution-code:`|stable_flwr_version|` represents a specific version of the image. - * | ``--root-certificates ca.crt``: This specifies the location of the CA certificate file - | inside the container. - | - | The ``ca.crt`` file is used to verify the identity of the SuperLink. - + * ``docker run``: This tells Docker to run a container from an image. + * ``--rm``: Remove the container once it is stopped or the command exits. + * | ``--volume ./ca.crt:/app/ca.crt/:ro``: Mount the ``ca.crt`` file from the + | current working directory of the host machine as a read-only volume at the ``/app/ca.crt`` + | directory inside the container. + * | :substitution-code:`flwr/supernode:|stable_flwr_version|`: The name of the image to be run and the specific + | tag of the image. The tag :substitution-code:`|stable_flwr_version|` represents a specific version of the image. + * | ``--root-certificates ca.crt``: This specifies the location of the CA certificate file + | inside the container. + | + | The ``ca.crt`` file is used to verify the identity of the SuperLink. SuperExec --------- -Assuming all files we need are in the local ``certificates`` directory where the SuperExec will be executed from, we can use the flag -``--volume`` to mount the local directory into the ``/app/certificates/`` directory of the container: +Assuming all files we need are in the local ``certificates`` directory where the +SuperExec will be executed from, we can use the flag ``--volume`` to mount the local +directory into the ``/app/certificates/`` directory of the container: .. code-block:: bash - :substitutions: - - $ docker run --rm \ - --volume ./certificates/:/app/certificates/:ro \ - flwr/superexec:|stable_flwr_version| \ - --ssl-ca-certfile certificates/ca.crt \ - --ssl-certfile certificates/server.pem \ - --ssl-keyfile certificates/server.key \ - --executor-config \ - root-certificates=\"certificates/superlink_ca.crt\" + :substitutions: + $ docker run --rm \ + --volume ./certificates/:/app/certificates/:ro \ + flwr/superexec:|stable_flwr_version| \ + --ssl-ca-certfile certificates/ca.crt \ + --ssl-certfile certificates/server.pem \ + --ssl-keyfile certificates/server.key \ + --executor-config \ + root-certificates=\"certificates/superlink_ca.crt\" .. dropdown:: Understanding the command - * ``docker run``: This tells Docker to run a container from an image. - * ``--rm``: Remove the container once it is stopped or the command exits. - * | ``--volume ./certificates/:/app/certificates/:ro``: Mount the ``certificates`` directory in - | the current working directory of the host machine as a read-only volume at the - | ``/app/certificates`` directory inside the container. - | - | This allows the container to access the TLS certificates that are stored in the certificates - | directory. - * | :substitution-code:`flwr/superexec:|stable_flwr_version|`: The name of the image to be run and the specific - | tag of the image. The tag :substitution-code:`|stable_flwr_version|` represents a specific version of the image. - * | ``--ssl-ca-certfile certificates/ca.crt``: Specify the location of the CA certificate file - | inside the container. - | - | The ``certificates/ca.crt`` file is a certificate that is used to verify the identity of the - | SuperExec. - * | ``--ssl-certfile certificates/server.pem``: Specify the location of the SuperExec's - | TLS certificate file inside the container. - | - | The ``certificates/server.pem`` file is used to identify the SuperExec and to encrypt the - | data that is transmitted over the network. - * | ``--ssl-keyfile certificates/server.key``: Specify the location of the SuperExec's - | TLS private key file inside the container. - | - | The ``certificates/server.key`` file is used to decrypt the data that is transmitted over - | the network. - * | ``--executor-config root-certificates=\"certificates/superlink_ca.crt\"``: Specify the - | location of the CA certificate file inside the container that the SuperExec executor - | should use to verify the SuperLink's identity. + * ``docker run``: This tells Docker to run a container from an image. + * ``--rm``: Remove the container once it is stopped or the command exits. + * | ``--volume ./certificates/:/app/certificates/:ro``: Mount the ``certificates`` directory in + | the current working directory of the host machine as a read-only volume at the + | ``/app/certificates`` directory inside the container. + | + | This allows the container to access the TLS certificates that are stored in the certificates + | directory. + * | :substitution-code:`flwr/superexec:|stable_flwr_version|`: The name of the image to be run and the specific + | tag of the image. The tag :substitution-code:`|stable_flwr_version|` represents a specific version of the image. + * | ``--ssl-ca-certfile certificates/ca.crt``: Specify the location of the CA certificate file + | inside the container. + | + | The ``certificates/ca.crt`` file is a certificate that is used to verify the identity of the + | SuperExec. + * | ``--ssl-certfile certificates/server.pem``: Specify the location of the SuperExec's + | TLS certificate file inside the container. + | + | The ``certificates/server.pem`` file is used to identify the SuperExec and to encrypt the + | data that is transmitted over the network. + * | ``--ssl-keyfile certificates/server.key``: Specify the location of the SuperExec's + | TLS private key file inside the container. + | + | The ``certificates/server.key`` file is used to decrypt the data that is transmitted over + | the network. + * | ``--executor-config root-certificates=\"certificates/superlink_ca.crt\"``: Specify the + | location of the CA certificate file inside the container that the SuperExec executor + | should use to verify the SuperLink's identity. diff --git a/doc/source/docker/index.rst b/doc/source/docker/index.rst index 2c972b805930..3fd391114dc1 100644 --- a/doc/source/docker/index.rst +++ b/doc/source/docker/index.rst @@ -1,48 +1,48 @@ Run Flower using Docker ======================= -Start your Flower journey with our pre-made Docker images on Docker Hub, supporting ``amd64`` -and ``arm64v8`` architectures. +Start your Flower journey with our pre-made Docker images on Docker Hub, supporting +``amd64`` and ``arm64v8`` architectures. -Our Quickstart guide walks you through containerizing a Flower project and running it end to -end using Docker. +Our Quickstart guide walks you through containerizing a Flower project and running it +end to end using Docker. Getting Started --------------- .. toctree:: - :maxdepth: 1 - - tutorial-quickstart-docker + :maxdepth: 1 + tutorial-quickstart-docker Running in Production --------------------- .. toctree:: - :maxdepth: 1 + :maxdepth: 1 - enable-tls - persist-superlink-state + enable-tls + persist-superlink-state Advanced Options ---------------- .. toctree:: - :maxdepth: 1 + :maxdepth: 1 - set-environment-variables - run-as-root-user - run-as-subprocess - pin-version - use-a-different-version + set-environment-variables + run-as-root-user + run-as-subprocess + pin-version + use-a-different-version Run Flower using Docker Compose ------------------------------- .. toctree:: - :maxdepth: 1 - tutorial-quickstart-docker-compose - run-quickstart-examples-docker-compose - tutorial-deploy-on-multiple-machines + :maxdepth: 1 + + tutorial-quickstart-docker-compose + run-quickstart-examples-docker-compose + tutorial-deploy-on-multiple-machines diff --git a/doc/source/docker/persist-superlink-state.rst b/doc/source/docker/persist-superlink-state.rst index 68e04ed33762..214e408c44c3 100644 --- a/doc/source/docker/persist-superlink-state.rst +++ b/doc/source/docker/persist-superlink-state.rst @@ -1,39 +1,40 @@ Persist the State of the SuperLink ================================== -By default, the Flower SuperLink keeps its state in-memory. When using the Docker flag ``--rm``, the -state is not persisted between container starts. +By default, the Flower SuperLink keeps its state in-memory. When using the Docker flag +``--rm``, the state is not persisted between container starts. -If you want to persist the state of the SuperLink on your host system, all you need to do is specify -a directory where you want to save the file on your host system and a name for the database file. +If you want to persist the state of the SuperLink on your host system, all you need to +do is specify a directory where you want to save the file on your host system and a name +for the database file. -By default, the SuperLink container runs with a non-root user called ``app`` with the user ID -``49999``. It is recommended to create a new directory and change the user ID of the directory to -``49999`` to ensure the mounted directory has the proper permissions. +By default, the SuperLink container runs with a non-root user called ``app`` with the +user ID ``49999``. It is recommended to create a new directory and change the user ID of +the directory to ``49999`` to ensure the mounted directory has the proper permissions. -If you later want to delete the directory, you can change the user ID back to the current user -ID by running ``sudo chown -R $USER:$(id -gn) state``. +If you later want to delete the directory, you can change the user ID back to the +current user ID by running ``sudo chown -R $USER:$(id -gn) state``. Example ------- -In the example below, we create a new directory called ``state``, change the user ID and tell -Docker via the flag ``--volume`` to mount the local ``state`` directory into the ``/app/state`` -directory of the container. Lastly, we use the flag ``--database`` to specify the name of the -database file. +In the example below, we create a new directory called ``state``, change the user ID and +tell Docker via the flag ``--volume`` to mount the local ``state`` directory into the +``/app/state`` directory of the container. Lastly, we use the flag ``--database`` to +specify the name of the database file. .. code-block:: bash - :substitutions: - - $ mkdir state - $ sudo chown -R 49999:49999 state - $ docker run --rm \ - --volume ./state/:/app/state flwr/superlink:|stable_flwr_version| \ - --database state.db \ - ... - -As soon as the SuperLink starts, the file ``state.db`` is created in the ``state`` directory on -your host system. If the file already exists, the SuperLink tries to restore the state from the -file. To start the SuperLink with an empty database, ensure that there is no database -called ``state.db`` in the ``state`` directory (``rm state.db``) before you execute the -``docker run`` command above. + :substitutions: + + $ mkdir state + $ sudo chown -R 49999:49999 state + $ docker run --rm \ + --volume ./state/:/app/state flwr/superlink:|stable_flwr_version| \ + --database state.db \ + ... + +As soon as the SuperLink starts, the file ``state.db`` is created in the ``state`` +directory on your host system. If the file already exists, the SuperLink tries to +restore the state from the file. To start the SuperLink with an empty database, ensure +that there is no database called ``state.db`` in the ``state`` directory (``rm +state.db``) before you execute the ``docker run`` command above. diff --git a/doc/source/docker/pin-version.rst b/doc/source/docker/pin-version.rst index 800e3ed95423..4a69860aa428 100644 --- a/doc/source/docker/pin-version.rst +++ b/doc/source/docker/pin-version.rst @@ -1,10 +1,11 @@ Pin a Docker Image to a Specific Version ======================================== -It may happen that we update the images behind the tags. Such updates usually include security -updates of system dependencies that should not change the functionality of Flower. However, if -you want to ensure that you use a fixed version of the Docker image in your deployments, you can -`specify the digest `_ +It may happen that we update the images behind the tags. Such updates usually include +security updates of system dependencies that should not change the functionality of +Flower. However, if you want to ensure that you use a fixed version of the Docker image +in your deployments, you can `specify the digest +`_ of the image instead of the tag. Example @@ -14,23 +15,23 @@ The following command returns the current image digest referenced by the :substitution-code:`superlink:|stable_flwr_version|` tag: .. code-block:: bash - :substitutions: + :substitutions: - $ docker pull flwr/superlink:|stable_flwr_version| - $ docker inspect --format='{{index .RepoDigests 0}}' flwr/superlink:|stable_flwr_version| + $ docker pull flwr/superlink:|stable_flwr_version| + $ docker inspect --format='{{index .RepoDigests 0}}' flwr/superlink:|stable_flwr_version| This will output .. code-block:: bash - :substitutions: + :substitutions: - flwr/superlink@sha256:|stable__flwr_superlink_docker_digest| + flwr/superlink@sha256:|stable__flwr_superlink_docker_digest| Next, we can pin the digest when running a new SuperLink container: .. code-block:: bash - :substitutions: + :substitutions: - $ docker run \ - --rm flwr/superlink@sha256:|latest_version_docker_sha| \ - [OPTIONS] + $ docker run \ + --rm flwr/superlink@sha256:|latest_version_docker_sha| \ + [OPTIONS] diff --git a/doc/source/docker/run-as-root-user.rst b/doc/source/docker/run-as-root-user.rst index d1b41a9b6168..5f8e5eae43af 100644 --- a/doc/source/docker/run-as-root-user.rst +++ b/doc/source/docker/run-as-root-user.rst @@ -2,11 +2,11 @@ Run with Root User Privileges ============================= Flower Docker images, by default, run with a non-root user (username/groupname: ``app``, -UID/GID: ``49999``). Using root user is **not recommended** unless it is necessary for specific -tasks during the build process. +UID/GID: ``49999``). Using root user is **not recommended** unless it is necessary for +specific tasks during the build process. -Always make sure to run the container as a non-root user in production to maintain security -best practices. +Always make sure to run the container as a non-root user in production to maintain +security best practices. Run a Container with Root User Privileges ----------------------------------------- @@ -14,32 +14,33 @@ Run a Container with Root User Privileges Run the Docker image with the ``-u`` flag and specify ``root`` as the username: .. code-block:: bash - :substitutions: + :substitutions: - $ docker run --rm -u root flwr/superlink:|stable_flwr_version| + $ docker run --rm -u root flwr/superlink:|stable_flwr_version| This command will run the Docker container with root user privileges. Run the Build Process with Root User Privileges ----------------------------------------------- -If you want to switch to the root user during the build process of the Docker image to install -missing system dependencies, you can use the ``USER root`` directive within your Dockerfile. +If you want to switch to the root user during the build process of the Docker image to +install missing system dependencies, you can use the ``USER root`` directive within your +Dockerfile. .. code-block:: dockerfile - :caption: SuperNode Dockerfile - :substitutions: + :caption: SuperNode Dockerfile + :substitutions: - FROM flwr/supernode:|stable_flwr_version| + FROM flwr/supernode:|stable_flwr_version| - # Switch to root user - USER root + # Switch to root user + USER root - # Install missing dependencies (requires root access) - RUN apt-get update && apt-get install -y + # Install missing dependencies (requires root access) + RUN apt-get update && apt-get install -y - # Switch back to non-root user app - USER app + # Switch back to non-root user app + USER app - # Continue with your Docker image build process - # ... + # Continue with your Docker image build process + # ... diff --git a/doc/source/docker/run-as-subprocess.rst b/doc/source/docker/run-as-subprocess.rst index f8c482f632a0..d97319ff52af 100644 --- a/doc/source/docker/run-as-subprocess.rst +++ b/doc/source/docker/run-as-subprocess.rst @@ -1,53 +1,53 @@ Run ClientApp as a Subprocess ============================= -In this mode, the ClientApp is executed as a subprocess within the SuperNode Docker container, -rather than running in a separate container. This approach reduces the number of running containers, -which can be beneficial for environments with limited resources. However, it also means that the -ClientApp is no longer isolated from the SuperNode, which may introduce additional security -concerns. +In this mode, the ClientApp is executed as a subprocess within the SuperNode Docker +container, rather than running in a separate container. This approach reduces the number +of running containers, which can be beneficial for environments with limited resources. +However, it also means that the ClientApp is no longer isolated from the SuperNode, +which may introduce additional security concerns. Prerequisites ------------- -#. Before running the ClientApp as a subprocess, ensure that the FAB dependencies have been installed - in the SuperNode images. This can be done by extending the SuperNode image: +1. Before running the ClientApp as a subprocess, ensure that the FAB dependencies have + been installed in the SuperNode images. This can be done by extending the SuperNode + image: .. code-block:: dockerfile - :caption: Dockerfile.supernode - :linenos: - :substitutions: + :caption: Dockerfile.supernode + :linenos: + :substitutions: - FROM flwr/supernode:|stable_flwr_version| + FROM flwr/supernode:|stable_flwr_version| - WORKDIR /app - COPY pyproject.toml . - RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml \ - && python -m pip install -U --no-cache-dir . + WORKDIR /app + COPY pyproject.toml . + RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml \ + && python -m pip install -U --no-cache-dir . - ENTRYPOINT ["flower-supernode"] + ENTRYPOINT ["flower-supernode"] -#. Next, build the SuperNode Docker image by running the following command in the directory where - Dockerfile is located: +2. Next, build the SuperNode Docker image by running the following command in the + directory where Dockerfile is located: .. code-block:: shell - $ docker build -f Dockerfile.supernode -t flwr_supernode:0.0.1 . - + $ docker build -f Dockerfile.supernode -t flwr_supernode:0.0.1 . Run the ClientApp as a Subprocess --------------------------------- -Start the SuperNode with the flag ``--isolation subprocess``, which tells the SuperNode to execute -the ClientApp as a subprocess: +Start the SuperNode with the flag ``--isolation subprocess``, which tells the SuperNode +to execute the ClientApp as a subprocess: .. code-block:: shell - $ docker run --rm \ - --detach \ - flwr_supernode:0.0.1 \ - --insecure \ - --superlink superlink:9092 \ - --node-config "partition-id=1 num-partitions=2" \ - --supernode-address localhost:9094 \ - --isolation subprocess + $ docker run --rm \ + --detach \ + flwr_supernode:0.0.1 \ + --insecure \ + --superlink superlink:9092 \ + --node-config "partition-id=1 num-partitions=2" \ + --supernode-address localhost:9094 \ + --isolation subprocess diff --git a/doc/source/docker/run-quickstart-examples-docker-compose.rst b/doc/source/docker/run-quickstart-examples-docker-compose.rst index 5bdb33e991dd..b31f0035e143 100644 --- a/doc/source/docker/run-quickstart-examples-docker-compose.rst +++ b/doc/source/docker/run-quickstart-examples-docker-compose.rst @@ -1,15 +1,16 @@ Run Flower Quickstart Examples with Docker Compose ================================================== -Flower provides a set of `quickstart examples `_ -to help you get started with the framework. These examples are designed to demonstrate the -capabilities of Flower and by default run using the Simulation Engine. This guide demonstrates -how to run them using Flower's Deployment Engine via Docker Compose. +Flower provides a set of `quickstart examples +`_ to help you get started with the +framework. These examples are designed to demonstrate the capabilities of Flower and by +default run using the Simulation Engine. This guide demonstrates how to run them using +Flower's Deployment Engine via Docker Compose. .. important:: - Some quickstart examples may have limitations or requirements that prevent them from running - on every environment. For more information, please see `Limitations`_. + Some quickstart examples may have limitations or requirements that prevent them from + running on every environment. For more information, please see Limitations_. Prerequisites ------------- @@ -23,68 +24,71 @@ Before you start, make sure that: Run the Quickstart Example -------------------------- -#. Clone the quickstart example you like to run. For example, ``quickstart-pytorch``: +1. Clone the quickstart example you like to run. For example, ``quickstart-pytorch``: .. code-block:: bash - $ git clone --depth=1 https://github.com/adap/flower.git \ - && mv flower/examples/quickstart-pytorch . \ - && rm -rf flower && cd quickstart-pytorch + $ git clone --depth=1 https://github.com/adap/flower.git \ + && mv flower/examples/quickstart-pytorch . \ + && rm -rf flower && cd quickstart-pytorch -#. Download the `compose.yml `_ file into the example directory: +2. Download the `compose.yml + `_ file + into the example directory: .. code-block:: bash - $ curl https://raw.githubusercontent.com/adap/flower/refs/heads/main/src/docker/complete/compose.yml \ - -o compose.yml + $ curl https://raw.githubusercontent.com/adap/flower/refs/heads/main/src/docker/complete/compose.yml \ + -o compose.yml -#. Build and start the services using the following command: +3. Build and start the services using the following command: .. code-block:: bash - $ docker compose up --build -d + $ docker compose up --build -d -#. Append the following lines to the end of the ``pyproject.toml`` file and save it: +4. Append the following lines to the end of the ``pyproject.toml`` file and save it: .. code-block:: toml - :caption: pyproject.toml + :caption: pyproject.toml - [tool.flwr.federations.local-deployment] - address = "127.0.0.1:9093" - insecure = true + [tool.flwr.federations.local-deployment] + address = "127.0.0.1:9093" + insecure = true .. note:: - You can customize the string that follows ``tool.flwr.federations.`` to fit your needs. - However, please note that the string cannot contain a dot (``.``). + You can customize the string that follows ``tool.flwr.federations.`` to fit your + needs. However, please note that the string cannot contain a dot (``.``). - In this example, ``local-deployment`` has been used. Just remember to replace - ``local-deployment`` with your chosen name in both the ``tool.flwr.federations.`` string - and the corresponding ``flwr run .`` command. + In this example, ``local-deployment`` has been used. Just remember to replace + ``local-deployment`` with your chosen name in both the ``tool.flwr.federations.`` + string and the corresponding ``flwr run .`` command. -#. Run the example: +5. Run the example: .. code-block:: bash - $ flwr run . local-deployment + $ flwr run . local-deployment -#. Follow the logs of the SuperExec service: +6. Follow the logs of the SuperExec service: .. code-block:: bash - $ docker compose logs superexec -f + $ docker compose logs superexec -f -That is all it takes! You can monitor the progress of the run through the logs of the SuperExec. +That is all it takes! You can monitor the progress of the run through the logs of the +SuperExec. Run a Different Quickstart Example ---------------------------------- -To run a different quickstart example, such as ``quickstart-tensorflow``, first, shut down the Docker -Compose services of the current example: +To run a different quickstart example, such as ``quickstart-tensorflow``, first, shut +down the Docker Compose services of the current example: .. code-block:: bash - $ docker compose down + $ docker compose down After that, you can repeat the steps above. @@ -92,31 +96,32 @@ Limitations ----------- .. list-table:: - :header-rows: 1 - - * - Quickstart Example - - Limitations - * - quickstart-fastai - - None - * - quickstart-huggingface - - None - * - quickstart-jax - - The example has not yet been updated to work with the latest ``flwr`` version. - * - quickstart-mlcube - - The example has not yet been updated to work with the latest ``flwr`` version. - * - quickstart-mlx - - `Requires to run on macOS with Apple Silicon `_. - * - quickstart-monai - - None - * - quickstart-pandas - - None - * - quickstart-pytorch-lightning - - Requires an older pip version that is not supported by the Flower Docker images. - * - quickstart-pytorch - - None - * - quickstart-sklearn-tabular - - None - * - quickstart-tabnet - - The example has not yet been updated to work with the latest ``flwr`` version. - * - quickstart-tensorflow - - Only runs on AMD64. + :header-rows: 1 + + - - Quickstart Example + - Limitations + - - quickstart-fastai + - None + - - quickstart-huggingface + - None + - - quickstart-jax + - The example has not yet been updated to work with the latest ``flwr`` version. + - - quickstart-mlcube + - The example has not yet been updated to work with the latest ``flwr`` version. + - - quickstart-mlx + - `Requires to run on macOS with Apple Silicon + `_. + - - quickstart-monai + - None + - - quickstart-pandas + - None + - - quickstart-pytorch-lightning + - Requires an older pip version that is not supported by the Flower Docker images. + - - quickstart-pytorch + - None + - - quickstart-sklearn-tabular + - None + - - quickstart-tabnet + - The example has not yet been updated to work with the latest ``flwr`` version. + - - quickstart-tensorflow + - Only runs on AMD64. diff --git a/doc/source/docker/set-environment-variables.rst b/doc/source/docker/set-environment-variables.rst index ff8d6dde0a29..f5d860812bab 100644 --- a/doc/source/docker/set-environment-variables.rst +++ b/doc/source/docker/set-environment-variables.rst @@ -8,7 +8,7 @@ Example ------- .. code-block:: bash - :substitutions: + :substitutions: - $ docker run -e FLWR_TELEMETRY_ENABLED=0 -e FLWR_TELEMETRY_LOGGING=0 \ - --rm flwr/superlink:|stable_flwr_version| + $ docker run -e FLWR_TELEMETRY_ENABLED=0 -e FLWR_TELEMETRY_LOGGING=0 \ + --rm flwr/superlink:|stable_flwr_version| diff --git a/doc/source/docker/tutorial-deploy-on-multiple-machines.rst b/doc/source/docker/tutorial-deploy-on-multiple-machines.rst index 7b6cec8292eb..72958c926ba9 100644 --- a/doc/source/docker/tutorial-deploy-on-multiple-machines.rst +++ b/doc/source/docker/tutorial-deploy-on-multiple-machines.rst @@ -1,191 +1,171 @@ -######################################################## - Deploy Flower on Multiple Machines with Docker Compose -######################################################## - -This guide will help you set up a Flower project on multiple machines -using Docker Compose. - -You will learn how to run the Flower client and server components on two -separate machines, with Flower configured to use TLS encryption and -persist SuperLink state across restarts. A server consists of a -SuperLink and ``SuperExec``. For more details about the Flower -architecture, refer to the :doc:`../explanation-flower-architecture` +Deploy Flower on Multiple Machines with Docker Compose +====================================================== + +This guide will help you set up a Flower project on multiple machines using Docker +Compose. + +You will learn how to run the Flower client and server components on two separate +machines, with Flower configured to use TLS encryption and persist SuperLink state +across restarts. A server consists of a SuperLink and ``SuperExec``. For more details +about the Flower architecture, refer to the :doc:`../explanation-flower-architecture` explainer page. -This guide assumes you have completed the -:doc:`tutorial-quickstart-docker-compose` tutorial. It is highly -recommended that you follow and understand the contents of that tutorial -before proceeding with this guide. +This guide assumes you have completed the :doc:`tutorial-quickstart-docker-compose` +tutorial. It is highly recommended that you follow and understand the contents of that +tutorial before proceeding with this guide. -*************** - Prerequisites -*************** +Prerequisites +------------- Before you begin, make sure you have the following prerequisites: -- The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` - locally. -- The Docker daemon is running on your local machine and the remote - machine. -- Docker Compose V2 is installed on both your local machine and the - remote machine. -- You can connect to the remote machine from your local machine. -- Ports ``9091`` and ``9093`` are accessible on the remote machine. +- The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally. +- The Docker daemon is running on your local machine and the remote machine. +- Docker Compose V2 is installed on both your local machine and the remote machine. +- You can connect to the remote machine from your local machine. +- Ports ``9091`` and ``9093`` are accessible on the remote machine. .. note:: - The guide uses the |quickstart_sklearn_tabular|_ example as an - example project. + The guide uses the |quickstart_sklearn_tabular|_ example as an example project. - If your project has a different name or location, please remember to - adjust the commands/paths accordingly. + If your project has a different name or location, please remember to adjust the + commands/paths accordingly. -**************** - Step 1: Set Up -**************** +Step 1: Set Up +-------------- -#. Clone the Flower repository and change to the ``distributed`` - directory: +1. Clone the Flower repository and change to the ``distributed`` directory: - .. code:: bash + .. code-block:: bash - $ git clone --depth=1 https://github.com/adap/flower.git - $ cd flower/src/docker/distributed + $ git clone --depth=1 https://github.com/adap/flower.git + $ cd flower/src/docker/distributed -#. Get the IP address from the remote machine and save it for later. - -#. Use the ``certs.yml`` Compose file to generate your own self-signed - certificates. If you have certificates, you can continue with Step 2. +2. Get the IP address from the remote machine and save it for later. +3. Use the ``certs.yml`` Compose file to generate your own self-signed certificates. If + you have certificates, you can continue with Step 2. .. important:: - These certificates should be used only for development purposes. + These certificates should be used only for development purposes. - For production environments, you may have to use dedicated - services to obtain your certificates. + For production environments, you may have to use dedicated services to obtain + your certificates. - First, set the environment variables ``SUPERLINK_IP`` and - ``SUPEREXEC_IP`` with the IP address from the remote machine. For - example, if the IP is ``192.168.2.33``, execute: + First, set the environment variables ``SUPERLINK_IP`` and ``SUPEREXEC_IP`` with the + IP address from the remote machine. For example, if the IP is ``192.168.2.33``, + execute: - .. code:: bash + .. code-block:: bash - $ export SUPERLINK_IP=192.168.2.33 - $ export SUPEREXEC_IP=192.168.2.33 + $ export SUPERLINK_IP=192.168.2.33 + $ export SUPEREXEC_IP=192.168.2.33 Next, generate the self-signed certificates: - .. code:: bash + .. code-block:: bash - $ docker compose -f certs.yml -f ../complete/certs.yml up --build + $ docker compose -f certs.yml -f ../complete/certs.yml up --build -*************************************** - Step 2: Copy the Server Compose Files -*************************************** +Step 2: Copy the Server Compose Files +------------------------------------- -Use the method that works best for you to copy the ``server`` directory, -the certificates, and your Flower project to the remote machine. +Use the method that works best for you to copy the ``server`` directory, the +certificates, and your Flower project to the remote machine. For example, you can use ``scp`` to copy the directories: -.. code:: bash +.. code-block:: bash - $ scp -r ./server \ - ./superexec-certificates \ - ./superlink-certificates \ - ../../../examples/quickstart-sklearn-tabular remote:~/distributed + $ scp -r ./server \ + ./superexec-certificates \ + ./superlink-certificates \ + ../../../examples/quickstart-sklearn-tabular remote:~/distributed -******************************************** - Step 3: Start the Flower Server Components -******************************************** +Step 3: Start the Flower Server Components +------------------------------------------ -Log into the remote machine using ``ssh`` and run the following command -to start the SuperLink and SuperExec services: +Log into the remote machine using ``ssh`` and run the following command to start the +SuperLink and SuperExec services: -.. code:: bash +.. code-block:: bash - $ ssh - # In your remote machine - $ cd - $ export PROJECT_DIR=../quickstart-sklearn-tabular - $ docker compose -f server/compose.yml up --build -d + $ ssh + # In your remote machine + $ cd + $ export PROJECT_DIR=../quickstart-sklearn-tabular + $ docker compose -f server/compose.yml up --build -d .. note:: - The Path of the ``PROJECT_DIR`` should be relative to the location of - the ``server`` Docker Compose files. + The Path of the ``PROJECT_DIR`` should be relative to the location of the ``server`` + Docker Compose files. Go back to your terminal on your local machine. -******************************************** - Step 4: Start the Flower Client Components -******************************************** +Step 4: Start the Flower Client Components +------------------------------------------ -On your local machine, run the following command to start the client -components: +On your local machine, run the following command to start the client components: -.. code:: bash +.. code-block:: bash - # In the `docker/distributed` directory - $ export PROJECT_DIR=../../../../examples/quickstart-sklearn-tabular - $ docker compose -f client/compose.yml up --build -d + # In the `docker/distributed` directory + $ export PROJECT_DIR=../../../../examples/quickstart-sklearn-tabular + $ docker compose -f client/compose.yml up --build -d .. note:: - The Path of the ``PROJECT_DIR`` should be relative to the location of - the ``client`` Docker Compose files. + The Path of the ``PROJECT_DIR`` should be relative to the location of the ``client`` + Docker Compose files. -********************************* - Step 5: Run Your Flower Project -********************************* +Step 5: Run Your Flower Project +------------------------------- -Specify the remote SuperExec IP addresses and the path to the root -certificate in the ``[tool.flwr.federations.remote-superexec]`` table in -the ``pyproject.toml`` file. Here, we have named our remote federation -``remote-superexec``: +Specify the remote SuperExec IP addresses and the path to the root certificate in the +``[tool.flwr.federations.remote-superexec]`` table in the ``pyproject.toml`` file. Here, +we have named our remote federation ``remote-superexec``: -.. code:: toml - :caption: examples/quickstart-sklearn-tabular/pyproject.toml +.. code-block:: toml + :caption: examples/quickstart-sklearn-tabular/pyproject.toml - [tool.flwr.federations.remote-superexec] - address = "192.168.2.33:9093" - root-certificates = "../../src/docker/distributed/superexec-certificates/ca.crt" + [tool.flwr.federations.remote-superexec] + address = "192.168.2.33:9093" + root-certificates = "../../src/docker/distributed/superexec-certificates/ca.crt" .. note:: - The Path of the ``root-certificates`` should be relative to the - location of the ``pyproject.toml`` file. + The Path of the ``root-certificates`` should be relative to the location of the + ``pyproject.toml`` file. To run the project, execute: -.. code:: bash +.. code-block:: bash - $ flwr run ../../../examples/quickstart-sklearn-tabular remote-superexec + $ flwr run ../../../examples/quickstart-sklearn-tabular remote-superexec -That's it! With these steps, you've set up Flower on two separate -machines and are ready to start using it. +That's it! With these steps, you've set up Flower on two separate machines and are ready +to start using it. -****************** - Step 6: Clean Up -****************** +Step 6: Clean Up +---------------- Shut down the Flower client components: -.. code:: bash +.. code-block:: bash - # In the `docker/distributed` directory - $ docker compose -f client/compose.yml down + # In the `docker/distributed` directory + $ docker compose -f client/compose.yml down Shut down the Flower server components and delete the SuperLink state: -.. code:: bash - - $ ssh - $ cd - $ docker compose -f server/compose.yml down -v +.. code-block:: bash -.. |quickstart_sklearn_tabular| replace:: + $ ssh + $ cd + $ docker compose -f server/compose.yml down -v - ``examples/quickstart-sklearn-tabular`` +.. |quickstart_sklearn_tabular| replace:: ``examples/quickstart-sklearn-tabular`` .. _quickstart_sklearn_tabular: https://github.com/adap/flower/tree/main/examples/quickstart-sklearn-tabular diff --git a/doc/source/docker/tutorial-quickstart-docker-compose.rst b/doc/source/docker/tutorial-quickstart-docker-compose.rst index 7aeae1e2fb6b..bff3125c1b16 100644 --- a/doc/source/docker/tutorial-quickstart-docker-compose.rst +++ b/doc/source/docker/tutorial-quickstart-docker-compose.rst @@ -2,11 +2,12 @@ Quickstart with Docker Compose ============================== This quickstart shows you how to set up Flower using Docker Compose in a single command, -allowing you to focus on developing your application without worrying about the underlying -infrastructure. +allowing you to focus on developing your application without worrying about the +underlying infrastructure. -You will also learn how to easily enable TLS encryption and persist application state locally, -giving you the freedom to choose the configuration that best suits your project's needs. +You will also learn how to easily enable TLS encryption and persist application state +locally, giving you the freedom to choose the configuration that best suits your +project's needs. Prerequisites ------------- @@ -20,55 +21,56 @@ Before you start, make sure that: Step 1: Set Up -------------- -#. Clone the Docker Compose ``complete`` directory: +1. Clone the Docker Compose ``complete`` directory: .. code-block:: bash - $ git clone --depth=1 https://github.com/adap/flower.git _tmp \ - && mv _tmp/src/docker/complete . \ - && rm -rf _tmp && cd complete + $ git clone --depth=1 https://github.com/adap/flower.git _tmp \ + && mv _tmp/src/docker/complete . \ + && rm -rf _tmp && cd complete -#. Create a new Flower project (PyTorch): +2. Create a new Flower project (PyTorch): .. code-block:: bash - $ flwr new quickstart-compose --framework PyTorch --username flower + $ flwr new quickstart-compose --framework PyTorch --username flower -#. Export the path of the newly created project. The path should be relative to the location of the - Docker Compose files: +3. Export the path of the newly created project. The path should be relative to the + location of the Docker Compose files: .. code-block:: bash - $ export PROJECT_DIR=quickstart-compose + $ export PROJECT_DIR=quickstart-compose - Setting the ``PROJECT_DIR`` helps Docker Compose locate the ``pyproject.toml`` file, allowing - it to install dependencies in the SuperExec and SuperNode images correctly. + Setting the ``PROJECT_DIR`` helps Docker Compose locate the ``pyproject.toml`` file, + allowing it to install dependencies in the SuperExec and SuperNode images correctly. Step 2: Run Flower in Insecure Mode ----------------------------------- -To begin, start Flower with the most basic configuration. In this setup, Flower -will run without TLS and without persisting the state. +To begin, start Flower with the most basic configuration. In this setup, Flower will run +without TLS and without persisting the state. .. note:: - Without TLS, the data sent between the services remains **unencrypted**. Use it only for development - purposes. + Without TLS, the data sent between the services remains **unencrypted**. Use it only + for development purposes. - For production-oriented use cases, :ref:`enable TLS` for secure data transmission. + For production-oriented use cases, :ref:`enable TLS` for secure data + transmission. Open your terminal and run: .. code-block:: bash - $ docker compose -f compose.yml up --build -d + $ docker compose -f compose.yml up --build -d .. dropdown:: Understand the command - * ``docker compose``: The Docker command to run the Docker Compose tool. - * ``-f compose.yml``: Specify the YAML file that contains the basic Flower service definitions. - * ``--build``: Rebuild the images for each service if they don't already exist. - * ``-d``: Detach the containers from the terminal and run them in the background. + * ``docker compose``: The Docker command to run the Docker Compose tool. + * ``-f compose.yml``: Specify the YAML file that contains the basic Flower service definitions. + * ``--build``: Rebuild the images for each service if they don't already exist. + * ``-d``: Detach the containers from the terminal and run them in the background. Step 3: Run the Quickstart Project ---------------------------------- @@ -76,316 +78,321 @@ Step 3: Run the Quickstart Project Now that the Flower services have been started via Docker Compose, it is time to run the quickstart example. -To ensure the ``flwr`` CLI connects to the SuperExec, you need to specify the SuperExec addresses -in the ``pyproject.toml`` file. +To ensure the ``flwr`` CLI connects to the SuperExec, you need to specify the SuperExec +addresses in the ``pyproject.toml`` file. -#. Add the following lines to the ``quickstart-compose/pyproject.toml``: +1. Add the following lines to the ``quickstart-compose/pyproject.toml``: .. code-block:: toml - :caption: quickstart-compose/pyproject.toml + :caption: quickstart-compose/pyproject.toml - [tool.flwr.federations.docker-compose] - address = "127.0.0.1:9093" - insecure = true + [tool.flwr.federations.docker-compose] + address = "127.0.0.1:9093" + insecure = true -#. Execute the command to run the quickstart example: +2. Execute the command to run the quickstart example: .. code-block:: bash - $ flwr run quickstart-compose docker-compose + $ flwr run quickstart-compose docker-compose -#. Monitor the SuperExec logs and wait for the summary to appear: +3. Monitor the SuperExec logs and wait for the summary to appear: .. code-block:: bash - $ docker compose logs superexec -f + $ docker compose logs superexec -f Step 4: Update the Application ------------------------------ In the next step, change the application code. -#. For example, go to the ``task.py`` file in the ``quickstart-compose/quickstart_compose/`` - directory and add a ``print`` call in the ``get_weights`` function: +1. For example, go to the ``task.py`` file in the + ``quickstart-compose/quickstart_compose/`` directory and add a ``print`` call in the + ``get_weights`` function: .. code-block:: python - :caption: quickstart-compose/quickstart_compose/task.py + :caption: quickstart-compose/quickstart_compose/task.py - # ... - def get_weights(net): - print("Get weights") - return [val.cpu().numpy() for _, val in net.state_dict().items()] - # ... + # ... + def get_weights(net): + print("Get weights") + return [val.cpu().numpy() for _, val in net.state_dict().items()] -#. Rebuild and restart the services. + + # ... + +2. Rebuild and restart the services. .. note:: - If you have modified the dependencies listed in your ``pyproject.toml`` file, it is essential - to rebuild images. + If you have modified the dependencies listed in your ``pyproject.toml`` file, it + is essential to rebuild images. - If you haven't made any changes, you can skip this step. + If you haven't made any changes, you can skip this step. Run the following command to rebuild and restart the services: .. code-block:: bash - $ docker compose -f compose.yml up --build -d + $ docker compose -f compose.yml up --build -d -#. Run the updated quickstart example: +3. Run the updated quickstart example: .. code-block:: bash - $ flwr run quickstart-compose docker-compose - $ docker compose logs superexec -f + $ flwr run quickstart-compose docker-compose + $ docker compose logs superexec -f In the SuperExec logs, you should find the ``Get weights`` line: .. code-block:: - :emphasize-lines: 9 - - superexec-1 | INFO : Starting Flower SuperExec - superexec-1 | WARNING : Option `--insecure` was set. Starting insecure HTTP server. - superexec-1 | INFO : Starting Flower SuperExec gRPC server on 0.0.0.0:9093 - superexec-1 | INFO : ExecServicer.StartRun - superexec-1 | 🎊 Successfully installed quickstart-compose to /app/.flwr/apps/flower/quickstart-compose/1.0.0. - superexec-1 | INFO : Created run -6767165609169293507 - superexec-1 | INFO : Started run -6767165609169293507 - superexec-1 | WARNING : Option `--insecure` was set. Starting insecure HTTP client connected to superlink:9091. - superexec-1 | Get weights - superexec-1 | INFO : Starting Flower ServerApp, config: num_rounds=3, no round_timeout + :emphasize-lines: 9 + + superexec-1 | INFO : Starting Flower SuperExec + superexec-1 | WARNING : Option `--insecure` was set. Starting insecure HTTP server. + superexec-1 | INFO : Starting Flower SuperExec gRPC server on 0.0.0.0:9093 + superexec-1 | INFO : ExecServicer.StartRun + superexec-1 | 🎊 Successfully installed quickstart-compose to /app/.flwr/apps/flower/quickstart-compose/1.0.0. + superexec-1 | INFO : Created run -6767165609169293507 + superexec-1 | INFO : Started run -6767165609169293507 + superexec-1 | WARNING : Option `--insecure` was set. Starting insecure HTTP client connected to superlink:9091. + superexec-1 | Get weights + superexec-1 | INFO : Starting Flower ServerApp, config: num_rounds=3, no round_timeout Step 5: Persisting the SuperLink State -------------------------------------- -In this step, Flower services are configured to persist the state of the SuperLink service, -ensuring that it maintains its state even after a restart. +In this step, Flower services are configured to persist the state of the SuperLink +service, ensuring that it maintains its state even after a restart. .. note:: - When working with Docker Compose on Linux, you may need to create the ``state`` directory first - and change its ownership to ensure proper access and permissions. + When working with Docker Compose on Linux, you may need to create the ``state`` + directory first and change its ownership to ensure proper access and permissions. For more information, consult the following page: :doc:`persist-superlink-state`. -#. Run the command: +1. Run the command: .. code-block:: bash - $ docker compose -f compose.yml -f with-state.yml up --build -d + $ docker compose -f compose.yml -f with-state.yml up --build -d .. dropdown:: Understand the command - * ``docker compose``: The Docker command to run the Docker Compose tool. - * ``-f compose.yml``: Specify the YAML file that contains the basic Flower service definitions. - * | ``-f with-state.yml``: Specifies the path to an additional Docker Compose file that - | contains the configuration for persisting the SuperLink state. - | - | Docker merges Compose files according to `merging rules `_. - * ``--build``: Rebuild the images for each service if they don't already exist. - * ``-d``: Detach the containers from the terminal and run them in the background. + * ``docker compose``: The Docker command to run the Docker Compose tool. + * ``-f compose.yml``: Specify the YAML file that contains the basic Flower service definitions. + * | ``-f with-state.yml``: Specifies the path to an additional Docker Compose file that + | contains the configuration for persisting the SuperLink state. + | + | Docker merges Compose files according to `merging rules `_. + * ``--build``: Rebuild the images for each service if they don't already exist. + * ``-d``: Detach the containers from the terminal and run them in the background. -#. Rerun the ``quickstart-compose`` project: +2. Rerun the ``quickstart-compose`` project: .. code-block:: bash - $ flwr run quickstart-compose docker-compose + $ flwr run quickstart-compose docker-compose -#. Check the content of the ``state`` directory: +3. Check the content of the ``state`` directory: .. code-block:: bash - $ ls state/ - state.db + $ ls state/ + state.db - You should see a ``state.db`` file in the ``state`` directory. If you restart the service, the - state file will be used to restore the state from the previously saved data. This ensures that - the data persists even if the containers are stopped and started again. + You should see a ``state.db`` file in the ``state`` directory. If you restart the + service, the state file will be used to restore the state from the previously saved + data. This ensures that the data persists even if the containers are stopped and + started again. -.. _TLS: +.. _tls: Step 6: Run Flower with TLS --------------------------- -#. To demonstrate how to enable TLS, generate self-signed certificates using the ``certs.yml`` - Compose file. +1. To demonstrate how to enable TLS, generate self-signed certificates using the + ``certs.yml`` Compose file. .. important:: - These certificates should be used only for development purposes. + These certificates should be used only for development purposes. - For production environments, use a service like `Let's Encrypt `_ - to obtain your certificates. + For production environments, use a service like `Let's Encrypt + `_ to obtain your certificates. Run the command: .. code-block:: bash - $ docker compose -f certs.yml up --build + $ docker compose -f certs.yml up --build -#. Add the following lines to the ``quickstart-compose/pyproject.toml``: +2. Add the following lines to the ``quickstart-compose/pyproject.toml``: .. code-block:: toml - :caption: quickstart-compose/pyproject.toml + :caption: quickstart-compose/pyproject.toml - [tool.flwr.federations.docker-compose-tls] - address = "127.0.0.1:9093" - root-certificates = "../superexec-certificates/ca.crt" + [tool.flwr.federations.docker-compose-tls] + address = "127.0.0.1:9093" + root-certificates = "../superexec-certificates/ca.crt" -#. Restart the services with TLS enabled: +3. Restart the services with TLS enabled: .. code-block:: bash - $ docker compose -f compose.yml -f with-tls.yml up --build -d + $ docker compose -f compose.yml -f with-tls.yml up --build -d -#. Rerun the ``quickstart-compose`` project: +4. Rerun the ``quickstart-compose`` project: .. code-block:: bash - $ flwr run quickstart-compose docker-compose-tls - $ docker compose logs superexec -f + $ flwr run quickstart-compose docker-compose-tls + $ docker compose logs superexec -f Step 7: Add another SuperNode ----------------------------- -You can add more SuperNodes and ClientApps by duplicating their definitions in the ``compose.yml`` -file. +You can add more SuperNodes and ClientApps by duplicating their definitions in the +``compose.yml`` file. -Just give each new SuperNode and ClientApp service a unique service name like ``supernode-3``, -``clientapp-3``, etc. +Just give each new SuperNode and ClientApp service a unique service name like +``supernode-3``, ``clientapp-3``, etc. In ``compose.yml``, add the following: .. code-block:: yaml - :caption: compose.yml - :substitutions: - - # other service definitions - - supernode-3: - image: flwr/supernode:${FLWR_VERSION:-|stable_flwr_version|} - command: - - --insecure - - --superlink - - superlink:9092 - - --supernode-address - - 0.0.0.0:9096 - - --isolation - - process - - --node-config - - "partition-id=1 num-partitions=2" - depends_on: - - superlink - - clientapp-3: - build: - context: ${PROJECT_DIR:-.} - dockerfile_inline: | - FROM flwr/clientapp:${FLWR_VERSION:-|stable_flwr_version|} - - USER root - RUN apt-get update \ - && apt-get -y --no-install-recommends install \ - build-essential \ - && rm -rf /var/lib/apt/lists/* - USER app - - WORKDIR /app - COPY --chown=app:app pyproject.toml . - RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml \ - && python -m pip install -U --no-cache-dir . - - ENTRYPOINT ["flwr-clientapp"] - command: - - --supernode - - supernode-3:9096 - deploy: - resources: - limits: - cpus: "2" - stop_signal: SIGINT - depends_on: - - supernode-3 - -If you also want to enable TLS for the new SuperNodes, duplicate the SuperNode definition for -each new SuperNode service in the ``with-tls.yml`` file. + :caption: compose.yml + :substitutions: + + # other service definitions + + supernode-3: + image: flwr/supernode:${FLWR_VERSION:-|stable_flwr_version|} + command: + - --insecure + - --superlink + - superlink:9092 + - --supernode-address + - 0.0.0.0:9096 + - --isolation + - process + - --node-config + - "partition-id=1 num-partitions=2" + depends_on: + - superlink + + clientapp-3: + build: + context: ${PROJECT_DIR:-.} + dockerfile_inline: | + FROM flwr/clientapp:${FLWR_VERSION:-|stable_flwr_version|} + + USER root + RUN apt-get update \ + && apt-get -y --no-install-recommends install \ + build-essential \ + && rm -rf /var/lib/apt/lists/* + USER app + + WORKDIR /app + COPY --chown=app:app pyproject.toml . + RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml \ + && python -m pip install -U --no-cache-dir . + + ENTRYPOINT ["flwr-clientapp"] + command: + - --supernode + - supernode-3:9096 + deploy: + resources: + limits: + cpus: "2" + stop_signal: SIGINT + depends_on: + - supernode-3 + +If you also want to enable TLS for the new SuperNodes, duplicate the SuperNode +definition for each new SuperNode service in the ``with-tls.yml`` file. Make sure that the names of the services match with the one in the ``compose.yml`` file. In ``with-tls.yml``, add the following: .. code-block:: yaml - :caption: with-tls.yml - - # other service definitions - - supernode-3: - command: - - --superlink - - superlink:9092 - - --supernode-address - - 0.0.0.0:9096 - - --isolation - - process - - --node-config - - "partition-id=1 num-partitions=2" - - --root-certificates - - certificates/ca.crt - secrets: - - source: superlink-ca-certfile - target: /app/certificates/ca.crt + :caption: with-tls.yml + + # other service definitions + + supernode-3: + command: + - --superlink + - superlink:9092 + - --supernode-address + - 0.0.0.0:9096 + - --isolation + - process + - --node-config + - "partition-id=1 num-partitions=2" + - --root-certificates + - certificates/ca.crt + secrets: + - source: superlink-ca-certfile + target: /app/certificates/ca.crt Step 8: Persisting the SuperLink State and Enabling TLS ------------------------------------------------------- -To run Flower with persisted SuperLink state and enabled TLS, a slight change in the ``with-state.yml`` -file is required: +To run Flower with persisted SuperLink state and enabled TLS, a slight change in the +``with-state.yml`` file is required: -#. Comment out the lines 2-4 and uncomment the lines 5-9: +1. Comment out the lines 2-4 and uncomment the lines 5-9: .. code-block:: yaml - :caption: with-state.yml - :linenos: - :emphasize-lines: 2-9 - - superlink: - # command: - # - --insecure - # - --database=state/state.db - command: - - --ssl-ca-certfile=certificates/ca.crt - - --ssl-certfile=certificates/server.pem - - --ssl-keyfile=certificates/server.key - - --database=state/state.db - volumes: - - ./state/:/app/state/:rw - -#. Restart the services: + :caption: with-state.yml + :linenos: + :emphasize-lines: 2-9 + + superlink: + # command: + # - --insecure + # - --database=state/state.db + command: + - --ssl-ca-certfile=certificates/ca.crt + - --ssl-certfile=certificates/server.pem + - --ssl-keyfile=certificates/server.key + - --database=state/state.db + volumes: + - ./state/:/app/state/:rw + +2. Restart the services: .. code-block:: bash - $ docker compose -f compose.yml -f with-tls.yml -f with-state.yml up --build -d + $ docker compose -f compose.yml -f with-tls.yml -f with-state.yml up --build -d -#. Rerun the ``quickstart-compose`` project: +3. Rerun the ``quickstart-compose`` project: .. code-block:: bash - $ flwr run quickstart-compose docker-compose-tls - $ docker compose logs superexec -f + $ flwr run quickstart-compose docker-compose-tls + $ docker compose logs superexec -f Step 9: Merge Multiple Compose Files ------------------------------------ -You can merge multiple Compose files into a single file. For instance, if you wish to combine -the basic configuration with the TLS configuration, execute the following command: +You can merge multiple Compose files into a single file. For instance, if you wish to +combine the basic configuration with the TLS configuration, execute the following +command: .. code-block:: bash - $ docker compose -f compose.yml \ - -f with-tls.yml config --no-path-resolution > my_compose.yml + $ docker compose -f compose.yml \ + -f with-tls.yml config --no-path-resolution > my_compose.yml -This will merge the contents of ``compose.yml`` and ``with-tls.yml`` into a new file called -``my_compose.yml``. +This will merge the contents of ``compose.yml`` and ``with-tls.yml`` into a new file +called ``my_compose.yml``. Step 10: Clean Up ----------------- @@ -394,10 +401,10 @@ Remove all services and volumes: .. code-block:: bash - $ docker compose down -v - $ docker compose -f certs.yml down -v + $ docker compose down -v + $ docker compose -f certs.yml down -v Where to Go Next ---------------- -* :doc:`run-quickstart-examples-docker-compose` +- :doc:`run-quickstart-examples-docker-compose` diff --git a/doc/source/docker/tutorial-quickstart-docker.rst b/doc/source/docker/tutorial-quickstart-docker.rst index 189d019cb097..993754dcf109 100644 --- a/doc/source/docker/tutorial-quickstart-docker.rst +++ b/doc/source/docker/tutorial-quickstart-docker.rst @@ -1,11 +1,11 @@ Quickstart with Docker ====================== -This quickstart aims to guide you through the process of containerizing a Flower project and -running it end to end using Docker on your local machine. +This quickstart aims to guide you through the process of containerizing a Flower project +and running it end to end using Docker on your local machine. -This tutorial does not use production-ready settings, so you can focus on understanding the basic -workflow that uses the minimum configurations. +This tutorial does not use production-ready settings, so you can focus on understanding +the basic workflow that uses the minimum configurations. Prerequisites ------------- @@ -18,33 +18,33 @@ Before you start, make sure that: Step 1: Set Up -------------- -#. Create a new Flower project (PyTorch): +1. Create a new Flower project (PyTorch): .. code-block:: bash - $ flwr new quickstart-docker --framework PyTorch --username flower + $ flwr new quickstart-docker --framework PyTorch --username flower - 🔨 Creating Flower project quickstart-docker... - 🎊 Project creation successful. + 🔨 Creating Flower project quickstart-docker... + 🎊 Project creation successful. - Use the following command to run your project: + Use the following command to run your project: - cd quickstart-docker - pip install -e . - flwr run + cd quickstart-docker + pip install -e . + flwr run - $ cd quickstart-docker - $ pip install -e . + $ cd quickstart-docker + $ pip install -e . -#. Create a new Docker bridge network called ``flwr-network``: +2. Create a new Docker bridge network called ``flwr-network``: .. code-block:: bash - $ docker network create --driver bridge flwr-network + $ docker network create --driver bridge flwr-network - User-defined networks, such as ``flwr-network``, enable IP resolution of container names, a feature - absent in the default bridge network. This simplifies quickstart example by avoiding the need to - determine host IP first. + User-defined networks, such as ``flwr-network``, enable IP resolution of container + names, a feature absent in the default bridge network. This simplifies quickstart + example by avoiding the need to determine host IP first. Step 2: Start the SuperLink --------------------------- @@ -52,334 +52,339 @@ Step 2: Start the SuperLink Open your terminal and run: .. code-block:: bash - :substitutions: + :substitutions: - $ docker run --rm \ - -p 9091:9091 -p 9092:9092 \ - --network flwr-network \ - --name superlink \ - --detach \ - flwr/superlink:|stable_flwr_version| --insecure + $ docker run --rm \ + -p 9091:9091 -p 9092:9092 \ + --network flwr-network \ + --name superlink \ + --detach \ + flwr/superlink:|stable_flwr_version| --insecure .. dropdown:: Understand the command - * ``docker run``: This tells Docker to run a container from an image. - * ``--rm``: Remove the container once it is stopped or the command exits. - * | ``-p 9091:9091 -p 9092:9092``: Map port ``9091`` and ``9092`` of the container to the same port of - | the host machine, allowing other services to access the Driver API on - | ``http://localhost:9091`` and the Fleet API on ``http://localhost:9092``. - * ``--network flwr-network``: Make the container join the network named ``flwr-network``. - * ``--name superlink``: Assign the name ``superlink`` to the container. - * ``--detach``: Run the container in the background, freeing up the terminal. - * | :substitution-code:`flwr/superlink:|stable_flwr_version|`: The name of the image to be run and the specific - | tag of the image. The tag :substitution-code:`|stable_flwr_version|` represents a :doc:`specific version ` of the image. - * | ``--insecure``: This flag tells the container to operate in an insecure mode, allowing - | unencrypted communication. + * ``docker run``: This tells Docker to run a container from an image. + * ``--rm``: Remove the container once it is stopped or the command exits. + * | ``-p 9091:9091 -p 9092:9092``: Map port ``9091`` and ``9092`` of the container to the same port of + | the host machine, allowing other services to access the Driver API on + | ``http://localhost:9091`` and the Fleet API on ``http://localhost:9092``. + * ``--network flwr-network``: Make the container join the network named ``flwr-network``. + * ``--name superlink``: Assign the name ``superlink`` to the container. + * ``--detach``: Run the container in the background, freeing up the terminal. + * | :substitution-code:`flwr/superlink:|stable_flwr_version|`: The name of the image to be run and the specific + | tag of the image. The tag :substitution-code:`|stable_flwr_version|` represents a :doc:`specific version ` of the image. + * | ``--insecure``: This flag tells the container to operate in an insecure mode, allowing + | unencrypted communication. Step 3: Start the SuperNode --------------------------- Start two SuperNode containers. -#. Start the first container: +1. Start the first container: .. code-block:: bash - :substitutions: - - $ docker run --rm \ - -p 9094:9094 \ - --network flwr-network \ - --name supernode-1 \ - --detach \ - flwr/supernode:|stable_flwr_version| \ - --insecure \ - --superlink superlink:9092 \ - --node-config "partition-id=0 num-partitions=2" \ - --supernode-address 0.0.0.0:9094 \ - --isolation process + :substitutions: + + $ docker run --rm \ + -p 9094:9094 \ + --network flwr-network \ + --name supernode-1 \ + --detach \ + flwr/supernode:|stable_flwr_version| \ + --insecure \ + --superlink superlink:9092 \ + --node-config "partition-id=0 num-partitions=2" \ + --supernode-address 0.0.0.0:9094 \ + --isolation process .. dropdown:: Understand the command - * ``docker run``: This tells Docker to run a container from an image. - * ``--rm``: Remove the container once it is stopped or the command exits. - * | ``-p 9094:9094``: Map port ``9094`` of the container to the same port of - | the host machine, allowing other services to access the SuperNode API on - | ``http://localhost:9094``. - * ``--network flwr-network``: Make the container join the network named ``flwr-network``. - * ``--name supernode-1``: Assign the name ``supernode-1`` to the container. - * ``--detach``: Run the container in the background, freeing up the terminal. - * | ``flwr/supernode:|stable_flwr_version|``: This is the name of the image to be run and the specific tag - | of the image. - * | ``--insecure``: This flag tells the container to operate in an insecure mode, allowing - | unencrypted communication. - * | ``--superlink superlink:9092``: Connect to the SuperLink's Fleet API at the address - | ``superlink:9092``. - * | ``--node-config "partition-id=0 num-partitions=2"``: Set the partition ID to ``0`` and the - | number of partitions to ``2`` for the SuperNode configuration. - * | ``--supernode-address 0.0.0.0:9094``: Set the address and port number that the SuperNode - | is listening on. - * | ``--isolation process``: Tells the SuperNode that the ClientApp is created by separate - | independent process. The SuperNode does not attempt to create it. - -#. Start the second container: + * ``docker run``: This tells Docker to run a container from an image. + * ``--rm``: Remove the container once it is stopped or the command exits. + * | ``-p 9094:9094``: Map port ``9094`` of the container to the same port of + | the host machine, allowing other services to access the SuperNode API on + | ``http://localhost:9094``. + * ``--network flwr-network``: Make the container join the network named ``flwr-network``. + * ``--name supernode-1``: Assign the name ``supernode-1`` to the container. + * ``--detach``: Run the container in the background, freeing up the terminal. + * | ``flwr/supernode:|stable_flwr_version|``: This is the name of the image to be run and the specific tag + | of the image. + * | ``--insecure``: This flag tells the container to operate in an insecure mode, allowing + | unencrypted communication. + * | ``--superlink superlink:9092``: Connect to the SuperLink's Fleet API at the address + | ``superlink:9092``. + * | ``--node-config "partition-id=0 num-partitions=2"``: Set the partition ID to ``0`` and the + | number of partitions to ``2`` for the SuperNode configuration. + * | ``--supernode-address 0.0.0.0:9094``: Set the address and port number that the SuperNode + | is listening on. + * | ``--isolation process``: Tells the SuperNode that the ClientApp is created by separate + | independent process. The SuperNode does not attempt to create it. + +2. Start the second container: .. code-block:: shell - :substitutions: - - $ docker run --rm \ - -p 9095:9095 \ - --network flwr-network \ - --name supernode-2 \ - --detach \ - flwr/supernode:|stable_flwr_version| \ - --insecure \ - --superlink superlink:9092 \ - --node-config "partition-id=1 num-partitions=2" \ - --supernode-address 0.0.0.0:9095 \ - --isolation process + :substitutions: + + $ docker run --rm \ + -p 9095:9095 \ + --network flwr-network \ + --name supernode-2 \ + --detach \ + flwr/supernode:|stable_flwr_version| \ + --insecure \ + --superlink superlink:9092 \ + --node-config "partition-id=1 num-partitions=2" \ + --supernode-address 0.0.0.0:9095 \ + --isolation process Step 4: Start the ClientApp --------------------------- -The ClientApp Docker image comes with a pre-installed version of Flower and serves as a base for -building your own ClientApp image. In order to install the FAB dependencies, you will need to create -a Dockerfile that extends the ClientApp image and installs the required dependencies. +The ClientApp Docker image comes with a pre-installed version of Flower and serves as a +base for building your own ClientApp image. In order to install the FAB dependencies, +you will need to create a Dockerfile that extends the ClientApp image and installs the +required dependencies. -#. Create a ClientApp Dockerfile called ``Dockerfile.clientapp`` and paste the following code into it: +1. Create a ClientApp Dockerfile called ``Dockerfile.clientapp`` and paste the following + code into it: .. code-block:: dockerfile - :caption: Dockerfile.clientapp - :linenos: - :substitutions: + :caption: Dockerfile.clientapp + :linenos: + :substitutions: - FROM flwr/clientapp:|stable_flwr_version| + FROM flwr/clientapp:|stable_flwr_version| - WORKDIR /app - COPY pyproject.toml . - RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml \ - && python -m pip install -U --no-cache-dir . + WORKDIR /app + COPY pyproject.toml . + RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml \ + && python -m pip install -U --no-cache-dir . - ENTRYPOINT ["flwr-clientapp"] + ENTRYPOINT ["flwr-clientapp"] .. dropdown:: Understand the Dockerfile - * | :substitution-code:`FROM flwr/clientapp:|stable_flwr_version|`: This line specifies that the Docker image - | to be built from is the ``flwr/clientapp image``, version :substitution-code:`|stable_flwr_version|`. - * | ``WORKDIR /app``: Set the working directory for the container to ``/app``. - | Any subsequent commands that reference a directory will be relative to this directory. - * | ``COPY pyproject.toml .``: Copy the ``pyproject.toml`` file - | from the current working directory into the container's ``/app`` directory. - * | ``RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml``: Remove the ``flwr`` dependency - | from the ``pyproject.toml``. - * | ``python -m pip install -U --no-cache-dir .``: Run the ``pip`` install command to - | install the dependencies defined in the ``pyproject.toml`` file - | - | The ``-U`` flag indicates that any existing packages should be upgraded, and - | ``--no-cache-dir`` prevents pip from using the cache to speed up the installation. - * | ``ENTRYPOINT ["flwr-clientapp"]``: Set the command ``flwr-clientapp`` to be - | the default command run when the container is started. + * | :substitution-code:`FROM flwr/clientapp:|stable_flwr_version|`: This line specifies that the Docker image + | to be built from is the ``flwr/clientapp image``, version :substitution-code:`|stable_flwr_version|`. + * | ``WORKDIR /app``: Set the working directory for the container to ``/app``. + | Any subsequent commands that reference a directory will be relative to this directory. + * | ``COPY pyproject.toml .``: Copy the ``pyproject.toml`` file + | from the current working directory into the container's ``/app`` directory. + * | ``RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml``: Remove the ``flwr`` dependency + | from the ``pyproject.toml``. + * | ``python -m pip install -U --no-cache-dir .``: Run the ``pip`` install command to + | install the dependencies defined in the ``pyproject.toml`` file + | + | The ``-U`` flag indicates that any existing packages should be upgraded, and + | ``--no-cache-dir`` prevents pip from using the cache to speed up the installation. + * | ``ENTRYPOINT ["flwr-clientapp"]``: Set the command ``flwr-clientapp`` to be + | the default command run when the container is started. .. important:: - Note that `flwr `__ is already installed in the ``flwr/clientapp`` - base image, so only other package dependencies such as ``flwr-datasets``, ``torch``, etc., - need to be installed. As a result, the ``flwr`` dependency is removed from the - ``pyproject.toml`` after it has been copied into the Docker image (see line 5). + Note that `flwr `__ is already installed in the + ``flwr/clientapp`` base image, so only other package dependencies such as + ``flwr-datasets``, ``torch``, etc., need to be installed. As a result, the + ``flwr`` dependency is removed from the ``pyproject.toml`` after it has been + copied into the Docker image (see line 5). -#. Next, build the ClientApp Docker image by running the following command in the directory where - the Dockerfile is located: +2. Next, build the ClientApp Docker image by running the following command in the + directory where the Dockerfile is located: .. code-block:: bash - $ docker build -f Dockerfile.clientapp -t flwr_clientapp:0.0.1 . + $ docker build -f Dockerfile.clientapp -t flwr_clientapp:0.0.1 . .. note:: - The image name was set as ``flwr_clientapp`` with the tag ``0.0.1``. Remember that - these values are merely examples, and you can customize them according to your requirements. + The image name was set as ``flwr_clientapp`` with the tag ``0.0.1``. Remember + that these values are merely examples, and you can customize them according to + your requirements. -#. Start the first ClientApp container: +3. Start the first ClientApp container: .. code-block:: bash - $ docker run --rm \ - --network flwr-network \ - --detach \ - flwr_clientapp:0.0.1 \ - --supernode supernode-1:9094 + $ docker run --rm \ + --network flwr-network \ + --detach \ + flwr_clientapp:0.0.1 \ + --supernode supernode-1:9094 .. dropdown:: Understand the command - * ``docker run``: This tells Docker to run a container from an image. - * ``--rm``: Remove the container once it is stopped or the command exits. - * ``--network flwr-network``: Make the container join the network named ``flwr-network``. - * ``--detach``: Run the container in the background, freeing up the terminal. - * | ``flwr_clientapp:0.0.1``: This is the name of the image to be run and the specific tag - | of the image. - * | ``--supernode supernode-1:9094``: Connect to the SuperNode's Fleet API at the address - | ``supernode-1:9094``. + * ``docker run``: This tells Docker to run a container from an image. + * ``--rm``: Remove the container once it is stopped or the command exits. + * ``--network flwr-network``: Make the container join the network named ``flwr-network``. + * ``--detach``: Run the container in the background, freeing up the terminal. + * | ``flwr_clientapp:0.0.1``: This is the name of the image to be run and the specific tag + | of the image. + * | ``--supernode supernode-1:9094``: Connect to the SuperNode's Fleet API at the address + | ``supernode-1:9094``. -#. Start the second ClientApp container: +4. Start the second ClientApp container: .. code-block:: shell - $ docker run --rm \ - --network flwr-network \ - --detach \ - flwr_clientapp:0.0.1 \ - --supernode supernode-2:9095 + $ docker run --rm \ + --network flwr-network \ + --detach \ + flwr_clientapp:0.0.1 \ + --supernode supernode-2:9095 Step 5: Start the SuperExec --------------------------- -The procedure for building and running a SuperExec image is almost identical to the ClientApp image. +The procedure for building and running a SuperExec image is almost identical to the +ClientApp image. -Similar to the ClientApp image, you will need to create a Dockerfile that extends the SuperExec -image and installs the required FAB dependencies. +Similar to the ClientApp image, you will need to create a Dockerfile that extends the +SuperExec image and installs the required FAB dependencies. -#. Create a SuperExec Dockerfile called ``Dockerfile.superexec`` and paste the following code in: +1. Create a SuperExec Dockerfile called ``Dockerfile.superexec`` and paste the following + code in: .. code-block:: dockerfile - :caption: Dockerfile.superexec - :substitutions: + :caption: Dockerfile.superexec + :substitutions: - FROM flwr/superexec:|stable_flwr_version| + FROM flwr/superexec:|stable_flwr_version| - WORKDIR /app + WORKDIR /app - COPY pyproject.toml . - RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml \ - && python -m pip install -U --no-cache-dir . + COPY pyproject.toml . + RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml \ + && python -m pip install -U --no-cache-dir . - ENTRYPOINT ["flower-superexec", "--executor", "flwr.superexec.deployment:executor"] + ENTRYPOINT ["flower-superexec", "--executor", "flwr.superexec.deployment:executor"] .. dropdown:: Understand the Dockerfile - * | :substitution-code:`FROM flwr/superexec:|stable_flwr_version|`: This line specifies that the Docker image - | to be built from is the ``flwr/superexec image``, version :substitution-code:`|stable_flwr_version|`. - * | ``WORKDIR /app``: Set the working directory for the container to ``/app``. - | Any subsequent commands that reference a directory will be relative to this directory. - * | ``COPY pyproject.toml .``: Copy the ``pyproject.toml`` file - | from the current working directory into the container's ``/app`` directory. - * | ``RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml``: Remove the ``flwr`` dependency - | from the ``pyproject.toml``. - * | ``python -m pip install -U --no-cache-dir .``: Run the ``pip`` install command to - | install the dependencies defined in the ``pyproject.toml`` file - | - | The ``-U`` flag indicates that any existing packages should be upgraded, and - | ``--no-cache-dir`` prevents pip from using the cache to speed up the installation. - * | ``ENTRYPOINT ["flower-superexec"``: Set the command ``flower-superexec`` to be - | the default command run when the container is started. - | - | ``"--executor", "flwr.superexec.deployment:executor"]`` Use the - | ``flwr.superexec.deployment:executor`` executor to run the ServerApps. - -#. Afterward, in the directory that holds the Dockerfile, execute this Docker command to + * | :substitution-code:`FROM flwr/superexec:|stable_flwr_version|`: This line specifies that the Docker image + | to be built from is the ``flwr/superexec image``, version :substitution-code:`|stable_flwr_version|`. + * | ``WORKDIR /app``: Set the working directory for the container to ``/app``. + | Any subsequent commands that reference a directory will be relative to this directory. + * | ``COPY pyproject.toml .``: Copy the ``pyproject.toml`` file + | from the current working directory into the container's ``/app`` directory. + * | ``RUN sed -i 's/.*flwr\[simulation\].*//' pyproject.toml``: Remove the ``flwr`` dependency + | from the ``pyproject.toml``. + * | ``python -m pip install -U --no-cache-dir .``: Run the ``pip`` install command to + | install the dependencies defined in the ``pyproject.toml`` file + | + | The ``-U`` flag indicates that any existing packages should be upgraded, and + | ``--no-cache-dir`` prevents pip from using the cache to speed up the installation. + * | ``ENTRYPOINT ["flower-superexec"``: Set the command ``flower-superexec`` to be + | the default command run when the container is started. + | + | ``"--executor", "flwr.superexec.deployment:executor"]`` Use the + | ``flwr.superexec.deployment:executor`` executor to run the ServerApps. + +2. Afterward, in the directory that holds the Dockerfile, execute this Docker command to build the SuperExec image: .. code-block:: bash - $ docker build -f Dockerfile.superexec -t flwr_superexec:0.0.1 . + $ docker build -f Dockerfile.superexec -t flwr_superexec:0.0.1 . - -#. Start the SuperExec container: +3. Start the SuperExec container: .. code-block:: bash - $ docker run --rm \ - -p 9093:9093 \ - --network flwr-network \ - --name superexec \ - --detach \ - flwr_superexec:0.0.1 \ - --insecure \ - --executor-config superlink=\"superlink:9091\" + $ docker run --rm \ + -p 9093:9093 \ + --network flwr-network \ + --name superexec \ + --detach \ + flwr_superexec:0.0.1 \ + --insecure \ + --executor-config superlink=\"superlink:9091\" .. dropdown:: Understand the command - * ``docker run``: This tells Docker to run a container from an image. - * ``--rm``: Remove the container once it is stopped or the command exits. - * | ``-p 9093:9093``: Map port ``9093`` of the container to the same port of - | the host machine, allowing you to access the SuperExec API on ``http://localhost:9093``. - * ``--network flwr-network``: Make the container join the network named ``flwr-network``. - * ``--name superexec``: Assign the name ``superexec`` to the container. - * ``--detach``: Run the container in the background, freeing up the terminal. - * | ``flwr_superexec:0.0.1``: This is the name of the image to be run and the specific tag - | of the image. - * | ``--insecure``: This flag tells the container to operate in an insecure mode, allowing - | unencrypted communication. - * | ``--executor-config superlink=\"superlink:9091\"``: Configure the SuperExec executor to - | connect to the SuperLink running on port ``9091``. + * ``docker run``: This tells Docker to run a container from an image. + * ``--rm``: Remove the container once it is stopped or the command exits. + * | ``-p 9093:9093``: Map port ``9093`` of the container to the same port of + | the host machine, allowing you to access the SuperExec API on ``http://localhost:9093``. + * ``--network flwr-network``: Make the container join the network named ``flwr-network``. + * ``--name superexec``: Assign the name ``superexec`` to the container. + * ``--detach``: Run the container in the background, freeing up the terminal. + * | ``flwr_superexec:0.0.1``: This is the name of the image to be run and the specific tag + | of the image. + * | ``--insecure``: This flag tells the container to operate in an insecure mode, allowing + | unencrypted communication. + * | ``--executor-config superlink=\"superlink:9091\"``: Configure the SuperExec executor to + | connect to the SuperLink running on port ``9091``. Step 6: Run the Quickstart Project ---------------------------------- -#. Add the following lines to the ``pyproject.toml``: +1. Add the following lines to the ``pyproject.toml``: .. code-block:: toml - :caption: pyproject.toml + :caption: pyproject.toml - [tool.flwr.federations.docker] - address = "127.0.0.1:9093" - insecure = true + [tool.flwr.federations.docker] + address = "127.0.0.1:9093" + insecure = true -#. Run the ``quickstart-docker`` project by executing the command: +2. Run the ``quickstart-docker`` project by executing the command: .. code-block:: bash - $ flwr run . docker + $ flwr run . docker -#. Follow the SuperExec logs to track the execution of the run: +3. Follow the SuperExec logs to track the execution of the run: .. code-block:: bash - $ docker logs -f superexec + $ docker logs -f superexec Step 7: Update the Application ------------------------------ -#. Change the application code. For example, change the ``seed`` in ``quickstart_docker/task.py`` - to ``43`` and save it: +1. Change the application code. For example, change the ``seed`` in + ``quickstart_docker/task.py`` to ``43`` and save it: .. code-block:: python - :caption: quickstart_docker/task.py + :caption: quickstart_docker/task.py - # ... - partition_train_test = partition.train_test_split(test_size=0.2, seed=43) - # ... + # ... + partition_train_test = partition.train_test_split(test_size=0.2, seed=43) + # ... -#. Stop the current ClientApp containers: +2. Stop the current ClientApp containers: .. code-block:: bash - $ docker stop $(docker ps -a -q --filter ancestor=flwr_clientapp:0.0.1) + $ docker stop $(docker ps -a -q --filter ancestor=flwr_clientapp:0.0.1) -#. Rebuild the FAB and ClientApp image: +3. Rebuild the FAB and ClientApp image: .. code-block:: bash - $ docker build -f Dockerfile.clientapp -t flwr_clientapp:0.0.1 . + $ docker build -f Dockerfile.clientapp -t flwr_clientapp:0.0.1 . -#. Launch two new ClientApp containers based on the newly built image: +4. Launch two new ClientApp containers based on the newly built image: .. code-block:: bash - $ docker run --rm \ - --network flwr-network \ - --detach \ - flwr_clientapp:0.0.1 \ - --supernode supernode-1:9094 - $ docker run --rm \ - --network flwr-network \ - --detach \ - flwr_clientapp:0.0.1 \ - --supernode supernode-2:9095 + $ docker run --rm \ + --network flwr-network \ + --detach \ + flwr_clientapp:0.0.1 \ + --supernode supernode-1:9094 + $ docker run --rm \ + --network flwr-network \ + --detach \ + flwr_clientapp:0.0.1 \ + --supernode supernode-2:9095 -#. Run the updated project: +5. Run the updated project: .. code-block:: bash - $ flwr run . docker + $ flwr run . docker Step 8: Clean Up ---------------- @@ -388,16 +393,16 @@ Remove the containers and the bridge network: .. code-block:: bash - $ docker stop $(docker ps -a -q --filter ancestor=flwr_clientapp:0.0.1) \ - supernode-1 \ - supernode-2 \ - superexec \ - superlink - $ docker network rm flwr-network + $ docker stop $(docker ps -a -q --filter ancestor=flwr_clientapp:0.0.1) \ + supernode-1 \ + supernode-2 \ + superexec \ + superlink + $ docker network rm flwr-network Where to Go Next ---------------- -* :doc:`enable-tls` -* :doc:`persist-superlink-state` -* :doc:`tutorial-quickstart-docker-compose` +- :doc:`enable-tls` +- :doc:`persist-superlink-state` +- :doc:`tutorial-quickstart-docker-compose` diff --git a/doc/source/docker/use-a-different-version.rst b/doc/source/docker/use-a-different-version.rst index 73e5f4218663..9108f5157dcd 100644 --- a/doc/source/docker/use-a-different-version.rst +++ b/doc/source/docker/use-a-different-version.rst @@ -1,12 +1,13 @@ Use a Different Flower Version ============================== -If you want to use a different version of Flower, for example Flower nightly, you can do so by -changing the tag. All available versions are on `Docker Hub `__. +If you want to use a different version of Flower, for example Flower nightly, you can do +so by changing the tag. All available versions are on `Docker Hub +`__. .. important:: - When using Flower nightly, the SuperLink nightly image must be paired with the corresponding - SuperNode and ServerApp nightly images released on the same day. To ensure the versions are - in sync, using the concrete tag, e.g., ``1.10.0.dev20240610`` instead of ``nightly`` is - recommended. + When using Flower nightly, the SuperLink nightly image must be paired with the + corresponding SuperNode and ServerApp nightly images released on the same day. To + ensure the versions are in sync, using the concrete tag, e.g., + ``1.10.0.dev20240610`` instead of ``nightly`` is recommended. diff --git a/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst b/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst index 0139f3b8dc31..4a9d4607d9a5 100644 --- a/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst +++ b/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst @@ -1,16 +1,22 @@ Example: FedBN in PyTorch - From Centralized To Federated ========================================================= -This tutorial will show you how to use Flower to build a federated version of an existing machine learning workload with `FedBN `_, a federated training strategy designed for non-iid data. -We are using PyTorch to train a Convolutional Neural Network(with Batch Normalization layers) on the CIFAR-10 dataset. -When applying FedBN, only few changes needed compared to :doc:`Example: PyTorch - From Centralized To Federated `. +This tutorial will show you how to use Flower to build a federated version of an +existing machine learning workload with `FedBN `_, a +federated training strategy designed for non-iid data. We are using PyTorch to train a +Convolutional Neural Network(with Batch Normalization layers) on the CIFAR-10 dataset. +When applying FedBN, only few changes needed compared to :doc:`Example: PyTorch - From +Centralized To Federated `. Centralized Training -------------------- -All files are revised based on :doc:`Example: PyTorch - From Centralized To Federated `. -The only thing to do is modifying the file called :code:`cifar.py`, revised part is shown below: -The model architecture defined in class Net() is added with Batch Normalization layers accordingly. +All files are revised based on :doc:`Example: PyTorch - From Centralized To Federated +`. The only thing to do is modifying the +file called ``cifar.py``, revised part is shown below: + +The model architecture defined in class Net() is added with Batch Normalization layers +accordingly. .. code-block:: python @@ -40,26 +46,33 @@ The model architecture defined in class Net() is added with Batch Normalization You can now run your machine learning workload: -.. code-block:: python +.. code-block:: bash python3 cifar.py -So far this should all look fairly familiar if you've used PyTorch before. -Let's take the next step and use what we've built to create a federated learning system within FedBN, the system consists of one server and two clients. +So far this should all look fairly familiar if you've used PyTorch before. Let's take +the next step and use what we've built to create a federated learning system within +FedBN, the system consists of one server and two clients. Federated Training ------------------ -If you have read :doc:`Example: PyTorch - From Centralized To Federated `, the following parts are easy to follow, only :code:`get_parameters` and :code:`set_parameters` function in :code:`client.py` needed to revise. -If not, please read the :doc:`Example: PyTorch - From Centralized To Federated `. first. +If you have read :doc:`Example: PyTorch - From Centralized To Federated +`, the following parts are easy to +follow, only ``get_parameters`` and ``set_parameters`` function in ``client.py`` needed +to revise. If not, please read the :doc:`Example: PyTorch - From Centralized To +Federated `. first. -Our example consists of one *server* and two *clients*. In FedBN, :code:`server.py` keeps unchanged, we can start the server directly. +Our example consists of one *server* and two *clients*. In FedBN, ``server.py`` keeps +unchanged, we can start the server directly. -.. code-block:: python +.. code-block:: bash python3 server.py -Finally, we will revise our *client* logic by changing :code:`get_parameters` and :code:`set_parameters` in :code:`client.py`, we will exclude batch normalization parameters from model parameter list when sending to or receiving from the server. +Finally, we will revise our *client* logic by changing ``get_parameters`` and +``set_parameters`` in ``client.py``, we will exclude batch normalization parameters from +model parameter list when sending to or receiving from the server. .. code-block:: python @@ -71,11 +84,15 @@ Finally, we will revise our *client* logic by changing :code:`get_parameters` an def get_parameters(self, config) -> List[np.ndarray]: # Return model parameters as a list of NumPy ndarrays, excluding parameters of BN layers when using FedBN - return [val.cpu().numpy() for name, val in self.model.state_dict().items() if 'bn' not in name] + return [ + val.cpu().numpy() + for name, val in self.model.state_dict().items() + if "bn" not in name + ] def set_parameters(self, parameters: List[np.ndarray]) -> None: # Set model parameters from a list of NumPy ndarrays - keys = [k for k in self.model.state_dict().keys() if 'bn' not in k] + keys = [k for k in self.model.state_dict().keys() if "bn" not in k] params_dict = zip(keys, parameters) state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) self.model.load_state_dict(state_dict, strict=False) @@ -84,15 +101,20 @@ Finally, we will revise our *client* logic by changing :code:`get_parameters` an Now, you can now open two additional terminal windows and run -.. code-block:: python +.. code-block:: bash python3 client.py -in each window (make sure that the server is still running before you do so) and see your (previously centralized) PyTorch project run federated learning with FedBN strategy across two clients. Congratulations! +in each window (make sure that the server is still running before you do so) and see +your (previously centralized) PyTorch project run federated learning with FedBN strategy +across two clients. Congratulations! Next Steps ---------- -The full source code for this example can be found `here `_. -Our example is of course somewhat over-simplified because both clients load the exact same dataset, which isn't realistic. -You're now prepared to explore this topic further. How about using different subsets of CIFAR-10 on each client? How about adding more clients? +The full source code for this example can be found `here +`_. +Our example is of course somewhat over-simplified because both clients load the exact +same dataset, which isn't realistic. You're now prepared to explore this topic further. +How about using different subsets of CIFAR-10 on each client? How about adding more +clients? diff --git a/doc/source/example-pytorch-from-centralized-to-federated.rst b/doc/source/example-pytorch-from-centralized-to-federated.rst index 0c458a136a81..9629a7fed6e8 100644 --- a/doc/source/example-pytorch-from-centralized-to-federated.rst +++ b/doc/source/example-pytorch-from-centralized-to-federated.rst @@ -1,21 +1,25 @@ Example: PyTorch - From Centralized To Federated ================================================ -This tutorial will show you how to use Flower to build a federated version of an existing machine learning workload. -We are using PyTorch to train a Convolutional Neural Network on the CIFAR-10 dataset. -First, we introduce this machine learning task with a centralized training approach based on the `Deep Learning with PyTorch `_ tutorial. -Then, we build upon the centralized training code to run the training in a federated fashion. +This tutorial will show you how to use Flower to build a federated version of an +existing machine learning workload. We are using PyTorch to train a Convolutional Neural +Network on the CIFAR-10 dataset. First, we introduce this machine learning task with a +centralized training approach based on the `Deep Learning with PyTorch +`_ tutorial. Then, +we build upon the centralized training code to run the training in a federated fashion. Centralized Training -------------------- -We begin with a brief description of the centralized CNN training code. -If you want a more in-depth explanation of what's going on then have a look at the official `PyTorch tutorial `_. +We begin with a brief description of the centralized CNN training code. If you want a +more in-depth explanation of what's going on then have a look at the official `PyTorch +tutorial `_. -Let's create a new file called :code:`cifar.py` with all the components required for a traditional (centralized) training on CIFAR-10. -First, all required packages (such as :code:`torch` and :code:`torchvision`) need to be imported. -You can see that we do not import any package for federated learning. -You can keep all these imports as they are even when we add the federated learning components at a later point. +Let's create a new file called ``cifar.py`` with all the components required for a +traditional (centralized) training on CIFAR-10. First, all required packages (such as +``torch`` and ``torchvision``) need to be imported. You can see that we do not import +any package for federated learning. You can keep all these imports as they are even when +we add the federated learning components at a later point. .. code-block:: python @@ -29,7 +33,9 @@ You can keep all these imports as they are even when we add the federated learni from torch import Tensor from torchvision.datasets import CIFAR10 -As already mentioned we will use the CIFAR-10 dataset for this machine learning workload. The model architecture (a very simple Convolutional Neural Network) is defined in :code:`class Net()`. +As already mentioned we will use the CIFAR-10 dataset for this machine learning +workload. The model architecture (a very simple Convolutional Neural Network) is defined +in ``class Net()``. .. code-block:: python @@ -53,13 +59,17 @@ As already mentioned we will use the CIFAR-10 dataset for this machine learning x = self.fc3(x) return x -The :code:`load_data()` function loads the CIFAR-10 training and test sets. The :code:`transform` normalized the data after loading. +The ``load_data()`` function loads the CIFAR-10 training and test sets. The +``transform`` normalized the data after loading. .. code-block:: python DATA_ROOT = "~/data/cifar-10" - def load_data() -> Tuple[torch.utils.data.DataLoader, torch.utils.data.DataLoader, Dict]: + + def load_data() -> ( + Tuple[torch.utils.data.DataLoader, torch.utils.data.DataLoader, Dict] + ): """Load CIFAR-10 (training and test set).""" transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] @@ -68,12 +78,15 @@ The :code:`load_data()` function loads the CIFAR-10 training and test sets. The trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True) testset = CIFAR10(DATA_ROOT, train=False, download=True, transform=transform) testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False) - num_examples = {"trainset" : len(trainset), "testset" : len(testset)} + num_examples = {"trainset": len(trainset), "testset": len(testset)} return trainloader, testloader, num_examples -We now need to define the training (function :code:`train()`) which loops over the training set, measures the loss, backpropagates it, and then takes one optimizer step for each batch of training examples. +We now need to define the training (function ``train()``) which loops over the training +set, measures the loss, backpropagates it, and then takes one optimizer step for each +batch of training examples. -The evaluation of the model is defined in the function :code:`test()`. The function loops over all test samples and measures the loss of the model based on the test dataset. +The evaluation of the model is defined in the function ``test()``. The function loops +over all test samples and measures the loss of the model based on the test dataset. .. code-block:: python @@ -133,7 +146,8 @@ The evaluation of the model is defined in the function :code:`test()`. The funct accuracy = correct / total return loss, accuracy -Having defined the data loading, model architecture, training, and evaluation we can put everything together and train our CNN on CIFAR-10. +Having defined the data loading, model architecture, training, and evaluation we can put +everything together and train our CNN on CIFAR-10. .. code-block:: python @@ -143,7 +157,7 @@ Having defined the data loading, model architecture, training, and evaluation we print("Load data") trainloader, testloader, _ = load_data() print("Start training") - net=Net().to(DEVICE) + net = Net().to(DEVICE) train(net=net, trainloader=trainloader, epochs=2, device=DEVICE) print("Evaluate model") loss, accuracy = test(net=net, testloader=testloader, device=DEVICE) @@ -156,46 +170,57 @@ Having defined the data loading, model architecture, training, and evaluation we You can now run your machine learning workload: -.. code-block:: python +.. code-block:: bash python3 cifar.py -So far, this should all look fairly familiar if you've used PyTorch before. -Let's take the next step and use what we've built to create a simple federated learning system consisting of one server and two clients. +So far, this should all look fairly familiar if you've used PyTorch before. Let's take +the next step and use what we've built to create a simple federated learning system +consisting of one server and two clients. Federated Training ------------------ -The simple machine learning project discussed in the previous section trains the model on a single dataset (CIFAR-10), we call this centralized learning. -This concept of centralized learning, as shown in the previous section, is probably known to most of you, and many of you have used it previously. -Normally, if you'd want to run machine learning workloads in a federated fashion, then you'd have to change most of your code and set everything up from scratch. This can be a considerable effort. +The simple machine learning project discussed in the previous section trains the model +on a single dataset (CIFAR-10), we call this centralized learning. This concept of +centralized learning, as shown in the previous section, is probably known to most of +you, and many of you have used it previously. Normally, if you'd want to run machine +learning workloads in a federated fashion, then you'd have to change most of your code +and set everything up from scratch. This can be a considerable effort. -However, with Flower you can evolve your pre-existing code into a federated learning setup without the need for a major rewrite. +However, with Flower you can evolve your pre-existing code into a federated learning +setup without the need for a major rewrite. -The concept is easy to understand. -We have to start a *server* and then use the code in :code:`cifar.py` for the *clients* that are connected to the *server*. -The *server* sends model parameters to the clients. The *clients* run the training and update the parameters. -The updated parameters are sent back to the *server* which averages all received parameter updates. -This describes one round of the federated learning process and we repeat this for multiple rounds. +The concept is easy to understand. We have to start a *server* and then use the code in +``cifar.py`` for the *clients* that are connected to the *server*. The *server* sends +model parameters to the clients. The *clients* run the training and update the +parameters. The updated parameters are sent back to the *server* which averages all +received parameter updates. This describes one round of the federated learning process +and we repeat this for multiple rounds. -Our example consists of one *server* and two *clients*. Let's set up :code:`server.py` first. The *server* needs to import the Flower package :code:`flwr`. -Next, we use the :code:`start_server` function to start a server and tell it to perform three rounds of federated learning. +Our example consists of one *server* and two *clients*. Let's set up ``server.py`` +first. The *server* needs to import the Flower package ``flwr``. Next, we use the +``start_server`` function to start a server and tell it to perform three rounds of +federated learning. .. code-block:: python import flwr as fl if __name__ == "__main__": - fl.server.start_server(server_address="0.0.0.0:8080", config=fl.server.ServerConfig(num_rounds=3)) + fl.server.start_server( + server_address="0.0.0.0:8080", config=fl.server.ServerConfig(num_rounds=3) + ) We can already start the *server*: -.. code-block:: python +.. code-block:: bash python3 server.py -Finally, we will define our *client* logic in :code:`client.py` and build upon the previously defined centralized training in :code:`cifar.py`. -Our *client* needs to import :code:`flwr`, but also :code:`torch` to update the parameters on our PyTorch model: +Finally, we will define our *client* logic in ``client.py`` and build upon the +previously defined centralized training in ``cifar.py``. Our *client* needs to import +``flwr``, but also ``torch`` to update the parameters on our PyTorch model: .. code-block:: python @@ -210,28 +235,38 @@ Our *client* needs to import :code:`flwr`, but also :code:`torch` to update the DEVICE: str = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") -Implementing a Flower *client* basically means implementing a subclass of either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. -Our implementation will be based on :code:`flwr.client.NumPyClient` and we'll call it :code:`CifarClient`. -:code:`NumPyClient` is slightly easier to implement than :code:`Client` if you use a framework with good NumPy interoperability (like PyTorch or TensorFlow/Keras) because it avoids some of the boilerplate that would otherwise be necessary. -:code:`CifarClient` needs to implement four methods, two methods for getting/setting model parameters, one method for training the model, and one method for testing the model: - -#. :code:`set_parameters` - * set the model parameters on the local model that are received from the server - * loop over the list of model parameters received as NumPy :code:`ndarray`'s (think list of neural network layers) -#. :code:`get_parameters` - * get the model parameters and return them as a list of NumPy :code:`ndarray`'s (which is what :code:`flwr.client.NumPyClient` expects) -#. :code:`fit` - * update the parameters of the local model with the parameters received from the server - * train the model on the local training set - * get the updated local model weights and return them to the server -#. :code:`evaluate` - * update the parameters of the local model with the parameters received from the server - * evaluate the updated model on the local test set - * return the local loss and accuracy to the server - -The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make use of the functions :code:`train()` and :code:`test()` previously defined in :code:`cifar.py`. -So what we really do here is we tell Flower through our :code:`NumPyClient` subclass which of our already defined functions to call for training and evaluation. -We included type annotations to give you a better understanding of the data types that get passed around. +Implementing a Flower *client* basically means implementing a subclass of either +``flwr.client.Client`` or ``flwr.client.NumPyClient``. Our implementation will be based +on ``flwr.client.NumPyClient`` and we'll call it ``CifarClient``. ``NumPyClient`` is +slightly easier to implement than ``Client`` if you use a framework with good NumPy +interoperability (like PyTorch or TensorFlow/Keras) because it avoids some of the +boilerplate that would otherwise be necessary. ``CifarClient`` needs to implement four +methods, two methods for getting/setting model parameters, one method for training the +model, and one method for testing the model: + +1. ``set_parameters`` + - set the model parameters on the local model that are received from the server + - loop over the list of model parameters received as NumPy ``ndarray``'s (think + list of neural network layers) +2. ``get_parameters`` + - get the model parameters and return them as a list of NumPy ``ndarray``'s + (which is what ``flwr.client.NumPyClient`` expects) +3. ``fit`` + - update the parameters of the local model with the parameters received from the + server + - train the model on the local training set + - get the updated local model weights and return them to the server +4. ``evaluate`` + - update the parameters of the local model with the parameters received from the + server + - evaluate the updated model on the local test set + - return the local loss and accuracy to the server + +The two ``NumPyClient`` methods ``fit`` and ``evaluate`` make use of the functions +``train()`` and ``test()`` previously defined in ``cifar.py``. So what we really do here +is we tell Flower through our ``NumPyClient`` subclass which of our already defined +functions to call for training and evaluation. We included type annotations to give you +a better understanding of the data types that get passed around. .. code-block:: python @@ -277,8 +312,10 @@ We included type annotations to give you a better understanding of the data type loss, accuracy = cifar.test(self.model, self.testloader, device=DEVICE) return float(loss), self.num_examples["testset"], {"accuracy": float(accuracy)} -All that's left to do it to define a function that loads both model and data, creates a :code:`CifarClient`, and starts this client. -You load your data and model by using :code:`cifar.py`. Start :code:`CifarClient` with the function :code:`fl.client.start_client()` by pointing it at the same IP address we used in :code:`server.py`: +All that's left to do it to define a function that loads both model and data, creates a +``CifarClient``, and starts this client. You load your data and model by using +``cifar.py``. Start ``CifarClient`` with the function ``fl.client.start_client()`` by +pointing it at the same IP address we used in ``server.py``: .. code-block:: python @@ -300,15 +337,20 @@ You load your data and model by using :code:`cifar.py`. Start :code:`CifarClient And that's it. You can now open two additional terminal windows and run -.. code-block:: python +.. code-block:: bash python3 client.py -in each window (make sure that the server is running before you do so) and see your (previously centralized) PyTorch project run federated learning across two clients. Congratulations! +in each window (make sure that the server is running before you do so) and see your +(previously centralized) PyTorch project run federated learning across two clients. +Congratulations! Next Steps ---------- -The full source code for this example: `PyTorch: From Centralized To Federated (Code) `_. -Our example is, of course, somewhat over-simplified because both clients load the exact same dataset, which isn't realistic. -You're now prepared to explore this topic further. How about using different subsets of CIFAR-10 on each client? How about adding more clients? +The full source code for this example: `PyTorch: From Centralized To Federated (Code) +`_. +Our example is, of course, somewhat over-simplified because both clients load the exact +same dataset, which isn't realistic. You're now prepared to explore this topic further. +How about using different subsets of CIFAR-10 on each client? How about adding more +clients? diff --git a/doc/source/explanation-differential-privacy.rst b/doc/source/explanation-differential-privacy.rst index e488f5ccbd57..06e9dbdedd39 100644 --- a/doc/source/explanation-differential-privacy.rst +++ b/doc/source/explanation-differential-privacy.rst @@ -1,133 +1,171 @@ Differential Privacy ==================== -The information in datasets like healthcare, financial transactions, user preferences, etc., is valuable and has the potential for scientific breakthroughs and provides important business insights. -However, such data is also sensitive and there is a risk of compromising individual privacy. -Traditional methods like anonymization alone would not work because of attacks like Re-identification and Data Linkage. -That's where differential privacy comes in. It provides the possibility of analyzing data while ensuring the privacy of individuals. +The information in datasets like healthcare, financial transactions, user preferences, +etc., is valuable and has the potential for scientific breakthroughs and provides +important business insights. However, such data is also sensitive and there is a risk of +compromising individual privacy. +Traditional methods like anonymization alone would not work because of attacks like +Re-identification and Data Linkage. That's where differential privacy comes in. It +provides the possibility of analyzing data while ensuring the privacy of individuals. Differential Privacy -------------------- -Imagine two datasets that are identical except for a single record (for instance, Alice's data). -Differential Privacy (DP) guarantees that any analysis (M), like calculating the average income, will produce nearly identical results for both datasets (O and O' would be similar). -This preserves group patterns while obscuring individual details, ensuring the individual's information remains hidden in the crowd. -.. image:: ./_static/DP/dp-intro.png - :align: center - :width: 400 - :alt: DP Intro +Imagine two datasets that are identical except for a single record (for instance, +Alice's data). Differential Privacy (DP) guarantees that any analysis (M), like +calculating the average income, will produce nearly identical results for both datasets +(O and O' would be similar). This preserves group patterns while obscuring individual +details, ensuring the individual's information remains hidden in the crowd. +.. image:: ./_static/DP/dp-intro.png + :align: center + :width: 400 + :alt: DP Intro -One of the most commonly used mechanisms to achieve DP is adding enough noise to the output of the analysis to mask the contribution of each individual in the data while preserving the overall accuracy of the analysis. +One of the most commonly used mechanisms to achieve DP is adding enough noise to the +output of the analysis to mask the contribution of each individual in the data while +preserving the overall accuracy of the analysis. Formal Definition ~~~~~~~~~~~~~~~~~ -Differential Privacy (DP) provides statistical guarantees against the information an adversary can infer through the output of a randomized algorithm. -It provides an unconditional upper bound on the influence of a single individual on the output of the algorithm by adding noise [1]. -A randomized mechanism -M provides (:math:`\epsilon`, :math:`\delta`)-differential privacy if for any two neighboring databases, D :sub:`1` and D :sub:`2`, that differ in only a single record, -and for all possible outputs S ⊆ Range(A): - -.. math:: - \small - P[M(D_{1} \in A)] \leq e^{\epsilon} P[M(D_{2} \in A)] + \delta +Differential Privacy (DP) provides statistical guarantees against the information an +adversary can infer through the output of a randomized algorithm. It provides an +unconditional upper bound on the influence of a single individual on the output of the +algorithm by adding noise [1]. A randomized mechanism M provides (:math:`\epsilon`, +:math:`\delta`)-differential privacy if for any two neighboring databases, D :sub:`1` +and D :sub:`2`, that differ in only a single record, and for all possible outputs S ⊆ +Range(A): +.. math:: -The :math:`\epsilon` parameter, also known as the privacy budget, is a metric of privacy loss. -It also controls the privacy-utility trade-off; lower :math:`\epsilon` values indicate higher levels of privacy but are likely to reduce utility as well. -The :math:`\delta` parameter accounts for a small probability on which the upper bound :math:`\epsilon` does not hold. -The amount of noise needed to achieve differential privacy is proportional to the sensitivity of the output, which measures the maximum change in the output due to the inclusion or removal of a single record. + \small + P[M(D_{1} \in A)] \leq e^{\epsilon} P[M(D_{2} \in A)] + \delta +The :math:`\epsilon` parameter, also known as the privacy budget, is a metric of privacy +loss. It also controls the privacy-utility trade-off; lower :math:`\epsilon` values +indicate higher levels of privacy but are likely to reduce utility as well. The +:math:`\delta` parameter accounts for a small probability on which the upper bound +:math:`\epsilon` does not hold. The amount of noise needed to achieve differential +privacy is proportional to the sensitivity of the output, which measures the maximum +change in the output due to the inclusion or removal of a single record. Differential Privacy in Machine Learning ---------------------------------------- + DP can be utilized in machine learning to preserve the privacy of the training data. -Differentially private machine learning algorithms are designed in a way to prevent the algorithm to learn any specific information about any individual data points and subsequently prevent the model from revealing sensitive information. -Depending on the stage at which noise is introduced, various methods exist for applying DP to machine learning algorithms. -One approach involves adding noise to the training data (either to the features or labels), while another method entails injecting noise into the gradients of the loss function during model training. -Additionally, such noise can be incorporated into the model's output. +Differentially private machine learning algorithms are designed in a way to prevent the +algorithm to learn any specific information about any individual data points and +subsequently prevent the model from revealing sensitive information. Depending on the +stage at which noise is introduced, various methods exist for applying DP to machine +learning algorithms. One approach involves adding noise to the training data (either to +the features or labels), while another method entails injecting noise into the gradients +of the loss function during model training. Additionally, such noise can be incorporated +into the model's output. Differential Privacy in Federated Learning ------------------------------------------ -Federated learning is a data minimization approach that allows multiple parties to collaboratively train a model without sharing their raw data. -However, federated learning also introduces new privacy challenges. The model updates between parties and the central server can leak information about the local data. -These leaks can be exploited by attacks such as membership inference and property inference attacks, or model inversion attacks. -DP can play a crucial role in federated learning to provide privacy for the clients' data. +Federated learning is a data minimization approach that allows multiple parties to +collaboratively train a model without sharing their raw data. However, federated +learning also introduces new privacy challenges. The model updates between parties and +the central server can leak information about the local data. These leaks can be +exploited by attacks such as membership inference and property inference attacks, or +model inversion attacks. -Depending on the granularity of privacy provision or the location of noise addition, different forms of DP exist in federated learning. -In this explainer, we focus on two approaches of DP utilization in federated learning based on where the noise is added: at the server (also known as the center) or at the client (also known as the local). +DP can play a crucial role in federated learning to provide privacy for the clients' +data. -- **Central Differential Privacy**: DP is applied by the server and the goal is to prevent the aggregated model from leaking information about each client's data. +Depending on the granularity of privacy provision or the location of noise addition, +different forms of DP exist in federated learning. In this explainer, we focus on two +approaches of DP utilization in federated learning based on where the noise is added: at +the server (also known as the center) or at the client (also known as the local). -- **Local Differential Privacy**: DP is applied on the client side before sending any information to the server and the goal is to prevent the updates that are sent to the server from leaking any information about the client's data. +- **Central Differential Privacy**: DP is applied by the server and the goal is to + prevent the aggregated model from leaking information about each client's data. +- **Local Differential Privacy**: DP is applied on the client side before sending any + information to the server and the goal is to prevent the updates that are sent to the + server from leaking any information about the client's data. Central Differential Privacy ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -In this approach, which is also known as user-level DP, the central server is responsible for adding noise to the globally aggregated parameters. It should be noted that trust in the server is required. + +In this approach, which is also known as user-level DP, the central server is +responsible for adding noise to the globally aggregated parameters. It should be noted +that trust in the server is required. .. image:: ./_static/DP/CDP.png - :align: center - :width: 400 - :alt: Central Differential Privacy - -While there are various ways to implement central DP in federated learning, we concentrate on the algorithms proposed by [2] and [3]. -The overall approach is to clip the model updates sent by the clients and add some amount of noise to the aggregated model. -In each iteration, a random set of clients is chosen with a specific probability for training. -Each client performs local training on its own data. -The update of each client is then clipped by some value `S` (sensitivity `S`). -This would limit the impact of any individual client which is crucial for privacy and often beneficial for robustness. -A common approach to achieve this is by restricting the `L2` norm of the clients' model updates, ensuring that larger updates are scaled down to fit within the norm `S`. + :align: center + :width: 400 + :alt: Central Differential Privacy + +While there are various ways to implement central DP in federated learning, we +concentrate on the algorithms proposed by [2] and [3]. The overall approach is to clip +the model updates sent by the clients and add some amount of noise to the aggregated +model. In each iteration, a random set of clients is chosen with a specific probability +for training. Each client performs local training on its own data. The update of each +client is then clipped by some value `S` (sensitivity `S`). This would limit the impact +of any individual client which is crucial for privacy and often beneficial for +robustness. A common approach to achieve this is by restricting the `L2` norm of the +clients' model updates, ensuring that larger updates are scaled down to fit within the +norm `S`. .. image:: ./_static/DP/clipping.png - :align: center - :width: 300 - :alt: clipping + :align: center + :width: 300 + :alt: clipping -Afterwards, the Gaussian mechanism is used to add noise in order to distort the sum of all clients' updates. -The amount of noise is scaled to the sensitivity value to obtain a privacy guarantee. -The Gaussian mechanism is used with a noise sampled from `N (0, σ²)` where `σ = ( noise_scale * S ) / (number of sampled clients)`. +Afterwards, the Gaussian mechanism is used to add noise in order to distort the sum of +all clients' updates. The amount of noise is scaled to the sensitivity value to obtain a +privacy guarantee. The Gaussian mechanism is used with a noise sampled from `N (0, σ²)` +where `σ = ( noise_scale * S ) / (number of sampled clients)`. Clipping -^^^^^^^^ - -There are two forms of clipping commonly used in Central DP: Fixed Clipping and Adaptive Clipping. +++++++++ -- **Fixed Clipping** : A predefined fix threshold is set for the magnitude of clients' updates. Any update exceeding this threshold is clipped back to the threshold value. +There are two forms of clipping commonly used in Central DP: Fixed Clipping and Adaptive +Clipping. -- **Adaptive Clipping** : The clipping threshold dynamically adjusts based on the observed update distribution [4]. It means that the clipping value is tuned during the rounds with respect to the quantile of the update norm distribution. +- **Fixed Clipping** : A predefined fix threshold is set for the magnitude of clients' + updates. Any update exceeding this threshold is clipped back to the threshold value. +- **Adaptive Clipping** : The clipping threshold dynamically adjusts based on the + observed update distribution [4]. It means that the clipping value is tuned during the + rounds with respect to the quantile of the update norm distribution. -The choice between fixed and adaptive clipping depends on various factors such as privacy requirements, data distribution, model complexity, and others. +The choice between fixed and adaptive clipping depends on various factors such as +privacy requirements, data distribution, model complexity, and others. Local Differential Privacy ~~~~~~~~~~~~~~~~~~~~~~~~~~ -In this approach, each client is responsible for performing DP. -Local DP avoids the need for a fully trusted aggregator, but it should be noted that local DP leads to a decrease in accuracy but better privacy in comparison to central DP. +In this approach, each client is responsible for performing DP. Local DP avoids the need +for a fully trusted aggregator, but it should be noted that local DP leads to a decrease +in accuracy but better privacy in comparison to central DP. .. image:: ./_static/DP/LDP.png - :align: center - :width: 400 - :alt: Local Differential Privacy - + :align: center + :width: 400 + :alt: Local Differential Privacy In this explainer, we focus on two forms of achieving Local DP: -- Each client adds noise to the local updates before sending them to the server. To achieve (:math:`\epsilon`, :math:`\delta`)-DP, considering the sensitivity of the local model to be ∆, Gaussian noise is applied with a noise scale of σ where: +- Each client adds noise to the local updates before sending them to the server. To + achieve (:math:`\epsilon`, :math:`\delta`)-DP, considering the sensitivity of the + local model to be ∆, Gaussian noise is applied with a noise scale of σ where: .. math:: + \small \frac{∆ \times \sqrt{2 \times \log\left(\frac{1.25}{\delta}\right)}}{\epsilon} - -- Each client adds noise to the gradients of the model during the local training (DP-SGD). More specifically, in this approach, gradients are clipped and an amount of calibrated noise is injected into the gradients. - +- Each client adds noise to the gradients of the model during the local training + (DP-SGD). More specifically, in this approach, gradients are clipped and an amount of + calibrated noise is injected into the gradients. Please note that these two approaches are providing privacy at different levels. - **References:** [1] Dwork et al. The Algorithmic Foundations of Differential Privacy. diff --git a/doc/source/explanation-federated-evaluation.rst b/doc/source/explanation-federated-evaluation.rst index bcdca9bae700..c56a5d48b2f6 100644 --- a/doc/source/explanation-federated-evaluation.rst +++ b/doc/source/explanation-federated-evaluation.rst @@ -1,8 +1,8 @@ Federated evaluation ==================== -There are two main approaches to evaluating models in federated learning systems: centralized (or server-side) evaluation and federated (or client-side) evaluation. - +There are two main approaches to evaluating models in federated learning systems: +centralized (or server-side) evaluation and federated (or client-side) evaluation. Centralized Evaluation ---------------------- @@ -10,15 +10,17 @@ Centralized Evaluation Built-In Strategies ~~~~~~~~~~~~~~~~~~~ -All built-in strategies support centralized evaluation by providing an evaluation function during initialization. -An evaluation function is any function that can take the current global model parameters as input and return evaluation results: +All built-in strategies support centralized evaluation by providing an evaluation +function during initialization. An evaluation function is any function that can take the +current global model parameters as input and return evaluation results: .. code-block:: python - + from flwr.common import NDArrays, Scalar - + from typing import Dict, Optional, Tuple + def get_evaluate_fn(model): """Return an evaluation function for server-side evaluation.""" @@ -38,6 +40,7 @@ An evaluation function is any function that can take the current global model pa return evaluate + # Load and compile model for server-side parameter evaluation model = tf.keras.applications.EfficientNetB0( input_shape=(32, 32, 3), weights=None, classes=10 @@ -47,7 +50,7 @@ An evaluation function is any function that can take the current global model pa # Create strategy strategy = fl.server.strategy.FedAvg( - # ... other FedAvg arguments + # ... other FedAvg arguments evaluate_fn=get_evaluate_fn(model), ) @@ -57,9 +60,10 @@ An evaluation function is any function that can take the current global model pa Custom Strategies ~~~~~~~~~~~~~~~~~ -The :code:`Strategy` abstraction provides a method called :code:`evaluate` that can directly be used to evaluate the current global model parameters. -The current server implementation calls :code:`evaluate` after parameter aggregation and before federated evaluation (see next paragraph). - +The ``Strategy`` abstraction provides a method called ``evaluate`` that can directly be +used to evaluate the current global model parameters. The current server implementation +calls ``evaluate`` after parameter aggregation and before federated evaluation (see next +paragraph). Federated Evaluation -------------------- @@ -67,7 +71,8 @@ Federated Evaluation Implementing Federated Evaluation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Client-side evaluation happens in the :code:`Client.evaluate` method and can be configured from the server side. +Client-side evaluation happens in the ``Client.evaluate`` method and can be configured +from the server side. .. code-block:: python @@ -79,9 +84,11 @@ Client-side evaluation happens in the :code:`Client.evaluate` method and can be def get_parameters(self, config): # ... + pass def fit(self, parameters, config): # ... + pass def evaluate(self, parameters, config): """Evaluate parameters on the locally held test set.""" @@ -100,12 +107,27 @@ Client-side evaluation happens in the :code:`Client.evaluate` method and can be Configuring Federated Evaluation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Federated evaluation can be configured from the server side. Built-in strategies support the following arguments: - -- :code:`fraction_evaluate`: a :code:`float` defining the fraction of clients that will be selected for evaluation. If :code:`fraction_evaluate` is set to :code:`0.1` and :code:`100` clients are connected to the server, then :code:`10` will be randomly selected for evaluation. If :code:`fraction_evaluate` is set to :code:`0.0`, federated evaluation will be disabled. -- :code:`min_evaluate_clients`: an :code:`int`: the minimum number of clients to be selected for evaluation. If :code:`fraction_evaluate` is set to :code:`0.1`, :code:`min_evaluate_clients` is set to 20, and :code:`100` clients are connected to the server, then :code:`20` clients will be selected for evaluation. -- :code:`min_available_clients`: an :code:`int` that defines the minimum number of clients which need to be connected to the server before a round of federated evaluation can start. If fewer than :code:`min_available_clients` are connected to the server, the server will wait until more clients are connected before it continues to sample clients for evaluation. -- :code:`on_evaluate_config_fn`: a function that returns a configuration dictionary which will be sent to the selected clients. The function will be called during each round and provides a convenient way to customize client-side evaluation from the server side, for example, to configure the number of validation steps performed. +Federated evaluation can be configured from the server side. Built-in strategies support +the following arguments: + +- ``fraction_evaluate``: a ``float`` defining the fraction of clients that will be + selected for evaluation. If ``fraction_evaluate`` is set to ``0.1`` and ``100`` + clients are connected to the server, then ``10`` will be randomly selected for + evaluation. If ``fraction_evaluate`` is set to ``0.0``, federated evaluation will be + disabled. +- ``min_evaluate_clients``: an ``int``: the minimum number of clients to be selected for + evaluation. If ``fraction_evaluate`` is set to ``0.1``, ``min_evaluate_clients`` is + set to 20, and ``100`` clients are connected to the server, then ``20`` clients will + be selected for evaluation. +- ``min_available_clients``: an ``int`` that defines the minimum number of clients which + need to be connected to the server before a round of federated evaluation can start. + If fewer than ``min_available_clients`` are connected to the server, the server will + wait until more clients are connected before it continues to sample clients for + evaluation. +- ``on_evaluate_config_fn``: a function that returns a configuration dictionary which + will be sent to the selected clients. The function will be called during each round + and provides a convenient way to customize client-side evaluation from the server + side, for example, to configure the number of validation steps performed. .. code-block:: python @@ -118,6 +140,7 @@ Federated evaluation can be configured from the server side. Built-in strategies val_steps = 5 if server_round < 4 else 10 return {"val_steps": val_steps} + # Create strategy strategy = fl.server.strategy.FedAvg( # ... other FedAvg arguments @@ -130,11 +153,11 @@ Federated evaluation can be configured from the server side. Built-in strategies # Start Flower server for four rounds of federated learning fl.server.start_server(server_address="[::]:8080", strategy=strategy) - Evaluating Local Model Updates During Training ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Model parameters can also be evaluated during training. :code:`Client.fit` can return arbitrary evaluation results as a dictionary: +Model parameters can also be evaluated during training. ``Client.fit`` can return +arbitrary evaluation results as a dictionary: .. code-block:: python @@ -146,6 +169,7 @@ Model parameters can also be evaluated during training. :code:`Client.fit` can r def get_parameters(self, config): # ... + pass def fit(self, parameters, config): """Train parameters on the locally held training set.""" @@ -171,9 +195,12 @@ Model parameters can also be evaluated during training. :code:`Client.fit` can r def evaluate(self, parameters, config): # ... - + pass Full Code Example ----------------- -For a full code example that uses both centralized and federated evaluation, see the *Advanced TensorFlow Example* (the same approach can be applied to workloads implemented in any other framework): https://github.com/adap/flower/tree/main/examples/advanced-tensorflow +For a full code example that uses both centralized and federated evaluation, see the +*Advanced TensorFlow Example* (the same approach can be applied to workloads implemented +in any other framework): +https://github.com/adap/flower/tree/main/examples/advanced-tensorflow diff --git a/doc/source/explanation-flower-architecture.rst b/doc/source/explanation-flower-architecture.rst index 22691d6091ac..e82da56dcefa 100644 --- a/doc/source/explanation-flower-architecture.rst +++ b/doc/source/explanation-flower-architecture.rst @@ -1,180 +1,158 @@ -##################### - Flower Architecture -##################### +Flower Architecture +=================== -This page explains the architecture of deployed Flower federated -learning system. +This page explains the architecture of deployed Flower federated learning system. -In federated learning (FL), there is typically one server and a number -of clients that are connected to the server. This is often called a -federation. +In federated learning (FL), there is typically one server and a number of clients that +are connected to the server. This is often called a federation. -The role of the server is to coordinate the training process. The role -of each client is to receive tasks from the server, execute those tasks -and return the results back to the server. +The role of the server is to coordinate the training process. The role of each client is +to receive tasks from the server, execute those tasks and return the results back to the +server. This is sometimes called a hub-and-spoke topology: .. figure:: ./_static/flower-architecture-hub-and-spoke.svg - :align: center - :width: 600 - :alt: Hub-and-spoke topology in federated learning - :class: no-scaled-link + :align: center + :width: 600 + :alt: Hub-and-spoke topology in federated learning + :class: no-scaled-link - Hub-and-spoke topology in federated learning (one server, multiple clients). + Hub-and-spoke topology in federated learning (one server, multiple clients). -In a real-world deployment, we typically want to run different projects -on such a federation. Each project could use different hyperparameters, -different model architectures, different aggregation strategies, or even -different machine learning frameworks like PyTorch and TensorFlow. +In a real-world deployment, we typically want to run different projects on such a +federation. Each project could use different hyperparameters, different model +architectures, different aggregation strategies, or even different machine learning +frameworks like PyTorch and TensorFlow. -This is why, in Flower, both the server side and the client side are -split into two parts. One part is long-lived and responsible for -communicating across the network, the other part is short-lived and -executes task-specific code. +This is why, in Flower, both the server side and the client side are split into two +parts. One part is long-lived and responsible for communicating across the network, the +other part is short-lived and executes task-specific code. A Flower `server` consists of **SuperLink** and ``ServerApp``: -- **SuperLink**: a long-running process that forwards task instructions - to clients (SuperNodes) and receives task results back. - -- ``ServerApp``: a short-lived process with project-spcific code that - customizes all server-side aspects of federated learning systems - (client selection, client configuration, result aggregation). This is - what AI researchers and AI engineers write when they build Flower - apps. +- **SuperLink**: a long-running process that forwards task instructions to clients + (SuperNodes) and receives task results back. +- ``ServerApp``: a short-lived process with project-spcific code that customizes all + server-side aspects of federated learning systems (client selection, client + configuration, result aggregation). This is what AI researchers and AI engineers write + when they build Flower apps. A Flower `client` consists of **SuperNode** and ``ClientApp``: -- **SuperNode**: a long-running process that connects to the SuperLink, - asks for tasks, executes tasks (for example, "train this model on - your local data") and returns task results back to the SuperLink. - -- ``ClientApp``: a short-lived process with project-specific code that - customizes all client-side aspects of federated learning systems - (local model training and evaluation, pre- and post-processing). This - is what AI researchers and AI engineers write when they build Flower - apps. +- **SuperNode**: a long-running process that connects to the SuperLink, asks for tasks, + executes tasks (for example, "train this model on your local data") and returns task + results back to the SuperLink. +- ``ClientApp``: a short-lived process with project-specific code that customizes all + client-side aspects of federated learning systems (local model training and + evaluation, pre- and post-processing). This is what AI researchers and AI engineers + write when they build Flower apps. -Why SuperNode and SuperLink? Well, in federated learning, the clients -are the actual stars of the show. They hold the training data and they -run the actual training. This is why Flower decided to name them -**SuperNode**. The **SuperLink** is then responsible for acting as the -`missing link` between all those SuperNodes. +Why SuperNode and SuperLink? Well, in federated learning, the clients are the actual +stars of the show. They hold the training data and they run the actual training. This is +why Flower decided to name them **SuperNode**. The **SuperLink** is then responsible for +acting as the `missing link` between all those SuperNodes. .. figure:: ./_static/flower-architecture-basic-architecture.svg - :align: center - :width: 600 - :alt: Basic Flower architecture - :class: no-scaled-link + :align: center + :width: 600 + :alt: Basic Flower architecture + :class: no-scaled-link - The basic Flower architecture for federated learning. + The basic Flower architecture for federated learning. -In a Flower app project, users will typically develop the ``ServerApp`` -and the ``ClientApp``. All the network communication between `server` -and `clients` is taken care of by the SuperLink and SuperNodes. +In a Flower app project, users will typically develop the ``ServerApp`` and the +``ClientApp``. All the network communication between `server` and `clients` is taken +care of by the SuperLink and SuperNodes. .. tip:: - For more details, please refer to the |serverapp_link|_ and - |clientapp_link|_ documentation. + For more details, please refer to the |serverapp_link|_ and |clientapp_link|_ + documentation. -With *multi-run*, multiple ``ServerApp``\s and ``ClientApp``\s are now -capable of running on the same federation consisting of a single -long-running SuperLink and multiple long-running SuperNodes. This is -sometimes referred to as `multi-tenancy` or `multi-job`. +With *multi-run*, multiple ``ServerApp``\s and ``ClientApp``\s are now capable of +running on the same federation consisting of a single long-running SuperLink and +multiple long-running SuperNodes. This is sometimes referred to as `multi-tenancy` or +`multi-job`. -As shown in the figure below, two projects, each consisting of a -``ServerApp`` and a ``ClientApp``, could share the same SuperLink and -SuperNodes. +As shown in the figure below, two projects, each consisting of a ``ServerApp`` and a +``ClientApp``, could share the same SuperLink and SuperNodes. .. figure:: ./_static/flower-architecture-multi-run.svg - :align: center - :width: 600 - :alt: Multi-tenancy federated learning architecture - :class: no-scaled-link + :align: center + :width: 600 + :alt: Multi-tenancy federated learning architecture + :class: no-scaled-link - Multi-tenancy federated learning architecture with Flower + Multi-tenancy federated learning architecture with Flower -To illustrate how multi-run works, consider one federated learning -training run where a ``ServerApp`` and a ``ClientApp`` are participating -in ``[run 1]``. Note that a SuperNode will only run a ``ClientApp`` if -it is selected to participate in the training run. +To illustrate how multi-run works, consider one federated learning training run where a +``ServerApp`` and a ``ClientApp`` are participating in ``[run 1]``. Note that a +SuperNode will only run a ``ClientApp`` if it is selected to participate in the training +run. -In ``[run 1]`` below, all the SuperNodes are selected and therefore run -their corresponding ``ClientApp``\s: +In ``[run 1]`` below, all the SuperNodes are selected and therefore run their +corresponding ``ClientApp``\s: .. figure:: ./_static/flower-architecture-multi-run-1.svg - :align: center - :width: 600 - :alt: Multi-tenancy federated learning architecture - Run 1 - :class: no-scaled-link + :align: center + :width: 600 + :alt: Multi-tenancy federated learning architecture - Run 1 + :class: no-scaled-link - Run 1 in a multi-run federated learning architecture with Flower. - All SuperNodes participate in the training round. + Run 1 in a multi-run federated learning architecture with Flower. All SuperNodes + participate in the training round. -However, in ``[run 2]``, only the first and third SuperNodes are -selected to participate in the training: +However, in ``[run 2]``, only the first and third SuperNodes are selected to participate +in the training: .. figure:: ./_static/flower-architecture-multi-run-2.svg - :align: center - :width: 600 - :alt: Multi-tenancy federated learning architecture - Run 2 - :class: no-scaled-link - - Run 2 in a multi-run federated learning architecture with Flower. - Only the first and third SuperNodes are selected to participate in the - training round. - -Therefore, with Flower multi-run, different projects (each consisting of -a ``ServerApp`` and ``ClientApp``) can run on different sets of clients. - -To help you start and manage all of the concurrently executing training -runs, Flower offers one additional long-running server-side service -called **SuperExec**. When you type ``flwr run`` to start a new training -run, the ``flwr`` CLI bundles your local project (mainly your -``ServerApp`` and ``ClientApp``) and sends it to the **SuperExec**. The -**SuperExec** will then take care of starting and managing your -``ServerApp``, which in turn selects SuperNodes to execute your -``ClientApp``. - -This architecture allows many users to (concurrently) run their projects -on the same federation, simply by typing ``flwr run`` on their local -developer machine. + :align: center + :width: 600 + :alt: Multi-tenancy federated learning architecture - Run 2 + :class: no-scaled-link + + Run 2 in a multi-run federated learning architecture with Flower. Only the first and + third SuperNodes are selected to participate in the training round. + +Therefore, with Flower multi-run, different projects (each consisting of a ``ServerApp`` +and ``ClientApp``) can run on different sets of clients. + +To help you start and manage all of the concurrently executing training runs, Flower +offers one additional long-running server-side service called **SuperExec**. When you +type ``flwr run`` to start a new training run, the ``flwr`` CLI bundles your local +project (mainly your ``ServerApp`` and ``ClientApp``) and sends it to the **SuperExec**. +The **SuperExec** will then take care of starting and managing your ``ServerApp``, which +in turn selects SuperNodes to execute your ``ClientApp``. + +This architecture allows many users to (concurrently) run their projects on the same +federation, simply by typing ``flwr run`` on their local developer machine. .. figure:: ./_static/flower-architecture-deployment-engine.svg - :align: center - :width: 800 - :alt: Flower Deployment Engine with SuperExec - :class: no-scaled-link + :align: center + :width: 800 + :alt: Flower Deployment Engine with SuperExec + :class: no-scaled-link - The SuperExec service for managing concurrent training runs in - Flower. + The SuperExec service for managing concurrent training runs in Flower. .. note:: - This explanation covers the Flower Deployment Engine. An explanation - covering the Flower Simulation Engine will follow. + This explanation covers the Flower Deployment Engine. An explanation covering the + Flower Simulation Engine will follow. .. important:: - As we continue to enhance Flower at a rapid pace, we'll periodically - update this explainer document. Feel free to share any feedback with - us. - -.. |clientapp_link| replace:: + As we continue to enhance Flower at a rapid pace, we'll periodically update this + explainer document. Feel free to share any feedback with us. - ``ClientApp`` +.. |clientapp_link| replace:: ``ClientApp`` -.. |serverapp_link| replace:: - - ``ServerApp`` +.. |serverapp_link| replace:: ``ServerApp`` .. _clientapp_link: ref-api/flwr.client.ClientApp.html .. _serverapp_link: ref-api/flwr.server.ServerApp.html -.. title:: Flower federated learning architecture - .. meta:: - :description: Explore the federated learning architecture of the Flower framework, featuring multi-run, concurrent execution, and scalable, secure machine learning while preserving data privacy. + :description: Explore the federated learning architecture of the Flower framework, featuring multi-run, concurrent execution, and scalable, secure machine learning while preserving data privacy. diff --git a/doc/source/how-to-aggregate-evaluation-results.rst b/doc/source/how-to-aggregate-evaluation-results.rst index fa4ba88b8ff0..be6e20068c88 100644 --- a/doc/source/how-to-aggregate-evaluation-results.rst +++ b/doc/source/how-to-aggregate-evaluation-results.rst @@ -1,14 +1,15 @@ Aggregate evaluation results ============================ -The Flower server does not prescribe a way to aggregate evaluation results, but it enables the user to fully customize result aggregation. - +The Flower server does not prescribe a way to aggregate evaluation results, but it +enables the user to fully customize result aggregation. Aggregate Custom Evaluation Results ----------------------------------- -The same :code:`Strategy`-customization approach can be used to aggregate custom evaluation results coming from individual clients. -Clients can return custom metrics to the server by returning a dictionary: +The same ``Strategy``-customization approach can be used to aggregate custom evaluation +results coming from individual clients. Clients can return custom metrics to the server +by returning a dictionary: .. code-block:: python @@ -16,9 +17,11 @@ Clients can return custom metrics to the server by returning a dictionary: def get_parameters(self, config): # ... + pass def fit(self, parameters, config): # ... + pass def evaluate(self, parameters, config): """Evaluate parameters on the locally held test set.""" @@ -33,7 +36,8 @@ Clients can return custom metrics to the server by returning a dictionary: num_examples_test = len(self.x_test) return loss, num_examples_test, {"accuracy": accuracy} -The server can then use a customized strategy to aggregate the metrics provided in these dictionaries: +The server can then use a customized strategy to aggregate the metrics provided in these +dictionaries: .. code-block:: python @@ -50,7 +54,9 @@ The server can then use a customized strategy to aggregate the metrics provided return None, {} # Call aggregate_evaluate from base class (FedAvg) to aggregate loss and metrics - aggregated_loss, aggregated_metrics = super().aggregate_evaluate(server_round, results, failures) + aggregated_loss, aggregated_metrics = super().aggregate_evaluate( + server_round, results, failures + ) # Weigh accuracy of each client by number of examples used accuracies = [r.metrics["accuracy"] * r.num_examples for _, r in results] @@ -58,11 +64,14 @@ The server can then use a customized strategy to aggregate the metrics provided # Aggregate and print custom metric aggregated_accuracy = sum(accuracies) / sum(examples) - print(f"Round {server_round} accuracy aggregated from client results: {aggregated_accuracy}") + print( + f"Round {server_round} accuracy aggregated from client results: {aggregated_accuracy}" + ) # Return aggregated loss and metrics (i.e., aggregated accuracy) return aggregated_loss, {"accuracy": aggregated_accuracy} + # Create strategy and run server strategy = AggregateCustomMetricStrategy( # (same arguments as FedAvg here) diff --git a/doc/source/how-to-authenticate-supernodes.rst b/doc/source/how-to-authenticate-supernodes.rst index 9b001531ee33..a2dd499dbc10 100644 --- a/doc/source/how-to-authenticate-supernodes.rst +++ b/doc/source/how-to-authenticate-supernodes.rst @@ -1,29 +1,38 @@ Authenticate SuperNodes ======================= -Flower has built-in support for authenticated SuperNodes that you can use to verify the identities of each SuperNode connecting to a SuperLink. -Flower node authentication works similar to how GitHub SSH authentication works: +Flower has built-in support for authenticated SuperNodes that you can use to verify the +identities of each SuperNode connecting to a SuperLink. Flower node authentication works +similar to how GitHub SSH authentication works: -* SuperLink (server) stores a list of known (client) node public keys -* Using ECDH, both SuperNode and SuperLink independently derive a shared secret -* Shared secret is used to compute the HMAC value of the message sent from SuperNode to SuperLink as a token -* SuperLink verifies the token +- SuperLink (server) stores a list of known (client) node public keys +- Using ECDH, both SuperNode and SuperLink independently derive a shared secret +- Shared secret is used to compute the HMAC value of the message sent from SuperNode to + SuperLink as a token +- SuperLink verifies the token -We recommend you to check out the complete `code example `_ demonstrating federated learning with Flower in an authenticated setting. +We recommend you to check out the complete `code example +`_ +demonstrating federated learning with Flower in an authenticated setting. .. note:: + This guide covers a preview feature that might change in future versions of Flower. .. note:: - For increased security, node authentication can only be used when encrypted connections (SSL/TLS) are enabled. -Enable node authentication in :code:`SuperLink` ------------------------------------------------ + For increased security, node authentication can only be used when encrypted + connections (SSL/TLS) are enabled. + +Enable node authentication in ``SuperLink`` +------------------------------------------- -To enable node authentication, first you need to configure SSL/TLS connections to secure the SuperLink<>SuperNode communication. You can find the complete guide -`here `_. -After configuring secure connections, you can enable client authentication in a long-running Flower :code:`SuperLink`. -Use the following terminal command to start a Flower :code:`SuperNode` that has both secure connections and node authentication enabled: +To enable node authentication, first you need to configure SSL/TLS connections to secure +the SuperLink<>SuperNode communication. You can find the complete guide `here +`_. After +configuring secure connections, you can enable client authentication in a long-running +Flower ``SuperLink``. Use the following terminal command to start a Flower ``SuperNode`` +that has both secure connections and node authentication enabled: .. code-block:: bash @@ -37,43 +46,59 @@ Use the following terminal command to start a Flower :code:`SuperNode` that has Let's break down the authentication flags: -1. The first flag :code:`--auth-list-public-keys` expects a path to a CSV file storing all known node public keys. You need to store all known node public keys that are allowed to participate in a federation in one CSV file (:code:`.csv`). +1. The first flag ``--auth-list-public-keys`` expects a path to a CSV file storing all + known node public keys. You need to store all known node public keys that are allowed + to participate in a federation in one CSV file (``.csv``). - A valid CSV file storing known node public keys should list the keys in OpenSSH format, separated by commas and without any comments. For an example, refer to our code sample, which contains a CSV file with two known node public keys. + A valid CSV file storing known node public keys should list the keys in OpenSSH + format, separated by commas and without any comments. For an example, refer to + our code sample, which contains a CSV file with two known node public keys. -2. The second and third flags :code:`--auth-superlink-private-key` and :code:`--auth-superlink-public-key` expect paths to the server's private and public keys. For development purposes, you can generate a private and public key pair using :code:`ssh-keygen -t ecdsa -b 384`. +2. The second and third flags ``--auth-superlink-private-key`` and + ``--auth-superlink-public-key`` expect paths to the server's private and public keys. + For development purposes, you can generate a private and public key pair using + ``ssh-keygen -t ecdsa -b 384``. .. note:: - In Flower 1.9, there is no support for dynamically removing, editing, or adding known node public keys to the SuperLink. - To change the set of known nodes, you need to shut the server down, edit the CSV file, and start the server again. - Support for dynamically changing the set of known nodes is on the roadmap to be released in Flower 1.10 (ETA: June). + In Flower 1.9, there is no support for dynamically removing, editing, or adding + known node public keys to the SuperLink. To change the set of known nodes, you need + to shut the server down, edit the CSV file, and start the server again. Support for + dynamically changing the set of known nodes is on the roadmap to be released in + Flower 1.10 (ETA: June). -Enable node authentication in :code:`SuperNode` -------------------------------------------------- +Enable node authentication in ``SuperNode`` +------------------------------------------- -Similar to the long-running Flower server (:code:`SuperLink`), you can easily enable node authentication in the long-running Flower client (:code:`SuperNode`). -Use the following terminal command to start an authenticated :code:`SuperNode`: +Similar to the long-running Flower server (``SuperLink``), you can easily enable node +authentication in the long-running Flower client (``SuperNode``). Use the following +terminal command to start an authenticated ``SuperNode``: .. code-block:: bash - flower-supernode - --root-certificates certificates/ca.crt - --superlink 127.0.0.1:9092 - --auth-supernode-private-key keys/client_credentials - --auth-supernode-public-key keys/client_credentials.pub - -The :code:`--auth-supernode-private-key` flag expects a path to the node's private key file and the :code:`--auth-supernode-public-key` flag expects a path to the node's public key file. For development purposes, you can generate a private and public key pair using :code:`ssh-keygen -t ecdsa -b 384`. + flower-supernode + --root-certificates certificates/ca.crt + --superlink 127.0.0.1:9092 + --auth-supernode-private-key keys/client_credentials + --auth-supernode-public-key keys/client_credentials.pub +The ``--auth-supernode-private-key`` flag expects a path to the node's private key file +and the ``--auth-supernode-public-key`` flag expects a path to the node's public key +file. For development purposes, you can generate a private and public key pair using +``ssh-keygen -t ecdsa -b 384``. Security notice --------------- -The system's security relies on the credentials of the SuperLink and each SuperNode. Therefore, it is imperative to safeguard and safely store the credentials to avoid security risks such as Public Key Infrastructure (PKI) impersonation attacks. -The node authentication mechanism also involves human interaction, so please ensure that all of the communication is done in a secure manner, using trusted communication methods. - +The system's security relies on the credentials of the SuperLink and each SuperNode. +Therefore, it is imperative to safeguard and safely store the credentials to avoid +security risks such as Public Key Infrastructure (PKI) impersonation attacks. The node +authentication mechanism also involves human interaction, so please ensure that all of +the communication is done in a secure manner, using trusted communication methods. Conclusion ---------- -You should now have learned how to start a long-running Flower server (:code:`SuperLink`) and client (:code:`SuperNode`) with node authentication enabled. You should also know the significance of the private key and store it safely to minimize security risks. +You should now have learned how to start a long-running Flower server (``SuperLink``) +and client (``SuperNode``) with node authentication enabled. You should also know the +significance of the private key and store it safely to minimize security risks. diff --git a/doc/source/how-to-configure-clients.rst b/doc/source/how-to-configure-clients.rst index ff0a2f4033df..c950ab3be9e7 100644 --- a/doc/source/how-to-configure-clients.rst +++ b/doc/source/how-to-configure-clients.rst @@ -1,37 +1,55 @@ Configure clients ================= -Along with model parameters, Flower can send configuration values to clients. Configuration values can be used for various purposes. They are, for example, a popular way to control client-side hyperparameters from the server. +Along with model parameters, Flower can send configuration values to clients. +Configuration values can be used for various purposes. They are, for example, a popular +way to control client-side hyperparameters from the server. Configuration values -------------------- -Configuration values are represented as a dictionary with ``str`` keys and values of type ``bool``, ``bytes``, ``double`` (64-bit precision float), ``int``, or ``str`` (or equivalent types in different languages). Here is an example of a configuration dictionary in Python: +Configuration values are represented as a dictionary with ``str`` keys and values of +type ``bool``, ``bytes``, ``double`` (64-bit precision float), ``int``, or ``str`` (or +equivalent types in different languages). Here is an example of a configuration +dictionary in Python: .. code-block:: python config_dict = { - "dropout": True, # str key, bool value + "dropout": True, # str key, bool value "learning_rate": 0.01, # str key, float value - "batch_size": 32, # str key, int value - "optimizer": "sgd", # str key, str value + "batch_size": 32, # str key, int value + "optimizer": "sgd", # str key, str value } -Flower serializes these configuration dictionaries (or *config dict* for short) to their ProtoBuf representation, transports them to the client using gRPC, and then deserializes them back to Python dictionaries. +Flower serializes these configuration dictionaries (or *config dict* for short) to their +ProtoBuf representation, transports them to the client using gRPC, and then deserializes +them back to Python dictionaries. .. note:: - Currently, there is no support for directly sending collection types (e.g., ``Set``, ``List``, ``Map``) as values in configuration dictionaries. There are several workarounds to send collections as values by converting them to one of the supported value types (and converting them back on the client-side). - - One can, for example, convert a list of floating-point numbers to a JSON string, then send the JSON string using the configuration dictionary, and then convert the JSON string back to a list of floating-point numbers on the client. + Currently, there is no support for directly sending collection types (e.g., ``Set``, + ``List``, ``Map``) as values in configuration dictionaries. There are several + workarounds to send collections as values by converting them to one of the supported + value types (and converting them back on the client-side). + One can, for example, convert a list of floating-point numbers to a JSON string, + then send the JSON string using the configuration dictionary, and then convert the + JSON string back to a list of floating-point numbers on the client. Configuration through built-in strategies ----------------------------------------- -The easiest way to send configuration values to clients is to use a built-in strategy like :code:`FedAvg`. Built-in strategies support so-called configuration functions. A configuration function is a function that the built-in strategy calls to get the configuration dictionary for the current round. It then forwards the configuration dictionary to all the clients selected during that round. +The easiest way to send configuration values to clients is to use a built-in strategy +like ``FedAvg``. Built-in strategies support so-called configuration functions. A +configuration function is a function that the built-in strategy calls to get the +configuration dictionary for the current round. It then forwards the configuration +dictionary to all the clients selected during that round. -Let's start with a simple example. Imagine we want to send (a) the batch size that the client should use, (b) the current global round of federated learning, and (c) the number of epochs to train on the client-side. Our configuration function could look like this: +Let's start with a simple example. Imagine we want to send (a) the batch size that the +client should use, (b) the current global round of federated learning, and (c) the +number of epochs to train on the client-side. Our configuration function could look like +this: .. code-block:: python @@ -44,12 +62,13 @@ Let's start with a simple example. Imagine we want to send (a) the batch size th } return config -To make the built-in strategies use this function, we can pass it to ``FedAvg`` during initialization using the parameter :code:`on_fit_config_fn`: +To make the built-in strategies use this function, we can pass it to ``FedAvg`` during +initialization using the parameter ``on_fit_config_fn``: .. code-block:: python strategy = FedAvg( - ..., # Other FedAvg parameters + ..., # Other FedAvg parameters on_fit_config_fn=fit_config, # The fit_config function we defined earlier ) @@ -64,9 +83,15 @@ One the client side, we receive the configuration dictionary in ``fit``: print(config["local_epochs"]) # Prints `2` # ... (rest of `fit` method) -There is also an `on_evaluate_config_fn` to configure evaluation, which works the same way. They are separate functions because one might want to send different configuration values to `evaluate` (for example, to use a different batch size). +There is also an `on_evaluate_config_fn` to configure evaluation, which works the same +way. They are separate functions because one might want to send different configuration +values to `evaluate` (for example, to use a different batch size). -The built-in strategies call this function every round (that is, every time `Strategy.configure_fit` or `Strategy.configure_evaluate` runs). Calling `on_evaluate_config_fn` every round allows us to vary/change the config dict over consecutive rounds. If we wanted to implement a hyperparameter schedule, for example, to increase the number of local epochs during later rounds, we could do the following: +The built-in strategies call this function every round (that is, every time +`Strategy.configure_fit` or `Strategy.configure_evaluate` runs). Calling +`on_evaluate_config_fn` every round allows us to vary/change the config dict over +consecutive rounds. If we wanted to implement a hyperparameter schedule, for example, to +increase the number of local epochs during later rounds, we could do the following: .. code-block:: python @@ -79,14 +104,19 @@ The built-in strategies call this function every round (that is, every time `Str } return config -The :code:`FedAvg` strategy will call this function *every round*. +The ``FedAvg`` strategy will call this function *every round*. Configuring individual clients ------------------------------ -In some cases, it is necessary to send different configuration values to different clients. +In some cases, it is necessary to send different configuration values to different +clients. -This can be achieved by customizing an existing strategy or by :doc:`implementing a custom strategy from scratch `. Here's a nonsensical example that customizes :code:`FedAvg` by adding a custom ``"hello": "world"`` configuration key/value pair to the config dict of a *single client* (only the first client in the list, the other clients in this round to not receive this "special" config value): +This can be achieved by customizing an existing strategy or by :doc:`implementing a +custom strategy from scratch `. Here's a nonsensical +example that customizes ``FedAvg`` by adding a custom ``"hello": "world"`` configuration +key/value pair to the config dict of a *single client* (only the first client in the +list, the other clients in this round to not receive this "special" config value): .. code-block:: python @@ -94,7 +124,9 @@ This can be achieved by customizing an existing strategy or by :doc:`implementin def configure_fit( self, server_round: int, parameters: Parameters, client_manager: ClientManager ) -> List[Tuple[ClientProxy, FitIns]]: - client_instructions = super().configure_fit(server_round, parameters, client_manager) + client_instructions = super().configure_fit( + server_round, parameters, client_manager + ) # Add special "hello": "world" config key/value pair, # but only to the first client in the list @@ -103,6 +135,7 @@ This can be achieved by customizing an existing strategy or by :doc:`implementin return client_instructions + # Create strategy and run server strategy = CustomClientConfigStrategy( # ... (same arguments as plain FedAvg here) diff --git a/doc/source/how-to-configure-logging.rst b/doc/source/how-to-configure-logging.rst index d5559429a73c..bb7461390b42 100644 --- a/doc/source/how-to-configure-logging.rst +++ b/doc/source/how-to-configure-logging.rst @@ -1,17 +1,19 @@ Configure logging ================= -The Flower logger keeps track of all core events that take place in federated learning workloads. -It presents information by default following a standard message format: +The Flower logger keeps track of all core events that take place in federated learning +workloads. It presents information by default following a standard message format: .. code-block:: python DEFAULT_FORMATTER = logging.Formatter( - "%(levelname)s %(name)s %(asctime)s | %(filename)s:%(lineno)d | %(message)s" + "%(levelname)s %(name)s %(asctime)s | %(filename)s:%(lineno)d | %(message)s" ) -containing relevant information including: log message level (e.g. :code:`INFO`, :code:`DEBUG`), a timestamp, the line where the logging took place from, as well as the log message itself. -In this way, the logger would typically display information on your terminal as follows: +containing relevant information including: log message level (e.g. ``INFO``, ``DEBUG``), +a timestamp, the line where the logging took place from, as well as the log message +itself. In this way, the logger would typically display information on your terminal as +follows: .. code-block:: bash @@ -29,29 +31,35 @@ In this way, the logger would typically display information on your terminal as INFO flwr 2023-07-15 15:32:36,118 | server.py:125 | fit progress: (5, 358.6936808824539, {'accuracy': 0.3467}, 18.964264554999318) ... - Saving log to file -------------------- +------------------ -By default, the Flower log is outputted to the terminal where you launch your Federated Learning workload from. This applies for both gRPC-based federation (i.e. when you do :code:`fl.server.start_server`) and when using the :code:`VirtualClientEngine` (i.e. when you do :code:`fl.simulation.start_simulation`). -In some situations you might want to save this log to disk. You can do so by calling the `fl.common.logger.configure() `_ function. For example: +By default, the Flower log is outputted to the terminal where you launch your Federated +Learning workload from. This applies for both gRPC-based federation (i.e. when you do +``fl.server.start_server``) and when using the ``VirtualClientEngine`` (i.e. when you do +``fl.simulation.start_simulation``). In some situations you might want to save this log +to disk. You can do so by calling the `fl.common.logger.configure() +`_ function. For +example: .. code-block:: python - - import flwr as fl - - ... - # in your main file and before launching your experiment - # add an identifier to your logger - # then specify the name of the file where the log should be outputted to - fl.common.logger.configure(identifier="myFlowerExperiment", filename="log.txt") + import flwr as fl + + ... - # then start your workload - fl.simulation.start_simulation(...) # or fl.server.start_server(...) + # in your main file and before launching your experiment + # add an identifier to your logger + # then specify the name of the file where the log should be outputted to + fl.common.logger.configure(identifier="myFlowerExperiment", filename="log.txt") -With the above, Flower will record the log you see on your terminal to :code:`log.txt`. This file will be created in the same directory as were you are running the code from. -If we inspect we see the log above is also recorded but prefixing with :code:`identifier` each line: + # then start your workload + fl.simulation.start_simulation(...) # or fl.server.start_server(...) + +With the above, Flower will record the log you see on your terminal to ``log.txt``. This +file will be created in the same directory as were you are running the code from. If we +inspect we see the log above is also recorded but prefixing with ``identifier`` each +line: .. code-block:: bash @@ -69,12 +77,11 @@ If we inspect we see the log above is also recorded but prefixing with :code:`id myFlowerExperiment | INFO flwr 2023-07-15 15:32:36,118 | server.py:125 | fit progress: (5, 358.6936808824539, {'accuracy': 0.3467}, 18.964264554999318) ... - Log your own messages --------------------- -You might expand the information shown by default with the Flower logger by adding more messages relevant to your application. -You can achieve this easily as follows. +You might expand the information shown by default with the Flower logger by adding more +messages relevant to your application. You can achieve this easily as follows. .. code-block:: python @@ -84,25 +91,31 @@ You can achieve this easily as follows. # For example, let's say you want to add to the log some info about the training on your client for debugging purposes + class FlowerClient(fl.client.NumPyClient): - def __init__(self, cid: int ...): + def __init__( + self, + cid: int, + # ... + ): self.cid = cid - self.net = ... - ... + self.net = net + # ... def fit(self, parameters, config): log(INFO, f"Printing a custom INFO message at the start of fit() :)") - + set_params(self.net, parameters) log(DEBUG, f"Client {self.cid} is doing fit() with config: {config}") - ... + # ... -In this way your logger will show, in addition to the default messages, the ones introduced by the clients as specified above. +In this way your logger will show, in addition to the default messages, the ones +introduced by the clients as specified above. .. code-block:: bash - + ... INFO flwr 2023-07-15 16:18:21,726 | server.py:89 | Initializing global parameters INFO flwr 2023-07-15 16:18:21,726 | server.py:276 | Requesting initial parameters from one random client @@ -123,10 +136,13 @@ In this way your logger will show, in addition to the default messages, the ones DEBUG flwr 2023-07-15 16:18:28,617 | main.py:63 | Client 13 is doing fit() with config: {'epochs': 5, 'batch_size': 64} ... - Log to a remote service ----------------------- -The :code:`fl.common.logger.configure` function, also allows specifying a host to which logs can be pushed (via :code:`POST`) through a native Python :code:`logging.handler.HTTPHandler`. -This is a particularly useful feature in :code:`gRPC`-based Federated Learning workloads where otherwise gathering logs from all entities (i.e. the server and the clients) might be cumbersome. -Note that in Flower simulation, the server automatically displays all logs. You can still specify a :code:`HTTPHandler` should you wish to backup or analyze the logs somewhere else. +The ``fl.common.logger.configure`` function, also allows specifying a host to which logs +can be pushed (via ``POST``) through a native Python ``logging.handler.HTTPHandler``. +This is a particularly useful feature in ``gRPC``-based Federated Learning workloads +where otherwise gathering logs from all entities (i.e. the server and the clients) might +be cumbersome. Note that in Flower simulation, the server automatically displays all +logs. You can still specify a ``HTTPHandler`` should you wish to backup or analyze the +logs somewhere else. diff --git a/doc/source/how-to-enable-ssl-connections.rst b/doc/source/how-to-enable-ssl-connections.rst index fc8e89914ac2..cd8590bc3436 100644 --- a/doc/source/how-to-enable-ssl-connections.rst +++ b/doc/source/how-to-enable-ssl-connections.rst @@ -1,44 +1,46 @@ Enable SSL connections ====================== -This guide describes how to a SSL-enabled secure Flower server (:code:`SuperLink`) can be started and -how a Flower client (:code:`SuperNode`) can establish a secure connections to it. +This guide describes how to a SSL-enabled secure Flower server (``SuperLink``) can be +started and how a Flower client (``SuperNode``) can establish a secure connections to +it. -A complete code example demonstrating a secure connection can be found -`here `_. - -The code example comes with a :code:`README.md` file which explains how to start it. Although it is -already SSL-enabled, it might be less descriptive on how it does so. Stick to this guide for a deeper -introduction to the topic. +A complete code example demonstrating a secure connection can be found `here +`_. +The code example comes with a ``README.md`` file which explains how to start it. +Although it is already SSL-enabled, it might be less descriptive on how it does so. +Stick to this guide for a deeper introduction to the topic. Certificates ------------ -Using SSL-enabled connections requires certificates to be passed to the server and client. For -the purpose of this guide we are going to generate self-signed certificates. As this can become -quite complex we are going to ask you to run the script in -:code:`examples/advanced-tensorflow/certificates/generate.sh` -with the following command sequence: +Using SSL-enabled connections requires certificates to be passed to the server and +client. For the purpose of this guide we are going to generate self-signed certificates. +As this can become quite complex we are going to ask you to run the script in +``examples/advanced-tensorflow/certificates/generate.sh`` with the following command +sequence: .. code-block:: bash - cd examples/advanced-tensorflow/certificates - ./generate.sh - -This will generate the certificates in :code:`examples/advanced-tensorflow/.cache/certificates`. + cd examples/advanced-tensorflow/certificates + ./generate.sh -The approach for generating SSL certificates in the context of this example can serve as an inspiration and -starting point, but it should not be used as a reference for production environments. Please refer to other -sources regarding the issue of correctly generating certificates for production environments. -For non-critical prototyping or research projects, it might be sufficient to use the self-signed certificates generated using -the scripts mentioned in this guide. +This will generate the certificates in +``examples/advanced-tensorflow/.cache/certificates``. +The approach for generating SSL certificates in the context of this example can serve as +an inspiration and starting point, but it should not be used as a reference for +production environments. Please refer to other sources regarding the issue of correctly +generating certificates for production environments. For non-critical prototyping or +research projects, it might be sufficient to use the self-signed certificates generated +using the scripts mentioned in this guide. Server (SuperLink) ------------------ -Use the following terminal command to start a sever (SuperLink) that uses the previously generated certificates: +Use the following terminal command to start a sever (SuperLink) that uses the previously +generated certificates: .. code-block:: bash @@ -47,34 +49,36 @@ Use the following terminal command to start a sever (SuperLink) that uses the pr --ssl-certfile certificates/server.pem --ssl-keyfile certificates/server.key -When providing certificates, the server expects a tuple of three certificates paths: CA certificate, server certificate and server private key. - +When providing certificates, the server expects a tuple of three certificates paths: CA +certificate, server certificate and server private key. Client (SuperNode) ------------------ -Use the following terminal command to start a client (SuperNode) that uses the previously generated certificates: +Use the following terminal command to start a client (SuperNode) that uses the +previously generated certificates: .. code-block:: bash - flower-supernode - --root-certificates certificates/ca.crt - --superlink 127.0.0.1:9092 - -When setting :code:`root_certificates`, the client expects a file path to PEM-encoded root certificates. + flower-supernode + --root-certificates certificates/ca.crt + --superlink 127.0.0.1:9092 +When setting ``root_certificates``, the client expects a file path to PEM-encoded root +certificates. Conclusion ---------- -You should now have learned how to generate self-signed certificates using the given script, start an -SSL-enabled server and have a client establish a secure connection to it. - +You should now have learned how to generate self-signed certificates using the given +script, start an SSL-enabled server and have a client establish a secure connection to +it. Additional resources -------------------- -These additional sources might be relevant if you would like to dive deeper into the topic of certificates: +These additional sources might be relevant if you would like to dive deeper into the +topic of certificates: -* `Let's Encrypt `_ -* `certbot `_ +- `Let's Encrypt `_ +- `certbot `_ diff --git a/doc/source/how-to-implement-strategies.rst b/doc/source/how-to-implement-strategies.rst index 01bbb3042973..075d8a0116c4 100644 --- a/doc/source/how-to-implement-strategies.rst +++ b/doc/source/how-to-implement-strategies.rst @@ -1,22 +1,21 @@ Implement strategies ==================== -The strategy abstraction enables implementation of fully custom strategies. A -strategy is basically the federated learning algorithm that runs on the server. -Strategies decide how to sample clients, how to configure clients for training, -how to aggregate updates, and how to evaluate models. Flower provides a few -built-in strategies which are based on the same API described below. +The strategy abstraction enables implementation of fully custom strategies. A strategy +is basically the federated learning algorithm that runs on the server. Strategies decide +how to sample clients, how to configure clients for training, how to aggregate updates, +and how to evaluate models. Flower provides a few built-in strategies which are based on +the same API described below. -The :code:`Strategy` abstraction --------------------------------- +The ``Strategy`` abstraction +---------------------------- All strategy implementation are derived from the abstract base class -:code:`flwr.server.strategy.Strategy`, both built-in implementations and third -party implementations. This means that custom strategy implementations have the -exact same capabilities at their disposal as built-in ones. +``flwr.server.strategy.Strategy``, both built-in implementations and third party +implementations. This means that custom strategy implementations have the exact same +capabilities at their disposal as built-in ones. -The strategy abstraction defines a few abstract methods that need to be -implemented: +The strategy abstraction defines a few abstract methods that need to be implemented: .. code-block:: python @@ -31,10 +30,7 @@ implemented: @abstractmethod def configure_fit( - self, - server_round: int, - parameters: Parameters, - client_manager: ClientManager + self, server_round: int, parameters: Parameters, client_manager: ClientManager ) -> List[Tuple[ClientProxy, FitIns]]: """Configure the next round of training.""" @@ -49,10 +45,7 @@ implemented: @abstractmethod def configure_evaluate( - self, - server_round: int, - parameters: Parameters, - client_manager: ClientManager + self, server_round: int, parameters: Parameters, client_manager: ClientManager ) -> List[Tuple[ClientProxy, EvaluateIns]]: """Configure the next round of evaluation.""" @@ -71,31 +64,35 @@ implemented: ) -> Optional[Tuple[float, Dict[str, Scalar]]]: """Evaluate the current model parameters.""" - -Creating a new strategy means implementing a new :code:`class` (derived from the -abstract base class :code:`Strategy`) that implements for the previously shown -abstract methods: +Creating a new strategy means implementing a new ``class`` (derived from the abstract +base class ``Strategy``) that implements for the previously shown abstract methods: .. code-block:: python class SotaStrategy(Strategy): def initialize_parameters(self, client_manager): # Your implementation here + pass def configure_fit(self, server_round, parameters, client_manager): # Your implementation here + pass def aggregate_fit(self, server_round, results, failures): # Your implementation here + pass def configure_evaluate(self, server_round, parameters, client_manager): # Your implementation here + pass def aggregate_evaluate(self, server_round, results, failures): # Your implementation here + pass def evaluate(self, parameters): # Your implementation here + pass The Flower server calls these methods in the following order: @@ -176,12 +173,15 @@ The Flower server calls these methods in the following order: The following sections describe each of those methods in more detail. -The :code:`initialize_parameters` method ----------------------------------------- +The ``initialize_parameters`` method +------------------------------------ -:code:`initialize_parameters` is called only once, at the very beginning of an execution. It is responsible for providing the initial global model parameters in a serialized form (i.e., as a :code:`Parameters` object). +``initialize_parameters`` is called only once, at the very beginning of an execution. It +is responsible for providing the initial global model parameters in a serialized form +(i.e., as a ``Parameters`` object). -Built-in strategies return user-provided initial parameters. The following example shows how initial parameters can be passed to :code:`FedAvg`: +Built-in strategies return user-provided initial parameters. The following example shows +how initial parameters can be passed to ``FedAvg``: .. code-block:: python @@ -200,49 +200,68 @@ Built-in strategies return user-provided initial parameters. The following examp # Serialize ndarrays to `Parameters` parameters = fl.common.ndarrays_to_parameters(weights) - # Use the serialized parameters as the initial global parameters + # Use the serialized parameters as the initial global parameters strategy = fl.server.strategy.FedAvg( initial_parameters=parameters, ) fl.server.start_server(config=fl.server.ServerConfig(num_rounds=3), strategy=strategy) -The Flower server will call :code:`initialize_parameters`, which either returns the parameters that were passed to :code:`initial_parameters`, or :code:`None`. If no parameters are returned from :code:`initialize_parameters` (i.e., :code:`None`), the server will randomly select one client and ask it to provide its parameters. This is a convenience feature and not recommended in practice, but it can be useful for prototyping. In practice, it is recommended to always use server-side parameter initialization. +The Flower server will call ``initialize_parameters``, which either returns the +parameters that were passed to ``initial_parameters``, or ``None``. If no parameters are +returned from ``initialize_parameters`` (i.e., ``None``), the server will randomly +select one client and ask it to provide its parameters. This is a convenience feature +and not recommended in practice, but it can be useful for prototyping. In practice, it +is recommended to always use server-side parameter initialization. .. note:: - Server-side parameter initialization is a powerful mechanism. It can be used, for example, to resume training from a previously saved checkpoint. It is also the fundamental capability needed to implement hybrid approaches, for example, to fine-tune a pre-trained model using federated learning. + Server-side parameter initialization is a powerful mechanism. It can be used, for + example, to resume training from a previously saved checkpoint. It is also the + fundamental capability needed to implement hybrid approaches, for example, to + fine-tune a pre-trained model using federated learning. -The :code:`configure_fit` method --------------------------------- +The ``configure_fit`` method +---------------------------- -:code:`configure_fit` is responsible for configuring the upcoming round of training. What does *configure* mean in this context? Configuring a round means selecting clients and deciding what instructions to send to these clients. The signature of :code:`configure_fit` makes this clear: +``configure_fit`` is responsible for configuring the upcoming round of training. What +does *configure* mean in this context? Configuring a round means selecting clients and +deciding what instructions to send to these clients. The signature of ``configure_fit`` +makes this clear: .. code-block:: python @abstractmethod def configure_fit( - self, - server_round: int, - parameters: Parameters, - client_manager: ClientManager + self, server_round: int, parameters: Parameters, client_manager: ClientManager ) -> List[Tuple[ClientProxy, FitIns]]: """Configure the next round of training.""" -The return value is a list of tuples, each representing the instructions that will be sent to a particular client. Strategy implementations usually perform the following steps in :code:`configure_fit`: +The return value is a list of tuples, each representing the instructions that will be +sent to a particular client. Strategy implementations usually perform the following +steps in ``configure_fit``: -* Use the :code:`client_manager` to randomly sample all (or a subset of) available clients (each represented as a :code:`ClientProxy` object) -* Pair each :code:`ClientProxy` with the same :code:`FitIns` holding the current global model :code:`parameters` and :code:`config` dict +- Use the ``client_manager`` to randomly sample all (or a subset of) available clients + (each represented as a ``ClientProxy`` object) +- Pair each ``ClientProxy`` with the same ``FitIns`` holding the current global model + ``parameters`` and ``config`` dict -More sophisticated implementations can use :code:`configure_fit` to implement custom client selection logic. A client will only participate in a round if the corresponding :code:`ClientProxy` is included in the list returned from :code:`configure_fit`. +More sophisticated implementations can use ``configure_fit`` to implement custom client +selection logic. A client will only participate in a round if the corresponding +``ClientProxy`` is included in the list returned from ``configure_fit``. .. note:: - The structure of this return value provides a lot of flexibility to the user. Since instructions are defined on a per-client basis, different instructions can be sent to each client. This enables custom strategies to train, for example, different models on different clients, or use different hyperparameters on different clients (via the :code:`config` dict). + The structure of this return value provides a lot of flexibility to the user. Since + instructions are defined on a per-client basis, different instructions can be sent + to each client. This enables custom strategies to train, for example, different + models on different clients, or use different hyperparameters on different clients + (via the ``config`` dict). -The :code:`aggregate_fit` method --------------------------------- +The ``aggregate_fit`` method +---------------------------- -:code:`aggregate_fit` is responsible for aggregating the results returned by the clients that were selected and asked to train in :code:`configure_fit`. +``aggregate_fit`` is responsible for aggregating the results returned by the clients +that were selected and asked to train in ``configure_fit``. .. code-block:: python @@ -255,42 +274,58 @@ The :code:`aggregate_fit` method ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]: """Aggregate training results.""" -Of course, failures can happen, so there is no guarantee that the server will get results from all the clients it sent instructions to (via :code:`configure_fit`). :code:`aggregate_fit` therefore receives a list of :code:`results`, but also a list of :code:`failures`. +Of course, failures can happen, so there is no guarantee that the server will get +results from all the clients it sent instructions to (via ``configure_fit``). +``aggregate_fit`` therefore receives a list of ``results``, but also a list of +``failures``. -:code:`aggregate_fit` returns an optional :code:`Parameters` object and a dictionary of aggregated metrics. The :code:`Parameters` return value is optional because :code:`aggregate_fit` might decide that the results provided are not sufficient for aggregation (e.g., too many failures). +``aggregate_fit`` returns an optional ``Parameters`` object and a dictionary of +aggregated metrics. The ``Parameters`` return value is optional because +``aggregate_fit`` might decide that the results provided are not sufficient for +aggregation (e.g., too many failures). -The :code:`configure_evaluate` method -------------------------------------- +The ``configure_evaluate`` method +--------------------------------- -:code:`configure_evaluate` is responsible for configuring the upcoming round of evaluation. What does *configure* mean in this context? Configuring a round means selecting clients and deciding what instructions to send to these clients. The signature of :code:`configure_evaluate` makes this clear: +``configure_evaluate`` is responsible for configuring the upcoming round of evaluation. +What does *configure* mean in this context? Configuring a round means selecting clients +and deciding what instructions to send to these clients. The signature of +``configure_evaluate`` makes this clear: .. code-block:: python @abstractmethod def configure_evaluate( - self, - server_round: int, - parameters: Parameters, - client_manager: ClientManager + self, server_round: int, parameters: Parameters, client_manager: ClientManager ) -> List[Tuple[ClientProxy, EvaluateIns]]: """Configure the next round of evaluation.""" -The return value is a list of tuples, each representing the instructions that will be sent to a particular client. Strategy implementations usually perform the following steps in :code:`configure_evaluate`: +The return value is a list of tuples, each representing the instructions that will be +sent to a particular client. Strategy implementations usually perform the following +steps in ``configure_evaluate``: -* Use the :code:`client_manager` to randomly sample all (or a subset of) available clients (each represented as a :code:`ClientProxy` object) -* Pair each :code:`ClientProxy` with the same :code:`EvaluateIns` holding the current global model :code:`parameters` and :code:`config` dict +- Use the ``client_manager`` to randomly sample all (or a subset of) available clients + (each represented as a ``ClientProxy`` object) +- Pair each ``ClientProxy`` with the same ``EvaluateIns`` holding the current global + model ``parameters`` and ``config`` dict -More sophisticated implementations can use :code:`configure_evaluate` to implement custom client selection logic. A client will only participate in a round if the corresponding :code:`ClientProxy` is included in the list returned from :code:`configure_evaluate`. +More sophisticated implementations can use ``configure_evaluate`` to implement custom +client selection logic. A client will only participate in a round if the corresponding +``ClientProxy`` is included in the list returned from ``configure_evaluate``. .. note:: - The structure of this return value provides a lot of flexibility to the user. Since instructions are defined on a per-client basis, different instructions can be sent to each client. This enables custom strategies to evaluate, for example, different models on different clients, or use different hyperparameters on different clients (via the :code:`config` dict). - + The structure of this return value provides a lot of flexibility to the user. Since + instructions are defined on a per-client basis, different instructions can be sent + to each client. This enables custom strategies to evaluate, for example, different + models on different clients, or use different hyperparameters on different clients + (via the ``config`` dict). -The :code:`aggregate_evaluate` method -------------------------------------- +The ``aggregate_evaluate`` method +--------------------------------- -:code:`aggregate_evaluate` is responsible for aggregating the results returned by the clients that were selected and asked to evaluate in :code:`configure_evaluate`. +``aggregate_evaluate`` is responsible for aggregating the results returned by the +clients that were selected and asked to evaluate in ``configure_evaluate``. .. code-block:: python @@ -303,21 +338,29 @@ The :code:`aggregate_evaluate` method ) -> Tuple[Optional[float], Dict[str, Scalar]]: """Aggregate evaluation results.""" -Of course, failures can happen, so there is no guarantee that the server will get results from all the clients it sent instructions to (via :code:`configure_evaluate`). :code:`aggregate_evaluate` therefore receives a list of :code:`results`, but also a list of :code:`failures`. +Of course, failures can happen, so there is no guarantee that the server will get +results from all the clients it sent instructions to (via ``configure_evaluate``). +``aggregate_evaluate`` therefore receives a list of ``results``, but also a list of +``failures``. -:code:`aggregate_evaluate` returns an optional :code:`float` (loss) and a dictionary of aggregated metrics. The :code:`float` return value is optional because :code:`aggregate_evaluate` might decide that the results provided are not sufficient for aggregation (e.g., too many failures). +``aggregate_evaluate`` returns an optional ``float`` (loss) and a dictionary of +aggregated metrics. The ``float`` return value is optional because +``aggregate_evaluate`` might decide that the results provided are not sufficient for +aggregation (e.g., too many failures). -The :code:`evaluate` method ---------------------------- +The ``evaluate`` method +----------------------- -:code:`evaluate` is responsible for evaluating model parameters on the server-side. Having :code:`evaluate` in addition to :code:`configure_evaluate`/:code:`aggregate_evaluate` enables strategies to perform both servers-side and client-side (federated) evaluation. +``evaluate`` is responsible for evaluating model parameters on the server-side. Having +``evaluate`` in addition to ``configure_evaluate``/``aggregate_evaluate`` enables +strategies to perform both servers-side and client-side (federated) evaluation. .. code-block:: python @abstractmethod - def evaluate( - self, parameters: Parameters - ) -> Optional[Tuple[float, Dict[str, Scalar]]]: + def evaluate(self, parameters: Parameters) -> Optional[Tuple[float, Dict[str, Scalar]]]: """Evaluate the current model parameters.""" -The return value is again optional because the strategy might not need to implement server-side evaluation or because the user-defined :code:`evaluate` method might not complete successfully (e.g., it might fail to load the server-side evaluation data). +The return value is again optional because the strategy might not need to implement +server-side evaluation or because the user-defined ``evaluate`` method might not +complete successfully (e.g., it might fail to load the server-side evaluation data). diff --git a/doc/source/how-to-install-flower.rst b/doc/source/how-to-install-flower.rst index a621377c8ce6..89cdf8b836cf 100644 --- a/doc/source/how-to-install-flower.rst +++ b/doc/source/how-to-install-flower.rst @@ -1,12 +1,11 @@ Install Flower ============== - Python version -------------- -Flower requires at least `Python 3.9 `_, but `Python 3.10 `_ or above is recommended. - +Flower requires at least `Python 3.9 `_, but `Python 3.10 +`_ or above is recommended. Install stable release ---------------------- @@ -14,45 +13,56 @@ Install stable release Using pip ~~~~~~~~~ -Stable releases are available on `PyPI `_:: +Stable releases are available on `PyPI `_: + +:: - python -m pip install flwr + python -m pip install flwr -For simulations that use the Virtual Client Engine, ``flwr`` should be installed with the ``simulation`` extra:: +For simulations that use the Virtual Client Engine, ``flwr`` should be installed with +the ``simulation`` extra: - python -m pip install "flwr[simulation]" +:: + python -m pip install "flwr[simulation]" Using conda (or mamba) ~~~~~~~~~~~~~~~~~~~~~~ Flower can also be installed from the ``conda-forge`` channel. -If you have not added ``conda-forge`` to your channels, you will first need to run the following:: +If you have not added ``conda-forge`` to your channels, you will first need to run the +following: + +:: + + conda config --add channels conda-forge + conda config --set channel_priority strict - conda config --add channels conda-forge - conda config --set channel_priority strict +Once the ``conda-forge`` channel has been enabled, ``flwr`` can be installed with +``conda``: -Once the ``conda-forge`` channel has been enabled, ``flwr`` can be installed with ``conda``:: +:: - conda install flwr + conda install flwr -or with ``mamba``:: +or with ``mamba``: - mamba install flwr +:: + mamba install flwr Verify installation ------------------- -The following command can be used to verify if Flower was successfully installed. If everything worked, it should print the version of Flower to the command line: +The following command can be used to verify if Flower was successfully installed. If +everything worked, it should print the version of Flower to the command line: .. code-block:: bash - :substitutions: - - python -c "import flwr;print(flwr.__version__)" - |stable_flwr_version| + :substitutions: + python -c "import flwr;print(flwr.__version__)" + |stable_flwr_version| Advanced installation options ----------------------------- @@ -65,21 +75,32 @@ Install via Docker Install pre-release ~~~~~~~~~~~~~~~~~~~ -New (possibly unstable) versions of Flower are sometimes available as pre-release versions (alpha, beta, release candidate) before the stable release happens:: +New (possibly unstable) versions of Flower are sometimes available as pre-release +versions (alpha, beta, release candidate) before the stable release happens: + +:: + + python -m pip install -U --pre flwr - python -m pip install -U --pre flwr +For simulations that use the Virtual Client Engine, ``flwr`` pre-releases should be +installed with the ``simulation`` extra: -For simulations that use the Virtual Client Engine, ``flwr`` pre-releases should be installed with the ``simulation`` extra:: +:: - python -m pip install -U --pre 'flwr[simulation]' + python -m pip install -U --pre 'flwr[simulation]' Install nightly release ~~~~~~~~~~~~~~~~~~~~~~~ -The latest (potentially unstable) changes in Flower are available as nightly releases:: +The latest (potentially unstable) changes in Flower are available as nightly releases: + +:: + + python -m pip install -U flwr-nightly - python -m pip install -U flwr-nightly +For simulations that use the Virtual Client Engine, ``flwr-nightly`` should be installed +with the ``simulation`` extra: -For simulations that use the Virtual Client Engine, ``flwr-nightly`` should be installed with the ``simulation`` extra:: +:: - python -m pip install -U flwr-nightly[simulation] + python -m pip install -U flwr-nightly[simulation] diff --git a/doc/source/how-to-monitor-simulation.rst b/doc/source/how-to-monitor-simulation.rst index f6c26a701d94..f540e22a6a77 100644 --- a/doc/source/how-to-monitor-simulation.rst +++ b/doc/source/how-to-monitor-simulation.rst @@ -1,109 +1,120 @@ Monitor simulation ================== -Flower allows you to monitor system resources while running your simulation. Moreover, the Flower simulation engine is powerful and enables you to decide how to allocate resources per client manner and constrain the total usage. Insights from resource consumption can help you make smarter decisions and speed up the execution time. - -The specific instructions assume you are using macOS and have the `Homebrew `_ package manager installed. +Flower allows you to monitor system resources while running your simulation. Moreover, +the Flower simulation engine is powerful and enables you to decide how to allocate +resources per client manner and constrain the total usage. Insights from resource +consumption can help you make smarter decisions and speed up the execution time. +The specific instructions assume you are using macOS and have the `Homebrew +`_ package manager installed. Downloads --------- .. code-block:: bash - brew install prometheus grafana + brew install prometheus grafana -`Prometheus `_ is used for data collection, while `Grafana `_ will enable you to visualize the collected data. They are both well integrated with `Ray `_ which Flower uses under the hood. +`Prometheus `_ is used for data collection, while `Grafana +`_ will enable you to visualize the collected data. They are both +well integrated with `Ray `_ which Flower uses under the hood. -Overwrite the configuration files (depending on your device, it might be installed on a different path). +Overwrite the configuration files (depending on your device, it might be installed on a +different path). If you are on an M1 Mac, it should be: .. code-block:: bash - /opt/homebrew/etc/prometheus.yml - /opt/homebrew/etc/grafana/grafana.ini + /opt/homebrew/etc/prometheus.yml + /opt/homebrew/etc/grafana/grafana.ini On the previous generation Intel Mac devices, it should be: .. code-block:: bash - /usr/local/etc/prometheus.yml - /usr/local/etc/grafana/grafana.ini + /usr/local/etc/prometheus.yml + /usr/local/etc/grafana/grafana.ini -Open the respective configuration files and change them. Depending on your device, use one of the two following commands: +Open the respective configuration files and change them. Depending on your device, use +one of the two following commands: .. code-block:: bash - # M1 macOS - open /opt/homebrew/etc/prometheus.yml + # M1 macOS + open /opt/homebrew/etc/prometheus.yml - # Intel macOS - open /usr/local/etc/prometheus.yml + # Intel macOS + open /usr/local/etc/prometheus.yml -and then delete all the text in the file and paste a new Prometheus config you see below. You may adjust the time intervals to your requirements: +and then delete all the text in the file and paste a new Prometheus config you see +below. You may adjust the time intervals to your requirements: .. code-block:: bash - global: - scrape_interval: 1s - evaluation_interval: 1s + global: + scrape_interval: 1s + evaluation_interval: 1s - scrape_configs: - # Scrape from each ray node as defined in the service_discovery.json provided by ray. - - job_name: 'ray' - file_sd_configs: - - files: - - '/tmp/ray/prom_metrics_service_discovery.json' + scrape_configs: + # Scrape from each ray node as defined in the service_discovery.json provided by ray. + - job_name: 'ray' + file_sd_configs: + - files: + - '/tmp/ray/prom_metrics_service_discovery.json' -Now after you have edited the Prometheus configuration, do the same with the Grafana configuration files. Open those using one of the following commands as before: +Now after you have edited the Prometheus configuration, do the same with the Grafana +configuration files. Open those using one of the following commands as before: .. code-block:: python - # M1 macOS - open /opt/homebrew/etc/grafana/grafana.ini + # M1 macOS + open / opt / homebrew / etc / grafana / grafana.ini - # Intel macOS - open /usr/local/etc/grafana/grafana.ini + # Intel macOS + open / usr / local / etc / grafana / grafana.ini -Your terminal editor should open and allow you to apply the following configuration as before. +Your terminal editor should open and allow you to apply the following configuration as +before. .. code-block:: bash - [security] - allow_embedding = true - - [auth.anonymous] - enabled = true - org_name = Main Org. - org_role = Viewer + [security] + allow_embedding = true - [paths] - provisioning = /tmp/ray/session_latest/metrics/grafana/provisioning + [auth.anonymous] + enabled = true + org_name = Main Org. + org_role = Viewer -Congratulations, you just downloaded all the necessary software needed for metrics tracking. Now, let’s start it. + [paths] + provisioning = /tmp/ray/session_latest/metrics/grafana/provisioning +Congratulations, you just downloaded all the necessary software needed for metrics +tracking. Now, let’s start it. Tracking metrics ---------------- -Before running your Flower simulation, you have to start the monitoring tools you have just installed and configured. +Before running your Flower simulation, you have to start the monitoring tools you have +just installed and configured. .. code-block:: bash - brew services start prometheus - brew services start grafana + brew services start prometheus + brew services start grafana Please include the following argument in your Python code when starting a simulation. .. code-block:: python - fl.simulation.start_simulation( - # ... - # all the args you used before - # ... - ray_init_args = {"include_dashboard": True} - ) + fl.simulation.start_simulation( + # ... + # all the args you used before + # ... + ray_init_args={"include_dashboard": True} + ) Now, you are ready to start your workload. @@ -111,126 +122,140 @@ Shortly after the simulation starts, you should see the following logs in your t .. code-block:: bash - 2023-01-20 16:22:58,620 INFO [worker.py:1529](http://worker.py:1529/) -- Started a local Ray instance. View the dashboard at http://127.0.0.1:8265 - + 2023-01-20 16:22:58,620 INFO [worker.py:1529](http://worker.py:1529/) -- Started a local Ray instance. View the dashboard at http://127.0.0.1:8265 -You can look at everything at ``_ . +You can look at everything at http://127.0.0.1:8265 . -It's a Ray Dashboard. You can navigate to Metrics (on the left panel, the lowest option). +It's a Ray Dashboard. You can navigate to Metrics (on the left panel, the lowest +option). -Or alternatively, you can just see them in Grafana by clicking on the right-up corner, “View in Grafana”. Please note that the Ray dashboard is only accessible during the simulation. After the simulation ends, you can only use Grafana to explore the metrics. You can start Grafana by going to ``http://localhost:3000/``. +Or alternatively, you can just see them in Grafana by clicking on the right-up corner, +“View in Grafana”. Please note that the Ray dashboard is only accessible during the +simulation. After the simulation ends, you can only use Grafana to explore the metrics. +You can start Grafana by going to ``http://localhost:3000/``. -After you finish the visualization, stop Prometheus and Grafana. This is important as they will otherwise block, for example port :code:`3000` on your machine as long as they are running. +After you finish the visualization, stop Prometheus and Grafana. This is important as +they will otherwise block, for example port ``3000`` on your machine as long as they are +running. .. code-block:: bash - brew services stop prometheus - brew services stop grafana - + brew services stop prometheus + brew services stop grafana Resource allocation ------------------- -You must understand how the Ray library works to efficiently allocate system resources to simulation clients on your own. +You must understand how the Ray library works to efficiently allocate system resources +to simulation clients on your own. -Initially, the simulation (which Ray handles under the hood) starts by default with all the available resources on the system, which it shares among the clients. It doesn't mean it divides it equally among all of them, nor that the model training happens at all of them simultaneously. You will learn more about that in the later part of this blog. You can check the system resources by running the following: +Initially, the simulation (which Ray handles under the hood) starts by default with all +the available resources on the system, which it shares among the clients. It doesn't +mean it divides it equally among all of them, nor that the model training happens at all +of them simultaneously. You will learn more about that in the later part of this blog. +You can check the system resources by running the following: .. code-block:: python - import ray - ray.available_resources() + import ray + + ray.available_resources() In Google Colab, the result you see might be similar to this: .. code-block:: bash - {'memory': 8020104807.0, - 'GPU': 1.0, - 'object_store_memory': 4010052403.0, - 'CPU': 2.0, - 'accelerator_type:T4': 1.0, - 'node:172.28.0.2': 1.0} + {'memory': 8020104807.0, + 'GPU': 1.0, + 'object_store_memory': 4010052403.0, + 'CPU': 2.0, + 'accelerator_type:T4': 1.0, + 'node:172.28.0.2': 1.0} - -However, you can overwrite the defaults. When starting a simulation, do the following (you don't need to overwrite all of them): +However, you can overwrite the defaults. When starting a simulation, do the following +(you don't need to overwrite all of them): .. code-block:: python - num_cpus = 2 - num_gpus = 1 - ram_memory = 16_000 * 1024 * 1024 # 16 GB - fl.simulation.start_simulation( - # ... - # all the args you were specifying before - # ... - ray_init_args = { - "include_dashboard": True, # we need this one for tracking - "num_cpus": num_cpus, - "num_gpus": num_gpus, - "memory": ram_memory, - } - ) - + num_cpus = 2 + num_gpus = 1 + ram_memory = 16_000 * 1024 * 1024 # 16 GB + fl.simulation.start_simulation( + # ... + # all the args you were specifying before + # ... + ray_init_args={ + "include_dashboard": True, # we need this one for tracking + "num_cpus": num_cpus, + "num_gpus": num_gpus, + "memory": ram_memory, + } + ) Let’s also specify the resource for a single client. .. code-block:: python - # Total resources for simulation - num_cpus = 4 - num_gpus = 1 - ram_memory = 16_000 * 1024 * 1024 # 16 GB - - # Single client resources - client_num_cpus = 2 - client_num_gpus = 1 - - fl.simulation.start_simulation( - # ... - # all the args you were specifying before - # ... - ray_init_args = { - "include_dashboard": True, # we need this one for tracking - "num_cpus": num_cpus, - "num_gpus": num_gpus, - "memory": ram_memory, - }, - # The argument below is new - client_resources = { - "num_cpus": client_num_cpus, - "num_gpus": client_num_gpus, - } - ) - -Now comes the crucial part. Ray will start a new client only when it has all the required resources (such that they run in parallel) when the resources allow. - -In the example above, only one client will be run, so your clients won't run concurrently. Setting :code:`client_num_gpus = 0.5` would allow running two clients and therefore enable them to run concurrently. -Be careful not to require more resources than available. If you specified :code:`client_num_gpus = 2`, the simulation wouldn't start (even if you had 2 GPUs but decided to set 1 in :code:`ray_init_args`). - + # Total resources for simulation + num_cpus = 4 + num_gpus = 1 + ram_memory = 16_000 * 1024 * 1024 # 16 GB + + # Single client resources + client_num_cpus = 2 + client_num_gpus = 1 + + fl.simulation.start_simulation( + # ... + # all the args you were specifying before + # ... + ray_init_args={ + "include_dashboard": True, # we need this one for tracking + "num_cpus": num_cpus, + "num_gpus": num_gpus, + "memory": ram_memory, + }, + # The argument below is new + client_resources={ + "num_cpus": client_num_cpus, + "num_gpus": client_num_gpus, + }, + ) + +Now comes the crucial part. Ray will start a new client only when it has all the +required resources (such that they run in parallel) when the resources allow. + +In the example above, only one client will be run, so your clients won't run +concurrently. Setting ``client_num_gpus = 0.5`` would allow running two clients and +therefore enable them to run concurrently. Be careful not to require more resources than +available. If you specified ``client_num_gpus = 2``, the simulation wouldn't start (even +if you had 2 GPUs but decided to set 1 in ``ray_init_args``). FAQ --- Q: I don't see any metrics logged. -A: The timeframe might not be properly set. The setting is in the top right corner ("Last 30 minutes" by default). Please change the timeframe to reflect the period when the simulation was running. +A: The timeframe might not be properly set. The setting is in the top right corner +("Last 30 minutes" by default). Please change the timeframe to reflect the period when +the simulation was running. -Q: I see “Grafana server not detected. Please make sure the Grafana server is running and refresh this page” after going to the Metrics tab in Ray Dashboard. +Q: I see “Grafana server not detected. Please make sure the Grafana server is running +and refresh this page” after going to the Metrics tab in Ray Dashboard. A: You probably don't have Grafana running. Please check the running services .. code-block:: bash - brew services list + brew services list -Q: I see "This site can't be reached" when going to ``_. +Q: I see "This site can't be reached" when going to http://127.0.0.1:8265. A: Either the simulation has already finished, or you still need to start Prometheus. - Resources --------- -Ray Dashboard: ``_ +Ray Dashboard: https://docs.ray.io/en/latest/ray-observability/getting-started.html -Ray Metrics: ``_ +Ray Metrics: https://docs.ray.io/en/latest/cluster/metrics.html diff --git a/doc/source/how-to-run-simulations.rst b/doc/source/how-to-run-simulations.rst index d1dcb511ed51..fb4eed17b4e7 100644 --- a/doc/source/how-to-run-simulations.rst +++ b/doc/source/how-to-run-simulations.rst @@ -1,48 +1,85 @@ Run simulations =============== -.. youtube:: cRebUIGB5RU - :url_parameters: ?list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB - :width: 100% - -Simulating Federated Learning workloads is useful for a multitude of use-cases: you might want to run your workload on a large cohort of clients but without having to source, configure and mange a large number of physical devices; you might want to run your FL workloads as fast as possible on the compute systems you have access to without having to go through a complex setup process; you might want to validate your algorithm on different scenarios at varying levels of data and system heterogeneity, client availability, privacy budgets, etc. These are among some of the use-cases where simulating FL workloads makes sense. Flower can accommodate these scenarios by means of its `VirtualClientEngine `_ or VCE. - -The :code:`VirtualClientEngine` schedules, launches and manages `virtual` clients. These clients are identical to `non-virtual` clients (i.e. the ones you launch via the command `flwr.client.start_client `_) in the sense that they can be configure by creating a class inheriting, for example, from `flwr.client.NumPyClient `_ and therefore behave in an identical way. In addition to that, clients managed by the :code:`VirtualClientEngine` are: - -* resource-aware: this means that each client gets assigned a portion of the compute and memory on your system. You as a user can control this at the beginning of the simulation and allows you to control the degree of parallelism of your Flower FL simulation. The fewer the resources per client, the more clients can run concurrently on the same hardware. -* self-managed: this means that you as a user do not need to launch clients manually, instead this gets delegated to :code:`VirtualClientEngine`'s internals. -* ephemeral: this means that a client is only materialized when it is required in the FL process (e.g. to do `fit() `_). The object is destroyed afterwards, releasing the resources it was assigned and allowing in this way other clients to participate. - -The :code:`VirtualClientEngine` implements `virtual` clients using `Ray `_, an open-source framework for scalable Python workloads. In particular, Flower's :code:`VirtualClientEngine` makes use of `Actors `_ to spawn `virtual` clients and run their workload. - +.. youtube:: cRebUIGB5RU + :url_parameters: ?list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB + :width: 100% + +Simulating Federated Learning workloads is useful for a multitude of use-cases: you +might want to run your workload on a large cohort of clients but without having to +source, configure and mange a large number of physical devices; you might want to run +your FL workloads as fast as possible on the compute systems you have access to without +having to go through a complex setup process; you might want to validate your algorithm +on different scenarios at varying levels of data and system heterogeneity, client +availability, privacy budgets, etc. These are among some of the use-cases where +simulating FL workloads makes sense. Flower can accommodate these scenarios by means of +its `VirtualClientEngine +`_ or VCE. + +The ``VirtualClientEngine`` schedules, launches and manages `virtual` clients. These +clients are identical to `non-virtual` clients (i.e. the ones you launch via the command +`flwr.client.start_client `_) in the sense that they can +be configure by creating a class inheriting, for example, from `flwr.client.NumPyClient +`_ and therefore behave in an identical way. +In addition to that, clients managed by the ``VirtualClientEngine`` are: + +- resource-aware: this means that each client gets assigned a portion of the compute and + memory on your system. You as a user can control this at the beginning of the + simulation and allows you to control the degree of parallelism of your Flower FL + simulation. The fewer the resources per client, the more clients can run concurrently + on the same hardware. +- self-managed: this means that you as a user do not need to launch clients manually, + instead this gets delegated to ``VirtualClientEngine``'s internals. +- ephemeral: this means that a client is only materialized when it is required in the FL + process (e.g. to do `fit() `_). The object + is destroyed afterwards, releasing the resources it was assigned and allowing in this + way other clients to participate. + +The ``VirtualClientEngine`` implements `virtual` clients using `Ray +`_, an open-source framework for scalable Python workloads. In +particular, Flower's ``VirtualClientEngine`` makes use of `Actors +`_ to spawn `virtual` clients and +run their workload. Launch your Flower simulation ----------------------------- -Running Flower simulations still require you to define your client class, a strategy, and utility functions to download and load (and potentially partition) your dataset. With that out of the way, launching your simulation is done with `start_simulation `_ and a minimal example looks as follows: - +Running Flower simulations still require you to define your client class, a strategy, +and utility functions to download and load (and potentially partition) your dataset. +With that out of the way, launching your simulation is done with `start_simulation +`_ and a minimal example looks as +follows: .. code-block:: python import flwr as fl from flwr.server.strategy import FedAvg - + + def client_fn(cid: str): # Return a standard Flower client return MyFlowerClient().to_client() + # Launch the simulation hist = fl.simulation.start_simulation( - client_fn=client_fn, # A function to run a _virtual_ client when required - num_clients=50, # Total number of clients available - config=fl.server.ServerConfig(num_rounds=3), # Specify number of FL rounds - strategy=FedAvg() # A Flower strategy + client_fn=client_fn, # A function to run a _virtual_ client when required + num_clients=50, # Total number of clients available + config=fl.server.ServerConfig(num_rounds=3), # Specify number of FL rounds + strategy=FedAvg(), # A Flower strategy ) - VirtualClientEngine resources ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -By default the VCE has access to all system resources (i.e. all CPUs, all GPUs, etc) since that is also the default behavior when starting Ray. However, in some settings you might want to limit how many of your system resources are used for simulation. You can do this via the :code:`ray_init_args` input argument to :code:`start_simulation` which the VCE internally passes to Ray's :code:`ray.init` command. For a complete list of settings you can configure check the `ray.init `_ documentation. Do not set :code:`ray_init_args` if you want the VCE to use all your system's CPUs and GPUs. + +By default the VCE has access to all system resources (i.e. all CPUs, all GPUs, etc) +since that is also the default behavior when starting Ray. However, in some settings you +might want to limit how many of your system resources are used for simulation. You can +do this via the ``ray_init_args`` input argument to ``start_simulation`` which the VCE +internally passes to Ray's ``ray.init`` command. For a complete list of settings you can +configure check the `ray.init +`_ documentation. +Do not set ``ray_init_args`` if you want the VCE to use all your system's CPUs and GPUs. .. code-block:: python @@ -50,22 +87,28 @@ By default the VCE has access to all system resources (i.e. all CPUs, all GPUs, # Launch the simulation by limiting resources visible to Flower's VCE hist = fl.simulation.start_simulation( - ... + # ... # Out of all CPUs and GPUs available in your system, # only 8xCPUs and 1xGPUs would be used for simulation. - ray_init_args = {'num_cpus': 8, 'num_gpus': 1} + ray_init_args={"num_cpus": 8, "num_gpus": 1} ) - - Assigning client resources ~~~~~~~~~~~~~~~~~~~~~~~~~~ -By default the :code:`VirtualClientEngine` assigns a single CPU core (and nothing else) to each virtual client. This means that if your system has 10 cores, that many virtual clients can be concurrently running. -More often than not, you would probably like to adjust the resources your clients get assigned based on the complexity (i.e. compute and memory footprint) of your FL workload. You can do so when starting your simulation by setting the argument `client_resources` to `start_simulation `_. Two keys are internally used by Ray to schedule and spawn workloads (in our case Flower clients): +By default the ``VirtualClientEngine`` assigns a single CPU core (and nothing else) to +each virtual client. This means that if your system has 10 cores, that many virtual +clients can be concurrently running. -* :code:`num_cpus` indicates the number of CPU cores a client would get. -* :code:`num_gpus` indicates the **ratio** of GPU memory a client gets assigned. +More often than not, you would probably like to adjust the resources your clients get +assigned based on the complexity (i.e. compute and memory footprint) of your FL +workload. You can do so when starting your simulation by setting the argument +`client_resources` to `start_simulation +`_. Two keys are internally used by +Ray to schedule and spawn workloads (in our case Flower clients): + +- ``num_cpus`` indicates the number of CPU cores a client would get. +- ``num_gpus`` indicates the **ratio** of GPU memory a client gets assigned. Let's see a few examples: @@ -74,90 +117,140 @@ Let's see a few examples: import flwr as fl # each client gets 1xCPU (this is the default if no resources are specified) - my_client_resources = {'num_cpus': 1, 'num_gpus': 0.0} + my_client_resources = {"num_cpus": 1, "num_gpus": 0.0} # each client gets 2xCPUs and half a GPU. (with a single GPU, 2 clients run concurrently) - my_client_resources = {'num_cpus': 2, 'num_gpus': 0.5} + my_client_resources = {"num_cpus": 2, "num_gpus": 0.5} # 10 client can run concurrently on a single GPU, but only if you have 20 CPU threads. - my_client_resources = {'num_cpus': 2, 'num_gpus': 0.1} + my_client_resources = {"num_cpus": 2, "num_gpus": 0.1} # Launch the simulation hist = fl.simulation.start_simulation( - ... - client_resources = my_client_resources # A Python dict specifying CPU/GPU resources + # ... + client_resources=my_client_resources # A Python dict specifying CPU/GPU resources ) -While the :code:`client_resources` can be used to control the degree of concurrency in your FL simulation, this does not stop you from running dozens, hundreds or even thousands of clients in the same round and having orders of magnitude more `dormant` (i.e. not participating in a round) clients. Let's say you want to have 100 clients per round but your system can only accommodate 8 clients concurrently. The :code:`VirtualClientEngine` will schedule 100 jobs to run (each simulating a client sampled by the strategy) and then will execute them in a resource-aware manner in batches of 8. +While the ``client_resources`` can be used to control the degree of concurrency in your +FL simulation, this does not stop you from running dozens, hundreds or even thousands of +clients in the same round and having orders of magnitude more `dormant` (i.e. not +participating in a round) clients. Let's say you want to have 100 clients per round but +your system can only accommodate 8 clients concurrently. The ``VirtualClientEngine`` +will schedule 100 jobs to run (each simulating a client sampled by the strategy) and +then will execute them in a resource-aware manner in batches of 8. -To understand all the intricate details on how resources are used to schedule FL clients and how to define custom resources, please take a look at the `Ray documentation `_. +To understand all the intricate details on how resources are used to schedule FL clients +and how to define custom resources, please take a look at the `Ray documentation +`_. Simulation examples ~~~~~~~~~~~~~~~~~~~ -A few ready-to-run complete examples for Flower simulation in Tensorflow/Keras and PyTorch are provided in the `Flower repository `_. You can run them on Google Colab too: - -* `Tensorflow/Keras Simulation `_: 100 clients collaboratively train a MLP model on MNIST. -* `PyTorch Simulation `_: 100 clients collaboratively train a CNN model on MNIST. - +A few ready-to-run complete examples for Flower simulation in Tensorflow/Keras and +PyTorch are provided in the `Flower repository `_. You +can run them on Google Colab too: +- `Tensorflow/Keras Simulation + `_: 100 + clients collaboratively train a MLP model on MNIST. +- `PyTorch Simulation + `_: 100 clients + collaboratively train a CNN model on MNIST. Multi-node Flower simulations ----------------------------- -Flower's :code:`VirtualClientEngine` allows you to run FL simulations across multiple compute nodes. Before starting your multi-node simulation ensure that you: - -#. Have the same Python environment in all nodes. -#. Have a copy of your code (e.g. your entire repo) in all nodes. -#. Have a copy of your dataset in all nodes (more about this in :ref:`simulation considerations `) -#. Pass :code:`ray_init_args={"address"="auto"}` to `start_simulation `_ so the :code:`VirtualClientEngine` attaches to a running Ray instance. -#. Start Ray on you head node: on the terminal type :code:`ray start --head`. This command will print a few lines, one of which indicates how to attach other nodes to the head node. -#. Attach other nodes to the head node: copy the command shown after starting the head and execute it on terminal of a new node: for example :code:`ray start --address='192.168.1.132:6379'` - -With all the above done, you can run your code from the head node as you would if the simulation was running on a single node. - -Once your simulation is finished, if you'd like to dismantle your cluster you simply need to run the command :code:`ray stop` in each node's terminal (including the head node). +Flower's ``VirtualClientEngine`` allows you to run FL simulations across multiple +compute nodes. Before starting your multi-node simulation ensure that you: + +1. Have the same Python environment in all nodes. +2. Have a copy of your code (e.g. your entire repo) in all nodes. +3. Have a copy of your dataset in all nodes (more about this in :ref:`simulation + considerations `) +4. Pass ``ray_init_args={"address"="auto"}`` to `start_simulation + `_ so the ``VirtualClientEngine`` + attaches to a running Ray instance. +5. Start Ray on you head node: on the terminal type ``ray start --head``. This command + will print a few lines, one of which indicates how to attach other nodes to the head + node. +6. Attach other nodes to the head node: copy the command shown after starting the head + and execute it on terminal of a new node: for example ``ray start + --address='192.168.1.132:6379'`` + +With all the above done, you can run your code from the head node as you would if the +simulation was running on a single node. + +Once your simulation is finished, if you'd like to dismantle your cluster you simply +need to run the command ``ray stop`` in each node's terminal (including the head node). Multi-node simulation good-to-know ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Here we list a few interesting functionality when running multi-node FL simulations: -User :code:`ray status` to check all nodes connected to your head node as well as the total resources available to the :code:`VirtualClientEngine`. +User ``ray status`` to check all nodes connected to your head node as well as the total +resources available to the ``VirtualClientEngine``. -When attaching a new node to the head, all its resources (i.e. all CPUs, all GPUs) will be visible by the head node. This means that the :code:`VirtualClientEngine` can schedule as many `virtual` clients as that node can possible run. In some settings you might want to exclude certain resources from the simulation. You can do this by appending `--num-cpus=` and/or `--num-gpus=` in any :code:`ray start` command (including when starting the head) +When attaching a new node to the head, all its resources (i.e. all CPUs, all GPUs) will +be visible by the head node. This means that the ``VirtualClientEngine`` can schedule as +many `virtual` clients as that node can possible run. In some settings you might want to +exclude certain resources from the simulation. You can do this by appending +`--num-cpus=` and/or `--num-gpus=` in any ``ray +start`` command (including when starting the head) .. _considerations-for-simulations: - Considerations for simulations ------------------------------ .. note:: - We are actively working on these fronts so to make it trivial to run any FL workload with Flower simulation. + We are actively working on these fronts so to make it trivial to run any FL workload + with Flower simulation. -The current VCE allows you to run Federated Learning workloads in simulation mode whether you are prototyping simple scenarios on your personal laptop or you want to train a complex FL pipeline across multiple high-performance GPU nodes. While we add more capabilities to the VCE, the points below highlight some of the considerations to keep in mind when designing your FL pipeline with Flower. We also highlight a couple of current limitations in our implementation. +The current VCE allows you to run Federated Learning workloads in simulation mode +whether you are prototyping simple scenarios on your personal laptop or you want to +train a complex FL pipeline across multiple high-performance GPU nodes. While we add +more capabilities to the VCE, the points below highlight some of the considerations to +keep in mind when designing your FL pipeline with Flower. We also highlight a couple of +current limitations in our implementation. GPU resources ~~~~~~~~~~~~~ -The VCE assigns a share of GPU memory to a client that specifies the key :code:`num_gpus` in :code:`client_resources`. This being said, Ray (used internally by the VCE) is by default: - - -* not aware of the total VRAM available on the GPUs. This means that if you set :code:`num_gpus=0.5` and you have two GPUs in your system with different (e.g. 32GB and 8GB) VRAM amounts, they both would run 2 clients concurrently. -* not aware of other unrelated (i.e. not created by the VCE) workloads are running on the GPU. Two takeaways from this are: +The VCE assigns a share of GPU memory to a client that specifies the key ``num_gpus`` in +``client_resources``. This being said, Ray (used internally by the VCE) is by default: - * Your Flower server might need a GPU to evaluate the `global model` after aggregation (by instance when making use of the `evaluate method `_) - * If you want to run several independent Flower simulations on the same machine you need to mask-out your GPUs with :code:`CUDA_VISIBLE_DEVICES=""` when launching your experiment. +- not aware of the total VRAM available on the GPUs. This means that if you set + ``num_gpus=0.5`` and you have two GPUs in your system with different (e.g. 32GB and + 8GB) VRAM amounts, they both would run 2 clients concurrently. +- not aware of other unrelated (i.e. not created by the VCE) workloads are running on + the GPU. Two takeaways from this are: + - Your Flower server might need a GPU to evaluate the `global model` after aggregation + (by instance when making use of the `evaluate method + `_) + - If you want to run several independent Flower simulations on the same machine you + need to mask-out your GPUs with ``CUDA_VISIBLE_DEVICES=""`` when launching + your experiment. -In addition, the GPU resource limits passed to :code:`client_resources` are not `enforced` (i.e. they can be exceeded) which can result in the situation of client using more VRAM than the ratio specified when starting the simulation. +In addition, the GPU resource limits passed to ``client_resources`` are not `enforced` +(i.e. they can be exceeded) which can result in the situation of client using more VRAM +than the ratio specified when starting the simulation. TensorFlow with GPUs -"""""""""""""""""""" +++++++++++++++++++++ -When `using a GPU with TensorFlow `_ nearly your entire GPU memory of all your GPUs visible to the process will be mapped. This is done by TensorFlow for optimization purposes. However, in settings such as FL simulations where we want to split the GPU into multiple `virtual` clients, this is not a desirable mechanism. Luckily we can disable this default behavior by `enabling memory growth `_. +When `using a GPU with TensorFlow `_ nearly your +entire GPU memory of all your GPUs visible to the process will be mapped. This is done +by TensorFlow for optimization purposes. However, in settings such as FL simulations +where we want to split the GPU into multiple `virtual` clients, this is not a desirable +mechanism. Luckily we can disable this default behavior by `enabling memory growth +`_. -This would need to be done in the main process (which is where the server would run) and in each Actor created by the VCE. By means of :code:`actor_kwargs` we can pass the reserved key `"on_actor_init_fn"` in order to specify a function to be executed upon actor initialization. In this case, to enable GPU growth for TF workloads. It would look as follows: +This would need to be done in the main process (which is where the server would run) and +in each Actor created by the VCE. By means of ``actor_kwargs`` we can pass the reserved +key `"on_actor_init_fn"` in order to specify a function to be executed upon actor +initialization. In this case, to enable GPU growth for TF workloads. It would look as +follows: .. code-block:: python @@ -170,19 +263,29 @@ This would need to be done in the main process (which is where the server would # Start Flower simulation hist = fl.simulation.start_simulation( - ... + # ... actor_kwargs={ - "on_actor_init_fn": enable_tf_gpu_growth # <-- To be executed upon actor init. + "on_actor_init_fn": enable_tf_gpu_growth # <-- To be executed upon actor init. }, ) -This is precisely the mechanism used in `Tensorflow/Keras Simulation `_ example. - +This is precisely the mechanism used in `Tensorflow/Keras Simulation +`_ example. Multi-node setups ~~~~~~~~~~~~~~~~~ -* The VCE does not currently offer a way to control on which node a particular `virtual` client is executed. In other words, if more than a single node have the resources needed by a client to run, then any of those nodes could get the client workload scheduled onto. Later in the FL process (i.e. in a different round) the same client could be executed by a different node. Depending on how your clients access their datasets, this might require either having a copy of all dataset partitions on all nodes or a dataset serving mechanism (e.g. using nfs, a database) to circumvent data duplication. - -* By definition virtual clients are `stateless` due to their ephemeral nature. A client state can be implemented as part of the Flower client class but users need to ensure this saved to persistent storage (e.g. a database, disk) and that can be retrieve later by the same client regardless on which node it is running from. This is related to the point above also since, in some way, the client's dataset could be seen as a type of `state`. - +- The VCE does not currently offer a way to control on which node a particular `virtual` + client is executed. In other words, if more than a single node have the resources + needed by a client to run, then any of those nodes could get the client workload + scheduled onto. Later in the FL process (i.e. in a different round) the same client + could be executed by a different node. Depending on how your clients access their + datasets, this might require either having a copy of all dataset partitions on all + nodes or a dataset serving mechanism (e.g. using nfs, a database) to circumvent data + duplication. +- By definition virtual clients are `stateless` due to their ephemeral nature. A client + state can be implemented as part of the Flower client class but users need to ensure + this saved to persistent storage (e.g. a database, disk) and that can be retrieve + later by the same client regardless on which node it is running from. This is related + to the point above also since, in some way, the client's dataset could be seen as a + type of `state`. diff --git a/doc/source/how-to-save-and-load-model-checkpoints.rst b/doc/source/how-to-save-and-load-model-checkpoints.rst index 0d711e375cd8..f2f12dae97be 100644 --- a/doc/source/how-to-save-and-load-model-checkpoints.rst +++ b/doc/source/how-to-save-and-load-model-checkpoints.rst @@ -1,17 +1,19 @@ Save and load model checkpoints =============================== -Flower does not automatically save model updates on the server-side. This how-to guide describes the steps to save (and load) model checkpoints in Flower. - +Flower does not automatically save model updates on the server-side. This how-to guide +describes the steps to save (and load) model checkpoints in Flower. Model checkpointing ------------------- -Model updates can be persisted on the server-side by customizing :code:`Strategy` methods. -Implementing custom strategies is always an option, but for many cases it may be more convenient to simply customize an existing strategy. -The following code example defines a new :code:`SaveModelStrategy` which customized the existing built-in :code:`FedAvg` strategy. -In particular, it customizes :code:`aggregate_fit` by calling :code:`aggregate_fit` in the base class (:code:`FedAvg`). -It then continues to save returned (aggregated) weights before it returns those aggregated weights to the caller (i.e., the server): +Model updates can be persisted on the server-side by customizing ``Strategy`` methods. +Implementing custom strategies is always an option, but for many cases it may be more +convenient to simply customize an existing strategy. The following code example defines +a new ``SaveModelStrategy`` which customized the existing built-in ``FedAvg`` strategy. +In particular, it customizes ``aggregate_fit`` by calling ``aggregate_fit`` in the base +class (``FedAvg``). It then continues to save returned (aggregated) weights before it +returns those aggregated weights to the caller (i.e., the server): .. code-block:: python @@ -24,11 +26,15 @@ It then continues to save returned (aggregated) weights before it returns those ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]: # Call aggregate_fit from base class (FedAvg) to aggregate parameters and metrics - aggregated_parameters, aggregated_metrics = super().aggregate_fit(server_round, results, failures) - + aggregated_parameters, aggregated_metrics = super().aggregate_fit( + server_round, results, failures + ) + if aggregated_parameters is not None: # Convert `Parameters` to `List[np.ndarray]` - aggregated_ndarrays: List[np.ndarray] = fl.common.parameters_to_ndarrays(aggregated_parameters) + aggregated_ndarrays: List[np.ndarray] = fl.common.parameters_to_ndarrays( + aggregated_parameters + ) # Save aggregated_ndarrays print(f"Saving round {server_round} aggregated_ndarrays...") @@ -36,24 +42,27 @@ It then continues to save returned (aggregated) weights before it returns those return aggregated_parameters, aggregated_metrics + # Create strategy and run server strategy = SaveModelStrategy( # (same arguments as FedAvg here) ) fl.server.start_server(strategy=strategy) - Save and load PyTorch checkpoints --------------------------------- -Similar to the previous example but with a few extra steps, we'll show how to -store a PyTorch checkpoint we'll use the ``torch.save`` function. -Firstly, ``aggregate_fit`` returns a ``Parameters`` object that has to be transformed into a list of NumPy ``ndarray``'s, -then those are transformed into the PyTorch ``state_dict`` following the ``OrderedDict`` class structure. +Similar to the previous example but with a few extra steps, we'll show how to store a +PyTorch checkpoint we'll use the ``torch.save`` function. Firstly, ``aggregate_fit`` +returns a ``Parameters`` object that has to be transformed into a list of NumPy +``ndarray``'s, then those are transformed into the PyTorch ``state_dict`` following the +``OrderedDict`` class structure. .. code-block:: python net = cifar.Net().to(DEVICE) + + class SaveModelStrategy(fl.server.strategy.FedAvg): def aggregate_fit( self, @@ -64,14 +73,18 @@ then those are transformed into the PyTorch ``state_dict`` following the ``Order """Aggregate model weights using weighted average and store checkpoint""" # Call aggregate_fit from base class (FedAvg) to aggregate parameters and metrics - aggregated_parameters, aggregated_metrics = super().aggregate_fit(server_round, results, failures) - + aggregated_parameters, aggregated_metrics = super().aggregate_fit( + server_round, results, failures + ) + if aggregated_parameters is not None: print(f"Saving round {server_round} aggregated_parameters...") # Convert `Parameters` to `List[np.ndarray]` - aggregated_ndarrays: List[np.ndarray] = fl.common.parameters_to_ndarrays(aggregated_parameters) - + aggregated_ndarrays: List[np.ndarray] = fl.common.parameters_to_ndarrays( + aggregated_parameters + ) + # Convert `List[np.ndarray]` to PyTorch`state_dict` params_dict = zip(net.state_dict().keys(), aggregated_ndarrays) state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) @@ -82,7 +95,8 @@ then those are transformed into the PyTorch ``state_dict`` following the ``Order return aggregated_parameters, aggregated_metrics -To load your progress, you simply append the following lines to your code. Note that this will iterate over all saved checkpoints and load the latest one: +To load your progress, you simply append the following lines to your code. Note that +this will iterate over all saved checkpoints and load the latest one: .. code-block:: python @@ -94,4 +108,5 @@ To load your progress, you simply append the following lines to your code. Note state_dict_ndarrays = [v.cpu().numpy() for v in net.state_dict().values()] parameters = fl.common.ndarrays_to_parameters(state_dict_ndarrays) -Return/use this object of type ``Parameters`` wherever necessary, such as in the ``initial_parameters`` when defining a ``Strategy``. \ No newline at end of file +Return/use this object of type ``Parameters`` wherever necessary, such as in the +``initial_parameters`` when defining a ``Strategy``. diff --git a/doc/source/how-to-upgrade-to-flower-1.0.rst b/doc/source/how-to-upgrade-to-flower-1.0.rst index c0721b0f3736..5f10f16a551f 100644 --- a/doc/source/how-to-upgrade-to-flower-1.0.rst +++ b/doc/source/how-to-upgrade-to-flower-1.0.rst @@ -1,8 +1,10 @@ Upgrade to Flower 1.0 ===================== -Flower 1.0 is here. Along with new features, Flower 1.0 provides a stable foundation for future growth. Compared to Flower 0.19 (and other 0.x series releases), there are a few breaking changes that make it necessary to change the code of existing 0.x-series projects. - +Flower 1.0 is here. Along with new features, Flower 1.0 provides a stable foundation for +future growth. Compared to Flower 0.19 (and other 0.x series releases), there are a few +breaking changes that make it necessary to change the code of existing 0.x-series +projects. Install update -------------- @@ -14,11 +16,13 @@ Here's how to update an existing installation to Flower 1.0 using either pip or - ``python -m pip install -U flwr`` (when using ``start_server`` and ``start_client``) - ``python -m pip install -U 'flwr[simulation]'`` (when using ``start_simulation``) -- Poetry: update the ``flwr`` dependency in ``pyproject.toml`` and then reinstall (don't forget to delete ``poetry.lock`` via ``rm poetry.lock`` before running ``poetry install``). +- Poetry: update the ``flwr`` dependency in ``pyproject.toml`` and then reinstall (don't + forget to delete ``poetry.lock`` via ``rm poetry.lock`` before running ``poetry + install``). - ``flwr = "^1.0.0"`` (when using ``start_server`` and ``start_client``) - - ``flwr = { version = "^1.0.0", extras = ["simulation"] }`` (when using ``start_simulation``) - + - ``flwr = { version = "^1.0.0", extras = ["simulation"] }`` (when using + ``start_simulation``) Required changes ---------------- @@ -28,64 +32,96 @@ The following breaking changes require manual updates. General ~~~~~~~ -Pass all arguments as keyword arguments (not as positional arguments). Here's an example: +Pass all arguments as keyword arguments (not as positional arguments). Here's an +example: - Flower 0.19 (positional arguments): ``start_client("127.0.0.1:8080", FlowerClient())`` -- Flower 1.0 (keyword arguments): ``start_client(server_address="127.0.0.1:8080", client=FlowerClient())`` +- Flower 1.0 (keyword arguments): ``start_client(server_address="127.0.0.1:8080", + client=FlowerClient())`` Client ~~~~~~ -- Subclasses of ``NumPyClient``: change ``def get_parameters(self):``` to ``def get_parameters(self, config):`` -- Subclasses of ``Client``: change ``def get_parameters(self):``` to ``def get_parameters(self, ins: GetParametersIns):`` +- Subclasses of ``NumPyClient``: change ``def get_parameters(self):``` to ``def + get_parameters(self, config):`` +- Subclasses of ``Client``: change ``def get_parameters(self):``` to ``def + get_parameters(self, ins: GetParametersIns):`` Strategies / ``start_server`` / ``start_simulation`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -- Pass ``ServerConfig`` (instead of a dictionary) to ``start_server`` and ``start_simulation``. Here's an example: +- Pass ``ServerConfig`` (instead of a dictionary) to ``start_server`` and + ``start_simulation``. Here's an example: - - Flower 0.19: ``start_server(..., config={"num_rounds": 3, "round_timeout": 600.0}, ...)`` - - Flower 1.0: ``start_server(..., config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), ...)`` + - Flower 0.19: ``start_server(..., config={"num_rounds": 3, "round_timeout": 600.0}, + ...)`` + - Flower 1.0: ``start_server(..., config=flwr.server.ServerConfig(num_rounds=3, + round_timeout=600.0), ...)`` -- Replace ``num_rounds=1`` in ``start_simulation`` with the new ``config=ServerConfig(...)`` (see previous item) -- Remove ``force_final_distributed_eval`` parameter from calls to ``start_server``. Distributed evaluation on all clients can be enabled by configuring the strategy to sample all clients for evaluation after the last round of training. +- Replace ``num_rounds=1`` in ``start_simulation`` with the new + ``config=ServerConfig(...)`` (see previous item) +- Remove ``force_final_distributed_eval`` parameter from calls to ``start_server``. + Distributed evaluation on all clients can be enabled by configuring the strategy to + sample all clients for evaluation after the last round of training. - Rename parameter/ndarray conversion functions: - ``parameters_to_weights`` --> ``parameters_to_ndarrays`` - ``weights_to_parameters`` --> ``ndarrays_to_parameters`` -- Strategy initialization: if the strategy relies on the default values for ``fraction_fit`` and ``fraction_evaluate``, set ``fraction_fit`` and ``fraction_evaluate`` manually to ``0.1``. Projects that do not manually create a strategy (by calling ``start_server`` or ``start_simulation`` without passing a strategy instance) should now manually initialize FedAvg with ``fraction_fit`` and ``fraction_evaluate`` set to ``0.1``. +- Strategy initialization: if the strategy relies on the default values for + ``fraction_fit`` and ``fraction_evaluate``, set ``fraction_fit`` and + ``fraction_evaluate`` manually to ``0.1``. Projects that do not manually create a + strategy (by calling ``start_server`` or ``start_simulation`` without passing a + strategy instance) should now manually initialize FedAvg with ``fraction_fit`` and + ``fraction_evaluate`` set to ``0.1``. - Rename built-in strategy parameters (e.g., ``FedAvg``): - ``fraction_eval`` --> ``fraction_evaluate`` - ``min_eval_clients`` --> ``min_evaluate_clients`` - ``eval_fn`` --> ``evaluate_fn`` -- Rename ``rnd`` to ``server_round``. This impacts multiple methods and functions, for example, ``configure_fit``, ``aggregate_fit``, ``configure_evaluate``, ``aggregate_evaluate``, and ``evaluate_fn``. +- Rename ``rnd`` to ``server_round``. This impacts multiple methods and functions, for + example, ``configure_fit``, ``aggregate_fit``, ``configure_evaluate``, + ``aggregate_evaluate``, and ``evaluate_fn``. - Add ``server_round`` and ``config`` to ``evaluate_fn``: - - Flower 0.19: ``def evaluate(parameters: NDArrays) -> Optional[Tuple[float, Dict[str, Scalar]]]:`` - - Flower 1.0: ``def evaluate(server_round: int, parameters: NDArrays, config: Dict[str, Scalar]) -> Optional[Tuple[float, Dict[str, Scalar]]]:`` + - Flower 0.19: ``def evaluate(parameters: NDArrays) -> Optional[Tuple[float, Dict[str, + Scalar]]]:`` + - Flower 1.0: ``def evaluate(server_round: int, parameters: NDArrays, config: + Dict[str, Scalar]) -> Optional[Tuple[float, Dict[str, Scalar]]]:`` Custom strategies ~~~~~~~~~~~~~~~~~ -- The type of parameter ``failures`` has changed from ``List[BaseException]`` to ``List[Union[Tuple[ClientProxy, FitRes], BaseException]]`` (in ``aggregate_fit``) and ``List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]]`` (in ``aggregate_evaluate``) -- The ``Strategy`` method ``evaluate`` now receives the current round of federated learning/evaluation as the first parameter: +- The type of parameter ``failures`` has changed from ``List[BaseException]`` to + ``List[Union[Tuple[ClientProxy, FitRes], BaseException]]`` (in ``aggregate_fit``) and + ``List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]]`` (in + ``aggregate_evaluate``) +- The ``Strategy`` method ``evaluate`` now receives the current round of federated + learning/evaluation as the first parameter: - - Flower 0.19: ``def evaluate(self, parameters: Parameters) -> Optional[Tuple[float, Dict[str, Scalar]]]:`` - - Flower 1.0: ``def evaluate(self, server_round: int, parameters: Parameters) -> Optional[Tuple[float, Dict[str, Scalar]]]:`` + - Flower 0.19: ``def evaluate(self, parameters: Parameters) -> Optional[Tuple[float, + Dict[str, Scalar]]]:`` + - Flower 1.0: ``def evaluate(self, server_round: int, parameters: Parameters) -> + Optional[Tuple[float, Dict[str, Scalar]]]:`` Optional improvements --------------------- -Along with the necessary changes above, there are a number of potential improvements that just became possible: - -- Remove "placeholder" methods from subclasses of ``Client`` or ``NumPyClient``. If you, for example, use server-side evaluation, then empty placeholder implementations of ``evaluate`` are no longer necessary. -- Configure the round timeout via ``start_simulation``: ``start_simulation(..., config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), ...)`` +Along with the necessary changes above, there are a number of potential improvements +that just became possible: +- Remove "placeholder" methods from subclasses of ``Client`` or ``NumPyClient``. If you, + for example, use server-side evaluation, then empty placeholder implementations of + ``evaluate`` are no longer necessary. +- Configure the round timeout via ``start_simulation``: ``start_simulation(..., + config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), ...)`` Further help ------------ -Most official `Flower code examples `_ are already updated to Flower 1.0, they can serve as a reference for using the Flower 1.0 API. If there are further questions, `join the Flower Slack `_ and use the channel ``#questions``. +Most official `Flower code examples +`_ are already updated to Flower 1.0, +they can serve as a reference for using the Flower 1.0 API. If there are further +questions, `join the Flower Slack `_ and use the channel +``#questions``. diff --git a/doc/source/how-to-upgrade-to-flower-next.rst b/doc/source/how-to-upgrade-to-flower-next.rst index f378e92dbba4..e1fc350deb8b 100644 --- a/doc/source/how-to-upgrade-to-flower-next.rst +++ b/doc/source/how-to-upgrade-to-flower-next.rst @@ -1,11 +1,13 @@ Upgrade to Flower Next ====================== -Welcome to the migration guide for updating Flower to Flower Next! Whether you're a seasoned user -or just getting started, this guide will help you smoothly transition your existing setup to take -advantage of the latest features and improvements in Flower Next, starting from version 1.8. +Welcome to the migration guide for updating Flower to Flower Next! Whether you're a +seasoned user or just getting started, this guide will help you smoothly transition your +existing setup to take advantage of the latest features and improvements in Flower Next, +starting from version 1.8. .. note:: + This guide shows how to reuse pre-``1.8`` Flower code with minimum code changes by using the *compatibility layer* in Flower Next. In another guide, we will show how to run Flower Next end-to-end with pure Flower Next APIs. @@ -18,26 +20,44 @@ Let's dive in! - https://github.com/jgm/pandoc/issues/3973#issuecomment-337087394 .. |clientapp_link| replace:: ``ClientApp()`` + .. |serverapp_link| replace:: ``ServerApp()`` + .. |startclient_link| replace:: ``start_client()`` + .. |startserver_link| replace:: ``start_server()`` + .. |startsim_link| replace:: ``start_simulation()`` + .. |runsim_link| replace:: ``run_simulation()`` + .. |flowernext_superlink_link| replace:: ``flower-superlink`` + .. |flowernext_clientapp_link| replace:: ``flower-client-app`` + .. |flowernext_serverapp_link| replace:: ``flower-server-app`` + .. |flower_simulation_link| replace:: ``flower-simulation`` + .. _clientapp_link: ref-api/flwr.client.ClientApp.html + +.. _flower_simulation_link: ref-api-cli.html#flower-simulation + +.. _flowernext_clientapp_link: ref-api-cli.html#flower-client-app + +.. _flowernext_serverapp_link: ref-api-cli.html#flower-server-app + +.. _flowernext_superlink_link: ref-api-cli.html#flower-superlink + +.. _runsim_link: ref-api/flwr.simulation.run_simulation.html + .. _serverapp_link: ref-api/flwr.server.ServerApp.html + .. _startclient_link: ref-api/flwr.client.start_client.html + .. _startserver_link: ref-api/flwr.server.start_server.html -.. _startsim_link: ref-api/flwr.simulation.start_simulation.html -.. _runsim_link: ref-api/flwr.simulation.run_simulation.html -.. _flowernext_superlink_link: ref-api-cli.html#flower-superlink -.. _flowernext_clientapp_link: ref-api-cli.html#flower-client-app -.. _flowernext_serverapp_link: ref-api-cli.html#flower-server-app -.. _flower_simulation_link: ref-api-cli.html#flower-simulation +.. _startsim_link: ref-api/flwr.simulation.start_simulation.html Install update -------------- @@ -48,19 +68,18 @@ Using pip Here's how to update an existing installation of Flower to Flower Next with ``pip``: .. code-block:: bash - + $ python -m pip install -U flwr or if you need Flower Next with simulation: .. code-block:: bash - - $ python -m pip install -U "flwr[simulation]" + $ python -m pip install -U "flwr[simulation]" Ensure you set the following version constraint in your ``requirements.txt`` -.. code-block:: +.. code-block:: # Without simulation support flwr>=1.8,<2.0 @@ -81,7 +100,8 @@ or ``pyproject.toml``: Using Poetry ~~~~~~~~~~~~ -Update the ``flwr`` dependency in ``pyproject.toml`` and then reinstall (don't forget to delete ``poetry.lock`` via ``rm poetry.lock`` before running ``poetry install``). +Update the ``flwr`` dependency in ``pyproject.toml`` and then reinstall (don't forget to +delete ``poetry.lock`` via ``rm poetry.lock`` before running ``poetry install``). Ensure you set the following version constraint in your ``pyproject.toml``: @@ -100,13 +120,16 @@ Required changes ---------------- In Flower Next, the *infrastructure* and *application layers* have been decoupled. -Instead of starting a client in code via ``start_client()``, you create a |clientapp_link|_ and start it via the command line. -Instead of starting a server in code via ``start_server()``, you create a |serverapp_link|_ and start it via the command line. -The long-running components of server and client are called SuperLink and SuperNode. -The following non-breaking changes that require manual updates and allow you to run your project both in the traditional way and in the Flower Next way: +Instead of starting a client in code via ``start_client()``, you create a +|clientapp_link|_ and start it via the command line. Instead of starting a server in +code via ``start_server()``, you create a |serverapp_link|_ and start it via the command +line. The long-running components of server and client are called SuperLink and +SuperNode. The following non-breaking changes that require manual updates and allow you +to run your project both in the traditional way and in the Flower Next way: |clientapp_link|_ ~~~~~~~~~~~~~~~~~ + - Wrap your existing client with |clientapp_link|_ instead of launching it via |startclient_link|_. Here's an example: @@ -115,23 +138,25 @@ The following non-breaking changes that require manual updates and allow you to # Flower 1.8 def client_fn(cid: str): - return flwr.client.FlowerClient().to_client() - + return flwr.client.FlowerClient().to_client() + + app = flwr.client.ClientApp( - client_fn=client_fn, + client_fn=client_fn, ) # Flower 1.7 if __name__ == "__main__": flwr.client.start_client( - server_address="127.0.0.1:8080", - client=flwr.client.FlowerClient().to_client(), + server_address="127.0.0.1:8080", + client=flwr.client.FlowerClient().to_client(), ) |serverapp_link|_ ~~~~~~~~~~~~~~~~~ -- Wrap your existing strategy with |serverapp_link|_ instead of starting the server - via |startserver_link|_. Here's an example: + +- Wrap your existing strategy with |serverapp_link|_ instead of starting the server via + |startserver_link|_. Here's an example: .. code-block:: python :emphasize-lines: 2,9 @@ -152,13 +177,14 @@ The following non-breaking changes that require manual updates and allow you to Deployment ~~~~~~~~~~ + - Run the ``SuperLink`` using |flowernext_superlink_link|_ before running, in sequence, - |flowernext_clientapp_link|_ (2x) and |flowernext_serverapp_link|_. There is no need to - execute `client.py` and `server.py` as Python scripts. + |flowernext_clientapp_link|_ (2x) and |flowernext_serverapp_link|_. There is no need + to execute `client.py` and `server.py` as Python scripts. - Here's an example to start the server without HTTPS (only for prototyping): .. code-block:: bash - + # Start a Superlink $ flower-superlink --insecure @@ -171,8 +197,9 @@ Deployment # In yet another terminal window, run the ServerApp (this starts the actual training run) $ flower-server-app server:app --insecure -- Here's another example to start with HTTPS. Use the ``--ssl-ca-certfile``, ``--ssl-certfile``, and ``--ssl-keyfile`` command line - options to pass paths to (CA certificate, server certificate, and server private key). +- Here's another example to start with HTTPS. Use the ``--ssl-ca-certfile``, + ``--ssl-certfile``, and ``--ssl-keyfile`` command line options to pass paths to (CA + certificate, server certificate, and server private key). .. code-block:: bash @@ -199,6 +226,7 @@ Deployment Simulation in CLI ~~~~~~~~~~~~~~~~~ + - Wrap your existing client and strategy with |clientapp_link|_ and |serverapp_link|_, respectively. There is no need to use |startsim_link|_ anymore. Here's an example: @@ -208,13 +236,16 @@ Simulation in CLI # Regular Flower client implementation class FlowerClient(NumPyClient): # ... + pass + # Flower 1.8 def client_fn(cid: str): - return FlowerClient().to_client() - + return FlowerClient().to_client() + + client_app = flwr.client.ClientApp( - client_fn=client_fn, + client_fn=client_fn, ) server_app = flwr.server.ServerApp( @@ -226,12 +257,12 @@ Simulation in CLI if __name__ == "__main__": hist = flwr.simulation.start_simulation( num_clients=100, - ... + # ... ) -- Run |flower_simulation_link|_ in CLI and point to the ``server_app`` / ``client_app`` object in the - code instead of executing the Python script. Here's an example (assuming the - ``server_app`` and ``client_app`` objects are in a ``sim.py`` module): +- Run |flower_simulation_link|_ in CLI and point to the ``server_app`` / ``client_app`` + object in the code instead of executing the Python script. Here's an example (assuming + the ``server_app`` and ``client_app`` objects are in a ``sim.py`` module): .. code-block:: bash @@ -246,8 +277,8 @@ Simulation in CLI # Flower 1.7 $ python sim.py -- Set default resources for each |clientapp_link|_ using the ``--backend-config`` command - line argument instead of setting the ``client_resources`` argument in +- Set default resources for each |clientapp_link|_ using the ``--backend-config`` + command line argument instead of setting the ``client_resources`` argument in |startsim_link|_. Here's an example: .. code-block:: bash @@ -266,26 +297,27 @@ Simulation in CLI # Flower 1.7 (in `sim.py`) if __name__ == "__main__": hist = flwr.simulation.start_simulation( - num_clients=100, - client_resources = {'num_cpus': 2, "num_gpus": 0.25}, - ... + num_clients=100, client_resources={"num_cpus": 2, "num_gpus": 0.25}, ... ) Simulation in a Notebook ~~~~~~~~~~~~~~~~~~~~~~~~ + - Run |runsim_link|_ in your notebook instead of |startsim_link|_. Here's an example: .. code-block:: python :emphasize-lines: 19,27 - NUM_CLIENTS = + NUM_CLIENTS = 10 # Replace by any integer greater than zero + def client_fn(cid: str): # ... - return FlowerClient().to_client() - + return FlowerClient().to_client() + + client_app = flwr.client.ClientApp( - client_fn=client_fn, + client_fn=client_fn, ) server_app = flwr.server.ServerApp( @@ -297,7 +329,7 @@ Simulation in a Notebook # Flower 1.8 flwr.simulation.run_simulation( - server_app=server_app, + server_app=server_app, client_app=client_app, num_supernodes=NUM_CLIENTS, backend_config=backend_config, @@ -312,18 +344,17 @@ Simulation in a Notebook client_resources=backend_config["client_resources"], ) - Further help ------------ Some official `Flower code examples `_ are already -updated to Flower Next so they can serve as a reference for using the Flower Next API. If there are -further questions, `join the Flower Slack `_ and use the channel ``#questions``. -You can also `participate in Flower Discuss `_ where you can find us -answering questions, or share and learn from others about migrating to Flower Next. +updated to Flower Next so they can serve as a reference for using the Flower Next API. +If there are further questions, `join the Flower Slack `_ +and use the channel ``#questions``. You can also `participate in Flower Discuss +`_ where you can find us answering questions, or share and +learn from others about migrating to Flower Next. .. admonition:: Important - :class: important As we continuously enhance Flower Next at a rapid pace, we'll be periodically updating this guide. Please feel free to share any feedback with us! diff --git a/doc/source/how-to-use-built-in-mods.rst b/doc/source/how-to-use-built-in-mods.rst index 341139175074..970b2055ec23 100644 --- a/doc/source/how-to-use-built-in-mods.rst +++ b/doc/source/how-to-use-built-in-mods.rst @@ -1,14 +1,19 @@ Use Built-in Mods ================= -**Note: This tutorial covers experimental features. The functionality and interfaces may change in future versions.** +**Note: This tutorial covers experimental features. The functionality and interfaces may +change in future versions.** -In this tutorial, we will learn how to utilize built-in mods to augment the behavior of a ``ClientApp``. Mods (sometimes also called Modifiers) allow us to perform operations before and after a task is processed in the ``ClientApp``. +In this tutorial, we will learn how to utilize built-in mods to augment the behavior of +a ``ClientApp``. Mods (sometimes also called Modifiers) allow us to perform operations +before and after a task is processed in the ``ClientApp``. What are Mods? -------------- -A Mod is a callable that wraps around a ``ClientApp``. It can manipulate or inspect the incoming ``Message`` and the resulting outgoing ``Message``. The signature for a ``Mod`` is as follows: +A Mod is a callable that wraps around a ``ClientApp``. It can manipulate or inspect the +incoming ``Message`` and the resulting outgoing ``Message``. The signature for a ``Mod`` +is as follows: .. code-block:: python @@ -51,12 +56,13 @@ Define your client function (``client_fn``) that will be wrapped by the mod(s): def client_fn(cid): # Your client code goes here. - return # your client + return # your client 3. Create the ``ClientApp`` with mods -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Create your ``ClientApp`` and pass the mods as a list to the ``mods`` argument. The order in which you provide the mods matters: +Create your ``ClientApp`` and pass the mods as a list to the ``mods`` argument. The +order in which you provide the mods matters: .. code-block:: python @@ -65,25 +71,31 @@ Create your ``ClientApp`` and pass the mods as a list to the ``mods`` argument. mods=[ example_mod_1, # Mod 1 example_mod_2, # Mod 2 - ] + ], ) Order of execution ------------------ -When the ``ClientApp`` runs, the mods are executed in the order they are provided in the list: +When the ``ClientApp`` runs, the mods are executed in the order they are provided in the +list: 1. ``example_mod_1`` (outermost mod) 2. ``example_mod_2`` (next mod) -3. Message handler (core function that handles the incoming ``Message`` and returns the outgoing ``Message``) +3. Message handler (core function that handles the incoming ``Message`` and returns the + outgoing ``Message``) 4. ``example_mod_2`` (on the way back) 5. ``example_mod_1`` (outermost mod on the way back) -Each mod has a chance to inspect and modify the incoming ``Message`` before passing it to the next mod, and likewise with the outgoing ``Message`` before returning it up the stack. +Each mod has a chance to inspect and modify the incoming ``Message`` before passing it +to the next mod, and likewise with the outgoing ``Message`` before returning it up the +stack. Conclusion ---------- -By following this guide, you have learned how to effectively use mods to enhance your ``ClientApp``'s functionality. Remember that the order of mods is crucial and affects how the input and output are processed. +By following this guide, you have learned how to effectively use mods to enhance your +``ClientApp``'s functionality. Remember that the order of mods is crucial and affects +how the input and output are processed. Enjoy building a more robust and flexible ``ClientApp`` with mods! diff --git a/doc/source/how-to-use-differential-privacy.rst b/doc/source/how-to-use-differential-privacy.rst index 5d4fa3dca1a4..67e54271bb2e 100644 --- a/doc/source/how-to-use-differential-privacy.rst +++ b/doc/source/how-to-use-differential-privacy.rst @@ -1,126 +1,151 @@ Use Differential Privacy ------------------------- -This guide explains how you can utilize differential privacy in the Flower framework. If you are not yet familiar with differential privacy, you can refer to :doc:`explanation-differential-privacy`. +======================== -.. warning:: +This guide explains how you can utilize differential privacy in the Flower framework. If +you are not yet familiar with differential privacy, you can refer to +:doc:`explanation-differential-privacy`. - Differential Privacy in Flower is in a preview phase. If you plan to use these features in a production environment with sensitive data, feel free contact us to discuss your requirements and to receive guidance on how to best use these features. +.. warning:: + Differential Privacy in Flower is in a preview phase. If you plan to use these + features in a production environment with sensitive data, feel free contact us to + discuss your requirements and to receive guidance on how to best use these features. Central Differential Privacy -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -This approach consists of two separate phases: clipping of the updates and adding noise to the aggregated model. -For the clipping phase, Flower framework has made it possible to decide whether to perform clipping on the server side or the client side. - -- **Server-side Clipping**: This approach has the advantage of the server enforcing uniform clipping across all clients' updates and reducing the communication overhead for clipping values. However, it also has the disadvantage of increasing the computational load on the server due to the need to perform the clipping operation for all clients. -- **Client-side Clipping**: This approach has the advantage of reducing the computational overhead on the server. However, it also has the disadvantage of lacking centralized control, as the server has less control over the clipping process. +---------------------------- +This approach consists of two separate phases: clipping of the updates and adding noise +to the aggregated model. For the clipping phase, Flower framework has made it possible +to decide whether to perform clipping on the server side or the client side. +- **Server-side Clipping**: This approach has the advantage of the server enforcing + uniform clipping across all clients' updates and reducing the communication overhead + for clipping values. However, it also has the disadvantage of increasing the + computational load on the server due to the need to perform the clipping operation for + all clients. +- **Client-side Clipping**: This approach has the advantage of reducing the + computational overhead on the server. However, it also has the disadvantage of lacking + centralized control, as the server has less control over the clipping process. Server-side Clipping -^^^^^^^^^^^^^^^^^^^^ -For central DP with server-side clipping, there are two :code:`Strategy` classes that act as wrappers around the actual :code:`Strategy` instance (for example, :code:`FedAvg`). -The two wrapper classes are :code:`DifferentialPrivacyServerSideFixedClipping` and :code:`DifferentialPrivacyServerSideAdaptiveClipping` for fixed and adaptive clipping. +~~~~~~~~~~~~~~~~~~~~ -.. image:: ./_static/DP/serversideCDP.png - :align: center - :width: 700 - :alt: server side clipping +For central DP with server-side clipping, there are two ``Strategy`` classes that act as +wrappers around the actual ``Strategy`` instance (for example, ``FedAvg``). The two +wrapper classes are ``DifferentialPrivacyServerSideFixedClipping`` and +``DifferentialPrivacyServerSideAdaptiveClipping`` for fixed and adaptive clipping. +.. image:: ./_static/DP/serversideCDP.png + :align: center + :width: 700 + :alt: server side clipping -The code sample below enables the :code:`FedAvg` strategy to use server-side fixed clipping using the :code:`DifferentialPrivacyServerSideFixedClipping` wrapper class. -The same approach can be used with :code:`DifferentialPrivacyServerSideAdaptiveClipping` by adjusting the corresponding input parameters. +The code sample below enables the ``FedAvg`` strategy to use server-side fixed clipping +using the ``DifferentialPrivacyServerSideFixedClipping`` wrapper class. The same +approach can be used with ``DifferentialPrivacyServerSideAdaptiveClipping`` by adjusting +the corresponding input parameters. .. code-block:: python - from flwr.server.strategy import DifferentialPrivacyClientSideFixedClipping - - # Create the strategy - strategy = fl.server.strategy.FedAvg(...) - - # Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping wrapper - dp_strategy = DifferentialPrivacyServerSideFixedClipping( - strategy, - cfg.noise_multiplier, - cfg.clipping_norm, - cfg.num_sampled_clients, - ) + from flwr.server.strategy import DifferentialPrivacyClientSideFixedClipping + # Create the strategy + strategy = fl.server.strategy.FedAvg(...) + # Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping wrapper + dp_strategy = DifferentialPrivacyServerSideFixedClipping( + strategy, + cfg.noise_multiplier, + cfg.clipping_norm, + cfg.num_sampled_clients, + ) Client-side Clipping -^^^^^^^^^^^^^^^^^^^^ -For central DP with client-side clipping, the server sends the clipping value to selected clients on each round. -Clients can use existing Flower :code:`Mods` to perform the clipping. -Two mods are available for fixed and adaptive client-side clipping: :code:`fixedclipping_mod` and :code:`adaptiveclipping_mod` with corresponding server-side wrappers :code:`DifferentialPrivacyClientSideFixedClipping` and :code:`DifferentialPrivacyClientSideAdaptiveClipping`. +~~~~~~~~~~~~~~~~~~~~ -.. image:: ./_static/DP/clientsideCDP.png - :align: center - :width: 800 - :alt: client side clipping +For central DP with client-side clipping, the server sends the clipping value to +selected clients on each round. Clients can use existing Flower ``Mods`` to perform the +clipping. Two mods are available for fixed and adaptive client-side clipping: +``fixedclipping_mod`` and ``adaptiveclipping_mod`` with corresponding server-side +wrappers ``DifferentialPrivacyClientSideFixedClipping`` and +``DifferentialPrivacyClientSideAdaptiveClipping``. +.. image:: ./_static/DP/clientsideCDP.png + :align: center + :width: 800 + :alt: client side clipping -The code sample below enables the :code:`FedAvg` strategy to use differential privacy with client-side fixed clipping using both the :code:`DifferentialPrivacyClientSideFixedClipping` wrapper class and, on the client, :code:`fixedclipping_mod`: +The code sample below enables the ``FedAvg`` strategy to use differential privacy with +client-side fixed clipping using both the ``DifferentialPrivacyClientSideFixedClipping`` +wrapper class and, on the client, ``fixedclipping_mod``: .. code-block:: python - from flwr.server.strategy import DifferentialPrivacyClientSideFixedClipping + from flwr.server.strategy import DifferentialPrivacyClientSideFixedClipping - # Create the strategy - strategy = fl.server.strategy.FedAvg(...) + # Create the strategy + strategy = fl.server.strategy.FedAvg(...) - # Wrap the strategy with the DifferentialPrivacyClientSideFixedClipping wrapper - dp_strategy = DifferentialPrivacyClientSideFixedClipping( - strategy, - cfg.noise_multiplier, - cfg.clipping_norm, - cfg.num_sampled_clients, - ) + # Wrap the strategy with the DifferentialPrivacyClientSideFixedClipping wrapper + dp_strategy = DifferentialPrivacyClientSideFixedClipping( + strategy, + cfg.noise_multiplier, + cfg.clipping_norm, + cfg.num_sampled_clients, + ) -In addition to the server-side strategy wrapper, the :code:`ClientApp` needs to configure the matching :code:`fixedclipping_mod` to perform the client-side clipping: +In addition to the server-side strategy wrapper, the ``ClientApp`` needs to configure +the matching ``fixedclipping_mod`` to perform the client-side clipping: .. code-block:: python - from flwr.client.mod import fixedclipping_mod - - # Add fixedclipping_mod to the client-side mods - app = fl.client.ClientApp( - client_fn=client_fn, - mods=[ - fixedclipping_mod, - ] - ) + from flwr.client.mod import fixedclipping_mod + # Add fixedclipping_mod to the client-side mods + app = fl.client.ClientApp( + client_fn=client_fn, + mods=[ + fixedclipping_mod, + ], + ) Local Differential Privacy -~~~~~~~~~~~~~~~~~~~~~~~~~~ -To utilize local differential privacy (DP) and add noise to the client model parameters before transmitting them to the server in Flower, you can use the `LocalDpMod`. The following hyperparameters need to be set: clipping norm value, sensitivity, epsilon, and delta. +-------------------------- + +To utilize local differential privacy (DP) and add noise to the client model parameters +before transmitting them to the server in Flower, you can use the `LocalDpMod`. The +following hyperparameters need to be set: clipping norm value, sensitivity, epsilon, and +delta. .. image:: ./_static/DP/localdp.png - :align: center - :width: 700 - :alt: local DP mod + :align: center + :width: 700 + :alt: local DP mod -Below is a code example that shows how to use :code:`LocalDpMod`: +Below is a code example that shows how to use ``LocalDpMod``: .. code-block:: python - from flwr.client.mod.localdp_mod import LocalDpMod - - # Create an instance of the mod with the required params - local_dp_obj = LocalDpMod( - cfg.clipping_norm, cfg.sensitivity, cfg.epsilon, cfg.delta - ) - # Add local_dp_obj to the client-side mods + from flwr.client.mod.localdp_mod import LocalDpMod - app = fl.client.ClientApp( - client_fn=client_fn, - mods=[local_dp_obj], - ) + # Create an instance of the mod with the required params + local_dp_obj = LocalDpMod(cfg.clipping_norm, cfg.sensitivity, cfg.epsilon, cfg.delta) + # Add local_dp_obj to the client-side mods + app = fl.client.ClientApp( + client_fn=client_fn, + mods=[local_dp_obj], + ) -Please note that the order of mods, especially those that modify parameters, is important when using multiple modifiers. Typically, differential privacy (DP) modifiers should be the last to operate on parameters. +Please note that the order of mods, especially those that modify parameters, is +important when using multiple modifiers. Typically, differential privacy (DP) modifiers +should be the last to operate on parameters. Local Training using Privacy Engines -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -For ensuring data instance-level privacy during local model training on the client side, consider leveraging privacy engines such as Opacus and TensorFlow Privacy. For examples of using Flower with these engines, please refer to the Flower examples directory (`Opacus `_, `Tensorflow Privacy `_). \ No newline at end of file +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +For ensuring data instance-level privacy during local model training on the client side, +consider leveraging privacy engines such as Opacus and TensorFlow Privacy. For examples +of using Flower with these engines, please refer to the Flower examples directory +(`Opacus `_, `Tensorflow +Privacy `_). diff --git a/doc/source/how-to-use-strategies.rst b/doc/source/how-to-use-strategies.rst index 8ac120124951..b4803c6059b3 100644 --- a/doc/source/how-to-use-strategies.rst +++ b/doc/source/how-to-use-strategies.rst @@ -1,19 +1,21 @@ Use strategies ============== -Flower allows full customization of the learning process through the :code:`Strategy` abstraction. A number of built-in strategies are provided in the core framework. +Flower allows full customization of the learning process through the ``Strategy`` +abstraction. A number of built-in strategies are provided in the core framework. -There are three ways to customize the way Flower orchestrates the learning process on the server side: - -* Use an existing strategy, for example, :code:`FedAvg` -* Customize an existing strategy with callback functions -* Implement a novel strategy +There are three ways to customize the way Flower orchestrates the learning process on +the server side: +- Use an existing strategy, for example, ``FedAvg`` +- Customize an existing strategy with callback functions +- Implement a novel strategy Use an existing strategy ------------------------ -Flower comes with a number of popular federated learning strategies built-in. A built-in strategy can be instantiated as follows: +Flower comes with a number of popular federated learning strategies built-in. A built-in +strategy can be instantiated as follows: .. code-block:: python @@ -22,7 +24,9 @@ Flower comes with a number of popular federated learning strategies built-in. A strategy = fl.server.strategy.FedAvg() fl.server.start_server(config=fl.server.ServerConfig(num_rounds=3), strategy=strategy) -This creates a strategy with all parameters left at their default values and passes it to the :code:`start_server` function. It is usually recommended to adjust a few parameters during instantiation: +This creates a strategy with all parameters left at their default values and passes it +to the ``start_server`` function. It is usually recommended to adjust a few parameters +during instantiation: .. code-block:: python @@ -35,22 +39,26 @@ This creates a strategy with all parameters left at their default values and pas ) fl.server.start_server(config=fl.server.ServerConfig(num_rounds=3), strategy=strategy) - Customize an existing strategy with callback functions ------------------------------------------------------ -Existing strategies provide several ways to customize their behaviour. Callback functions allow strategies to call user-provided code during execution. +Existing strategies provide several ways to customize their behaviour. Callback +functions allow strategies to call user-provided code during execution. Configuring client fit and client evaluate ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The server can pass new configuration values to the client each round by providing a function to :code:`on_fit_config_fn`. The provided function will be called by the strategy and must return a dictionary of configuration key values pairs that will be sent to the client. -It must return a dictionary of arbitrary configuration values :code:`client.fit` and :code:`client.evaluate` functions during each round of federated learning. +The server can pass new configuration values to the client each round by providing a +function to ``on_fit_config_fn``. The provided function will be called by the strategy +and must return a dictionary of configuration key values pairs that will be sent to the +client. It must return a dictionary of arbitrary configuration values ``client.fit`` and +``client.evaluate`` functions during each round of federated learning. .. code-block:: python import flwr as fl + def get_on_fit_config_fn() -> Callable[[int], Dict[str, str]]: """Return a function which returns training configurations.""" @@ -64,6 +72,7 @@ It must return a dictionary of arbitrary configuration values :code:`client.fit return fit_config + strategy = fl.server.strategy.FedAvg( fraction_fit=0.1, min_fit_clients=10, @@ -72,18 +81,23 @@ It must return a dictionary of arbitrary configuration values :code:`client.fit ) fl.server.start_server(config=fl.server.ServerConfig(num_rounds=3), strategy=strategy) -The :code:`on_fit_config_fn` can be used to pass arbitrary configuration values from server to client, and potentially change these values each round, for example, to adjust the learning rate. -The client will receive the dictionary returned by the :code:`on_fit_config_fn` in its own :code:`client.fit()` function. +The ``on_fit_config_fn`` can be used to pass arbitrary configuration values from server +to client, and potentially change these values each round, for example, to adjust the +learning rate. The client will receive the dictionary returned by the +``on_fit_config_fn`` in its own ``client.fit()`` function. -Similar to :code:`on_fit_config_fn`, there is also :code:`on_evaluate_config_fn` to customize the configuration sent to :code:`client.evaluate()` +Similar to ``on_fit_config_fn``, there is also ``on_evaluate_config_fn`` to customize +the configuration sent to ``client.evaluate()`` Configuring server-side evaluation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Server-side evaluation can be enabled by passing an evaluation function to :code:`evaluate_fn`. - +Server-side evaluation can be enabled by passing an evaluation function to +``evaluate_fn``. Implement a novel strategy -------------------------- -Writing a fully custom strategy is a bit more involved, but it provides the most flexibility. Read the `Implementing Strategies `_ guide to learn more. +Writing a fully custom strategy is a bit more involved, but it provides the most +flexibility. Read the `Implementing Strategies `_ +guide to learn more. diff --git a/doc/source/index.rst b/doc/source/index.rst index fe996db62ffb..197599d595a8 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -2,15 +2,16 @@ Flower Framework Documentation ============================== .. meta:: - :description: Check out the documentation of the main Flower Framework enabling easy Python development for Federated Learning. - -Welcome to Flower's documentation. `Flower `_ is a friendly federated learning framework. + :description: Check out the documentation of the main Flower Framework enabling easy Python development for Federated Learning. +Welcome to Flower's documentation. `Flower `_ is a friendly federated +learning framework. Join the Flower Community ------------------------- -The Flower Community is growing quickly - we're a friendly group of researchers, engineers, students, professionals, academics, and other enthusiasts. +The Flower Community is growing quickly - we're a friendly group of researchers, +engineers, students, professionals, academics, and other enthusiasts. .. button-link:: https://flower.ai/join-slack :color: primary @@ -18,13 +19,12 @@ The Flower Community is growing quickly - we're a friendly group of researchers, Join us on Slack - Flower Framework ---------------- -The user guide is targeted at researchers and developers who want to use Flower -to bring existing machine learning workloads into a federated setting. One of -Flower's design goals was to make this simple. Read on to learn more. +The user guide is targeted at researchers and developers who want to use Flower to bring +existing machine learning workloads into a federated setting. One of Flower's design +goals was to make this simple. Read on to learn more. Tutorials ~~~~~~~~~ @@ -32,44 +32,50 @@ Tutorials A learning-oriented series of federated learning tutorials, the best place to start. .. toctree:: - :maxdepth: 1 - :caption: Tutorial + :maxdepth: 1 + :caption: Tutorial - tutorial-series-what-is-federated-learning - tutorial-series-get-started-with-flower-pytorch - tutorial-series-use-a-federated-learning-strategy-pytorch - tutorial-series-build-a-strategy-from-scratch-pytorch - tutorial-series-customize-the-client-pytorch + tutorial-series-what-is-federated-learning + tutorial-series-get-started-with-flower-pytorch + tutorial-series-use-a-federated-learning-strategy-pytorch + tutorial-series-build-a-strategy-from-scratch-pytorch + tutorial-series-customize-the-client-pytorch .. toctree:: - :maxdepth: 1 - :caption: Quickstart tutorials - :hidden: - - tutorial-quickstart-pytorch - tutorial-quickstart-tensorflow - tutorial-quickstart-mlx - tutorial-quickstart-huggingface - tutorial-quickstart-jax - tutorial-quickstart-pandas - tutorial-quickstart-fastai - tutorial-quickstart-pytorch-lightning - tutorial-quickstart-scikitlearn - tutorial-quickstart-xgboost - tutorial-quickstart-android - tutorial-quickstart-ios - -QUICKSTART TUTORIALS: :doc:`PyTorch ` | :doc:`TensorFlow ` | :doc:`MLX ` | :doc:`🤗 Transformers ` | :doc:`JAX ` | :doc:`Pandas ` | :doc:`fastai ` | :doc:`PyTorch Lightning ` | :doc:`scikit-learn ` | :doc:`XGBoost ` | :doc:`Android ` | :doc:`iOS ` + :maxdepth: 1 + :caption: Quickstart tutorials + :hidden: + + tutorial-quickstart-pytorch + tutorial-quickstart-tensorflow + tutorial-quickstart-mlx + tutorial-quickstart-huggingface + tutorial-quickstart-jax + tutorial-quickstart-pandas + tutorial-quickstart-fastai + tutorial-quickstart-pytorch-lightning + tutorial-quickstart-scikitlearn + tutorial-quickstart-xgboost + tutorial-quickstart-android + tutorial-quickstart-ios + +QUICKSTART TUTORIALS: :doc:`PyTorch ` | :doc:`TensorFlow +` | :doc:`MLX ` | :doc:`🤗 +Transformers ` | :doc:`JAX ` | +:doc:`Pandas ` | :doc:`fastai ` +| :doc:`PyTorch Lightning ` | :doc:`scikit-learn +` | :doc:`XGBoost ` | +:doc:`Android ` | :doc:`iOS ` We also made video tutorials for PyTorch: -.. youtube:: jOmmuzMIQ4c - :width: 80% +.. youtube:: jOmmuzMIQ4c + :width: 80% And TensorFlow: -.. youtube:: FGTc2TQq7VM - :width: 80% +.. youtube:: FGTc2TQq7VM + :width: 80% How-to guides ~~~~~~~~~~~~~ @@ -77,45 +83,46 @@ How-to guides Problem-oriented how-to guides show step-by-step how to achieve a specific goal. .. toctree:: - :maxdepth: 1 - :caption: How-to guides - - how-to-install-flower - how-to-configure-clients - how-to-use-strategies - how-to-implement-strategies - how-to-aggregate-evaluation-results - how-to-save-and-load-model-checkpoints - how-to-run-simulations - how-to-monitor-simulation - how-to-configure-logging - how-to-enable-ssl-connections - how-to-use-built-in-mods - how-to-use-differential-privacy - how-to-authenticate-supernodes - docker/index - how-to-upgrade-to-flower-1.0 - how-to-upgrade-to-flower-next + :maxdepth: 1 + :caption: How-to guides + + how-to-install-flower + how-to-configure-clients + how-to-use-strategies + how-to-implement-strategies + how-to-aggregate-evaluation-results + how-to-save-and-load-model-checkpoints + how-to-run-simulations + how-to-monitor-simulation + how-to-configure-logging + how-to-enable-ssl-connections + how-to-use-built-in-mods + how-to-use-differential-privacy + how-to-authenticate-supernodes + docker/index + how-to-upgrade-to-flower-1.0 + how-to-upgrade-to-flower-next .. toctree:: - :maxdepth: 1 - :caption: Legacy example guides + :maxdepth: 1 + :caption: Legacy example guides - example-pytorch-from-centralized-to-federated - example-fedbn-pytorch-from-centralized-to-federated + example-pytorch-from-centralized-to-federated + example-fedbn-pytorch-from-centralized-to-federated Explanations ~~~~~~~~~~~~ -Understanding-oriented concept guides explain and discuss key topics and underlying ideas behind Flower and collaborative AI. +Understanding-oriented concept guides explain and discuss key topics and underlying +ideas behind Flower and collaborative AI. .. toctree:: - :maxdepth: 1 - :caption: Explanations + :maxdepth: 1 + :caption: Explanations - explanation-federated-evaluation - explanation-differential-privacy - explanation-flower-architecture + explanation-federated-evaluation + explanation-differential-privacy + explanation-flower-architecture References ~~~~~~~~~~ @@ -123,71 +130,77 @@ References Information-oriented API reference and other reference material. .. autosummary:: - :toctree: ref-api - :template: autosummary/module.rst - :caption: API reference - :recursive: + :toctree: ref-api + :template: autosummary/module.rst + :caption: API reference + :recursive: - flwr + flwr .. toctree:: - :maxdepth: 2 + :maxdepth: 2 - ref-api-cli + ref-api-cli .. toctree:: - :maxdepth: 1 - :caption: Reference docs - - ref-example-projects - ref-telemetry - ref-changelog - ref-faq + :maxdepth: 1 + :caption: Reference docs + ref-example-projects + ref-telemetry + ref-changelog + ref-faq Contributor docs ---------------- -The Flower community welcomes contributions. The following docs are intended to help along the way. - +The Flower community welcomes contributions. The following docs are intended to help +along the way. .. toctree:: - :maxdepth: 1 - :caption: Contributor tutorials + :maxdepth: 1 + :caption: Contributor tutorials - contributor-tutorial-contribute-on-github - contributor-tutorial-get-started-as-a-contributor + contributor-tutorial-contribute-on-github + contributor-tutorial-get-started-as-a-contributor .. toctree:: - :maxdepth: 1 - :caption: Contributor how-to guides + :maxdepth: 1 + :caption: Contributor how-to guides - contributor-how-to-install-development-versions - contributor-how-to-set-up-a-virtual-env - contributor-how-to-develop-in-vscode-dev-containers - contributor-how-to-write-documentation - contributor-how-to-release-flower - contributor-how-to-contribute-translations - contributor-how-to-build-docker-images + contributor-how-to-install-development-versions + contributor-how-to-set-up-a-virtual-env + contributor-how-to-develop-in-vscode-dev-containers + contributor-how-to-write-documentation + contributor-how-to-release-flower + contributor-how-to-contribute-translations + contributor-how-to-build-docker-images .. toctree:: - :maxdepth: 1 - :caption: Contributor explanations + :maxdepth: 1 + :caption: Contributor explanations - contributor-explanation-public-and-private-apis + contributor-explanation-public-and-private-apis .. toctree:: - :maxdepth: 1 - :caption: Contributor references + :maxdepth: 1 + :caption: Contributor references + + fed/index + contributor-ref-good-first-contributions + contributor-ref-secure-aggregation-protocols + +.. + Indices and tables - fed/index - contributor-ref-good-first-contributions - contributor-ref-secure-aggregation-protocols +.. + ------------------ +.. + * :ref:`genindex` -.. Indices and tables -.. ------------------ +.. + * :ref:`modindex` -.. * :ref:`genindex` -.. * :ref:`modindex` -.. * :ref:`search` +.. + * :ref:`search` diff --git a/doc/source/ref-api-cli.rst b/doc/source/ref-api-cli.rst index 95664b2f490a..e95132bbadba 100644 --- a/doc/source/ref-api-cli.rst +++ b/doc/source/ref-api-cli.rst @@ -4,64 +4,66 @@ Flower CLI reference .. _flwr-apiref: flwr CLI -~~~~~~~~ +-------- .. click:: flwr.cli.app:typer_click_object - :prog: flwr - :nested: full + :prog: flwr + :nested: full .. _flower-simulation-apiref: flower-simulation -~~~~~~~~~~~~~~~~~ +----------------- .. argparse:: - :module: flwr.simulation.run_simulation - :func: _parse_args_run_simulation - :prog: flower-simulation + :module: flwr.simulation.run_simulation + :func: _parse_args_run_simulation + :prog: flower-simulation .. _flower-superlink-apiref: flower-superlink -~~~~~~~~~~~~~~~~ +---------------- .. argparse:: - :module: flwr.server.app - :func: _parse_args_run_superlink - :prog: flower-superlink + :module: flwr.server.app + :func: _parse_args_run_superlink + :prog: flower-superlink .. _flower-supernode-apiref: flower-supernode -~~~~~~~~~~~~~~~~~ +---------------- .. argparse:: - :module: flwr.client.supernode.app - :func: _parse_args_run_supernode - :prog: flower-supernode + :module: flwr.client.supernode.app + :func: _parse_args_run_supernode + :prog: flower-supernode .. _flower-server-app-apiref: flower-server-app -~~~~~~~~~~~~~~~~~ +----------------- .. note:: - Note that since version :code:`1.11.0`, :code:`flower-server-app` no longer supports passing a reference to a `ServerApp` attribute. - Instead, you need to pass the path to Flower app via the argument :code:`--app`. - This is the path to a directory containing a `pyproject.toml`. - You can create a valid Flower app by executing :code:`flwr new` and following the prompt. + + Note that since version ``1.11.0``, ``flower-server-app`` no longer supports passing + a reference to a `ServerApp` attribute. Instead, you need to pass the path to Flower + app via the argument ``--app``. This is the path to a directory containing a + `pyproject.toml`. You can create a valid Flower app by executing ``flwr new`` and + following the prompt. .. argparse:: - :module: flwr.server.run_serverapp - :func: _parse_args_run_server_app - :prog: flower-server-app + :module: flwr.server.run_serverapp + :func: _parse_args_run_server_app + :prog: flower-server-app .. _flower-superexec-apiref: flower-superexec -~~~~~~~~~~~~~~~~~ +---------------- .. argparse:: - :module: flwr.superexec.app - :func: _parse_args_run_superexec - :prog: flower-superexec \ No newline at end of file + :module: flwr.superexec.app + :func: _parse_args_run_superexec + :prog: flower-superexec diff --git a/doc/source/ref-example-projects.rst b/doc/source/ref-example-projects.rst index 597e3a596c51..4f0a3014e1d4 100644 --- a/doc/source/ref-example-projects.rst +++ b/doc/source/ref-example-projects.rst @@ -1,48 +1,52 @@ Example projects ================ -Flower comes with a number of usage examples. The examples demonstrate how -Flower can be used to federate different kinds of existing machine learning -pipelines, usually leveraging popular machine learning frameworks such as -`PyTorch `_ or -`TensorFlow `_. +Flower comes with a number of usage examples. The examples demonstrate how Flower can be +used to federate different kinds of existing machine learning pipelines, usually +leveraging popular machine learning frameworks such as `PyTorch `_ +or `TensorFlow `_. The following examples are available as standalone projects. + Quickstart TensorFlow/Keras --------------------------- -The TensorFlow/Keras quickstart example shows CIFAR-10 image classification -with MobileNetV2: +The TensorFlow/Keras quickstart example shows CIFAR-10 image classification with +MobileNetV2: -- `Quickstart TensorFlow (Code) `_ +- `Quickstart TensorFlow (Code) + `_ - :doc:`Quickstart TensorFlow (Tutorial) ` -- `Quickstart TensorFlow (Blog Post) `_ - +- `Quickstart TensorFlow (Blog Post) + `_ Quickstart PyTorch ------------------ -The PyTorch quickstart example shows CIFAR-10 image classification -with a simple Convolutional Neural Network: +The PyTorch quickstart example shows CIFAR-10 image classification with a simple +Convolutional Neural Network: -- `Quickstart PyTorch (Code) `_ +- `Quickstart PyTorch (Code) + `_ - :doc:`Quickstart PyTorch (Tutorial) ` - PyTorch: From Centralized To Federated -------------------------------------- This example shows how a regular PyTorch project can be federated using Flower: -- `PyTorch: From Centralized To Federated (Code) `_ -- :doc:`PyTorch: From Centralized To Federated (Tutorial) ` - +- `PyTorch: From Centralized To Federated (Code) + `_ +- :doc:`PyTorch: From Centralized To Federated (Tutorial) + ` Federated Learning on Raspberry Pi and Nvidia Jetson ---------------------------------------------------- -This example shows how Flower can be used to build a federated learning system that run across Raspberry Pi and Nvidia Jetson: - -- `Federated Learning on Raspberry Pi and Nvidia Jetson (Code) `_ -- `Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) `_ +This example shows how Flower can be used to build a federated learning system that run +across Raspberry Pi and Nvidia Jetson: +- `Federated Learning on Raspberry Pi and Nvidia Jetson (Code) + `_ +- `Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) + `_ diff --git a/doc/source/ref-faq.rst b/doc/source/ref-faq.rst index e3bd754d481c..0bd004f81858 100644 --- a/doc/source/ref-faq.rst +++ b/doc/source/ref-faq.rst @@ -1,7 +1,8 @@ FAQ === -This page collects answers to commonly asked questions about Federated Learning with Flower. +This page collects answers to commonly asked questions about Federated Learning with +Flower. .. dropdown:: :fa:`eye,mr-1` Can Flower run on Jupyter Notebooks / Google Colab? diff --git a/doc/source/tutorial-quickstart-android.rst b/doc/source/tutorial-quickstart-android.rst index 9177236d5a7c..f2691203078c 100644 --- a/doc/source/tutorial-quickstart-android.rst +++ b/doc/source/tutorial-quickstart-android.rst @@ -1,12 +1,12 @@ .. _quickstart-android: - Quickstart Android ================== .. meta:: - :description: Read this Federated Learning quickstart tutorial for creating an Android app using Flower. + :description: Read this Federated Learning quickstart tutorial for creating an Android app using Flower. Let's build a federated learning system using TFLite and Flower on Android! -Please refer to the `full code example `_ to learn more. +Please refer to the `full code example +`_ to learn more. diff --git a/doc/source/tutorial-quickstart-fastai.rst b/doc/source/tutorial-quickstart-fastai.rst index e42328e6f712..d52c570b0195 100644 --- a/doc/source/tutorial-quickstart-fastai.rst +++ b/doc/source/tutorial-quickstart-fastai.rst @@ -1,113 +1,110 @@ .. _quickstart-fastai: -################### - Quickstart fastai -################### +Quickstart fastai +================= -In this federated learning tutorial we will learn how to train a -SqueezeNet model on MNIST using Flower and fastai. It is recommended to -create a virtual environment and run everything within a -:doc:`virtualenv `. +In this federated learning tutorial we will learn how to train a SqueezeNet model on +MNIST using Flower and fastai. It is recommended to create a virtual environment and run +everything within a :doc:`virtualenv `. Then, clone the code example directly from GitHub: -.. code:: shell +.. code-block:: shell - git clone --depth=1 https://github.com/adap/flower.git _tmp \ - && mv _tmp/examples/quickstart-fastai . \ - && rm -rf _tmp && cd quickstart-fastai + git clone --depth=1 https://github.com/adap/flower.git _tmp \ + && mv _tmp/examples/quickstart-fastai . \ + && rm -rf _tmp && cd quickstart-fastai -This will create a new directory called `quickstart-fastai` containing -the following files: +This will create a new directory called `quickstart-fastai` containing the following +files: -.. code:: shell +.. code-block:: shell - quickstart-fastai - ├── fastai_example - │ ├── client_app.py # Defines your ClientApp - │ ├── server_app.py # Defines your ServerApp - │ └── task.py # Defines your model, training and data loading - ├── pyproject.toml # Project metadata like dependencies and configs - └── README.md + quickstart-fastai + ├── fastai_example + │ ├── client_app.py # Defines your ClientApp + │ ├── server_app.py # Defines your ServerApp + │ └── task.py # Defines your model, training and data loading + ├── pyproject.toml # Project metadata like dependencies and configs + └── README.md Next, activate your environment, then run: -.. code:: shell +.. code-block:: shell - # Navigate to the example directory - $ cd path/to/quickstart-fastai + # Navigate to the example directory + $ cd path/to/quickstart-fastai - # Install project and dependencies - $ pip install -e . + # Install project and dependencies + $ pip install -e . -This example by default runs the Flower Simulation Engine, creating a -federation of 10 nodes using `FedAvg +This example by default runs the Flower Simulation Engine, creating a federation of 10 +nodes using `FedAvg `_ -as the aggregation strategy. The dataset will be partitioned using -Flower Dataset's `IidPartitioner +as the aggregation strategy. The dataset will be partitioned using Flower Dataset's +`IidPartitioner `_. Let's run the project: -.. code:: shell +.. code-block:: shell - # Run with default arguments - $ flwr run . + # Run with default arguments + $ flwr run . With default arguments you will see an output like this one: -.. code:: shell - - Loading project configuration... - Success - INFO : Starting Flower ServerApp, config: num_rounds=3, no round_timeout - INFO : - INFO : [INIT] - INFO : Using initial global parameters provided by strategy - INFO : Starting evaluation of initial global parameters - INFO : Evaluation returned no results (`None`) - INFO : - INFO : [ROUND 1] - INFO : configure_fit: strategy sampled 5 clients (out of 10) - INFO : aggregate_fit: received 5 results and 0 failures - WARNING : No fit_metrics_aggregation_fn provided - INFO : configure_evaluate: strategy sampled 5 clients (out of 10) - INFO : aggregate_evaluate: received 5 results and 0 failures - INFO : - INFO : [ROUND 2] - INFO : configure_fit: strategy sampled 5 clients (out of 10) - INFO : aggregate_fit: received 5 results and 0 failures - INFO : configure_evaluate: strategy sampled 5 clients (out of 10) - INFO : aggregate_evaluate: received 5 results and 0 failures - INFO : - INFO : [ROUND 3] - INFO : configure_fit: strategy sampled 5 clients (out of 10) - INFO : aggregate_fit: received 5 results and 0 failures - INFO : configure_evaluate: strategy sampled 5 clients (out of 10) - INFO : aggregate_evaluate: received 5 results and 0 failures - INFO : - INFO : [SUMMARY] - INFO : Run finished 3 round(s) in 143.02s - INFO : History (loss, distributed): - INFO : round 1: 2.699497365951538 - INFO : round 2: 0.9549586296081543 - INFO : round 3: 0.6627192616462707 - INFO : History (metrics, distributed, evaluate): - INFO : {'accuracy': [(1, 0.09766666889190674), - INFO : (2, 0.6948333323001862), - INFO : (3, 0.7721666693687439)]} - INFO : - -You can also override the parameters defined in the -``[tool.flwr.app.config]`` section in ``pyproject.toml`` like this: - -.. code:: shell - - # Override some arguments - $ flwr run . --run-config num-server-rounds=5 +.. code-block:: shell + + Loading project configuration... + Success + INFO : Starting Flower ServerApp, config: num_rounds=3, no round_timeout + INFO : + INFO : [INIT] + INFO : Using initial global parameters provided by strategy + INFO : Starting evaluation of initial global parameters + INFO : Evaluation returned no results (`None`) + INFO : + INFO : [ROUND 1] + INFO : configure_fit: strategy sampled 5 clients (out of 10) + INFO : aggregate_fit: received 5 results and 0 failures + WARNING : No fit_metrics_aggregation_fn provided + INFO : configure_evaluate: strategy sampled 5 clients (out of 10) + INFO : aggregate_evaluate: received 5 results and 0 failures + INFO : + INFO : [ROUND 2] + INFO : configure_fit: strategy sampled 5 clients (out of 10) + INFO : aggregate_fit: received 5 results and 0 failures + INFO : configure_evaluate: strategy sampled 5 clients (out of 10) + INFO : aggregate_evaluate: received 5 results and 0 failures + INFO : + INFO : [ROUND 3] + INFO : configure_fit: strategy sampled 5 clients (out of 10) + INFO : aggregate_fit: received 5 results and 0 failures + INFO : configure_evaluate: strategy sampled 5 clients (out of 10) + INFO : aggregate_evaluate: received 5 results and 0 failures + INFO : + INFO : [SUMMARY] + INFO : Run finished 3 round(s) in 143.02s + INFO : History (loss, distributed): + INFO : round 1: 2.699497365951538 + INFO : round 2: 0.9549586296081543 + INFO : round 3: 0.6627192616462707 + INFO : History (metrics, distributed, evaluate): + INFO : {'accuracy': [(1, 0.09766666889190674), + INFO : (2, 0.6948333323001862), + INFO : (3, 0.7721666693687439)]} + INFO : + +You can also override the parameters defined in the ``[tool.flwr.app.config]`` section +in ``pyproject.toml`` like this: + +.. code-block:: shell + + # Override some arguments + $ flwr run . --run-config num-server-rounds=5 .. note:: - Check the `source code - `_ - of this tutorial in ``examples/quickstart-fasai`` in the Flower - GitHub repository. + Check the `source code + `_ of this + tutorial in ``examples/quickstart-fasai`` in the Flower GitHub repository. diff --git a/doc/source/tutorial-quickstart-huggingface.rst b/doc/source/tutorial-quickstart-huggingface.rst index e5caa3b19dd6..3c9d3981e587 100644 --- a/doc/source/tutorial-quickstart-huggingface.rst +++ b/doc/source/tutorial-quickstart-huggingface.rst @@ -1,419 +1,385 @@ .. _quickstart-huggingface: -########################### - Quickstart 🤗 Transformers -########################### +Quickstart 🤗 Transformers +========================== -In this federated learning tutorial we will learn how to train a large -language model (LLM) on the `IMDB -`_ dataset using -Flower and the 🤗 Hugging Face Transformers library. It is recommended to -create a virtual environment and run everything within a -:doc:`virtualenv `. +In this federated learning tutorial we will learn how to train a large language model +(LLM) on the `IMDB `_ dataset using +Flower and the 🤗 Hugging Face Transformers library. It is recommended to create a +virtual environment and run everything within a :doc:`virtualenv +`. -Let's use ``flwr new`` to create a complete Flower+🤗 Hugging Face -project. It will generate all the files needed to run, by default with -the Flower Simulation Engine, a federation of 10 nodes using |fedavg|_ -The dataset will be partitioned using |flowerdatasets|_'s -|iidpartitioner|_. +Let's use ``flwr new`` to create a complete Flower+🤗 Hugging Face project. It will +generate all the files needed to run, by default with the Flower Simulation Engine, a +federation of 10 nodes using |fedavg|_ The dataset will be partitioned using +|flowerdatasets|_'s |iidpartitioner|_. -Now that we have a rough idea of what this example is about, let's get -started. First, install Flower in your new environment: +Now that we have a rough idea of what this example is about, let's get started. First, +install Flower in your new environment: -.. code:: shell +.. code-block:: shell - # In a new Python environment - $ pip install flwr + # In a new Python environment + $ pip install flwr -Then, run the command below. You will be prompted to select one of the -available templates (choose ``HuggingFace``), give a name to your -project, and type in your developer name: +Then, run the command below. You will be prompted to select one of the available +templates (choose ``HuggingFace``), give a name to your project, and type in your +developer name: -.. code:: shell +.. code-block:: shell - $ flwr new + $ flwr new -After running it you'll notice a new directory with your project name -has been created. It should have the following structure: +After running it you'll notice a new directory with your project name has been created. +It should have the following structure: -.. code:: shell +.. code-block:: shell - - ├── - │ ├── __init__.py - │ ├── client_app.py # Defines your ClientApp - │ ├── server_app.py # Defines your ServerApp - │ └── task.py # Defines your model, training and data loading - ├── pyproject.toml # Project metadata like dependencies and configs - └── README.md + + ├── + │ ├── __init__.py + │ ├── client_app.py # Defines your ClientApp + │ ├── server_app.py # Defines your ServerApp + │ └── task.py # Defines your model, training and data loading + ├── pyproject.toml # Project metadata like dependencies and configs + └── README.md -If you haven't yet installed the project and its dependencies, you can -do so by: +If you haven't yet installed the project and its dependencies, you can do so by: -.. code:: shell +.. code-block:: shell - # From the directory where your pyproject.toml is - $ pip install -e . + # From the directory where your pyproject.toml is + $ pip install -e . To run the project, do: -.. code:: shell +.. code-block:: shell - # Run with default arguments - $ flwr run . + # Run with default arguments + $ flwr run . With default arguments you will see an output like this one: -.. code:: shell - - Loading project configuration... - Success - INFO : Starting Flower ServerApp, config: num_rounds=3, no round_timeout - INFO : - INFO : [INIT] - INFO : Using initial global parameters provided by strategy - INFO : Starting evaluation of initial global parameters - INFO : Evaluation returned no results (`None`) - INFO : - INFO : [ROUND 1] - INFO : configure_fit: strategy sampled 2 clients (out of 10) - INFO : aggregate_fit: received 2 results and 0 failures - WARNING : No fit_metrics_aggregation_fn provided - INFO : configure_evaluate: strategy sampled 10 clients (out of 10) - INFO : aggregate_evaluate: received 10 results and 0 failures - WARNING : No evaluate_metrics_aggregation_fn provided - INFO : - INFO : [ROUND 2] - INFO : configure_fit: strategy sampled 5 clients (out of 10) - INFO : aggregate_fit: received 5 results and 0 failures - INFO : configure_evaluate: strategy sampled 10 clients (out of 10) - INFO : aggregate_evaluate: received 10 results and 0 failures - INFO : - INFO : [ROUND 3] - INFO : configure_fit: strategy sampled 5 clients (out of 10) - INFO : aggregate_fit: received 5 results and 0 failures - INFO : configure_evaluate: strategy sampled 10 clients (out of 10) - INFO : aggregate_evaluate: received 10 results and 0 failures - INFO : - INFO : [SUMMARY] - INFO : Run finished 3 round(s) in 249.11s - INFO : History (loss, distributed): - INFO : round 1: 0.02111011856794357 - INFO : round 2: 0.019722302150726317 - INFO : round 3: 0.018227258533239362 - INFO : +.. code-block:: shell + + Loading project configuration... + Success + INFO : Starting Flower ServerApp, config: num_rounds=3, no round_timeout + INFO : + INFO : [INIT] + INFO : Using initial global parameters provided by strategy + INFO : Starting evaluation of initial global parameters + INFO : Evaluation returned no results (`None`) + INFO : + INFO : [ROUND 1] + INFO : configure_fit: strategy sampled 2 clients (out of 10) + INFO : aggregate_fit: received 2 results and 0 failures + WARNING : No fit_metrics_aggregation_fn provided + INFO : configure_evaluate: strategy sampled 10 clients (out of 10) + INFO : aggregate_evaluate: received 10 results and 0 failures + WARNING : No evaluate_metrics_aggregation_fn provided + INFO : + INFO : [ROUND 2] + INFO : configure_fit: strategy sampled 5 clients (out of 10) + INFO : aggregate_fit: received 5 results and 0 failures + INFO : configure_evaluate: strategy sampled 10 clients (out of 10) + INFO : aggregate_evaluate: received 10 results and 0 failures + INFO : + INFO : [ROUND 3] + INFO : configure_fit: strategy sampled 5 clients (out of 10) + INFO : aggregate_fit: received 5 results and 0 failures + INFO : configure_evaluate: strategy sampled 10 clients (out of 10) + INFO : aggregate_evaluate: received 10 results and 0 failures + INFO : + INFO : [SUMMARY] + INFO : Run finished 3 round(s) in 249.11s + INFO : History (loss, distributed): + INFO : round 1: 0.02111011856794357 + INFO : round 2: 0.019722302150726317 + INFO : round 3: 0.018227258533239362 + INFO : You can also run the project with GPU as follows: -.. code:: shell - - # Run with default arguments - $ flwr run . localhost-gpu - -This will use the default arguments where each ``ClientApp`` will use 2 -CPUs and at most 4 ``ClientApp``\s will run in a given GPU. - -You can also override the parameters defined in the -``[tool.flwr.app.config]`` section in ``pyproject.toml`` like this: - -.. code:: shell - - # Override some arguments - $ flwr run . --run-config "num-server-rounds=5 fraction-fit=0.2" - -What follows is an explanation of each component in the project you just -created: dataset partition, the model, defining the ``ClientApp`` and -defining the ``ServerApp``. - -********** - The Data -********** - -This tutorial uses |flowerdatasets|_ to easily download and partition -the `IMDB `_ dataset. -In this example you'll make use of the |iidpartitioner|_ to generate -``num_partitions`` partitions. You can choose |otherpartitioners|_ -available in Flower Datasets. To tokenize the text, we will also load -the tokenizer from the pre-trained Transformer model that we'll use -during training - more on that in the next section. Each ``ClientApp`` -will call this function to create dataloaders with the data that -correspond to their data partition. - -.. code:: python - - partitioner = IidPartitioner(num_partitions=num_partitions) - fds = FederatedDataset( - dataset="stanfordnlp/imdb", - partitioners={"train": partitioner}, - ) - partition = fds.load_partition(partition_id) - # Divide data: 80% train, 20% test - partition_train_test = partition.train_test_split(test_size=0.2, seed=42) - - tokenizer = AutoTokenizer.from_pretrained(model_name) - - def tokenize_function(examples): - return tokenizer( - examples["text"], truncation=True, add_special_tokens=True, max_length=512 - ) - - partition_train_test = partition_train_test.map(tokenize_function, batched=True) - partition_train_test = partition_train_test.remove_columns("text") - partition_train_test = partition_train_test.rename_column("label", "labels") - - data_collator = DataCollatorWithPadding(tokenizer=tokenizer) - trainloader = DataLoader( - partition_train_test["train"], - shuffle=True, - batch_size=32, - collate_fn=data_collator, - ) - - testloader = DataLoader( - partition_train_test["test"], batch_size=32, collate_fn=data_collator - ) - -*********** - The Model -*********** - -We will leverage 🤗 Hugging Face to federate the training of language -models over multiple clients using Flower. More specifically, we will -fine-tune a pre-trained Transformer model (|berttiny|_) for sequence -classification over the dataset of IMDB ratings. The end goal is to -detect if a movie rating is positive or negative. If you have access to -larger GPUs, feel free to use larger models! - -.. code:: python - - net = AutoModelForSequenceClassification.from_pretrained( - model_name, num_labels=num_labels - ) - -Note that here, ``model_name`` is a string that will be loaded from the -``Context`` in the ClientApp and ServerApp. - -In addition to loading the pretrained model weights and architecture, we -also include two utility functions to perform both training (i.e. -``train()``) and evaluation (i.e. ``test()``) using the above model. -These functions should look fairly familiar if you have some prior -experience with PyTorch. Note these functions do not have anything -specific to Flower. That being said, the training function will normally -be called, as we'll see later, from a Flower client passing its own -data. In summary, your clients can use standard training/testing -functions to perform local training or evaluation: - -.. code:: python - - def train(net, trainloader, epochs, device): - optimizer = AdamW(net.parameters(), lr=5e-5) - net.train() - for _ in range(epochs): - for batch in trainloader: - batch = {k: v.to(device) for k, v in batch.items()} - outputs = net(**batch) - loss = outputs.loss - loss.backward() - optimizer.step() - optimizer.zero_grad() - - - def test(net, testloader, device): - metric = load_metric("accuracy") - loss = 0 - net.eval() - for batch in testloader: - batch = {k: v.to(device) for k, v in batch.items()} - with torch.no_grad(): - outputs = net(**batch) - logits = outputs.logits - loss += outputs.loss.item() - predictions = torch.argmax(logits, dim=-1) - metric.add_batch(predictions=predictions, references=batch["labels"]) - loss /= len(testloader.dataset) - accuracy = metric.compute()["accuracy"] - return loss, accuracy - -*************** - The ClientApp -*************** - -The main changes we have to make to use 🤗 Hugging Face with Flower will -be found in the ``get_weights()`` and ``set_weights()`` functions. Under -the hood, the ``transformers`` library uses PyTorch, which means we can -reuse the ``get_weights()`` and ``set_weights()`` code that we defined -in the :doc:`Quickstart PyTorch ` tutorial. -As a reminder, in ``get_weights()``, PyTorch model parameters are -extracted and represented as a list of NumPy arrays. The -``set_weights()`` function that's the opposite: given a list of NumPy -arrays it applies them to an existing PyTorch model. Doing this in -fairly easy in PyTorch. +.. code-block:: shell -.. note:: - - The specific implementation of ``get_weights()`` and - ``set_weights()`` depends on the type of models you use. The ones - shown below work for a wide range of PyTorch models but you might - need to adjust them if you have more exotic model architectures. - -.. code:: python - - def get_weights(net): - return [val.cpu().numpy() for _, val in net.state_dict().items()] - - def set_weights(net, parameters): - params_dict = zip(net.state_dict().keys(), parameters) - state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) - net.load_state_dict(state_dict, strict=True) - -The rest of the functionality is directly inspired by the centralized -case. The ``fit()`` method in the client trains the model using the -local dataset. Similarly, the ``evaluate()`` method is used to evaluate -the model received on a held-out validation set that the client might -have: - -.. code:: python - - class FlowerClient(NumPyClient): - def __init__(self, net, trainloader, testloader, local_epochs): - self.net = net - self.trainloader = trainloader - self.testloader = testloader - self.local_epochs = local_epochs - self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - self.net.to(self.device) - - def fit(self, parameters, config): - set_weights(self.net, parameters) - train(self.net, self.trainloader, epochs=self.local_epochs, device=self.device) - return get_weights(self.net), len(self.trainloader), {} - - def evaluate(self, parameters, config): - set_weights(self.net, parameters) - loss, accuracy = test(self.net, self.testloader, self.device) - return float(loss), len(self.testloader), {"accuracy": accuracy} - -Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` -defined above by means of a ``client_fn()`` callback. Note that the -`context` enables you to get access to hyperparemeters defined in your -``pyproject.toml`` to configure the run. In this tutorial we access the -``local-epochs`` setting to control the number of epochs a ``ClientApp`` -will perform when running the ``fit()`` method. You could define -additional hyperparameters in ``pyproject.toml`` and access them here. + # Run with default arguments + $ flwr run . localhost-gpu -.. code:: python +This will use the default arguments where each ``ClientApp`` will use 2 CPUs and at most +4 ``ClientApp``\s will run in a given GPU. - def client_fn(context: Context): +You can also override the parameters defined in the ``[tool.flwr.app.config]`` section +in ``pyproject.toml`` like this: - # Get this client's dataset partition - partition_id = context.node_config["partition-id"] - num_partitions = context.node_config["num-partitions"] - model_name = context.run_config["model-name"] - trainloader, valloader = load_data(partition_id, num_partitions, model_name) +.. code-block:: shell - # Load model - num_labels = context.run_config["num-labels"] - net = AutoModelForSequenceClassification.from_pretrained( - model_name, num_labels=num_labels - ) + # Override some arguments + $ flwr run . --run-config "num-server-rounds=5 fraction-fit=0.2" + +What follows is an explanation of each component in the project you just created: +dataset partition, the model, defining the ``ClientApp`` and defining the ``ServerApp``. + +The Data +-------- + +This tutorial uses |flowerdatasets|_ to easily download and partition the `IMDB +`_ dataset. In this example you'll +make use of the |iidpartitioner|_ to generate ``num_partitions`` partitions. You can +choose |otherpartitioners|_ available in Flower Datasets. To tokenize the text, we will +also load the tokenizer from the pre-trained Transformer model that we'll use during +training - more on that in the next section. Each ``ClientApp`` will call this function +to create dataloaders with the data that correspond to their data partition. + +.. code-block:: python + + partitioner = IidPartitioner(num_partitions=num_partitions) + fds = FederatedDataset( + dataset="stanfordnlp/imdb", + partitioners={"train": partitioner}, + ) + partition = fds.load_partition(partition_id) + # Divide data: 80% train, 20% test + partition_train_test = partition.train_test_split(test_size=0.2, seed=42) + + tokenizer = AutoTokenizer.from_pretrained(model_name) + + + def tokenize_function(examples): + return tokenizer( + examples["text"], truncation=True, add_special_tokens=True, max_length=512 + ) + + + partition_train_test = partition_train_test.map(tokenize_function, batched=True) + partition_train_test = partition_train_test.remove_columns("text") + partition_train_test = partition_train_test.rename_column("label", "labels") + + data_collator = DataCollatorWithPadding(tokenizer=tokenizer) + trainloader = DataLoader( + partition_train_test["train"], + shuffle=True, + batch_size=32, + collate_fn=data_collator, + ) + + testloader = DataLoader( + partition_train_test["test"], batch_size=32, collate_fn=data_collator + ) + +The Model +--------- + +We will leverage 🤗 Hugging Face to federate the training of language models over +multiple clients using Flower. More specifically, we will fine-tune a pre-trained +Transformer model (|berttiny|_) for sequence classification over the dataset of IMDB +ratings. The end goal is to detect if a movie rating is positive or negative. If you +have access to larger GPUs, feel free to use larger models! + +.. code-block:: python + + net = AutoModelForSequenceClassification.from_pretrained( + model_name, num_labels=num_labels + ) + +Note that here, ``model_name`` is a string that will be loaded from the ``Context`` in +the ClientApp and ServerApp. + +In addition to loading the pretrained model weights and architecture, we also include +two utility functions to perform both training (i.e. ``train()``) and evaluation (i.e. +``test()``) using the above model. These functions should look fairly familiar if you +have some prior experience with PyTorch. Note these functions do not have anything +specific to Flower. That being said, the training function will normally be called, as +we'll see later, from a Flower client passing its own data. In summary, your clients can +use standard training/testing functions to perform local training or evaluation: + +.. code-block:: python + + def train(net, trainloader, epochs, device): + optimizer = AdamW(net.parameters(), lr=5e-5) + net.train() + for _ in range(epochs): + for batch in trainloader: + batch = {k: v.to(device) for k, v in batch.items()} + outputs = net(**batch) + loss = outputs.loss + loss.backward() + optimizer.step() + optimizer.zero_grad() + + + def test(net, testloader, device): + metric = load_metric("accuracy") + loss = 0 + net.eval() + for batch in testloader: + batch = {k: v.to(device) for k, v in batch.items()} + with torch.no_grad(): + outputs = net(**batch) + logits = outputs.logits + loss += outputs.loss.item() + predictions = torch.argmax(logits, dim=-1) + metric.add_batch(predictions=predictions, references=batch["labels"]) + loss /= len(testloader.dataset) + accuracy = metric.compute()["accuracy"] + return loss, accuracy + +The ClientApp +------------- + +The main changes we have to make to use 🤗 Hugging Face with Flower will be found in the +``get_weights()`` and ``set_weights()`` functions. Under the hood, the ``transformers`` +library uses PyTorch, which means we can reuse the ``get_weights()`` and +``set_weights()`` code that we defined in the :doc:`Quickstart PyTorch +` tutorial. As a reminder, in ``get_weights()``, PyTorch +model parameters are extracted and represented as a list of NumPy arrays. The +``set_weights()`` function that's the opposite: given a list of NumPy arrays it applies +them to an existing PyTorch model. Doing this in fairly easy in PyTorch. - local_epochs = context.run_config["local-epochs"] - - # Return Client instance - return FlowerClient(net, trainloader, valloader, local_epochs).to_client() - - # Flower ClientApp - app = ClientApp(client_fn) +.. note:: -*************** - The ServerApp -*************** + The specific implementation of ``get_weights()`` and ``set_weights()`` depends on + the type of models you use. The ones shown below work for a wide range of PyTorch + models but you might need to adjust them if you have more exotic model + architectures. + +.. code-block:: python + + def get_weights(net): + return [val.cpu().numpy() for _, val in net.state_dict().items()] + + + def set_weights(net, parameters): + params_dict = zip(net.state_dict().keys(), parameters) + state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) + net.load_state_dict(state_dict, strict=True) + +The rest of the functionality is directly inspired by the centralized case. The +``fit()`` method in the client trains the model using the local dataset. Similarly, the +``evaluate()`` method is used to evaluate the model received on a held-out validation +set that the client might have: + +.. code-block:: python + + class FlowerClient(NumPyClient): + def __init__(self, net, trainloader, testloader, local_epochs): + self.net = net + self.trainloader = trainloader + self.testloader = testloader + self.local_epochs = local_epochs + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + self.net.to(self.device) + + def fit(self, parameters, config): + set_weights(self.net, parameters) + train(self.net, self.trainloader, epochs=self.local_epochs, device=self.device) + return get_weights(self.net), len(self.trainloader), {} + + def evaluate(self, parameters, config): + set_weights(self.net, parameters) + loss, accuracy = test(self.net, self.testloader, self.device) + return float(loss), len(self.testloader), {"accuracy": accuracy} + +Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` defined above by +means of a ``client_fn()`` callback. Note that the `context` enables you to get access +to hyperparemeters defined in your ``pyproject.toml`` to configure the run. In this +tutorial we access the ``local-epochs`` setting to control the number of epochs a +``ClientApp`` will perform when running the ``fit()`` method. You could define +additional hyperparameters in ``pyproject.toml`` and access them here. -To construct a ``ServerApp`` we define a ``server_fn()`` callback with -an identical signature to that of ``client_fn()`` but the return type is -|serverappcomponents|_ as opposed to a |client|_ In this example we use -the `FedAvg` strategy. To it we pass a randomly initialized model that -will server as the global model to federated. Note that the value of -``fraction_fit`` is read from the run config. You can find the default -value defined in the ``pyproject.toml``. +.. code-block:: python -.. code:: python + def client_fn(context: Context): - def server_fn(context: Context): - # Read from config - num_rounds = context.run_config["num-server-rounds"] - fraction_fit = context.run_config["fraction-fit"] + # Get this client's dataset partition + partition_id = context.node_config["partition-id"] + num_partitions = context.node_config["num-partitions"] + model_name = context.run_config["model-name"] + trainloader, valloader = load_data(partition_id, num_partitions, model_name) - # Initialize global model - model_name = context.run_config["model-name"] - num_labels = context.run_config["num-labels"] - net = AutoModelForSequenceClassification.from_pretrained( - model_name, num_labels=num_labels - ) + # Load model + num_labels = context.run_config["num-labels"] + net = AutoModelForSequenceClassification.from_pretrained( + model_name, num_labels=num_labels + ) - weights = get_weights(net) - initial_parameters = ndarrays_to_parameters(weights) + local_epochs = context.run_config["local-epochs"] - # Define strategy - strategy = FedAvg( - fraction_fit=fraction_fit, - fraction_evaluate=1.0, - initial_parameters=initial_parameters, - ) - config = ServerConfig(num_rounds=num_rounds) + # Return Client instance + return FlowerClient(net, trainloader, valloader, local_epochs).to_client() - return ServerAppComponents(strategy=strategy, config=config) + # Flower ClientApp + app = ClientApp(client_fn) - # Create ServerApp - app = ServerApp(server_fn=server_fn) +The ServerApp +------------- -Congratulations! You've successfully built and run your first federated -learning system for an LLM. +To construct a ``ServerApp`` we define a ``server_fn()`` callback with an identical +signature to that of ``client_fn()`` but the return type is |serverappcomponents|_ as +opposed to a |client|_ In this example we use the `FedAvg` strategy. To it we pass a +randomly initialized model that will server as the global model to federated. Note that +the value of ``fraction_fit`` is read from the run config. You can find the default +value defined in the ``pyproject.toml``. -.. note:: +.. code-block:: python - Check the source code of the extended version of this tutorial in - |quickstart_hf_link|_ in the Flower GitHub repository. For a - comprehensive example of a federated fine-tuning of an LLM with - Flower, refer to the |flowertune|_ example in the Flower GitHub - repository. + def server_fn(context: Context): + # Read from config + num_rounds = context.run_config["num-server-rounds"] + fraction_fit = context.run_config["fraction-fit"] -.. |quickstart_hf_link| replace:: + # Initialize global model + model_name = context.run_config["model-name"] + num_labels = context.run_config["num-labels"] + net = AutoModelForSequenceClassification.from_pretrained( + model_name, num_labels=num_labels + ) - ``examples/quickstart-huggingface`` + weights = get_weights(net) + initial_parameters = ndarrays_to_parameters(weights) -.. |fedavg| replace:: + # Define strategy + strategy = FedAvg( + fraction_fit=fraction_fit, + fraction_evaluate=1.0, + initial_parameters=initial_parameters, + ) + config = ServerConfig(num_rounds=num_rounds) - ``FedAvg`` + return ServerAppComponents(strategy=strategy, config=config) -.. |iidpartitioner| replace:: - ``IidPartitioner`` + # Create ServerApp + app = ServerApp(server_fn=server_fn) -.. |otherpartitioners| replace:: +Congratulations! You've successfully built and run your first federated learning system +for an LLM. - other partitioners +.. note:: -.. |berttiny| replace:: + Check the source code of the extended version of this tutorial in + |quickstart_hf_link|_ in the Flower GitHub repository. For a comprehensive example + of a federated fine-tuning of an LLM with Flower, refer to the |flowertune|_ example + in the Flower GitHub repository. - ``bert-tiny`` +.. |quickstart_hf_link| replace:: ``examples/quickstart-huggingface`` -.. |serverappcomponents| replace:: +.. |fedavg| replace:: ``FedAvg`` - ``ServerAppComponents`` +.. |iidpartitioner| replace:: ``IidPartitioner`` -.. |client| replace:: +.. |otherpartitioners| replace:: other partitioners - ``Client`` +.. |berttiny| replace:: ``bert-tiny`` -.. |flowerdatasets| replace:: +.. |serverappcomponents| replace:: ``ServerAppComponents`` - Flower Datasets +.. |client| replace:: ``Client`` -.. |flowertune| replace:: +.. |flowerdatasets| replace:: Flower Datasets - FlowerTune LLM +.. |flowertune| replace:: FlowerTune LLM .. _berttiny: https://huggingface.co/prajjwal1/bert-tiny @@ -434,4 +400,4 @@ learning system for an LLM. .. _serverappcomponents: ref-api/flwr.server.ServerAppComponents.html#serverappcomponents .. meta:: - :description: Check out this Federating Learning quickstart tutorial for using Flower with 🤗 HuggingFace Transformers in order to fine-tune an LLM. + :description: Check out this Federating Learning quickstart tutorial for using Flower with 🤗 HuggingFace Transformers in order to fine-tune an LLM. diff --git a/doc/source/tutorial-quickstart-ios.rst b/doc/source/tutorial-quickstart-ios.rst index e4315ce569fb..8a9250f8dfb0 100644 --- a/doc/source/tutorial-quickstart-ios.rst +++ b/doc/source/tutorial-quickstart-ios.rst @@ -1,136 +1,155 @@ .. _quickstart-ios: - Quickstart iOS ============== .. meta:: - :description: Read this Federated Learning quickstart tutorial for creating an iOS app using Flower to train a neural network on MNIST. + :description: Read this Federated Learning quickstart tutorial for creating an iOS app using Flower to train a neural network on MNIST. -In this tutorial we will learn how to train a Neural Network on MNIST using Flower and CoreML on iOS devices. +In this tutorial we will learn how to train a Neural Network on MNIST using Flower and +CoreML on iOS devices. -First of all, for running the Flower Python server, it is recommended to create a virtual environment and run everything within a :doc:`virtualenv `. -For the Flower client implementation in iOS, it is recommended to use Xcode as our IDE. +First of all, for running the Flower Python server, it is recommended to create a +virtual environment and run everything within a :doc:`virtualenv +`. For the Flower client implementation in iOS, +it is recommended to use Xcode as our IDE. -Our example consists of one Python *server* and two iPhone *clients* that all have the same model. +Our example consists of one Python *server* and two iPhone *clients* that all have the +same model. -*Clients* are responsible for generating individual weight updates for the model based on their local datasets. -These updates are then sent to the *server* which will aggregate them to produce a better model. Finally, the *server* sends this improved version of the model back to each *client*. -A complete cycle of weight updates is called a *round*. +*Clients* are responsible for generating individual weight updates for the model based +on their local datasets. These updates are then sent to the *server* which will +aggregate them to produce a better model. Finally, the *server* sends this improved +version of the model back to each *client*. A complete cycle of weight updates is called +a *round*. -Now that we have a rough idea of what is going on, let's get started to setup our Flower server environment. We first need to install Flower. You can do this by using pip: +Now that we have a rough idea of what is going on, let's get started to setup our Flower +server environment. We first need to install Flower. You can do this by using pip: .. code-block:: shell - $ pip install flwr + $ pip install flwr Or Poetry: .. code-block:: shell - $ poetry add flwr + $ poetry add flwr Flower Client ------------- -Now that we have all our dependencies installed, let's run a simple distributed training using CoreML as our local training pipeline and MNIST as our dataset. -For simplicity reasons we will use the complete Flower client with CoreML, that has been implemented and stored inside the Swift SDK. The client implementation can be seen below: +Now that we have all our dependencies installed, let's run a simple distributed training +using CoreML as our local training pipeline and MNIST as our dataset. For simplicity +reasons we will use the complete Flower client with CoreML, that has been implemented +and stored inside the Swift SDK. The client implementation can be seen below: .. code-block:: swift - /// Parses the parameters from the local model and returns them as GetParametersRes struct - /// - /// - Returns: Parameters from the local model - public func getParameters() -> GetParametersRes { - let parameters = parameters.weightsToParameters() - let status = Status(code: .ok, message: String()) - - return GetParametersRes(parameters: parameters, status: status) - } - - /// Calls the routine to fit the local model - /// - /// - Returns: The result from the local training, e.g., updated parameters - public func fit(ins: FitIns) -> FitRes { - let status = Status(code: .ok, message: String()) - let result = runMLTask(configuration: parameters.parametersToWeights(parameters: ins.parameters), task: .train) - let parameters = parameters.weightsToParameters() - - return FitRes(parameters: parameters, numExamples: result.numSamples, status: status) - } + /// Parses the parameters from the local model and returns them as GetParametersRes struct + /// + /// - Returns: Parameters from the local model + public func getParameters() -> GetParametersRes { + let parameters = parameters.weightsToParameters() + let status = Status(code: .ok, message: String()) - /// Calls the routine to evaluate the local model - /// - /// - Returns: The result from the evaluation, e.g., loss - public func evaluate(ins: EvaluateIns) -> EvaluateRes { - let status = Status(code: .ok, message: String()) - let result = runMLTask(configuration: parameters.parametersToWeights(parameters: ins.parameters), task: .test) + return GetParametersRes(parameters: parameters, status: status) + } - return EvaluateRes(loss: Float(result.loss), numExamples: result.numSamples, status: status) - } + /// Calls the routine to fit the local model + /// + /// - Returns: The result from the local training, e.g., updated parameters + public func fit(ins: FitIns) -> FitRes { + let status = Status(code: .ok, message: String()) + let result = runMLTask(configuration: parameters.parametersToWeights(parameters: ins.parameters), task: .train) + let parameters = parameters.weightsToParameters() + + return FitRes(parameters: parameters, numExamples: result.numSamples, status: status) + } + + /// Calls the routine to evaluate the local model + /// + /// - Returns: The result from the evaluation, e.g., loss + public func evaluate(ins: EvaluateIns) -> EvaluateRes { + let status = Status(code: .ok, message: String()) + let result = runMLTask(configuration: parameters.parametersToWeights(parameters: ins.parameters), task: .test) + + return EvaluateRes(loss: Float(result.loss), numExamples: result.numSamples, status: status) + } -Let's create a new application project in Xcode and add :code:`flwr` as a dependency in your project. For our application, we will store the logic of our app in :code:`FLiOSModel.swift` and the UI elements in :code:`ContentView.swift`. -We will focus more on :code:`FLiOSModel.swift` in this quickstart. Please refer to the `full code example `_ to learn more about the app. +Let's create a new application project in Xcode and add ``flwr`` as a dependency in your +project. For our application, we will store the logic of our app in ``FLiOSModel.swift`` +and the UI elements in ``ContentView.swift``. We will focus more on ``FLiOSModel.swift`` +in this quickstart. Please refer to the `full code example +`_ to learn more about the app. -Import Flower and CoreML related packages in :code:`FLiOSModel.swift`: +Import Flower and CoreML related packages in ``FLiOSModel.swift``: .. code-block:: swift - import Foundation - import CoreML - import flwr + import Foundation + import CoreML + import flwr -Then add the mlmodel to the project simply by drag-and-drop, the mlmodel will be bundled inside the application during deployment to your iOS device. -We need to pass the url to access mlmodel and run CoreML machine learning processes, it can be retrieved by calling the function :code:`Bundle.main.url`. -For the MNIST dataset, we need to preprocess it into :code:`MLBatchProvider` object. The preprocessing is done inside :code:`DataLoader.swift`. +Then add the mlmodel to the project simply by drag-and-drop, the mlmodel will be bundled +inside the application during deployment to your iOS device. We need to pass the url to +access mlmodel and run CoreML machine learning processes, it can be retrieved by calling +the function ``Bundle.main.url``. For the MNIST dataset, we need to preprocess it into +``MLBatchProvider`` object. The preprocessing is done inside ``DataLoader.swift``. .. code-block:: swift - // prepare train dataset - let trainBatchProvider = DataLoader.trainBatchProvider() { _ in } + // prepare train dataset + let trainBatchProvider = DataLoader.trainBatchProvider() { _ in } - // prepare test dataset - let testBatchProvider = DataLoader.testBatchProvider() { _ in } + // prepare test dataset + let testBatchProvider = DataLoader.testBatchProvider() { _ in } - // load them together - let dataLoader = MLDataLoader(trainBatchProvider: trainBatchProvider, - testBatchProvider: testBatchProvider) + // load them together + let dataLoader = MLDataLoader(trainBatchProvider: trainBatchProvider, + testBatchProvider: testBatchProvider) -Since CoreML does not allow the model parameters to be seen before training, and accessing the model parameters during or after the training can only be done by specifying the layer name, -we need to know this information beforehand, through looking at the model specification, which are written as proto files. The implementation can be seen in :code:`MLModelInspect`. +Since CoreML does not allow the model parameters to be seen before training, and +accessing the model parameters during or after the training can only be done by +specifying the layer name, we need to know this information beforehand, through looking +at the model specification, which are written as proto files. The implementation can be +seen in ``MLModelInspect``. After we have all of the necessary information, let's create our Flower client. .. code-block:: swift - let compiledModelUrl = try MLModel.compileModel(at: url) + let compiledModelUrl = try MLModel.compileModel(at: url) - // inspect the model to be able to access the model parameters - // to access the model we need to know the layer name - // since the model parameters are stored as key value pairs - let modelInspect = try MLModelInspect(serializedData: Data(contentsOf: url)) - let layerWrappers = modelInspect.getLayerWrappers() - self.mlFlwrClient = MLFlwrClient(layerWrappers: layerWrappers, - dataLoader: dataLoader, - compiledModelUrl: compiledModelUrl) + // inspect the model to be able to access the model parameters + // to access the model we need to know the layer name + // since the model parameters are stored as key value pairs + let modelInspect = try MLModelInspect(serializedData: Data(contentsOf: url)) + let layerWrappers = modelInspect.getLayerWrappers() + self.mlFlwrClient = MLFlwrClient(layerWrappers: layerWrappers, + dataLoader: dataLoader, + compiledModelUrl: compiledModelUrl) -Then start the Flower gRPC client and start communicating to the server by passing our Flower client to the function :code:`startFlwrGRPC`. +Then start the Flower gRPC client and start communicating to the server by passing our +Flower client to the function ``startFlwrGRPC``. .. code-block:: swift - self.flwrGRPC = FlwrGRPC(serverHost: hostname, serverPort: port) - self.flwrGRPC.startFlwrGRPC(client: self.mlFlwrClient) + self.flwrGRPC = FlwrGRPC(serverHost: hostname, serverPort: port) + self.flwrGRPC.startFlwrGRPC(client: self.mlFlwrClient) -That's it for the client. We only have to implement :code:`Client` or call the provided -:code:`MLFlwrClient` and call :code:`startFlwrGRPC()`. The attribute :code:`hostname` and :code:`port` tells the client which server to connect to. -This can be done by entering the hostname and port in the application before clicking the start button to start the federated learning process. +That's it for the client. We only have to implement ``Client`` or call the provided +``MLFlwrClient`` and call ``startFlwrGRPC()``. The attribute ``hostname`` and ``port`` +tells the client which server to connect to. This can be done by entering the hostname +and port in the application before clicking the start button to start the federated +learning process. Flower Server ------------- -For simple workloads we can start a Flower server and leave all the -configuration possibilities at their default values. In a file named -:code:`server.py`, import Flower and start the server: +For simple workloads we can start a Flower server and leave all the configuration +possibilities at their default values. In a file named ``server.py``, import Flower and +start the server: .. code-block:: python @@ -141,18 +160,21 @@ configuration possibilities at their default values. In a file named Train the model, federated! --------------------------- -With both client and server ready, we can now run everything and see federated -learning in action. FL systems usually have a server and multiple clients. We -therefore have to start the server first: +With both client and server ready, we can now run everything and see federated learning +in action. FL systems usually have a server and multiple clients. We therefore have to +start the server first: .. code-block:: shell $ python server.py -Once the server is running we can start the clients in different terminals. -Build and run the client through your Xcode, one through Xcode Simulator and the other by deploying it to your iPhone. -To see more about how to deploy your app to iPhone or Simulator visit `here `_. +Once the server is running we can start the clients in different terminals. Build and +run the client through your Xcode, one through Xcode Simulator and the other by +deploying it to your iPhone. To see more about how to deploy your app to iPhone or +Simulator visit `here +`_. -Congratulations! -You've successfully built and run your first federated learning system in your ios device. -The full `source code `_ for this example can be found in :code:`examples/ios`. +Congratulations! You've successfully built and run your first federated learning system +in your ios device. The full `source code +`_ for this example can be found +in ``examples/ios``. diff --git a/doc/source/tutorial-quickstart-jax.rst b/doc/source/tutorial-quickstart-jax.rst index d2b9243e2bb3..0581e95d8d42 100644 --- a/doc/source/tutorial-quickstart-jax.rst +++ b/doc/source/tutorial-quickstart-jax.rst @@ -1,34 +1,42 @@ .. _quickstart-jax: - Quickstart JAX ============== .. meta:: - :description: Check out this Federated Learning quickstart tutorial for using Flower with Jax to train a linear regression model on a scikit-learn dataset. - -This tutorial will show you how to use Flower to build a federated version of an existing JAX workload. -We are using JAX to train a linear regression model on a scikit-learn dataset. -We will structure the example similar to our `PyTorch - From Centralized To Federated `_ walkthrough. -First, we build a centralized training approach based on the `Linear Regression with JAX `_ tutorial`. -Then, we build upon the centralized training code to run the training in a federated fashion. - -Before we start building our JAX example, we need install the packages :code:`jax`, :code:`jaxlib`, :code:`scikit-learn`, and :code:`flwr`: + :description: Check out this Federated Learning quickstart tutorial for using Flower with Jax to train a linear regression model on a scikit-learn dataset. + +This tutorial will show you how to use Flower to build a federated version of an +existing JAX workload. We are using JAX to train a linear regression model on a +scikit-learn dataset. We will structure the example similar to our `PyTorch - From +Centralized To Federated +`_ +walkthrough. First, we build a centralized training approach based on the `Linear +Regression with JAX +`_ tutorial`. +Then, we build upon the centralized training code to run the training in a federated +fashion. + +Before we start building our JAX example, we need install the packages ``jax``, +``jaxlib``, ``scikit-learn``, and ``flwr``: .. code-block:: shell - $ pip install jax jaxlib scikit-learn flwr - + $ pip install jax jaxlib scikit-learn flwr Linear Regression with JAX -------------------------- -We begin with a brief description of the centralized training code based on a :code:`Linear Regression` model. -If you want a more in-depth explanation of what's going on then have a look at the official `JAX documentation `_. +We begin with a brief description of the centralized training code based on a ``Linear +Regression`` model. If you want a more in-depth explanation of what's going on then have +a look at the official `JAX documentation `_. -Let's create a new file called :code:`jax_training.py` with all the components required for a traditional (centralized) linear regression training. -First, the JAX packages :code:`jax` and :code:`jaxlib` need to be imported. In addition, we need to import :code:`sklearn` since we use :code:`make_regression` for the dataset and :code:`train_test_split` to split the dataset into a training and test set. -You can see that we do not yet import the :code:`flwr` package for federated learning. This will be done later. +Let's create a new file called ``jax_training.py`` with all the components required for +a traditional (centralized) linear regression training. First, the JAX packages ``jax`` +and ``jaxlib`` need to be imported. In addition, we need to import ``sklearn`` since we +use ``make_regression`` for the dataset and ``train_test_split`` to split the dataset +into a training and test set. You can see that we do not yet import the ``flwr`` package +for federated learning. This will be done later. .. code-block:: python @@ -40,47 +48,52 @@ You can see that we do not yet import the :code:`flwr` package for federated lea key = jax.random.PRNGKey(0) -The :code:`load_data()` function loads the mentioned training and test sets. +The ``load_data()`` function loads the mentioned training and test sets. .. code-block:: python - def load_data() -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray], List[np.ndarray]]: + def load_data() -> ( + Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray], List[np.ndarray]] + ): # create our dataset and start with similar datasets for different clients X, y = make_regression(n_features=3, random_state=0) X, X_test, y, y_test = train_test_split(X, y) return X, y, X_test, y_test -The model architecture (a very simple :code:`Linear Regression` model) is defined in :code:`load_model()`. +The model architecture (a very simple ``Linear Regression`` model) is defined in +``load_model()``. .. code-block:: python def load_model(model_shape) -> Dict: # model weights - params = { - 'b' : jax.random.uniform(key), - 'w' : jax.random.uniform(key, model_shape) - } + params = {"b": jax.random.uniform(key), "w": jax.random.uniform(key, model_shape)} return params -We now need to define the training (function :code:`train()`), which loops over the training set and measures the loss (function :code:`loss_fn()`) for each batch of training examples. The loss function is separate since JAX takes derivatives with a :code:`grad()` function (defined in the :code:`main()` function and called in :code:`train()`). +We now need to define the training (function ``train()``), which loops over the training +set and measures the loss (function ``loss_fn()``) for each batch of training examples. +The loss function is separate since JAX takes derivatives with a ``grad()`` function +(defined in the ``main()`` function and called in ``train()``). .. code-block:: python def loss_fn(params, X, y) -> Callable: - err = jnp.dot(X, params['w']) + params['b'] - y + err = jnp.dot(X, params["w"]) + params["b"] - y return jnp.mean(jnp.square(err)) # mse + def train(params, grad_fn, X, y) -> Tuple[np.array, float, int]: num_examples = X.shape[0] for epochs in range(10): grads = grad_fn(params, X, y) params = jax.tree_multimap(lambda p, g: p - 0.05 * g, params, grads) - loss = loss_fn(params,X, y) + loss = loss_fn(params, X, y) # if epochs % 10 == 9: # print(f'For Epoch {epochs} loss {loss}') return params, loss, num_examples -The evaluation of the model is defined in the function :code:`evaluation()`. The function takes all test examples and measures the loss of the linear regression model. +The evaluation of the model is defined in the function ``evaluation()``. The function +takes all test examples and measures the loss of the linear regression model. .. code-block:: python @@ -91,7 +104,9 @@ The evaluation of the model is defined in the function :code:`evaluation()`. The # print(f'Test loss {loss_test}') return loss_test, num_examples -Having defined the data loading, model architecture, training, and evaluation we can put everything together and train our model using JAX. As already mentioned, the :code:`jax.grad()` function is defined in :code:`main()` and passed to :code:`train()`. +Having defined the data loading, model architecture, training, and evaluation we can put +everything together and train our model using JAX. As already mentioned, the +``jax.grad()`` function is defined in ``main()`` and passed to ``train()``. .. code-block:: python @@ -100,7 +115,7 @@ Having defined the data loading, model architecture, training, and evaluation we model_shape = X.shape[1:] grad_fn = jax.grad(loss_fn) print("Model Shape", model_shape) - params = load_model(model_shape) + params = load_model(model_shape) params, loss, num_examples = train(params, grad_fn, X, y) evaluation(params, grad_fn, X_test, y_test) @@ -110,40 +125,48 @@ Having defined the data loading, model architecture, training, and evaluation we You can now run your (centralized) JAX linear regression workload: -.. code-block:: python +.. code-block:: bash python3 jax_training.py -So far this should all look fairly familiar if you've used JAX before. -Let's take the next step and use what we've built to create a simple federated learning system consisting of one server and two clients. +So far this should all look fairly familiar if you've used JAX before. Let's take the +next step and use what we've built to create a simple federated learning system +consisting of one server and two clients. JAX meets Flower ---------------- -The concept of federating an existing workload is always the same and easy to understand. -We have to start a *server* and then use the code in :code:`jax_training.py` for the *clients* that are connected to the *server*. -The *server* sends model parameters to the clients. The *clients* run the training and update the parameters. -The updated parameters are sent back to the *server*, which averages all received parameter updates. -This describes one round of the federated learning process, and we repeat this for multiple rounds. +The concept of federating an existing workload is always the same and easy to +understand. We have to start a *server* and then use the code in ``jax_training.py`` for +the *clients* that are connected to the *server*. The *server* sends model parameters to +the clients. The *clients* run the training and update the parameters. The updated +parameters are sent back to the *server*, which averages all received parameter updates. +This describes one round of the federated learning process, and we repeat this for +multiple rounds. -Our example consists of one *server* and two *clients*. Let's set up :code:`server.py` first. The *server* needs to import the Flower package :code:`flwr`. -Next, we use the :code:`start_server` function to start a server and tell it to perform three rounds of federated learning. +Our example consists of one *server* and two *clients*. Let's set up ``server.py`` +first. The *server* needs to import the Flower package ``flwr``. Next, we use the +``start_server`` function to start a server and tell it to perform three rounds of +federated learning. .. code-block:: python import flwr as fl if __name__ == "__main__": - fl.server.start_server(server_address="0.0.0.0:8080", config=fl.server.ServerConfig(num_rounds=3)) + fl.server.start_server( + server_address="0.0.0.0:8080", config=fl.server.ServerConfig(num_rounds=3) + ) We can already start the *server*: -.. code-block:: python +.. code-block:: bash python3 server.py -Finally, we will define our *client* logic in :code:`client.py` and build upon the previously defined JAX training in :code:`jax_training.py`. -Our *client* needs to import :code:`flwr`, but also :code:`jax` and :code:`jaxlib` to update the parameters on our JAX model: +Finally, we will define our *client* logic in ``client.py`` and build upon the +previously defined JAX training in ``jax_training.py``. Our *client* needs to import +``flwr``, but also ``jax`` and ``jaxlib`` to update the parameters on our JAX model: .. code-block:: python @@ -156,36 +179,45 @@ Our *client* needs to import :code:`flwr`, but also :code:`jax` and :code:`jaxli import jax_training - -Implementing a Flower *client* basically means implementing a subclass of either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. -Our implementation will be based on :code:`flwr.client.NumPyClient` and we'll call it :code:`FlowerClient`. -:code:`NumPyClient` is slightly easier to implement than :code:`Client` if you use a framework with good NumPy interoperability (like JAX) because it avoids some of the boilerplate that would otherwise be necessary. -:code:`FlowerClient` needs to implement four methods, two methods for getting/setting model parameters, one method for training the model, and one method for testing the model: - -#. :code:`set_parameters (optional)` - * set the model parameters on the local model that are received from the server - * transform parameters to NumPy :code:`ndarray`'s - * loop over the list of model parameters received as NumPy :code:`ndarray`'s (think list of neural network layers) -#. :code:`get_parameters` - * get the model parameters and return them as a list of NumPy :code:`ndarray`'s (which is what :code:`flwr.client.NumPyClient` expects) -#. :code:`fit` - * update the parameters of the local model with the parameters received from the server - * train the model on the local training set - * get the updated local model parameters and return them to the server -#. :code:`evaluate` - * update the parameters of the local model with the parameters received from the server - * evaluate the updated model on the local test set - * return the local loss to the server - -The challenging part is to transform the JAX model parameters from :code:`DeviceArray` to :code:`NumPy ndarray` to make them compatible with `NumPyClient`. - -The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make use of the functions :code:`train()` and :code:`evaluate()` previously defined in :code:`jax_training.py`. -So what we really do here is we tell Flower through our :code:`NumPyClient` subclass which of our already defined functions to call for training and evaluation. -We included type annotations to give you a better understanding of the data types that get passed around. +Implementing a Flower *client* basically means implementing a subclass of either +``flwr.client.Client`` or ``flwr.client.NumPyClient``. Our implementation will be based +on ``flwr.client.NumPyClient`` and we'll call it ``FlowerClient``. ``NumPyClient`` is +slightly easier to implement than ``Client`` if you use a framework with good NumPy +interoperability (like JAX) because it avoids some of the boilerplate that would +otherwise be necessary. ``FlowerClient`` needs to implement four methods, two methods +for getting/setting model parameters, one method for training the model, and one method +for testing the model: + +1. ``set_parameters (optional)`` + - set the model parameters on the local model that are received from the server + - transform parameters to NumPy ``ndarray``'s + - loop over the list of model parameters received as NumPy ``ndarray``'s (think + list of neural network layers) +2. ``get_parameters`` + - get the model parameters and return them as a list of NumPy ``ndarray``'s + (which is what ``flwr.client.NumPyClient`` expects) +3. ``fit`` + - update the parameters of the local model with the parameters received from the + server + - train the model on the local training set + - get the updated local model parameters and return them to the server +4. ``evaluate`` + - update the parameters of the local model with the parameters received from the + server + - evaluate the updated model on the local test set + - return the local loss to the server + +The challenging part is to transform the JAX model parameters from ``DeviceArray`` to +``NumPy ndarray`` to make them compatible with `NumPyClient`. + +The two ``NumPyClient`` methods ``fit`` and ``evaluate`` make use of the functions +``train()`` and ``evaluate()`` previously defined in ``jax_training.py``. So what we +really do here is we tell Flower through our ``NumPyClient`` subclass which of our +already defined functions to call for training and evaluation. We included type +annotations to give you a better understanding of the data types that get passed around. .. code-block:: python - class FlowerClient(fl.client.NumPyClient): """Flower client implementing using linear regression and JAX.""" @@ -198,7 +230,7 @@ We included type annotations to give you a better understanding of the data type test_x: List[np.ndarray], test_y: List[np.ndarray], ) -> None: - self.params= params + self.params = params self.grad_fn = grad_fn self.train_x = train_x self.train_y = train_y @@ -211,25 +243,26 @@ We included type annotations to give you a better understanding of the data type for _, val in self.params.items(): parameter_value.append(np.array(val)) return parameter_value - + def set_parameters(self, parameters: List[np.ndarray]) -> Dict: # Collect model parameters and update the parameters of the local model - value=jnp.ndarray - params_item = list(zip(self.params.keys(),parameters)) + value = jnp.ndarray + params_item = list(zip(self.params.keys(), parameters)) for item in params_item: key = item[0] value = item[1] self.params[key] = value return self.params - def fit( self, parameters: List[np.ndarray], config: Dict ) -> Tuple[List[np.ndarray], int, Dict]: # Set model parameters, train model, return updated model parameters print("Start local training") self.params = self.set_parameters(parameters) - self.params, loss, num_examples = jax_training.train(self.params, self.grad_fn, self.train_x, self.train_y) + self.params, loss, num_examples = jax_training.train( + self.params, self.grad_fn, self.train_x, self.train_y + ) results = {"loss": float(loss)} print("Training results", results) return self.get_parameters(config={}), num_examples, results @@ -240,7 +273,9 @@ We included type annotations to give you a better understanding of the data type # Set model parameters, evaluate the model on a local test dataset, return result print("Start evaluation") self.params = self.set_parameters(parameters) - loss, num_examples = jax_training.evaluation(self.params,self.grad_fn, self.test_x, self.test_y) + loss, num_examples = jax_training.evaluation( + self.params, self.grad_fn, self.test_x, self.test_y + ) print("Evaluation accuracy & loss", loss) return ( float(loss), @@ -267,22 +302,25 @@ Having defined the federation process, we can run it. client = FlowerClient(params, grad_fn, train_x, train_y, test_x, test_y) fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client()) + if __name__ == "__main__": main() - And that's it. You can now open two additional terminal windows and run -.. code-block:: python +.. code-block:: bash python3 client.py -in each window (make sure that the server is still running before you do so) and see your JAX project run federated learning across two clients. Congratulations! +in each window (make sure that the server is still running before you do so) and see +your JAX project run federated learning across two clients. Congratulations! Next Steps ---------- -The source code of this example was improved over time and can be found here: `Quickstart JAX `_. +The source code of this example was improved over time and can be found here: +`Quickstart JAX `_. Our example is somewhat over-simplified because both clients load the same dataset. -You're now prepared to explore this topic further. How about using a more sophisticated model or using a different dataset? How about adding more clients? +You're now prepared to explore this topic further. How about using a more sophisticated +model or using a different dataset? How about adding more clients? diff --git a/doc/source/tutorial-quickstart-mlx.rst b/doc/source/tutorial-quickstart-mlx.rst index 675a08502d26..40e870ddc822 100644 --- a/doc/source/tutorial-quickstart-mlx.rst +++ b/doc/source/tutorial-quickstart-mlx.rst @@ -1,410 +1,393 @@ .. _quickstart-mlx: -################ - Quickstart MLX -################ - -In this federated learning tutorial we will learn how to train simple -MLP on MNIST using Flower and MLX. It is recommended to create a virtual -environment and run everything within a :doc:`virtualenv -`. - -Let's use `flwr new` to create a complete Flower+MLX project. It will -generate all the files needed to run, by default with the Simulation -Engine, a federation of 10 nodes using `FedAvg +Quickstart MLX +============== + +In this federated learning tutorial we will learn how to train simple MLP on MNIST using +Flower and MLX. It is recommended to create a virtual environment and run everything +within a :doc:`virtualenv `. + +Let's use `flwr new` to create a complete Flower+MLX project. It will generate all the +files needed to run, by default with the Simulation Engine, a federation of 10 nodes +using `FedAvg `_. The dataset will be partitioned using Flower Dataset's `IidPartitioner `_. -Now that we have a rough idea of what this example is about, let's get -started. First, install Flower in your new environment: +Now that we have a rough idea of what this example is about, let's get started. First, +install Flower in your new environment: -.. code:: shell +.. code-block:: shell - # In a new Python environment - $ pip install flwr + # In a new Python environment + $ pip install flwr -Then, run the command below. You will be prompted to select of the -available templates (choose ``MLX``), give a name to your project, and -type in your developer name: +Then, run the command below. You will be prompted to select of the available templates +(choose ``MLX``), give a name to your project, and type in your developer name: -.. code:: shell +.. code-block:: shell - $ flwr new + $ flwr new -After running it you'll notice a new directory with your project name -has been created. It should have the following structure: +After running it you'll notice a new directory with your project name has been created. +It should have the following structure: -.. code:: shell +.. code-block:: shell - - ├── - │ ├── __init__.py - │ ├── client_app.py # Defines your ClientApp - │ ├── server_app.py # Defines your ServerApp - │ └── task.py # Defines your model, training and data loading - ├── pyproject.toml # Project metadata like dependencies and configs - └── README.md + + ├── + │ ├── __init__.py + │ ├── client_app.py # Defines your ClientApp + │ ├── server_app.py # Defines your ServerApp + │ └── task.py # Defines your model, training and data loading + ├── pyproject.toml # Project metadata like dependencies and configs + └── README.md -If you haven't yet installed the project and its dependencies, you can -do so by: +If you haven't yet installed the project and its dependencies, you can do so by: -.. code:: shell +.. code-block:: shell - # From the directory where your pyproject.toml is - $ pip install -e . + # From the directory where your pyproject.toml is + $ pip install -e . To run the project do: -.. code:: shell +.. code-block:: shell - # Run with default arguments - $ flwr run . + # Run with default arguments + $ flwr run . With default arguments you will see an output like this one: -.. code:: shell - - Loading project configuration... - Success - INFO : Starting Flower ServerApp, config: num_rounds=3, no round_timeout - INFO : - INFO : [INIT] - INFO : Requesting initial parameters from one random client - WARNING : FAB ID is not provided; the default ClientApp will be loaded. - INFO : Received initial parameters from one random client - INFO : Evaluating initial global parameters - INFO : - INFO : [ROUND 1] - INFO : configure_fit: strategy sampled 10 clients (out of 10) - INFO : aggregate_fit: received 10 results and 0 failures - WARNING : No fit_metrics_aggregation_fn provided - INFO : configure_evaluate: strategy sampled 10 clients (out of 10) - INFO : aggregate_evaluate: received 10 results and 0 failures - WARNING : No evaluate_metrics_aggregation_fn provided - INFO : - INFO : [ROUND 2] - INFO : configure_fit: strategy sampled 10 clients (out of 10) - INFO : aggregate_fit: received 10 results and 0 failures - INFO : configure_evaluate: strategy sampled 10 clients (out of 10) - INFO : aggregate_evaluate: received 10 results and 0 failures - INFO : - INFO : [ROUND 3] - INFO : configure_fit: strategy sampled 10 clients (out of 10) - INFO : aggregate_fit: received 10 results and 0 failures - INFO : configure_evaluate: strategy sampled 10 clients (out of 10) - INFO : aggregate_evaluate: received 10 results and 0 failures - INFO : - INFO : [SUMMARY] - INFO : Run finished 3 round(s) in 8.15s - INFO : History (loss, distributed): - INFO : round 1: 2.243802046775818 - INFO : round 2: 2.101812958717346 - INFO : round 3: 1.7419301986694335 - INFO : - -You can also override the parameters defined in -``[tool.flwr.app.config]`` section in the ``pyproject.toml`` like this: - -.. code:: shell - - # Override some arguments - $ flwr run . --run-config "num-server-rounds=5 lr=0.05" - -What follows is an explanation of each component in the project you just -created: dataset partition, the model, defining the ``ClientApp`` and -defining the ``ServerApp``. - -********** - The Data -********** - -We will use `Flower Datasets `_ to -easily download and partition the `MNIST` dataset. In this example -you'll make use of the `IidPartitioner +.. code-block:: shell + + Loading project configuration... + Success + INFO : Starting Flower ServerApp, config: num_rounds=3, no round_timeout + INFO : + INFO : [INIT] + INFO : Requesting initial parameters from one random client + WARNING : FAB ID is not provided; the default ClientApp will be loaded. + INFO : Received initial parameters from one random client + INFO : Evaluating initial global parameters + INFO : + INFO : [ROUND 1] + INFO : configure_fit: strategy sampled 10 clients (out of 10) + INFO : aggregate_fit: received 10 results and 0 failures + WARNING : No fit_metrics_aggregation_fn provided + INFO : configure_evaluate: strategy sampled 10 clients (out of 10) + INFO : aggregate_evaluate: received 10 results and 0 failures + WARNING : No evaluate_metrics_aggregation_fn provided + INFO : + INFO : [ROUND 2] + INFO : configure_fit: strategy sampled 10 clients (out of 10) + INFO : aggregate_fit: received 10 results and 0 failures + INFO : configure_evaluate: strategy sampled 10 clients (out of 10) + INFO : aggregate_evaluate: received 10 results and 0 failures + INFO : + INFO : [ROUND 3] + INFO : configure_fit: strategy sampled 10 clients (out of 10) + INFO : aggregate_fit: received 10 results and 0 failures + INFO : configure_evaluate: strategy sampled 10 clients (out of 10) + INFO : aggregate_evaluate: received 10 results and 0 failures + INFO : + INFO : [SUMMARY] + INFO : Run finished 3 round(s) in 8.15s + INFO : History (loss, distributed): + INFO : round 1: 2.243802046775818 + INFO : round 2: 2.101812958717346 + INFO : round 3: 1.7419301986694335 + INFO : + +You can also override the parameters defined in ``[tool.flwr.app.config]`` section in +the ``pyproject.toml`` like this: + +.. code-block:: shell + + # Override some arguments + $ flwr run . --run-config "num-server-rounds=5 lr=0.05" + +What follows is an explanation of each component in the project you just created: +dataset partition, the model, defining the ``ClientApp`` and defining the ``ServerApp``. + +The Data +-------- + +We will use `Flower Datasets `_ to easily download and +partition the `MNIST` dataset. In this example you'll make use of the `IidPartitioner `_ -to generate `num_partitions` partitions. You can choose `other -partitioners -`_ -available in Flower Datasets: - -.. code:: python - - partitioner = IidPartitioner(num_partitions=num_partitions) - fds = FederatedDataset( - dataset="ylecun/mnist", - partitioners={"train": partitioner}, - ) - partition = fds.load_partition(partition_id) - partition_splits = partition.train_test_split(test_size=0.2, seed=42) - - partition_splits["train"].set_format("numpy") - partition_splits["test"].set_format("numpy") - - train_partition = partition_splits["train"].map( - lambda img: {"img": img.reshape(-1, 28 * 28).squeeze().astype(np.float32) / 255.0}, - input_columns="image", - ) - test_partition = partition_splits["test"].map( - lambda img: {"img": img.reshape(-1, 28 * 28).squeeze().astype(np.float32) / 255.0}, - input_columns="image", - ) - - data = ( - train_partition["img"], - train_partition["label"].astype(np.uint32), - test_partition["img"], - test_partition["label"].astype(np.uint32), - ) - - train_images, train_labels, test_images, test_labels = map(mx.array, data) - -*********** - The Model -*********** +to generate `num_partitions` partitions. You can choose `other partitioners +`_ available in +Flower Datasets: + +.. code-block:: python + + partitioner = IidPartitioner(num_partitions=num_partitions) + fds = FederatedDataset( + dataset="ylecun/mnist", + partitioners={"train": partitioner}, + ) + partition = fds.load_partition(partition_id) + partition_splits = partition.train_test_split(test_size=0.2, seed=42) + + partition_splits["train"].set_format("numpy") + partition_splits["test"].set_format("numpy") + + train_partition = partition_splits["train"].map( + lambda img: {"img": img.reshape(-1, 28 * 28).squeeze().astype(np.float32) / 255.0}, + input_columns="image", + ) + test_partition = partition_splits["test"].map( + lambda img: {"img": img.reshape(-1, 28 * 28).squeeze().astype(np.float32) / 255.0}, + input_columns="image", + ) + + data = ( + train_partition["img"], + train_partition["label"].astype(np.uint32), + test_partition["img"], + test_partition["label"].astype(np.uint32), + ) + + train_images, train_labels, test_images, test_labels = map(mx.array, data) + +The Model +--------- We define the model as in the `centralized MLX example -`_, it's a -simple MLP: +`_, it's a simple MLP: -.. code:: python +.. code-block:: python - class MLP(nn.Module): - """A simple MLP.""" + class MLP(nn.Module): + """A simple MLP.""" - def __init__( - self, num_layers: int, input_dim: int, hidden_dim: int, output_dim: int - ): - super().__init__() - layer_sizes = [input_dim] + [hidden_dim] * num_layers + [output_dim] - self.layers = [ - nn.Linear(idim, odim) - for idim, odim in zip(layer_sizes[:-1], layer_sizes[1:]) - ] + def __init__( + self, num_layers: int, input_dim: int, hidden_dim: int, output_dim: int + ): + super().__init__() + layer_sizes = [input_dim] + [hidden_dim] * num_layers + [output_dim] + self.layers = [ + nn.Linear(idim, odim) + for idim, odim in zip(layer_sizes[:-1], layer_sizes[1:]) + ] - def __call__(self, x): - for l in self.layers[:-1]: - x = mx.maximum(l(x), 0.0) - return self.layers[-1](x) + def __call__(self, x): + for l in self.layers[:-1]: + x = mx.maximum(l(x), 0.0) + return self.layers[-1](x) -We also define some utility functions to test our model and to iterate -over batches. +We also define some utility functions to test our model and to iterate over batches. -.. code:: python +.. code-block:: python - def loss_fn(model, X, y): - return mx.mean(nn.losses.cross_entropy(model(X), y)) + def loss_fn(model, X, y): + return mx.mean(nn.losses.cross_entropy(model(X), y)) - def eval_fn(model, X, y): - return mx.mean(mx.argmax(model(X), axis=1) == y) + def eval_fn(model, X, y): + return mx.mean(mx.argmax(model(X), axis=1) == y) - def batch_iterate(batch_size, X, y): - perm = mx.array(np.random.permutation(y.size)) - for s in range(0, y.size, batch_size): - ids = perm[s : s + batch_size] - yield X[ids], y[ids] + def batch_iterate(batch_size, X, y): + perm = mx.array(np.random.permutation(y.size)) + for s in range(0, y.size, batch_size): + ids = perm[s : s + batch_size] + yield X[ids], y[ids] The ClientApp -============= +~~~~~~~~~~~~~ -The main changes we have to make to use `MLX` with `Flower` will be -found in the ``get_params()`` and ``set_params()`` functions. Indeed, -MLX doesn't provide an easy way to convert the model parameters into a -list of ``np.array`` objects (the format we need for the serialization -of the messages to work). +The main changes we have to make to use `MLX` with `Flower` will be found in the +``get_params()`` and ``set_params()`` functions. Indeed, MLX doesn't provide an easy way +to convert the model parameters into a list of ``np.array`` objects (the format we need +for the serialization of the messages to work). The way MLX stores its parameters is as follows: -.. code:: shell - - { - "layers": [ - {"weight": mlx.core.array, "bias": mlx.core.array}, - {"weight": mlx.core.array, "bias": mlx.core.array}, - ..., - {"weight": mlx.core.array, "bias": mlx.core.array} - ] - } - -Therefore, to get our list of ``np.array`` objects, we need to extract -each array and convert them into a NumPy array: - -.. code:: python - - def get_params(model): - layers = model.parameters()["layers"] - return [np.array(val) for layer in layers for _, val in layer.items()] - -For the ``set_params()`` function, we perform the reverse operation. We -receive a list of NumPy arrays and want to convert them into MLX -parameters. Therefore, we iterate through pairs of parameters and assign -them to the `weight` and `bias` keys of each layer dict: - -.. code:: python - - def set_params(model, parameters): - new_params = {} - new_params["layers"] = [ - {"weight": mx.array(parameters[i]), "bias": mx.array(parameters[i + 1])} - for i in range(0, len(parameters), 2) - ] - model.update(new_params) - -The rest of the functionality is directly inspired by the centralized -case. The ``fit()`` method in the client trains the model using the -local dataset: - -.. code:: python - - def fit(self, parameters, config): - self.set_parameters(parameters) - for _ in range(self.num_epochs): - for X, y in batch_iterate( - self.batch_size, self.train_images, self.train_labels - ): - _, grads = self.loss_and_grad_fn(self.model, X, y) - self.optimizer.update(self.model, grads) - mx.eval(self.model.parameters(), self.optimizer.state) - return self.get_parameters(config={}), len(self.train_images), {} - -Here, after updating the parameters, we perform the training as in the -centralized case, and return the new parameters. +.. code-block:: shell + + { + "layers": [ + {"weight": mlx.core.array, "bias": mlx.core.array}, + {"weight": mlx.core.array, "bias": mlx.core.array}, + ..., + {"weight": mlx.core.array, "bias": mlx.core.array} + ] + } + +Therefore, to get our list of ``np.array`` objects, we need to extract each array and +convert them into a NumPy array: + +.. code-block:: python + + def get_params(model): + layers = model.parameters()["layers"] + return [np.array(val) for layer in layers for _, val in layer.items()] + +For the ``set_params()`` function, we perform the reverse operation. We receive a list +of NumPy arrays and want to convert them into MLX parameters. Therefore, we iterate +through pairs of parameters and assign them to the `weight` and `bias` keys of each +layer dict: + +.. code-block:: python + + def set_params(model, parameters): + new_params = {} + new_params["layers"] = [ + {"weight": mx.array(parameters[i]), "bias": mx.array(parameters[i + 1])} + for i in range(0, len(parameters), 2) + ] + model.update(new_params) + +The rest of the functionality is directly inspired by the centralized case. The +``fit()`` method in the client trains the model using the local dataset: + +.. code-block:: python + + def fit(self, parameters, config): + self.set_parameters(parameters) + for _ in range(self.num_epochs): + for X, y in batch_iterate( + self.batch_size, self.train_images, self.train_labels + ): + _, grads = self.loss_and_grad_fn(self.model, X, y) + self.optimizer.update(self.model, grads) + mx.eval(self.model.parameters(), self.optimizer.state) + return self.get_parameters(config={}), len(self.train_images), {} + +Here, after updating the parameters, we perform the training as in the centralized case, +and return the new parameters. And for the ``evaluate()`` method of the client: -.. code:: python +.. code-block:: python - def evaluate(self, parameters, config): - self.set_parameters(parameters) - accuracy = eval_fn(self.model, self.test_images, self.test_labels) - loss = loss_fn(self.model, self.test_images, self.test_labels) - return loss.item(), len(self.test_images), {"accuracy": accuracy.item()} + def evaluate(self, parameters, config): + self.set_parameters(parameters) + accuracy = eval_fn(self.model, self.test_images, self.test_labels) + loss = loss_fn(self.model, self.test_images, self.test_labels) + return loss.item(), len(self.test_images), {"accuracy": accuracy.item()} -We also begin by updating the parameters with the ones sent by the -server, and then we compute the loss and accuracy using the functions -defined above. In the constructor of the ``FlowerClient`` we instantiate -the `MLP` model as well as other components such as the optimizer. +We also begin by updating the parameters with the ones sent by the server, and then we +compute the loss and accuracy using the functions defined above. In the constructor of +the ``FlowerClient`` we instantiate the `MLP` model as well as other components such as +the optimizer. Putting everything together we have: -.. code:: python - - class FlowerClient(NumPyClient): - def __init__( - self, - data, - num_layers, - hidden_dim, - num_classes, - batch_size, - learning_rate, - num_epochs, - ): - self.num_layers = num_layers - self.hidden_dim = hidden_dim - self.num_classes = num_classes - self.batch_size = batch_size - self.learning_rate = learning_rate - self.num_epochs = num_epochs - - self.train_images, self.train_labels, self.test_images, self.test_labels = data - self.model = MLP( - num_layers, self.train_images.shape[-1], hidden_dim, num_classes - ) - self.optimizer = optim.SGD(learning_rate=learning_rate) - self.loss_and_grad_fn = nn.value_and_grad(self.model, loss_fn) - self.num_epochs = num_epochs - self.batch_size = batch_size - - def get_parameters(self, config): - return get_params(self.model) - - def set_parameters(self, parameters): - set_params(self.model, parameters) - - def fit(self, parameters, config): - self.set_parameters(parameters) - for _ in range(self.num_epochs): - for X, y in batch_iterate( - self.batch_size, self.train_images, self.train_labels - ): - _, grads = self.loss_and_grad_fn(self.model, X, y) - self.optimizer.update(self.model, grads) - mx.eval(self.model.parameters(), self.optimizer.state) - return self.get_parameters(config={}), len(self.train_images), {} - - def evaluate(self, parameters, config): - self.set_parameters(parameters) - accuracy = eval_fn(self.model, self.test_images, self.test_labels) - loss = loss_fn(self.model, self.test_images, self.test_labels) - return loss.item(), len(self.test_images), {"accuracy": accuracy.item()} - -Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` -defined above by means of a ``client_fn()`` callback. Note that -``context`` enables you to get access to hyperparemeters defined in -``pyproject.toml`` to configure the run. In this tutorial we access, -among other hyperparameters, the ``local-epochs`` setting to control the -number of epochs a ``ClientApp`` will perform when running the ``fit()`` -method. - -.. code:: python - - def client_fn(context: Context): - partition_id = context.node_config["partition-id"] - num_partitions = context.node_config["num-partitions"] - data = load_data(partition_id, num_partitions) - - num_layers = context.run_config["num-layers"] - hidden_dim = context.run_config["hidden-dim"] - num_classes = 10 - batch_size = context.run_config["batch-size"] - learning_rate = context.run_config["lr"] - num_epochs = context.run_config["local-epochs"] - - # Return Client instance - return FlowerClient( - data, num_layers, hidden_dim, num_classes, batch_size, learning_rate, num_epochs - ).to_client() - - - # Flower ClientApp - app = ClientApp(client_fn) +.. code-block:: python + + class FlowerClient(NumPyClient): + def __init__( + self, + data, + num_layers, + hidden_dim, + num_classes, + batch_size, + learning_rate, + num_epochs, + ): + self.num_layers = num_layers + self.hidden_dim = hidden_dim + self.num_classes = num_classes + self.batch_size = batch_size + self.learning_rate = learning_rate + self.num_epochs = num_epochs + + self.train_images, self.train_labels, self.test_images, self.test_labels = data + self.model = MLP( + num_layers, self.train_images.shape[-1], hidden_dim, num_classes + ) + self.optimizer = optim.SGD(learning_rate=learning_rate) + self.loss_and_grad_fn = nn.value_and_grad(self.model, loss_fn) + self.num_epochs = num_epochs + self.batch_size = batch_size + + def get_parameters(self, config): + return get_params(self.model) + + def set_parameters(self, parameters): + set_params(self.model, parameters) + + def fit(self, parameters, config): + self.set_parameters(parameters) + for _ in range(self.num_epochs): + for X, y in batch_iterate( + self.batch_size, self.train_images, self.train_labels + ): + _, grads = self.loss_and_grad_fn(self.model, X, y) + self.optimizer.update(self.model, grads) + mx.eval(self.model.parameters(), self.optimizer.state) + return self.get_parameters(config={}), len(self.train_images), {} + + def evaluate(self, parameters, config): + self.set_parameters(parameters) + accuracy = eval_fn(self.model, self.test_images, self.test_labels) + loss = loss_fn(self.model, self.test_images, self.test_labels) + return loss.item(), len(self.test_images), {"accuracy": accuracy.item()} + +Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` defined above by +means of a ``client_fn()`` callback. Note that ``context`` enables you to get access to +hyperparemeters defined in ``pyproject.toml`` to configure the run. In this tutorial we +access, among other hyperparameters, the ``local-epochs`` setting to control the number +of epochs a ``ClientApp`` will perform when running the ``fit()`` method. + +.. code-block:: python + + def client_fn(context: Context): + partition_id = context.node_config["partition-id"] + num_partitions = context.node_config["num-partitions"] + data = load_data(partition_id, num_partitions) + + num_layers = context.run_config["num-layers"] + hidden_dim = context.run_config["hidden-dim"] + num_classes = 10 + batch_size = context.run_config["batch-size"] + learning_rate = context.run_config["lr"] + num_epochs = context.run_config["local-epochs"] + + # Return Client instance + return FlowerClient( + data, num_layers, hidden_dim, num_classes, batch_size, learning_rate, num_epochs + ).to_client() + + + # Flower ClientApp + app = ClientApp(client_fn) The ServerApp -------------- ++++++++++++++ -To construct a ``ServerApp``, we define a ``server_fn()`` callback with -an identical signature to that of ``client_fn()``, but the return type -is `ServerAppComponents +To construct a ``ServerApp``, we define a ``server_fn()`` callback with an identical +signature to that of ``client_fn()``, but the return type is `ServerAppComponents `_ as opposed to `Client -`_. -In this example we use the ``FedAvg`` strategy. +`_. In this +example we use the ``FedAvg`` strategy. -.. code:: python +.. code-block:: python - def server_fn(context: Context): - # Read from config - num_rounds = context.run_config["num-server-rounds"] + def server_fn(context: Context): + # Read from config + num_rounds = context.run_config["num-server-rounds"] - # Define strategy - strategy = FedAvg() - config = ServerConfig(num_rounds=num_rounds) + # Define strategy + strategy = FedAvg() + config = ServerConfig(num_rounds=num_rounds) - return ServerAppComponents(strategy=strategy, config=config) + return ServerAppComponents(strategy=strategy, config=config) - # Create ServerApp - app = ServerApp(server_fn=server_fn) + # Create ServerApp + app = ServerApp(server_fn=server_fn) -Congratulations! You've successfully built and run your first federated -learning system. +Congratulations! You've successfully built and run your first federated learning system. .. note:: - Check the `source code - `_ - of the extended version of this tutorial in - ``examples/quickstart-mlx`` in the Flower GitHub repository. + Check the `source code + `_ of the extended + version of this tutorial in ``examples/quickstart-mlx`` in the Flower GitHub + repository. diff --git a/doc/source/tutorial-quickstart-pandas.rst b/doc/source/tutorial-quickstart-pandas.rst index bb9cb1b28b54..00d831a15736 100644 --- a/doc/source/tutorial-quickstart-pandas.rst +++ b/doc/source/tutorial-quickstart-pandas.rst @@ -1,12 +1,12 @@ .. _quickstart-pandas: - Quickstart Pandas ================= .. meta:: - :description: Check out this Federated Learning quickstart tutorial for using Flower with Pandas to perform Federated Analytics. + :description: Check out this Federated Learning quickstart tutorial for using Flower with Pandas to perform Federated Analytics. Let's build a federated analytics system using Pandas and Flower! -Please refer to the `full code example `_ to learn more. +Please refer to the `full code example +`_ to learn more. diff --git a/doc/source/tutorial-quickstart-pytorch-lightning.rst b/doc/source/tutorial-quickstart-pytorch-lightning.rst index 7c74c9a1682f..089865a2969d 100644 --- a/doc/source/tutorial-quickstart-pytorch-lightning.rst +++ b/doc/source/tutorial-quickstart-pytorch-lightning.rst @@ -1,119 +1,118 @@ .. _quickstart-pytorch-lightning: -############################## - Quickstart PyTorch Lightning -############################## +Quickstart PyTorch Lightning +============================ -In this federated learning tutorial we will learn how to train an -AutoEncoder model on MNIST using Flower and PyTorch Lightning. It is -recommended to create a virtual environment and run everything within a -:doc:`virtualenv `. +In this federated learning tutorial we will learn how to train an AutoEncoder model on +MNIST using Flower and PyTorch Lightning. It is recommended to create a virtual +environment and run everything within a :doc:`virtualenv +`. Then, clone the code example directly from GitHub: -.. code:: shell +.. code-block:: shell - git clone --depth=1 https://github.com/adap/flower.git _tmp \ - && mv _tmp/examples/quickstart-pytorch-lightning . \ - && rm -rf _tmp && cd quickstart-pytorch-lightning + git clone --depth=1 https://github.com/adap/flower.git _tmp \ + && mv _tmp/examples/quickstart-pytorch-lightning . \ + && rm -rf _tmp && cd quickstart-pytorch-lightning -This will create a new directory called `quickstart-pytorch-lightning` -containing the following files: +This will create a new directory called `quickstart-pytorch-lightning` containing the +following files: -.. code:: shell +.. code-block:: shell - quickstart-pytorch-lightning - ├── pytorchlightning_example - │ ├── client_app.py # Defines your ClientApp - │ ├── server_app.py # Defines your ServerApp - │ └── task.py # Defines your model, training and data loading - ├── pyproject.toml # Project metadata like dependencies and configs - └── README.md + quickstart-pytorch-lightning + ├── pytorchlightning_example + │ ├── client_app.py # Defines your ClientApp + │ ├── server_app.py # Defines your ServerApp + │ └── task.py # Defines your model, training and data loading + ├── pyproject.toml # Project metadata like dependencies and configs + └── README.md Next, activate your environment, then run: -.. code:: shell +.. code-block:: shell - # Navigate to the example directory - $ cd path/to/quickstart-pytorch-lightning + # Navigate to the example directory + $ cd path/to/quickstart-pytorch-lightning - # Install project and dependencies - $ pip install -e . + # Install project and dependencies + $ pip install -e . -By default, Flower Simulation Engine will be started and it will create -a federation of 4 nodes using `FedAvg +By default, Flower Simulation Engine will be started and it will create a federation of +4 nodes using `FedAvg `_ -as the aggregation strategy. The dataset will be partitioned using -Flower Dataset's `IidPartitioner +as the aggregation strategy. The dataset will be partitioned using Flower Dataset's +`IidPartitioner `_. To run the project, do: -.. code:: shell +.. code-block:: shell - # Run with default arguments - $ flwr run . + # Run with default arguments + $ flwr run . With default arguments you will see an output like this one: -.. code:: shell - - Loading project configuration... - Success - INFO : Starting Flower ServerApp, config: num_rounds=3, no round_timeout - INFO : - INFO : [INIT] - INFO : Using initial global parameters provided by strategy - INFO : Starting evaluation of initial global parameters - INFO : Evaluation returned no results (`None`) - INFO : - INFO : [ROUND 1] - INFO : configure_fit: strategy sampled 2 clients (out of 4) - INFO : aggregate_evaluate: received 2 results and 0 failures - WARNING : No evaluate_metrics_aggregation_fn provided - INFO : - INFO : [ROUND 2] - INFO : configure_fit: strategy sampled 2 clients (out of 4) - INFO : aggregate_fit: received 2 results and 0 failures - INFO : configure_evaluate: strategy sampled 2 clients (out of 4) - INFO : aggregate_evaluate: received 2 results and 0 failures - INFO : - INFO : [ROUND 3] - INFO : configure_fit: strategy sampled 2 clients (out of 4) - INFO : aggregate_fit: received 2 results and 0 failures - INFO : configure_evaluate: strategy sampled 2 clients (out of 4) - INFO : aggregate_evaluate: received 2 results and 0 failures - INFO : - INFO : [SUMMARY] - INFO : Run finished 3 round(s) in 136.92s - INFO : History (loss, distributed): - INFO : round 1: 0.04982871934771538 - INFO : round 2: 0.046457378193736076 - INFO : round 3: 0.04506748169660568 - INFO : - -Each simulated `ClientApp` (two per round) will also log a summary of -their local training process. Expect this output to be similar to: - -.. code:: shell - - # The left part indicates the process ID running the `ClientApp` - (ClientAppActor pid=38155) ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ - (ClientAppActor pid=38155) ┃ Test metric ┃ DataLoader 0 ┃ - (ClientAppActor pid=38155) ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ - (ClientAppActor pid=38155) │ test_loss │ 0.045175597071647644 │ - (ClientAppActor pid=38155) └───────────────────────────┴───────────────────────────┘ - -You can also override the parameters defined in the -``[tool.flwr.app.config]`` section in ``pyproject.toml`` like this: - -.. code:: shell - - # Override some arguments - $ flwr run . --run-config num-server-rounds=5 +.. code-block:: shell + + Loading project configuration... + Success + INFO : Starting Flower ServerApp, config: num_rounds=3, no round_timeout + INFO : + INFO : [INIT] + INFO : Using initial global parameters provided by strategy + INFO : Starting evaluation of initial global parameters + INFO : Evaluation returned no results (`None`) + INFO : + INFO : [ROUND 1] + INFO : configure_fit: strategy sampled 2 clients (out of 4) + INFO : aggregate_evaluate: received 2 results and 0 failures + WARNING : No evaluate_metrics_aggregation_fn provided + INFO : + INFO : [ROUND 2] + INFO : configure_fit: strategy sampled 2 clients (out of 4) + INFO : aggregate_fit: received 2 results and 0 failures + INFO : configure_evaluate: strategy sampled 2 clients (out of 4) + INFO : aggregate_evaluate: received 2 results and 0 failures + INFO : + INFO : [ROUND 3] + INFO : configure_fit: strategy sampled 2 clients (out of 4) + INFO : aggregate_fit: received 2 results and 0 failures + INFO : configure_evaluate: strategy sampled 2 clients (out of 4) + INFO : aggregate_evaluate: received 2 results and 0 failures + INFO : + INFO : [SUMMARY] + INFO : Run finished 3 round(s) in 136.92s + INFO : History (loss, distributed): + INFO : round 1: 0.04982871934771538 + INFO : round 2: 0.046457378193736076 + INFO : round 3: 0.04506748169660568 + INFO : + +Each simulated `ClientApp` (two per round) will also log a summary of their local +training process. Expect this output to be similar to: + +.. code-block:: shell + + # The left part indicates the process ID running the `ClientApp` + (ClientAppActor pid=38155) ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ + (ClientAppActor pid=38155) ┃ Test metric ┃ DataLoader 0 ┃ + (ClientAppActor pid=38155) ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ + (ClientAppActor pid=38155) │ test_loss │ 0.045175597071647644 │ + (ClientAppActor pid=38155) └───────────────────────────┴───────────────────────────┘ + +You can also override the parameters defined in the ``[tool.flwr.app.config]`` section +in ``pyproject.toml`` like this: + +.. code-block:: shell + + # Override some arguments + $ flwr run . --run-config num-server-rounds=5 .. note:: - Check the `source code - `_ - of this tutorial in ``examples/quickstart-pytorch-lightning`` in the - Flower GitHub repository. + Check the `source code + `_ + of this tutorial in ``examples/quickstart-pytorch-lightning`` in the Flower GitHub + repository. diff --git a/doc/source/tutorial-quickstart-pytorch.rst b/doc/source/tutorial-quickstart-pytorch.rst index d00b9efbe16b..6b99e378d086 100644 --- a/doc/source/tutorial-quickstart-pytorch.rst +++ b/doc/source/tutorial-quickstart-pytorch.rst @@ -1,384 +1,366 @@ .. _quickstart-pytorch: -#################### - Quickstart PyTorch -#################### - -In this federated learning tutorial we will learn how to train a -Convolutional Neural Network on CIFAR-10 using Flower and PyTorch. It is -recommended to create a virtual environment and run everything within a -:doc:`virtualenv `. - -Let's use `flwr new` to create a complete Flower+PyTorch project. It -will generate all the files needed to run, by default with the Flower -Simulation Engine, a federation of 10 nodes using `FedAvg +Quickstart PyTorch +================== + +In this federated learning tutorial we will learn how to train a Convolutional Neural +Network on CIFAR-10 using Flower and PyTorch. It is recommended to create a virtual +environment and run everything within a :doc:`virtualenv +`. + +Let's use `flwr new` to create a complete Flower+PyTorch project. It will generate all +the files needed to run, by default with the Flower Simulation Engine, a federation of +10 nodes using `FedAvg `_. The dataset will be partitioned using Flower Dataset's `IidPartitioner `_. -Now that we have a rough idea of what this example is about, let's get -started. First, install Flower in your new environment: +Now that we have a rough idea of what this example is about, let's get started. First, +install Flower in your new environment: -.. code:: shell +.. code-block:: shell - # In a new Python environment - $ pip install flwr + # In a new Python environment + $ pip install flwr -Then, run the command below. You will be prompted to select one of the -available templates (choose ``PyTorch``), give a name to your project, -and type in your developer name: +Then, run the command below. You will be prompted to select one of the available +templates (choose ``PyTorch``), give a name to your project, and type in your developer +name: -.. code:: shell +.. code-block:: shell - $ flwr new + $ flwr new -After running it you'll notice a new directory with your project name -has been created. It should have the following structure: +After running it you'll notice a new directory with your project name has been created. +It should have the following structure: -.. code:: shell +.. code-block:: shell - - ├── - │ ├── __init__.py - │ ├── client_app.py # Defines your ClientApp - │ ├── server_app.py # Defines your ServerApp - │ └── task.py # Defines your model, training and data loading - ├── pyproject.toml # Project metadata like dependencies and configs - └── README.md + + ├── + │ ├── __init__.py + │ ├── client_app.py # Defines your ClientApp + │ ├── server_app.py # Defines your ServerApp + │ └── task.py # Defines your model, training and data loading + ├── pyproject.toml # Project metadata like dependencies and configs + └── README.md -If you haven't yet installed the project and its dependencies, you can -do so by: +If you haven't yet installed the project and its dependencies, you can do so by: -.. code:: shell +.. code-block:: shell - # From the directory where your pyproject.toml is - $ pip install -e . + # From the directory where your pyproject.toml is + $ pip install -e . To run the project, do: -.. code:: shell +.. code-block:: shell - # Run with default arguments - $ flwr run . + # Run with default arguments + $ flwr run . With default arguments you will see an output like this one: -.. code:: shell - - Loading project configuration... - Success - WARNING : FAB ID is not provided; the default ClientApp will be loaded. - INFO : Starting Flower ServerApp, config: num_rounds=3, no round_timeout - INFO : - INFO : [INIT] - INFO : Using initial global parameters provided by strategy - INFO : Evaluating initial global parameters - INFO : - INFO : [ROUND 1] - INFO : configure_fit: strategy sampled 5 clients (out of 10) - INFO : aggregate_fit: received 5 results and 0 failures - WARNING : No fit_metrics_aggregation_fn provided - INFO : configure_evaluate: strategy sampled 10 clients (out of 10) - INFO : aggregate_evaluate: received 10 results and 0 failures - WARNING : No evaluate_metrics_aggregation_fn provided - INFO : - INFO : [ROUND 2] - INFO : configure_fit: strategy sampled 5 clients (out of 10) - INFO : aggregate_fit: received 5 results and 0 failures - INFO : configure_evaluate: strategy sampled 10 clients (out of 10) - INFO : aggregate_evaluate: received 10 results and 0 failures - INFO : - INFO : [ROUND 3] - INFO : configure_fit: strategy sampled 5 clients (out of 10) - INFO : aggregate_fit: received 5 results and 0 failures - INFO : configure_evaluate: strategy sampled 10 clients (out of 10) - INFO : aggregate_evaluate: received 10 results and 0 failures - INFO : - INFO : [SUMMARY] - INFO : Run finished 3 round(s) in 21.35s - INFO : History (loss, distributed): - INFO : round 1: 2.2978184528648855 - INFO : round 2: 2.173852103948593 - INFO : round 3: 2.039920600131154 - INFO : - -You can also override the parameters defined in the -``[tool.flwr.app.config]`` section in ``pyproject.toml`` like this: - -.. code:: shell - - # Override some arguments - $ flwr run . --run-config "num-server-rounds=5 local-epochs=3" - -What follows is an explanation of each component in the project you just -created: dataset partition, the model, defining the ``ClientApp`` and -defining the ``ServerApp``. - -********** - The Data -********** - -This tutorial uses `Flower Datasets `_ -to easily download and partition the `CIFAR-10` dataset. In this example -you'll make use of the `IidPartitioner +.. code-block:: shell + + Loading project configuration... + Success + WARNING : FAB ID is not provided; the default ClientApp will be loaded. + INFO : Starting Flower ServerApp, config: num_rounds=3, no round_timeout + INFO : + INFO : [INIT] + INFO : Using initial global parameters provided by strategy + INFO : Evaluating initial global parameters + INFO : + INFO : [ROUND 1] + INFO : configure_fit: strategy sampled 5 clients (out of 10) + INFO : aggregate_fit: received 5 results and 0 failures + WARNING : No fit_metrics_aggregation_fn provided + INFO : configure_evaluate: strategy sampled 10 clients (out of 10) + INFO : aggregate_evaluate: received 10 results and 0 failures + WARNING : No evaluate_metrics_aggregation_fn provided + INFO : + INFO : [ROUND 2] + INFO : configure_fit: strategy sampled 5 clients (out of 10) + INFO : aggregate_fit: received 5 results and 0 failures + INFO : configure_evaluate: strategy sampled 10 clients (out of 10) + INFO : aggregate_evaluate: received 10 results and 0 failures + INFO : + INFO : [ROUND 3] + INFO : configure_fit: strategy sampled 5 clients (out of 10) + INFO : aggregate_fit: received 5 results and 0 failures + INFO : configure_evaluate: strategy sampled 10 clients (out of 10) + INFO : aggregate_evaluate: received 10 results and 0 failures + INFO : + INFO : [SUMMARY] + INFO : Run finished 3 round(s) in 21.35s + INFO : History (loss, distributed): + INFO : round 1: 2.2978184528648855 + INFO : round 2: 2.173852103948593 + INFO : round 3: 2.039920600131154 + INFO : + +You can also override the parameters defined in the ``[tool.flwr.app.config]`` section +in ``pyproject.toml`` like this: + +.. code-block:: shell + + # Override some arguments + $ flwr run . --run-config "num-server-rounds=5 local-epochs=3" + +What follows is an explanation of each component in the project you just created: +dataset partition, the model, defining the ``ClientApp`` and defining the ``ServerApp``. + +The Data +-------- + +This tutorial uses `Flower Datasets `_ to easily +download and partition the `CIFAR-10` dataset. In this example you'll make use of the +`IidPartitioner `_ -to generate `num_partitions` partitions. You can choose `other -partitioners -`_ -available in Flower Datasets. Each ``ClientApp`` will call this function -to create dataloaders with the data that correspond to their data -partition. - -.. code:: python - - partitioner = IidPartitioner(num_partitions=num_partitions) - fds = FederatedDataset( - dataset="uoft-cs/cifar10", - partitioners={"train": partitioner}, - ) - partition = fds.load_partition(partition_id) - # Divide data on each node: 80% train, 20% test - partition_train_test = partition.train_test_split(test_size=0.2, seed=42) - pytorch_transforms = Compose([ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) - - - def apply_transforms(batch): - """Apply transforms to the partition from FederatedDataset.""" - batch["img"] = [pytorch_transforms(img) for img in batch["img"]] - return batch - - - partition_train_test = partition_train_test.with_transform(apply_transforms) - trainloader = DataLoader(partition_train_test["train"], batch_size=32, shuffle=True) - testloader = DataLoader(partition_train_test["test"], batch_size=32) - -*********** - The Model -*********** - -We defined a simple Convolutional Neural Network (CNN), but feel free to -replace it with a more sophisticated model if you'd like: - -.. code:: python - - class Net(nn.Module): - """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')""" - - def __init__(self): - super(Net, self).__init__() - self.conv1 = nn.Conv2d(3, 6, 5) - self.pool = nn.MaxPool2d(2, 2) - self.conv2 = nn.Conv2d(6, 16, 5) - self.fc1 = nn.Linear(16 * 5 * 5, 120) - self.fc2 = nn.Linear(120, 84) - self.fc3 = nn.Linear(84, 10) - - def forward(self, x): - x = self.pool(F.relu(self.conv1(x))) - x = self.pool(F.relu(self.conv2(x))) - x = x.view(-1, 16 * 5 * 5) - x = F.relu(self.fc1(x)) - x = F.relu(self.fc2(x)) - return self.fc3(x) - -In addition to defining the model architecture, we also include two -utility functions to perform both training (i.e. ``train()``) and -evaluation (i.e. ``test()``) using the above model. These functions -should look fairly familiar if you have some prior experience with -PyTorch. Note these functions do not have anything specific to Flower. -That being said, the training function will normally be called, as we'll -see later, from a Flower client passing its own data. In summary, your -clients can use standard training/testing functions to perform local -training or evaluation: - -.. code:: python - - def train(net, trainloader, epochs, device): - """Train the model on the training set.""" - net.to(device) # move model to GPU if available - criterion = torch.nn.CrossEntropyLoss().to(device) - optimizer = torch.optim.SGD(net.parameters(), lr=0.1, momentum=0.9) - net.train() - running_loss = 0.0 - for _ in range(epochs): - for batch in trainloader: - images = batch["img"] - labels = batch["label"] - optimizer.zero_grad() - loss = criterion(net(images.to(device)), labels.to(device)) - loss.backward() - optimizer.step() - running_loss += loss.item() - - avg_trainloss = running_loss / len(trainloader) - return avg_trainloss - - - def test(net, testloader, device): - """Validate the model on the test set.""" - net.to(device) - criterion = torch.nn.CrossEntropyLoss() - correct, loss = 0, 0.0 - with torch.no_grad(): - for batch in testloader: - images = batch["img"].to(device) - labels = batch["label"].to(device) - outputs = net(images) - loss += criterion(outputs, labels).item() - correct += (torch.max(outputs.data, 1)[1] == labels).sum().item() - accuracy = correct / len(testloader.dataset) - return loss, accuracy - -*************** - The ClientApp -*************** - -The main changes we have to make to use `PyTorch` with `Flower` will be -found in the ``get_weights()`` and ``set_weights()`` functions. In -``get_weights()`` PyTorch model parameters are extracted and represented -as a list of NumPy arrays. The ``set_weights()`` function that's the -oposite: given a list of NumPy arrays it applies them to an existing -PyTorch model. Doing this in fairly easy in PyTorch. +to generate `num_partitions` partitions. You can choose `other partitioners +`_ available in +Flower Datasets. Each ``ClientApp`` will call this function to create dataloaders with +the data that correspond to their data partition. + +.. code-block:: python + + partitioner = IidPartitioner(num_partitions=num_partitions) + fds = FederatedDataset( + dataset="uoft-cs/cifar10", + partitioners={"train": partitioner}, + ) + partition = fds.load_partition(partition_id) + # Divide data on each node: 80% train, 20% test + partition_train_test = partition.train_test_split(test_size=0.2, seed=42) + pytorch_transforms = Compose([ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) + + + def apply_transforms(batch): + """Apply transforms to the partition from FederatedDataset.""" + batch["img"] = [pytorch_transforms(img) for img in batch["img"]] + return batch + + + partition_train_test = partition_train_test.with_transform(apply_transforms) + trainloader = DataLoader(partition_train_test["train"], batch_size=32, shuffle=True) + testloader = DataLoader(partition_train_test["test"], batch_size=32) + +The Model +--------- + +We defined a simple Convolutional Neural Network (CNN), but feel free to replace it with +a more sophisticated model if you'd like: + +.. code-block:: python + + class Net(nn.Module): + """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')""" + + def __init__(self): + super(Net, self).__init__() + self.conv1 = nn.Conv2d(3, 6, 5) + self.pool = nn.MaxPool2d(2, 2) + self.conv2 = nn.Conv2d(6, 16, 5) + self.fc1 = nn.Linear(16 * 5 * 5, 120) + self.fc2 = nn.Linear(120, 84) + self.fc3 = nn.Linear(84, 10) + + def forward(self, x): + x = self.pool(F.relu(self.conv1(x))) + x = self.pool(F.relu(self.conv2(x))) + x = x.view(-1, 16 * 5 * 5) + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + return self.fc3(x) + +In addition to defining the model architecture, we also include two utility functions to +perform both training (i.e. ``train()``) and evaluation (i.e. ``test()``) using the +above model. These functions should look fairly familiar if you have some prior +experience with PyTorch. Note these functions do not have anything specific to Flower. +That being said, the training function will normally be called, as we'll see later, from +a Flower client passing its own data. In summary, your clients can use standard +training/testing functions to perform local training or evaluation: + +.. code-block:: python + + def train(net, trainloader, epochs, device): + """Train the model on the training set.""" + net.to(device) # move model to GPU if available + criterion = torch.nn.CrossEntropyLoss().to(device) + optimizer = torch.optim.SGD(net.parameters(), lr=0.1, momentum=0.9) + net.train() + running_loss = 0.0 + for _ in range(epochs): + for batch in trainloader: + images = batch["img"] + labels = batch["label"] + optimizer.zero_grad() + loss = criterion(net(images.to(device)), labels.to(device)) + loss.backward() + optimizer.step() + running_loss += loss.item() + + avg_trainloss = running_loss / len(trainloader) + return avg_trainloss + + + def test(net, testloader, device): + """Validate the model on the test set.""" + net.to(device) + criterion = torch.nn.CrossEntropyLoss() + correct, loss = 0, 0.0 + with torch.no_grad(): + for batch in testloader: + images = batch["img"].to(device) + labels = batch["label"].to(device) + outputs = net(images) + loss += criterion(outputs, labels).item() + correct += (torch.max(outputs.data, 1)[1] == labels).sum().item() + accuracy = correct / len(testloader.dataset) + return loss, accuracy + +The ClientApp +------------- + +The main changes we have to make to use `PyTorch` with `Flower` will be found in the +``get_weights()`` and ``set_weights()`` functions. In ``get_weights()`` PyTorch model +parameters are extracted and represented as a list of NumPy arrays. The +``set_weights()`` function that's the oposite: given a list of NumPy arrays it applies +them to an existing PyTorch model. Doing this in fairly easy in PyTorch. .. note:: - The specific implementation of ``get_weights()`` and - ``set_weights()`` depends on the type of models you use. The ones - shown below work for a wide range of PyTorch models but you might - need to adjust them if you have more exotic model architectures. - -.. code:: python - - def get_weights(net): - return [val.cpu().numpy() for _, val in net.state_dict().items()] - - - def set_weights(net, parameters): - params_dict = zip(net.state_dict().keys(), parameters) - state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) - net.load_state_dict(state_dict, strict=True) - -The rest of the functionality is directly inspired by the centralized -case. The ``fit()`` method in the client trains the model using the -local dataset. Similarly, the ``evaluate()`` method is used to evaluate -the model received on a held-out validation set that the client might -have: - -.. code:: python - - class FlowerClient(NumPyClient): - def __init__(self, net, trainloader, valloader, local_epochs): - self.net = net - self.trainloader = trainloader - self.valloader = valloader - self.local_epochs = local_epochs - self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - self.net.to(device) - - def fit(self, parameters, config): - set_weights(self.net, parameters) - results = train( - self.net, - self.trainloader, - self.valloader, - self.local_epochs, - self.device, - ) - return get_weights(self.net), len(self.trainloader.dataset), results - - def evaluate(self, parameters, config): - set_weights(self.net, parameters) - loss, accuracy = test(self.net, self.valloader, self.device) - return loss, len(self.valloader.dataset), {"accuracy": accuracy} - -Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` -defined above by means of a ``client_fn()`` callback. Note that the -`context` enables you to get access to hyperparemeters defined in your -``pyproject.toml`` to configure the run. In this tutorial we access the -`local-epochs` setting to control the number of epochs a ``ClientApp`` -will perform when running the ``fit()`` method. You could define + The specific implementation of ``get_weights()`` and ``set_weights()`` depends on + the type of models you use. The ones shown below work for a wide range of PyTorch + models but you might need to adjust them if you have more exotic model + architectures. + +.. code-block:: python + + def get_weights(net): + return [val.cpu().numpy() for _, val in net.state_dict().items()] + + + def set_weights(net, parameters): + params_dict = zip(net.state_dict().keys(), parameters) + state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict}) + net.load_state_dict(state_dict, strict=True) + +The rest of the functionality is directly inspired by the centralized case. The +``fit()`` method in the client trains the model using the local dataset. Similarly, the +``evaluate()`` method is used to evaluate the model received on a held-out validation +set that the client might have: + +.. code-block:: python + + class FlowerClient(NumPyClient): + def __init__(self, net, trainloader, valloader, local_epochs): + self.net = net + self.trainloader = trainloader + self.valloader = valloader + self.local_epochs = local_epochs + self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + self.net.to(device) + + def fit(self, parameters, config): + set_weights(self.net, parameters) + results = train( + self.net, + self.trainloader, + self.valloader, + self.local_epochs, + self.device, + ) + return get_weights(self.net), len(self.trainloader.dataset), results + + def evaluate(self, parameters, config): + set_weights(self.net, parameters) + loss, accuracy = test(self.net, self.valloader, self.device) + return loss, len(self.valloader.dataset), {"accuracy": accuracy} + +Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` defined above by +means of a ``client_fn()`` callback. Note that the `context` enables you to get access +to hyperparemeters defined in your ``pyproject.toml`` to configure the run. In this +tutorial we access the `local-epochs` setting to control the number of epochs a +``ClientApp`` will perform when running the ``fit()`` method. You could define additioinal hyperparameters in ``pyproject.toml`` and access them here. -.. code:: python +.. code-block:: python - def client_fn(context: Context): - # Load model and data - net = Net() - partition_id = context.node_config["partition-id"] - num_partitions = context.node_config["num-partitions"] - trainloader, valloader = load_data(partition_id, num_partitions) - local_epochs = context.run_config["local-epochs"] + def client_fn(context: Context): + # Load model and data + net = Net() + partition_id = context.node_config["partition-id"] + num_partitions = context.node_config["num-partitions"] + trainloader, valloader = load_data(partition_id, num_partitions) + local_epochs = context.run_config["local-epochs"] - # Return Client instance - return FlowerClient(net, trainloader, valloader, local_epochs).to_client() + # Return Client instance + return FlowerClient(net, trainloader, valloader, local_epochs).to_client() - # Flower ClientApp - app = ClientApp(client_fn) + # Flower ClientApp + app = ClientApp(client_fn) -*************** - The ServerApp -*************** +The ServerApp +------------- -To construct a ``ServerApp`` we define a ``server_fn()`` callback with -an identical signature to that of ``client_fn()`` but the return type is -`ServerAppComponents +To construct a ``ServerApp`` we define a ``server_fn()`` callback with an identical +signature to that of ``client_fn()`` but the return type is `ServerAppComponents `_ as opposed to a `Client -`_. -In this example we use the `FedAvg`. To it we pass a randomly -initialized model that will server as the global model to federated. -Note that the value of ``fraction_fit`` is read from the run config. You -can find the default value defined in the ``pyproject.toml``. +`_. In this +example we use the `FedAvg`. To it we pass a randomly initialized model that will server +as the global model to federated. Note that the value of ``fraction_fit`` is read from +the run config. You can find the default value defined in the ``pyproject.toml``. -.. code:: python +.. code-block:: python - def server_fn(context: Context): - # Read from config - num_rounds = context.run_config["num-server-rounds"] - fraction_fit = context.run_config["fraction-fit"] + def server_fn(context: Context): + # Read from config + num_rounds = context.run_config["num-server-rounds"] + fraction_fit = context.run_config["fraction-fit"] - # Initialize model parameters - ndarrays = get_weights(Net()) - parameters = ndarrays_to_parameters(ndarrays) + # Initialize model parameters + ndarrays = get_weights(Net()) + parameters = ndarrays_to_parameters(ndarrays) - # Define strategy - strategy = FedAvg( - fraction_fit=fraction_fit, - fraction_evaluate=1.0, - min_available_clients=2, - initial_parameters=parameters, - ) - config = ServerConfig(num_rounds=num_rounds) + # Define strategy + strategy = FedAvg( + fraction_fit=fraction_fit, + fraction_evaluate=1.0, + min_available_clients=2, + initial_parameters=parameters, + ) + config = ServerConfig(num_rounds=num_rounds) - return ServerAppComponents(strategy=strategy, config=config) + return ServerAppComponents(strategy=strategy, config=config) - # Create ServerApp - app = ServerApp(server_fn=server_fn) + # Create ServerApp + app = ServerApp(server_fn=server_fn) -Congratulations! You've successfully built and run your first federated -learning system. +Congratulations! You've successfully built and run your first federated learning system. .. note:: - Check the `source code - `_ - of the extended version of this tutorial in - ``examples/quickstart-pytorch`` in the Flower GitHub repository. + Check the `source code + `_ of the + extended version of this tutorial in ``examples/quickstart-pytorch`` in the Flower + GitHub repository. -**************** - Video tutorial -**************** +Video tutorial +-------------- .. note:: - The video shown below shows how to setup a PyTorch + Flower project - using our previously recommended APIs. A new video tutorial will be - released that shows the new APIs (as the content above does) + The video shown below shows how to setup a PyTorch + Flower project using our + previously recommended APIs. A new video tutorial will be released that shows the + new APIs (as the content above does) .. meta:: - :description: Check out this Federated Learning quickstart tutorial for using Flower with PyTorch to train a CNN model on MNIST. + :description: Check out this Federated Learning quickstart tutorial for using Flower with PyTorch to train a CNN model on MNIST. .. youtube:: jOmmuzMIQ4c - :width: 100% + :width: 100% diff --git a/doc/source/tutorial-quickstart-scikitlearn.rst b/doc/source/tutorial-quickstart-scikitlearn.rst index fc3b58925c06..56bdf18cad17 100644 --- a/doc/source/tutorial-quickstart-scikitlearn.rst +++ b/doc/source/tutorial-quickstart-scikitlearn.rst @@ -1,77 +1,89 @@ .. _quickstart-scikitlearn: - Quickstart scikit-learn ======================= .. meta:: - :description: Check out this Federated Learning quickstart tutorial for using Flower with scikit-learn to train a linear regression model. + :description: Check out this Federated Learning quickstart tutorial for using Flower with scikit-learn to train a linear regression model. -In this tutorial, we will learn how to train a :code:`Logistic Regression` model on MNIST using Flower and scikit-learn. +In this tutorial, we will learn how to train a ``Logistic Regression`` model on MNIST +using Flower and scikit-learn. -It is recommended to create a virtual environment and run everything within this :doc:`virtualenv `. +It is recommended to create a virtual environment and run everything within this +:doc:`virtualenv `. Our example consists of one *server* and two *clients* all having the same model. -*Clients* are responsible for generating individual model parameter updates for the model based on their local datasets. -These updates are then sent to the *server* which will aggregate them to produce an updated global model. Finally, the *server* sends this improved version of the model back to each *client*. -A complete cycle of parameters updates is called a *round*. +*Clients* are responsible for generating individual model parameter updates for the +model based on their local datasets. These updates are then sent to the *server* which +will aggregate them to produce an updated global model. Finally, the *server* sends this +improved version of the model back to each *client*. A complete cycle of parameters +updates is called a *round*. -Now that we have a rough idea of what is going on, let's get started. We first need to install Flower. You can do this by running: +Now that we have a rough idea of what is going on, let's get started. We first need to +install Flower. You can do this by running: .. code-block:: shell - $ pip install flwr + $ pip install flwr Since we want to use scikit-learn, let's go ahead and install it: .. code-block:: shell - $ pip install scikit-learn + $ pip install scikit-learn Or simply install all dependencies using Poetry: .. code-block:: shell - $ poetry install - + $ poetry install Flower Client ------------- -Now that we have all our dependencies installed, let's run a simple distributed training with two clients and one server. -However, before setting up the client and server, we will define all functionalities that we need for our federated learning setup within :code:`utils.py`. The :code:`utils.py` contains different functions defining all the machine learning basics: +Now that we have all our dependencies installed, let's run a simple distributed training +with two clients and one server. However, before setting up the client and server, we +will define all functionalities that we need for our federated learning setup within +``utils.py``. The ``utils.py`` contains different functions defining all the machine +learning basics: -* :code:`get_model_parameters()` - * Returns the parameters of a :code:`sklearn` LogisticRegression model -* :code:`set_model_params()` - * Sets the parameters of a :code:`sklearn` LogisticRegression model -* :code:`set_initial_params()` - * Initializes the model parameters that the Flower server will ask for +- ``get_model_parameters()`` + - Returns the parameters of a ``sklearn`` LogisticRegression model +- ``set_model_params()`` + - Sets the parameters of a ``sklearn`` LogisticRegression model +- ``set_initial_params()`` + - Initializes the model parameters that the Flower server will ask for -Please check out :code:`utils.py` `here `_ for more details. -The pre-defined functions are used in the :code:`client.py` and imported. The :code:`client.py` also requires to import several packages such as Flower and scikit-learn: +Please check out ``utils.py`` `here +`_ for +more details. The pre-defined functions are used in the ``client.py`` and imported. The +``client.py`` also requires to import several packages such as Flower and scikit-learn: .. code-block:: python - import argparse - import warnings - - from sklearn.linear_model import LogisticRegression - from sklearn.metrics import log_loss - - import flwr as fl - import utils - from flwr_datasets import FederatedDataset + import argparse + import warnings + + from sklearn.linear_model import LogisticRegression + from sklearn.metrics import log_loss + + import flwr as fl + import utils + from flwr_datasets import FederatedDataset -Prior to local training, we need to load the MNIST dataset, a popular image classification dataset of handwritten digits for machine learning, and partition the dataset for FL. This can be conveniently achieved using `Flower Datasets `_. -The :code:`FederatedDataset.load_partition()` method loads the partitioned training set for each partition ID defined in the :code:`--partition-id` argument. +Prior to local training, we need to load the MNIST dataset, a popular image +classification dataset of handwritten digits for machine learning, and partition the +dataset for FL. This can be conveniently achieved using `Flower Datasets +`_. The ``FederatedDataset.load_partition()`` method +loads the partitioned training set for each partition ID defined in the +``--partition-id`` argument. .. code-block:: python if __name__ == "__main__": N_CLIENTS = 10 - + parser = argparse.ArgumentParser(description="Flower") parser.add_argument( "--partition-id", @@ -82,17 +94,17 @@ The :code:`FederatedDataset.load_partition()` method loads the partitioned train ) args = parser.parse_args() partition_id = args.partition_id - + fds = FederatedDataset(dataset="mnist", partitioners={"train": N_CLIENTS}) - + dataset = fds.load_partition(partition_id, "train").with_format("numpy") X, y = dataset["image"].reshape((len(dataset), -1)), dataset["label"] - + X_train, X_test = X[: int(0.8 * len(X))], X[int(0.8 * len(X)) :] y_train, y_test = y[: int(0.8 * len(y))], y[int(0.8 * len(y)) :] - -Next, the logistic regression model is defined and initialized with :code:`utils.set_initial_params()`. +Next, the logistic regression model is defined and initialized with +``utils.set_initial_params()``. .. code-block:: python @@ -104,28 +116,27 @@ Next, the logistic regression model is defined and initialized with :code:`utils utils.set_initial_params(model) -The Flower server interacts with clients through an interface called -:code:`Client`. When the server selects a particular client for training, it -sends training instructions over the network. The client receives those -instructions and calls one of the :code:`Client` methods to run your code -(i.e., to fit the logistic regression we defined earlier). - -Flower provides a convenience class called :code:`NumPyClient` which makes it -easier to implement the :code:`Client` interface when your workload uses scikit-learn. -Implementing :code:`NumPyClient` usually means defining the following methods -(:code:`set_parameters` is optional though): - -#. :code:`get_parameters` - * return the model weight as a list of NumPy ndarrays -#. :code:`set_parameters` (optional) - * update the local model weights with the parameters received from the server - * is directly imported with :code:`utils.set_model_params()` -#. :code:`fit` - * set the local model weights - * train the local model - * return the updated local model weights -#. :code:`evaluate` - * test the local model +The Flower server interacts with clients through an interface called ``Client``. When +the server selects a particular client for training, it sends training instructions over +the network. The client receives those instructions and calls one of the ``Client`` +methods to run your code (i.e., to fit the logistic regression we defined earlier). + +Flower provides a convenience class called ``NumPyClient`` which makes it easier to +implement the ``Client`` interface when your workload uses scikit-learn. Implementing +``NumPyClient`` usually means defining the following methods (``set_parameters`` is +optional though): + +1. ``get_parameters`` + - return the model weight as a list of NumPy ndarrays +2. ``set_parameters`` (optional) + - update the local model weights with the parameters received from the server + - is directly imported with ``utils.set_model_params()`` +3. ``fit`` + - set the local model weights + - train the local model + - return the updated local model weights +4. ``evaluate`` + - test the local model The methods can be implemented in the following way: @@ -149,27 +160,29 @@ The methods can be implemented in the following way: accuracy = model.score(X_test, y_test) return loss, len(X_test), {"accuracy": accuracy} - -We can now create an instance of our class :code:`MnistClient` and add one line -to actually run this client: +We can now create an instance of our class ``MnistClient`` and add one line to actually +run this client: .. code-block:: python fl.client.start_client("0.0.0.0:8080", client=MnistClient().to_client()) -That's it for the client. We only have to implement :code:`Client` or -:code:`NumPyClient` and call :code:`fl.client.start_client()`. If you implement a client of type :code:`NumPyClient` you'll need to first call its :code:`to_client()` method. The string :code:`"0.0.0.0:8080"` tells the client which server to connect to. In our case we can run the server and the client on the same machine, therefore we use -:code:`"0.0.0.0:8080"`. If we run a truly federated workload with the server and -clients running on different machines, all that needs to change is the -:code:`server_address` we pass to the client. +That's it for the client. We only have to implement ``Client`` or ``NumPyClient`` and +call ``fl.client.start_client()``. If you implement a client of type ``NumPyClient`` +you'll need to first call its ``to_client()`` method. The string ``"0.0.0.0:8080"`` +tells the client which server to connect to. In our case we can run the server and the +client on the same machine, therefore we use ``"0.0.0.0:8080"``. If we run a truly +federated workload with the server and clients running on different machines, all that +needs to change is the ``server_address`` we pass to the client. Flower Server ------------- -The following Flower server is a little bit more advanced and returns an evaluation function for the server-side evaluation. -First, we import again all required libraries such as Flower and scikit-learn. +The following Flower server is a little bit more advanced and returns an evaluation +function for the server-side evaluation. First, we import again all required libraries +such as Flower and scikit-learn. -:code:`server.py`, import Flower and start the server: +``server.py``, import Flower and start the server: .. code-block:: python @@ -179,12 +192,14 @@ First, we import again all required libraries such as Flower and scikit-learn. from sklearn.metrics import log_loss from sklearn.linear_model import LogisticRegression from typing import Dict - + from flwr_datasets import FederatedDataset -The number of federated learning rounds is set in :code:`fit_round()` and the evaluation is defined in :code:`get_evaluate_fn()`. -The evaluation function is called after each federated learning round and gives you information about loss and accuracy. -Note that we also make use of Flower Datasets here to load the test split of the MNIST dataset for server-side evaluation. +The number of federated learning rounds is set in ``fit_round()`` and the evaluation is +defined in ``get_evaluate_fn()``. The evaluation function is called after each federated +learning round and gives you information about loss and accuracy. Note that we also make +use of Flower Datasets here to load the test split of the MNIST dataset for server-side +evaluation. .. code-block:: python @@ -210,7 +225,13 @@ Note that we also make use of Flower Datasets here to load the test split of the return evaluate -The :code:`main` contains the server-side parameter initialization :code:`utils.set_initial_params()` as well as the aggregation strategy :code:`fl.server.strategy:FedAvg()`. The strategy is the default one, federated averaging (or FedAvg), with two clients and evaluation after each federated learning round. The server can be started with the command :code:`fl.server.start_server(server_address="0.0.0.0:8080", strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`. +The ``main`` contains the server-side parameter initialization +``utils.set_initial_params()`` as well as the aggregation strategy +``fl.server.strategy:FedAvg()``. The strategy is the default one, federated averaging +(or FedAvg), with two clients and evaluation after each federated learning round. The +server can be started with the command +``fl.server.start_server(server_address="0.0.0.0:8080", strategy=strategy, +config=fl.server.ServerConfig(num_rounds=3))``. .. code-block:: python @@ -223,21 +244,25 @@ The :code:`main` contains the server-side parameter initialization :code:`utils. evaluate_fn=get_evaluate_fn(model), on_fit_config_fn=fit_round, ) - fl.server.start_server(server_address="0.0.0.0:8080", strategy=strategy, config=fl.server.ServerConfig(num_rounds=3)) - + fl.server.start_server( + server_address="0.0.0.0:8080", + strategy=strategy, + config=fl.server.ServerConfig(num_rounds=3), + ) Train the model, federated! --------------------------- -With both client and server ready, we can now run everything and see federated -learning in action. Federated learning systems usually have a server and multiple clients. We, therefore, have to start the server first: +With both client and server ready, we can now run everything and see federated learning +in action. Federated learning systems usually have a server and multiple clients. We, +therefore, have to start the server first: .. code-block:: shell $ python3 server.py -Once the server is running we can start the clients in different terminals. -Open a new terminal and start the first client: +Once the server is running we can start the clients in different terminals. Open a new +terminal and start the first client: .. code-block:: shell @@ -249,8 +274,8 @@ Open another terminal and start the second client: $ python3 client.py -Each client will have its own dataset. -You should now see how the training does in the very first terminal (the one that started the server): +Each client will have its own dataset. You should now see how the training does in the +very first terminal (the one that started the server): .. code-block:: shell @@ -283,6 +308,7 @@ You should now see how the training does in the very first terminal (the one tha INFO flower 2022-01-13 13:43:21,232 | app.py:122 | app_evaluate: results [('ipv4:127.0.0.1:53980', EvaluateRes(loss=0.5843629240989685, num_examples=10000, accuracy=0.0, metrics={'accuracy': 0.8217})), ('ipv4:127.0.0.1:53982', EvaluateRes(loss=0.5843629240989685, num_examples=10000, accuracy=0.0, metrics={'accuracy': 0.8217}))] INFO flower 2022-01-13 13:43:21,232 | app.py:127 | app_evaluate: failures [] -Congratulations! -You've successfully built and run your first federated learning system. -The full `source code `_ for this example can be found in :code:`examples/sklearn-logreg-mnist`. +Congratulations! You've successfully built and run your first federated learning system. +The full `source code +`_ for this +example can be found in ``examples/sklearn-logreg-mnist``. diff --git a/doc/source/tutorial-quickstart-tensorflow.rst b/doc/source/tutorial-quickstart-tensorflow.rst index ffcd9efeb9bc..66cf69de6390 100644 --- a/doc/source/tutorial-quickstart-tensorflow.rst +++ b/doc/source/tutorial-quickstart-tensorflow.rst @@ -1,307 +1,290 @@ .. _quickstart-tensorflow: -####################### - Quickstart TensorFlow -####################### - -In this tutorial we will learn how to train a Convolutional Neural -Network on CIFAR-10 using the Flower framework and TensorFlow. First of -all, it is recommended to create a virtual environment and run -everything within a :doc:`virtualenv +Quickstart TensorFlow +===================== + +In this tutorial we will learn how to train a Convolutional Neural Network on CIFAR-10 +using the Flower framework and TensorFlow. First of all, it is recommended to create a +virtual environment and run everything within a :doc:`virtualenv `. -Let's use `flwr new` to create a complete Flower+TensorFlow project. It -will generate all the files needed to run, by default with the Flower -Simulation Engine, a federation of 10 nodes using `FedAvg +Let's use `flwr new` to create a complete Flower+TensorFlow project. It will generate +all the files needed to run, by default with the Flower Simulation Engine, a federation +of 10 nodes using `FedAvg `_. The dataset will be partitioned using Flower Dataset's `IidPartitioner `_. -Now that we have a rough idea of what this example is about, let's get -started. First, install Flower in your new environment: +Now that we have a rough idea of what this example is about, let's get started. First, +install Flower in your new environment: -.. code:: shell +.. code-block:: shell - # In a new Python environment - $ pip install flwr + # In a new Python environment + $ pip install flwr -Then, run the command below. You will be prompted to select one of the -available templates (choose ``TensorFlow``), give a name to your -project, and type in your developer name: +Then, run the command below. You will be prompted to select one of the available +templates (choose ``TensorFlow``), give a name to your project, and type in your +developer name: -.. code:: shell +.. code-block:: shell - $ flwr new + $ flwr new -After running it you'll notice a new directory with your project name -has been created. It should have the following structure: +After running it you'll notice a new directory with your project name has been created. +It should have the following structure: -.. code:: shell +.. code-block:: shell - - ├── - │ ├── __init__.py - │ ├── client_app.py # Defines your ClientApp - │ ├── server_app.py # Defines your ServerApp - │ └── task.py # Defines your model, training and data loading - ├── pyproject.toml # Project metadata like dependencies and configs - └── README.md + + ├── + │ ├── __init__.py + │ ├── client_app.py # Defines your ClientApp + │ ├── server_app.py # Defines your ServerApp + │ └── task.py # Defines your model, training and data loading + ├── pyproject.toml # Project metadata like dependencies and configs + └── README.md -If you haven't yet installed the project and its dependencies, you can -do so by: +If you haven't yet installed the project and its dependencies, you can do so by: -.. code:: shell +.. code-block:: shell - # From the directory where your pyproject.toml is - $ pip install -e . + # From the directory where your pyproject.toml is + $ pip install -e . To run the project, do: -.. code:: shell +.. code-block:: shell - # Run with default arguments - $ flwr run . + # Run with default arguments + $ flwr run . With default arguments you will see an output like this one: -.. code:: shell - - Loading project configuration... - Success - INFO : Starting Flower ServerApp, config: num_rounds=3, no round_timeout - INFO : - INFO : [INIT] - INFO : Using initial global parameters provided by strategy - INFO : Starting evaluation of initial global parameters - INFO : Evaluation returned no results (`None`) - INFO : - INFO : [ROUND 1] - INFO : configure_fit: strategy sampled 10 clients (out of 10) - INFO : aggregate_fit: received 10 results and 0 failures - WARNING : No fit_metrics_aggregation_fn provided - INFO : configure_evaluate: strategy sampled 10 clients (out of 10) - INFO : aggregate_evaluate: received 10 results and 0 failures - WARNING : No evaluate_metrics_aggregation_fn provided - INFO : - INFO : [ROUND 2] - INFO : configure_fit: strategy sampled 10 clients (out of 10) - INFO : aggregate_fit: received 10 results and 0 failures - INFO : configure_evaluate: strategy sampled 10 clients (out of 10) - INFO : aggregate_evaluate: received 10 results and 0 failures - INFO : - INFO : [ROUND 3] - INFO : configure_fit: strategy sampled 10 clients (out of 10) - INFO : aggregate_fit: received 10 results and 0 failures - INFO : configure_evaluate: strategy sampled 10 clients (out of 10) - INFO : aggregate_evaluate: received 10 results and 0 failures - INFO : - INFO : [SUMMARY] - INFO : Run finished 3 round(s) in 31.31s - INFO : History (loss, distributed): - INFO : round 1: 1.9066195368766785 - INFO : round 2: 1.657227087020874 - INFO : round 3: 1.559039831161499 - INFO : - -You can also override the parameters defined in the -``[tool.flwr.app.config]`` section in ``pyproject.toml`` like this: - -.. code:: shell - - # Override some arguments - $ flwr run . --run-config "num-server-rounds=5 batch-size=16" - -********** - The Data -********** - -This tutorial uses `Flower Datasets `_ -to easily download and partition the `CIFAR-10` dataset. In this example -you'll make use of the `IidPartitioner +.. code-block:: shell + + Loading project configuration... + Success + INFO : Starting Flower ServerApp, config: num_rounds=3, no round_timeout + INFO : + INFO : [INIT] + INFO : Using initial global parameters provided by strategy + INFO : Starting evaluation of initial global parameters + INFO : Evaluation returned no results (`None`) + INFO : + INFO : [ROUND 1] + INFO : configure_fit: strategy sampled 10 clients (out of 10) + INFO : aggregate_fit: received 10 results and 0 failures + WARNING : No fit_metrics_aggregation_fn provided + INFO : configure_evaluate: strategy sampled 10 clients (out of 10) + INFO : aggregate_evaluate: received 10 results and 0 failures + WARNING : No evaluate_metrics_aggregation_fn provided + INFO : + INFO : [ROUND 2] + INFO : configure_fit: strategy sampled 10 clients (out of 10) + INFO : aggregate_fit: received 10 results and 0 failures + INFO : configure_evaluate: strategy sampled 10 clients (out of 10) + INFO : aggregate_evaluate: received 10 results and 0 failures + INFO : + INFO : [ROUND 3] + INFO : configure_fit: strategy sampled 10 clients (out of 10) + INFO : aggregate_fit: received 10 results and 0 failures + INFO : configure_evaluate: strategy sampled 10 clients (out of 10) + INFO : aggregate_evaluate: received 10 results and 0 failures + INFO : + INFO : [SUMMARY] + INFO : Run finished 3 round(s) in 31.31s + INFO : History (loss, distributed): + INFO : round 1: 1.9066195368766785 + INFO : round 2: 1.657227087020874 + INFO : round 3: 1.559039831161499 + INFO : + +You can also override the parameters defined in the ``[tool.flwr.app.config]`` section +in ``pyproject.toml`` like this: + +.. code-block:: shell + + # Override some arguments + $ flwr run . --run-config "num-server-rounds=5 batch-size=16" + +The Data +-------- + +This tutorial uses `Flower Datasets `_ to easily +download and partition the `CIFAR-10` dataset. In this example you'll make use of the +`IidPartitioner `_ -to generate `num_partitions` partitions. You can choose `other -partitioners -`_ -available in Flower Datasets. Each ``ClientApp`` will call this function -to create the ``NumPy`` arrays that correspond to their data partition. - -.. code:: python - - partitioner = IidPartitioner(num_partitions=num_partitions) - fds = FederatedDataset( - dataset="uoft-cs/cifar10", - partitioners={"train": partitioner}, - ) - partition = fds.load_partition(partition_id, "train") - partition.set_format("numpy") - - # Divide data on each node: 80% train, 20% test - partition = partition.train_test_split(test_size=0.2) - x_train, y_train = partition["train"]["img"] / 255.0, partition["train"]["label"] - x_test, y_test = partition["test"]["img"] / 255.0, partition["test"]["label"] - -*********** - The Model -*********** - -Next, we need a model. We defined a simple Convolutional Neural Network -(CNN), but feel free to replace it with a more sophisticated model if -you'd like: - -.. code:: python - - def load_model(learning_rate: float = 0.001): - # Define a simple CNN for CIFAR-10 and set Adam optimizer - model = keras.Sequential( - [ - keras.Input(shape=(32, 32, 3)), - layers.Conv2D(32, kernel_size=(3, 3), activation="relu"), - layers.MaxPooling2D(pool_size=(2, 2)), - layers.Conv2D(64, kernel_size=(3, 3), activation="relu"), - layers.MaxPooling2D(pool_size=(2, 2)), - layers.Flatten(), - layers.Dropout(0.5), - layers.Dense(10, activation="softmax"), - ] - ) - model.compile( - "adam", - loss="sparse_categorical_crossentropy", - metrics=["accuracy"], - ) - return model - -*************** - The ClientApp -*************** - -With `TensorFlow`, we can use the built-in ``get_weights()`` and -``set_weights()`` functions, which simplifies the implementation with -`Flower`. The rest of the functionality in the ClientApp is directly -inspired by the centralized case. The ``fit()`` method in the client -trains the model using the local dataset. Similarly, the ``evaluate()`` -method is used to evaluate the model received on a held-out validation +to generate `num_partitions` partitions. You can choose `other partitioners +`_ available in +Flower Datasets. Each ``ClientApp`` will call this function to create the ``NumPy`` +arrays that correspond to their data partition. + +.. code-block:: python + + partitioner = IidPartitioner(num_partitions=num_partitions) + fds = FederatedDataset( + dataset="uoft-cs/cifar10", + partitioners={"train": partitioner}, + ) + partition = fds.load_partition(partition_id, "train") + partition.set_format("numpy") + + # Divide data on each node: 80% train, 20% test + partition = partition.train_test_split(test_size=0.2) + x_train, y_train = partition["train"]["img"] / 255.0, partition["train"]["label"] + x_test, y_test = partition["test"]["img"] / 255.0, partition["test"]["label"] + +The Model +--------- + +Next, we need a model. We defined a simple Convolutional Neural Network (CNN), but feel +free to replace it with a more sophisticated model if you'd like: + +.. code-block:: python + + def load_model(learning_rate: float = 0.001): + # Define a simple CNN for CIFAR-10 and set Adam optimizer + model = keras.Sequential( + [ + keras.Input(shape=(32, 32, 3)), + layers.Conv2D(32, kernel_size=(3, 3), activation="relu"), + layers.MaxPooling2D(pool_size=(2, 2)), + layers.Conv2D(64, kernel_size=(3, 3), activation="relu"), + layers.MaxPooling2D(pool_size=(2, 2)), + layers.Flatten(), + layers.Dropout(0.5), + layers.Dense(10, activation="softmax"), + ] + ) + model.compile( + "adam", + loss="sparse_categorical_crossentropy", + metrics=["accuracy"], + ) + return model + +The ClientApp +------------- + +With `TensorFlow`, we can use the built-in ``get_weights()`` and ``set_weights()`` +functions, which simplifies the implementation with `Flower`. The rest of the +functionality in the ClientApp is directly inspired by the centralized case. The +``fit()`` method in the client trains the model using the local dataset. Similarly, the +``evaluate()`` method is used to evaluate the model received on a held-out validation set that the client might have: -.. code:: python - - class FlowerClient(NumPyClient): - def __init__(self, model, data, epochs, batch_size, verbose): - self.model = model - self.x_train, self.y_train, self.x_test, self.y_test = data - self.epochs = epochs - self.batch_size = batch_size - self.verbose = verbose - - def fit(self, parameters, config): - self.model.set_weights(parameters) - self.model.fit( - self.x_train, - self.y_train, - epochs=self.epochs, - batch_size=self.batch_size, - verbose=self.verbose, - ) - return self.model.get_weights(), len(self.x_train), {} - - def evaluate(self, parameters, config): - self.model.set_weights(parameters) - loss, accuracy = self.model.evaluate(self.x_test, self.y_test, verbose=0) - return loss, len(self.x_test), {"accuracy": accuracy} - -Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` -defined above by means of a ``client_fn()`` callback. Note that the -`context` enables you to get access to hyperparameters defined in your -``pyproject.toml`` to configure the run. For example, in this tutorial -we access the `local-epochs` setting to control the number of epochs a -``ClientApp`` will perform when running the ``fit()`` method, in -addition to `batch-size`. You could define additional hyperparameters in -``pyproject.toml`` and access them here. - -.. code:: python - - def client_fn(context: Context): - # Load model and data - net = load_model() - - partition_id = context.node_config["partition-id"] - num_partitions = context.node_config["num-partitions"] - data = load_data(partition_id, num_partitions) - epochs = context.run_config["local-epochs"] - batch_size = context.run_config["batch-size"] - verbose = context.run_config.get("verbose") - - # Return Client instance - return FlowerClient( - net, data, epochs, batch_size, verbose - ).to_client() - - - # Flower ClientApp - app = ClientApp(client_fn=client_fn) - -*************** - The ServerApp -*************** - -To construct a ``ServerApp`` we define a ``server_fn()`` callback with -an identical signature to that of ``client_fn()`` but the return type is -`ServerAppComponents +.. code-block:: python + + class FlowerClient(NumPyClient): + def __init__(self, model, data, epochs, batch_size, verbose): + self.model = model + self.x_train, self.y_train, self.x_test, self.y_test = data + self.epochs = epochs + self.batch_size = batch_size + self.verbose = verbose + + def fit(self, parameters, config): + self.model.set_weights(parameters) + self.model.fit( + self.x_train, + self.y_train, + epochs=self.epochs, + batch_size=self.batch_size, + verbose=self.verbose, + ) + return self.model.get_weights(), len(self.x_train), {} + + def evaluate(self, parameters, config): + self.model.set_weights(parameters) + loss, accuracy = self.model.evaluate(self.x_test, self.y_test, verbose=0) + return loss, len(self.x_test), {"accuracy": accuracy} + +Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` defined above by +means of a ``client_fn()`` callback. Note that the `context` enables you to get access +to hyperparameters defined in your ``pyproject.toml`` to configure the run. For example, +in this tutorial we access the `local-epochs` setting to control the number of epochs a +``ClientApp`` will perform when running the ``fit()`` method, in addition to +`batch-size`. You could define additional hyperparameters in ``pyproject.toml`` and +access them here. + +.. code-block:: python + + def client_fn(context: Context): + # Load model and data + net = load_model() + + partition_id = context.node_config["partition-id"] + num_partitions = context.node_config["num-partitions"] + data = load_data(partition_id, num_partitions) + epochs = context.run_config["local-epochs"] + batch_size = context.run_config["batch-size"] + verbose = context.run_config.get("verbose") + + # Return Client instance + return FlowerClient(net, data, epochs, batch_size, verbose).to_client() + + + # Flower ClientApp + app = ClientApp(client_fn=client_fn) + +The ServerApp +------------- + +To construct a ``ServerApp`` we define a ``server_fn()`` callback with an identical +signature to that of ``client_fn()`` but the return type is `ServerAppComponents `_ as opposed to a `Client -`_. -In this example we use the `FedAvg`. To it we pass a randomly -initialized model that will serve as the global model to federate. +`_. In this +example we use the `FedAvg`. To it we pass a randomly initialized model that will serve +as the global model to federate. -.. code:: python +.. code-block:: python - def server_fn(context: Context): - # Read from config - num_rounds = context.run_config["num-server-rounds"] + def server_fn(context: Context): + # Read from config + num_rounds = context.run_config["num-server-rounds"] - # Get parameters to initialize global model - parameters = ndarrays_to_parameters(load_model().get_weights()) + # Get parameters to initialize global model + parameters = ndarrays_to_parameters(load_model().get_weights()) - # Define strategy - strategy = strategy = FedAvg( - fraction_fit=1.0, - fraction_evaluate=1.0, - min_available_clients=2, - initial_parameters=parameters, - ) - config = ServerConfig(num_rounds=num_rounds) + # Define strategy + strategy = strategy = FedAvg( + fraction_fit=1.0, + fraction_evaluate=1.0, + min_available_clients=2, + initial_parameters=parameters, + ) + config = ServerConfig(num_rounds=num_rounds) - return ServerAppComponents(strategy=strategy, config=config) + return ServerAppComponents(strategy=strategy, config=config) - # Create ServerApp - app = ServerApp(server_fn=server_fn) -Congratulations! You've successfully built and run your first federated -learning system. + # Create ServerApp + app = ServerApp(server_fn=server_fn) -.. note:: +Congratulations! You've successfully built and run your first federated learning system. - Check the source code of the extended version of this tutorial in - |quickstart_tf_link|_ in the Flower GitHub repository. +.. note:: -.. |quickstart_tf_link| replace:: + Check the source code of the extended version of this tutorial in + |quickstart_tf_link|_ in the Flower GitHub repository. - :code:`examples/quickstart-tensorflow` +.. |quickstart_tf_link| replace:: ``examples/quickstart-tensorflow`` .. _quickstart_tf_link: https://github.com/adap/flower/blob/main/examples/quickstart-tensorflow -**************** - Video tutorial -**************** +Video tutorial +-------------- .. note:: - The video shown below shows how to setup a TensorFlow + Flower - project using our previously recommended APIs. A new video tutorial - will be released that shows the new APIs (as the content above does) + The video shown below shows how to setup a TensorFlow + Flower project using our + previously recommended APIs. A new video tutorial will be released that shows the + new APIs (as the content above does) .. meta:: - :description: Check out this Federated Learning quickstart tutorial for using Flower with TensorFlow to train a CNN model on CIFAR-10. + :description: Check out this Federated Learning quickstart tutorial for using Flower with TensorFlow to train a CNN model on CIFAR-10. .. youtube:: FGTc2TQq7VM - :width: 100% + :width: 100% diff --git a/doc/source/tutorial-quickstart-xgboost.rst b/doc/source/tutorial-quickstart-xgboost.rst index 34ad5f6e99c0..fe15227fdf11 100644 --- a/doc/source/tutorial-quickstart-xgboost.rst +++ b/doc/source/tutorial-quickstart-xgboost.rst @@ -1,63 +1,75 @@ .. _quickstart-xgboost: - Quickstart XGBoost -===================== +================== .. meta:: - :description: Check out this Federated Learning quickstart tutorial for using Flower with XGBoost to train classification models on trees. + :description: Check out this Federated Learning quickstart tutorial for using Flower with XGBoost to train classification models on trees. -.. youtube:: AY1vpXUpesc - :width: 100% +.. youtube:: AY1vpXUpesc + :width: 100% Federated XGBoost -------------------- +----------------- -EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient implementation of gradient-boosted decision tree (**GBDT**), that maximises the computational boundaries for boosted tree methods. -It's primarily designed to enhance both the performance and computational speed of machine learning models. -In XGBoost, trees are constructed concurrently, unlike the sequential approach taken by GBDT. +EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient implementation of +gradient-boosted decision tree (**GBDT**), that maximises the computational boundaries +for boosted tree methods. It's primarily designed to enhance both the performance and +computational speed of machine learning models. In XGBoost, trees are constructed +concurrently, unlike the sequential approach taken by GBDT. -Often, for tabular data on medium-sized datasets with fewer than 10k training examples, XGBoost surpasses the results of deep learning techniques. +Often, for tabular data on medium-sized datasets with fewer than 10k training examples, +XGBoost surpasses the results of deep learning techniques. Why federated XGBoost? -~~~~~~~~~~~~~~~~~~~~~~~~~ - -Indeed, as the demand for data privacy and decentralized learning grows, there's an increasing requirement to implement federated XGBoost systems for specialised applications, like survival analysis and financial fraud detection. +~~~~~~~~~~~~~~~~~~~~~~ -Federated learning ensures that raw data remains on the local device, making it an attractive approach for sensitive domains where data security and privacy are paramount. -Given the robustness and efficiency of XGBoost, combining it with federated learning offers a promising solution for these specific challenges. +Indeed, as the demand for data privacy and decentralized learning grows, there's an +increasing requirement to implement federated XGBoost systems for specialised +applications, like survival analysis and financial fraud detection. -In this tutorial we will learn how to train a federated XGBoost model on HIGGS dataset using Flower and :code:`xgboost` package. -We use a simple example (`full code xgboost-quickstart `_) with two *clients* and one *server* -to demonstrate how federated XGBoost works, -and then we dive into a more complex example (`full code xgboost-comprehensive `_) to run various experiments. +Federated learning ensures that raw data remains on the local device, making it an +attractive approach for sensitive domains where data security and privacy are paramount. +Given the robustness and efficiency of XGBoost, combining it with federated learning +offers a promising solution for these specific challenges. +In this tutorial we will learn how to train a federated XGBoost model on HIGGS dataset +using Flower and ``xgboost`` package. We use a simple example (`full code +xgboost-quickstart +`_) with two +*clients* and one *server* to demonstrate how federated XGBoost works, and then we dive +into a more complex example (`full code xgboost-comprehensive +`_) to run +various experiments. Environment Setup --------------------- +----------------- -First of all, it is recommended to create a virtual environment and run everything within a :doc:`virtualenv `. +First of all, it is recommended to create a virtual environment and run everything +within a :doc:`virtualenv `. We first need to install Flower and Flower Datasets. You can do this by running : .. code-block:: shell - $ pip install flwr flwr-datasets + $ pip install flwr flwr-datasets -Since we want to use :code:`xgboost` package to build up XGBoost trees, let's go ahead and install :code:`xgboost`: +Since we want to use ``xgboost`` package to build up XGBoost trees, let's go ahead and +install ``xgboost``: .. code-block:: shell - $ pip install xgboost - + $ pip install xgboost Flower Client ------------------ +------------- -*Clients* are responsible for generating individual weight-updates for the model based on their local datasets. -Now that we have all our dependencies installed, let's run a simple distributed training with two clients and one server. +*Clients* are responsible for generating individual weight-updates for the model based +on their local datasets. Now that we have all our dependencies installed, let's run a +simple distributed training with two clients and one server. -In a file called :code:`client.py`, import xgboost, Flower, Flower Datasets and other related functions: +In a file called ``client.py``, import xgboost, Flower, Flower Datasets and other +related functions: .. code-block:: python @@ -84,9 +96,10 @@ In a file called :code:`client.py`, import xgboost, Flower, Flower Datasets and from flwr_datasets.partitioner import IidPartitioner Dataset partition and hyper-parameter selection -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Prior to local training, we require loading the HIGGS dataset from Flower Datasets and conduct data partitioning for FL: +Prior to local training, we require loading the HIGGS dataset from Flower Datasets and +conduct data partitioning for FL: .. code-block:: python @@ -99,8 +112,9 @@ Prior to local training, we require loading the HIGGS dataset from Flower Datase partition = fds.load_partition(partition_id=args.partition_id, split="train") partition.set_format("numpy") -In this example, we split the dataset into 30 partitions with uniform distribution (:code:`IidPartitioner(num_partitions=30)`). -Then, we load the partition for the given client based on :code:`partition_id`: +In this example, we split the dataset into 30 partitions with uniform distribution +(``IidPartitioner(num_partitions=30)``). Then, we load the partition for the given +client based on ``partition_id``: .. code-block:: python @@ -118,7 +132,8 @@ Then, we load the partition for the given client based on :code:`partition_id`: partition = fds.load_partition(idx=args.partition_id, split="train") partition.set_format("numpy") -After that, we do train/test splitting on the given partition (client's local data), and transform data format for :code:`xgboost` package. +After that, we do train/test splitting on the given partition (client's local data), and +transform data format for ``xgboost`` package. .. code-block:: python @@ -131,7 +146,8 @@ After that, we do train/test splitting on the given partition (client's local da train_dmatrix = transform_dataset_to_dmatrix(train_data) valid_dmatrix = transform_dataset_to_dmatrix(valid_data) -The functions of :code:`train_test_split` and :code:`transform_dataset_to_dmatrix` are defined as below: +The functions of ``train_test_split`` and ``transform_dataset_to_dmatrix`` are defined +as below: .. code-block:: python @@ -171,40 +187,39 @@ Finally, we define the hyper-parameters used for XGBoost training. "tree_method": "hist", } -The :code:`num_local_round` represents the number of iterations for local tree boost. -We use CPU for the training in default. -One can shift it to GPU by setting :code:`tree_method` to :code:`gpu_hist`. -We use AUC as evaluation metric. - +The ``num_local_round`` represents the number of iterations for local tree boost. We use +CPU for the training in default. One can shift it to GPU by setting ``tree_method`` to +``gpu_hist``. We use AUC as evaluation metric. Flower client definition for XGBoost -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -After loading the dataset we define the Flower client. -We follow the general rule to define :code:`XgbClient` class inherited from :code:`fl.client.Client`. +After loading the dataset we define the Flower client. We follow the general rule to +define ``XgbClient`` class inherited from ``fl.client.Client``. .. code-block:: python class XgbClient(fl.client.Client): - def __init__( - self, - train_dmatrix, - valid_dmatrix, - num_train, - num_val, - num_local_round, - params, - ): - self.train_dmatrix = train_dmatrix - self.valid_dmatrix = valid_dmatrix - self.num_train = num_train - self.num_val = num_val - self.num_local_round = num_local_round - self.params = params - -All required parameters defined above are passed to :code:`XgbClient`'s constructor. - -Then, we override :code:`get_parameters`, :code:`fit` and :code:`evaluate` methods insides :code:`XgbClient` class as follows. + def __init__( + self, + train_dmatrix, + valid_dmatrix, + num_train, + num_val, + num_local_round, + params, + ): + self.train_dmatrix = train_dmatrix + self.valid_dmatrix = valid_dmatrix + self.num_train = num_train + self.num_val = num_val + self.num_local_round = num_local_round + self.params = params + +All required parameters defined above are passed to ``XgbClient``'s constructor. + +Then, we override ``get_parameters``, ``fit`` and ``evaluate`` methods insides +``XgbClient`` class as follows. .. code-block:: python @@ -218,9 +233,10 @@ Then, we override :code:`get_parameters`, :code:`fit` and :code:`evaluate` metho parameters=Parameters(tensor_type="", tensors=[]), ) -Unlike neural network training, XGBoost trees are not started from a specified random weights. -In this case, we do not use :code:`get_parameters` and :code:`set_parameters` to initialise model parameters for XGBoost. -As a result, let's return an empty tensor in :code:`get_parameters` when it is called by the server at the first round. +Unlike neural network training, XGBoost trees are not started from a specified random +weights. In this case, we do not use ``get_parameters`` and ``set_parameters`` to +initialise model parameters for XGBoost. As a result, let's return an empty tensor in +``get_parameters`` when it is called by the server at the first round. .. code-block:: python @@ -259,9 +275,10 @@ As a result, let's return an empty tensor in :code:`get_parameters` when it is c metrics={}, ) -In :code:`fit`, at the first round, we call :code:`xgb.train()` to build up the first set of trees. -From the second round, we load the global model sent from server to new build Booster object, -and then update model weights on local training data with function :code:`local_boost` as follows: +In ``fit``, at the first round, we call ``xgb.train()`` to build up the first set of +trees. From the second round, we load the global model sent from server to new build +Booster object, and then update model weights on local training data with function +``local_boost`` as follows: .. code-block:: python @@ -278,8 +295,8 @@ and then update model weights on local training data with function :code:`local_ return bst -Given :code:`num_local_round`, we update trees by calling :code:`bst_input.update` method. -After training, the last :code:`N=num_local_round` trees will be extracted to send to the server. +Given ``num_local_round``, we update trees by calling ``bst_input.update`` method. After +training, the last ``N=num_local_round`` trees will be extracted to send to the server. .. code-block:: python @@ -310,40 +327,42 @@ After training, the last :code:`N=num_local_round` trees will be extracted to se metrics={"AUC": auc}, ) -In :code:`evaluate`, after loading the global model, we call :code:`bst.eval_set` function to conduct evaluation on valid set. -The AUC value will be returned. +In ``evaluate``, after loading the global model, we call ``bst.eval_set`` function to +conduct evaluation on valid set. The AUC value will be returned. -Now, we can create an instance of our class :code:`XgbClient` and add one line to actually run this client: +Now, we can create an instance of our class ``XgbClient`` and add one line to actually +run this client: .. code-block:: python - fl.client.start_client( - server_address="127.0.0.1:8080", - client=XgbClient( - train_dmatrix, - valid_dmatrix, - num_train, - num_val, - num_local_round, - params, - ).to_client(), - ) - -That's it for the client. We only have to implement :code:`Client` and call :code:`fl.client.start_client()`. -The string :code:`"[::]:8080"` tells the client which server to connect to. -In our case we can run the server and the client on the same machine, therefore we use -:code:`"[::]:8080"`. If we run a truly federated workload with the server and -clients running on different machines, all that needs to change is the -:code:`server_address` we point the client at. + fl.client.start_client( + server_address="127.0.0.1:8080", + client=XgbClient( + train_dmatrix, + valid_dmatrix, + num_train, + num_val, + num_local_round, + params, + ).to_client(), + ) +That's it for the client. We only have to implement ``Client`` and call +``fl.client.start_client()``. The string ``"[::]:8080"`` tells the client which server +to connect to. In our case we can run the server and the client on the same machine, +therefore we use ``"[::]:8080"``. If we run a truly federated workload with the server +and clients running on different machines, all that needs to change is the +``server_address`` we point the client at. Flower Server ------------------- +------------- -These updates are then sent to the *server* which will aggregate them to produce a better model. -Finally, the *server* sends this improved version of the model back to each *client* to finish a complete FL round. +These updates are then sent to the *server* which will aggregate them to produce a +better model. Finally, the *server* sends this improved version of the model back to +each *client* to finish a complete FL round. -In a file named :code:`server.py`, import Flower and FedXgbBagging from :code:`flwr.server.strategy`. +In a file named ``server.py``, import Flower and FedXgbBagging from +``flwr.server.strategy``. We first define a strategy for XGBoost bagging aggregation. @@ -361,6 +380,7 @@ We first define a strategy for XGBoost bagging aggregation. on_fit_config_fn=config_func, ) + def evaluate_metrics_aggregation(eval_metrics): """Return an aggregated metric (AUC) for evaluation.""" total_num = sum([num for num, _ in eval_metrics]) @@ -370,6 +390,7 @@ We first define a strategy for XGBoost bagging aggregation. metrics_aggregated = {"AUC": auc_aggregated} return metrics_aggregated + def config_func(rnd: int) -> Dict[str, str]: """Return a configuration with global epochs.""" config = { @@ -377,9 +398,10 @@ We first define a strategy for XGBoost bagging aggregation. } return config -We use two clients for this example. -An :code:`evaluate_metrics_aggregation` function is defined to collect and wighted average the AUC values from clients. -The :code:`config_func` function is to return the current FL round number to client's :code:`fit()` and :code:`evaluate()` methods. +We use two clients for this example. An ``evaluate_metrics_aggregation`` function is +defined to collect and wighted average the AUC values from clients. The ``config_func`` +function is to return the current FL round number to client's ``fit()`` and +``evaluate()`` methods. Then, we start the server: @@ -393,12 +415,13 @@ Then, we start the server: ) Tree-based bagging aggregation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ You must be curious about how bagging aggregation works. Let's look into the details. -In file :code:`flwr.server.strategy.fedxgb_bagging.py`, we define :code:`FedXgbBagging` inherited from :code:`flwr.server.strategy.FedAvg`. -Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` and :code:`evaluate` methods as follows: +In file ``flwr.server.strategy.fedxgb_bagging.py``, we define ``FedXgbBagging`` +inherited from ``flwr.server.strategy.FedAvg``. Then, we override the ``aggregate_fit``, +``aggregate_evaluate`` and ``evaluate`` methods as follows: .. code-block:: python @@ -493,7 +516,8 @@ Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` and :cod loss, metrics = eval_res return loss, metrics -In :code:`aggregate_fit`, we sequentially aggregate the clients' XGBoost trees by calling :code:`aggregate()` function: +In ``aggregate_fit``, we sequentially aggregate the clients' XGBoost trees by calling +``aggregate()`` function: .. code-block:: python @@ -552,28 +576,27 @@ In :code:`aggregate_fit`, we sequentially aggregate the clients' XGBoost trees b ) return tree_num, paral_tree_num -In this function, we first fetch the number of trees and the number of parallel trees for the current and previous model -by calling :code:`_get_tree_nums`. -Then, the fetched information will be aggregated. -After that, the trees (containing model weights) are aggregated to generate a new tree model. - -After traversal of all clients' models, a new global model is generated, -followed by the serialisation, and sending back to each client. +In this function, we first fetch the number of trees and the number of parallel trees +for the current and previous model by calling ``_get_tree_nums``. Then, the fetched +information will be aggregated. After that, the trees (containing model weights) are +aggregated to generate a new tree model. +After traversal of all clients' models, a new global model is generated, followed by the +serialisation, and sending back to each client. Launch Federated XGBoost! -------------------------------- +------------------------- -With both client and server ready, we can now run everything and see federated -learning in action. FL systems usually have a server and multiple clients. We -therefore have to start the server first: +With both client and server ready, we can now run everything and see federated learning +in action. FL systems usually have a server and multiple clients. We therefore have to +start the server first: .. code-block:: shell $ python3 server.py -Once the server is running we can start the clients in different terminals. -Open a new terminal and start the first client: +Once the server is running we can start the clients in different terminals. Open a new +terminal and start the first client: .. code-block:: shell @@ -585,8 +608,8 @@ Open another terminal and start the second client: $ python3 client.py --partition-id=1 -Each client will have its own dataset. -You should now see how the training does in the very first terminal (the one that started the server): +Each client will have its own dataset. You should now see how the training does in the +very first terminal (the one that started the server): .. code-block:: shell @@ -629,192 +652,197 @@ You should now see how the training does in the very first terminal (the one tha INFO : INFO : [SUMMARY] INFO : Run finished 5 round(s) in 1.67s - INFO : History (loss, distributed): - INFO : round 1: 0 - INFO : round 2: 0 - INFO : round 3: 0 - INFO : round 4: 0 - INFO : round 5: 0 - INFO : History (metrics, distributed, evaluate): - INFO : {'AUC': [(1, 0.76755), (2, 0.775), (3, 0.77935), (4, 0.7836), (5, 0.7872)]} - -Congratulations! -You've successfully built and run your first federated XGBoost system. -The AUC values can be checked in :code:`metrics_distributed`. -One can see that the average AUC increases over FL rounds. - -The full `source code `_ for this example can be found in :code:`examples/xgboost-quickstart`. - + INFO : History (loss, distributed): + INFO : round 1: 0 + INFO : round 2: 0 + INFO : round 3: 0 + INFO : round 4: 0 + INFO : round 5: 0 + INFO : History (metrics, distributed, evaluate): + INFO : {'AUC': [(1, 0.76755), (2, 0.775), (3, 0.77935), (4, 0.7836), (5, 0.7872)]} + +Congratulations! You've successfully built and run your first federated XGBoost system. +The AUC values can be checked in ``metrics_distributed``. One can see that the average +AUC increases over FL rounds. + +The full `source code +`_ for this +example can be found in ``examples/xgboost-quickstart``. Comprehensive Federated XGBoost ------------------------------------ +------------------------------- -Now that you have known how federated XGBoost work with Flower, it's time to run some more comprehensive experiments by customising the experimental settings. -In the xgboost-comprehensive example (`full code `_), -we provide more options to define various experimental setups, including aggregation strategies, data partitioning and centralised/distributed evaluation. -We also support :doc:`Flower simulation ` making it easy to simulate large client cohorts in a resource-aware manner. -Let's take a look! +Now that you have known how federated XGBoost work with Flower, it's time to run some +more comprehensive experiments by customising the experimental settings. In the +xgboost-comprehensive example (`full code +`_), we provide +more options to define various experimental setups, including aggregation strategies, +data partitioning and centralised/distributed evaluation. We also support :doc:`Flower +simulation ` making it easy to simulate large client cohorts in +a resource-aware manner. Let's take a look! Cyclic training -~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~ -In addition to bagging aggregation, we offer a cyclic training scheme, which performs FL in a client-by-client fashion. -Instead of aggregating multiple clients, there is only one single client participating in the training per round in the cyclic training scenario. -The trained local XGBoost trees will be passed to the next client as an initialised model for next round's boosting. +In addition to bagging aggregation, we offer a cyclic training scheme, which performs FL +in a client-by-client fashion. Instead of aggregating multiple clients, there is only +one single client participating in the training per round in the cyclic training +scenario. The trained local XGBoost trees will be passed to the next client as an +initialised model for next round's boosting. -To do this, we first customise a :code:`ClientManager` in :code:`server_utils.py`: +To do this, we first customise a ``ClientManager`` in ``server_utils.py``: .. code-block:: python - class CyclicClientManager(SimpleClientManager): - """Provides a cyclic client selection rule.""" - - def sample( - self, - num_clients: int, - min_num_clients: Optional[int] = None, - criterion: Optional[Criterion] = None, - ) -> List[ClientProxy]: - """Sample a number of Flower ClientProxy instances.""" - - # Block until at least num_clients are connected. - if min_num_clients is None: - min_num_clients = num_clients - self.wait_for(min_num_clients) - - # Sample clients which meet the criterion - available_cids = list(self.clients) - if criterion is not None: - available_cids = [ - cid for cid in available_cids if criterion.select(self.clients[cid]) - ] - - if num_clients > len(available_cids): - log( - INFO, - "Sampling failed: number of available clients" - " (%s) is less than number of requested clients (%s).", - len(available_cids), - num_clients, - ) - return [] - - # Return all available clients - return [self.clients[cid] for cid in available_cids] - -The customised :code:`ClientManager` samples all available clients in each FL round based on the order of connection to the server. -Then, we define a new strategy :code:`FedXgbCyclic` in :code:`flwr.server.strategy.fedxgb_cyclic.py`, -in order to sequentially select only one client in given round and pass the received model to next client. + class CyclicClientManager(SimpleClientManager): + """Provides a cyclic client selection rule.""" + + def sample( + self, + num_clients: int, + min_num_clients: Optional[int] = None, + criterion: Optional[Criterion] = None, + ) -> List[ClientProxy]: + """Sample a number of Flower ClientProxy instances.""" + + # Block until at least num_clients are connected. + if min_num_clients is None: + min_num_clients = num_clients + self.wait_for(min_num_clients) + + # Sample clients which meet the criterion + available_cids = list(self.clients) + if criterion is not None: + available_cids = [ + cid for cid in available_cids if criterion.select(self.clients[cid]) + ] + + if num_clients > len(available_cids): + log( + INFO, + "Sampling failed: number of available clients" + " (%s) is less than number of requested clients (%s).", + len(available_cids), + num_clients, + ) + return [] + + # Return all available clients + return [self.clients[cid] for cid in available_cids] + +The customised ``ClientManager`` samples all available clients in each FL round based on +the order of connection to the server. Then, we define a new strategy ``FedXgbCyclic`` +in ``flwr.server.strategy.fedxgb_cyclic.py``, in order to sequentially select only one +client in given round and pass the received model to next client. .. code-block:: python - class FedXgbCyclic(FedAvg): - """Configurable FedXgbCyclic strategy implementation.""" - - # pylint: disable=too-many-arguments,too-many-instance-attributes, line-too-long - def __init__( - self, - **kwargs: Any, - ): - self.global_model: Optional[bytes] = None - super().__init__(**kwargs) - - def aggregate_fit( - self, - server_round: int, - results: List[Tuple[ClientProxy, FitRes]], - failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]], - ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]: - """Aggregate fit results using bagging.""" - if not results: - return None, {} - # Do not aggregate if there are failures and failures are not accepted - if not self.accept_failures and failures: - return None, {} - - # Fetch the client model from last round as global model - for _, fit_res in results: - update = fit_res.parameters.tensors - for bst in update: - self.global_model = bst - - return ( - Parameters(tensor_type="", tensors=[cast(bytes, self.global_model)]), - {}, - ) - -Unlike the original :code:`FedAvg`, we don't perform aggregation here. -Instead, we just make a copy of the received client model as global model by overriding :code:`aggregate_fit`. - -Also, the customised :code:`configure_fit` and :code:`configure_evaluate` methods ensure the clients to be sequentially selected given FL round: + class FedXgbCyclic(FedAvg): + """Configurable FedXgbCyclic strategy implementation.""" + + # pylint: disable=too-many-arguments,too-many-instance-attributes, line-too-long + def __init__( + self, + **kwargs: Any, + ): + self.global_model: Optional[bytes] = None + super().__init__(**kwargs) + + def aggregate_fit( + self, + server_round: int, + results: List[Tuple[ClientProxy, FitRes]], + failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]], + ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]: + """Aggregate fit results using bagging.""" + if not results: + return None, {} + # Do not aggregate if there are failures and failures are not accepted + if not self.accept_failures and failures: + return None, {} + + # Fetch the client model from last round as global model + for _, fit_res in results: + update = fit_res.parameters.tensors + for bst in update: + self.global_model = bst + + return ( + Parameters(tensor_type="", tensors=[cast(bytes, self.global_model)]), + {}, + ) + +Unlike the original ``FedAvg``, we don't perform aggregation here. Instead, we just make +a copy of the received client model as global model by overriding ``aggregate_fit``. + +Also, the customised ``configure_fit`` and ``configure_evaluate`` methods ensure the +clients to be sequentially selected given FL round: .. code-block:: python - def configure_fit( - self, server_round: int, parameters: Parameters, client_manager: ClientManager - ) -> List[Tuple[ClientProxy, FitIns]]: - """Configure the next round of training.""" - config = {} - if self.on_fit_config_fn is not None: - # Custom fit config function provided - config = self.on_fit_config_fn(server_round) - fit_ins = FitIns(parameters, config) - - # Sample clients - sample_size, min_num_clients = self.num_fit_clients( - client_manager.num_available() - ) - clients = client_manager.sample( - num_clients=sample_size, - min_num_clients=min_num_clients, - ) - - # Sample the clients sequentially given server_round - sampled_idx = (server_round - 1) % len(clients) - sampled_clients = [clients[sampled_idx]] - - # Return client/config pairs - return [(client, fit_ins) for client in sampled_clients] - - def configure_evaluate( - self, server_round: int, parameters: Parameters, client_manager: ClientManager - ) -> List[Tuple[ClientProxy, EvaluateIns]]: - """Configure the next round of evaluation.""" - # Do not configure federated evaluation if fraction eval is 0. - if self.fraction_evaluate == 0.0: - return [] - - # Parameters and config - config = {} - if self.on_evaluate_config_fn is not None: - # Custom evaluation config function provided - config = self.on_evaluate_config_fn(server_round) - evaluate_ins = EvaluateIns(parameters, config) - - # Sample clients - sample_size, min_num_clients = self.num_evaluation_clients( - client_manager.num_available() - ) - clients = client_manager.sample( - num_clients=sample_size, - min_num_clients=min_num_clients, - ) - - # Sample the clients sequentially given server_round - sampled_idx = (server_round - 1) % len(clients) - sampled_clients = [clients[sampled_idx]] - - # Return client/config pairs - return [(client, evaluate_ins) for client in sampled_clients] + def configure_fit( + self, server_round: int, parameters: Parameters, client_manager: ClientManager + ) -> List[Tuple[ClientProxy, FitIns]]: + """Configure the next round of training.""" + config = {} + if self.on_fit_config_fn is not None: + # Custom fit config function provided + config = self.on_fit_config_fn(server_round) + fit_ins = FitIns(parameters, config) + + # Sample clients + sample_size, min_num_clients = self.num_fit_clients(client_manager.num_available()) + clients = client_manager.sample( + num_clients=sample_size, + min_num_clients=min_num_clients, + ) + + # Sample the clients sequentially given server_round + sampled_idx = (server_round - 1) % len(clients) + sampled_clients = [clients[sampled_idx]] + + # Return client/config pairs + return [(client, fit_ins) for client in sampled_clients] + def configure_evaluate( + self, server_round: int, parameters: Parameters, client_manager: ClientManager + ) -> List[Tuple[ClientProxy, EvaluateIns]]: + """Configure the next round of evaluation.""" + # Do not configure federated evaluation if fraction eval is 0. + if self.fraction_evaluate == 0.0: + return [] + + # Parameters and config + config = {} + if self.on_evaluate_config_fn is not None: + # Custom evaluation config function provided + config = self.on_evaluate_config_fn(server_round) + evaluate_ins = EvaluateIns(parameters, config) + + # Sample clients + sample_size, min_num_clients = self.num_evaluation_clients( + client_manager.num_available() + ) + clients = client_manager.sample( + num_clients=sample_size, + min_num_clients=min_num_clients, + ) + + # Sample the clients sequentially given server_round + sampled_idx = (server_round - 1) % len(clients) + sampled_clients = [clients[sampled_idx]] + + # Return client/config pairs + return [(client, evaluate_ins) for client in sampled_clients] Customised data partitioning -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -In :code:`dataset.py`, we have a function :code:`instantiate_partitioner` to instantiate the data partitioner -based on the given :code:`num_partitions` and :code:`partitioner_type`. -Currently, we provide four supported partitioner type to simulate the uniformity/non-uniformity in data quantity (uniform, linear, square, exponential). +In ``dataset.py``, we have a function ``instantiate_partitioner`` to instantiate the +data partitioner based on the given ``num_partitions`` and ``partitioner_type``. +Currently, we provide four supported partitioner type to simulate the +uniformity/non-uniformity in data quantity (uniform, linear, square, exponential). .. code-block:: python @@ -841,11 +869,10 @@ Currently, we provide four supported partitioner type to simulate the uniformity ) return partitioner - Customised centralised/distributed evaluation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -To facilitate centralised evaluation, we define a function in :code:`server_utils.py`: +To facilitate centralised evaluation, we define a function in ``server_utils.py``: .. code-block:: python @@ -877,105 +904,112 @@ To facilitate centralised evaluation, we define a function in :code:`server_util return evaluate_fn -This function returns a evaluation function which instantiates a :code:`Booster` object and loads the global model weights to it. -The evaluation is conducted by calling :code:`eval_set()` method, and the tested AUC value is reported. +This function returns a evaluation function which instantiates a ``Booster`` object and +loads the global model weights to it. The evaluation is conducted by calling +``eval_set()`` method, and the tested AUC value is reported. As for distributed evaluation on the clients, it's same as the quick-start example by -overriding the :code:`evaluate()` method insides the :code:`XgbClient` class in :code:`client_utils.py`. +overriding the ``evaluate()`` method insides the ``XgbClient`` class in +``client_utils.py``. Flower simulation -~~~~~~~~~~~~~~~~~~~~ -We also provide an example code (:code:`sim.py`) to use the simulation capabilities of Flower to simulate federated XGBoost training on either a single machine or a cluster of machines. - -.. code-block:: python +~~~~~~~~~~~~~~~~~ - from logging import INFO - import xgboost as xgb - from tqdm import tqdm - - import flwr as fl - from flwr_datasets import FederatedDataset - from flwr.common.logger import log - from flwr.server.strategy import FedXgbBagging, FedXgbCyclic - - from dataset import ( - instantiate_partitioner, - train_test_split, - transform_dataset_to_dmatrix, - separate_xy, - resplit, - ) - from utils import ( - sim_args_parser, - NUM_LOCAL_ROUND, - BST_PARAMS, - ) - from server_utils import ( - eval_config, - fit_config, - evaluate_metrics_aggregation, - get_evaluate_fn, - CyclicClientManager, - ) - from client_utils import XgbClient - -After importing all required packages, we define a :code:`main()` function to perform the simulation process: +We also provide an example code (``sim.py``) to use the simulation capabilities of +Flower to simulate federated XGBoost training on either a single machine or a cluster of +machines. .. code-block:: python - def main(): - # Parse arguments for experimental settings - args = sim_args_parser() + from logging import INFO + import xgboost as xgb + from tqdm import tqdm - # Load (HIGGS) dataset and conduct partitioning - partitioner = instantiate_partitioner( - partitioner_type=args.partitioner_type, num_partitions=args.pool_size + import flwr as fl + from flwr_datasets import FederatedDataset + from flwr.common.logger import log + from flwr.server.strategy import FedXgbBagging, FedXgbCyclic + + from dataset import ( + instantiate_partitioner, + train_test_split, + transform_dataset_to_dmatrix, + separate_xy, + resplit, + ) + from utils import ( + sim_args_parser, + NUM_LOCAL_ROUND, + BST_PARAMS, ) - fds = FederatedDataset( - dataset="jxie/higgs", - partitioners={"train": partitioner}, - resplitter=resplit, + from server_utils import ( + eval_config, + fit_config, + evaluate_metrics_aggregation, + get_evaluate_fn, + CyclicClientManager, ) + from client_utils import XgbClient - # Load centralised test set - if args.centralised_eval or args.centralised_eval_client: - log(INFO, "Loading centralised test set...") - test_data = fds.load_split("test") - test_data.set_format("numpy") - num_test = test_data.shape[0] - test_dmatrix = transform_dataset_to_dmatrix(test_data) - - # Load partitions and reformat data to DMatrix for xgboost - log(INFO, "Loading client local partitions...") - train_data_list = [] - valid_data_list = [] - - # Load and process all client partitions. This upfront cost is amortized soon - # after the simulation begins since clients wont need to preprocess their partition. - for node_id in tqdm(range(args.pool_size), desc="Extracting client partition"): - # Extract partition for client with node_id - partition = fds.load_partition(node_id=node_id, split="train") - partition.set_format("numpy") - - if args.centralised_eval_client: - # Use centralised test set for evaluation - train_data = partition - num_train = train_data.shape[0] - x_test, y_test = separate_xy(test_data) - valid_data_list.append(((x_test, y_test), num_test)) - else: - # Train/test splitting - train_data, valid_data, num_train, num_val = train_test_split( - partition, test_fraction=args.test_fraction, seed=args.seed - ) - x_valid, y_valid = separate_xy(valid_data) - valid_data_list.append(((x_valid, y_valid), num_val)) +After importing all required packages, we define a ``main()`` function to perform the +simulation process: - x_train, y_train = separate_xy(train_data) - train_data_list.append(((x_train, y_train), num_train)) +.. code-block:: python + + def main(): + # Parse arguments for experimental settings + args = sim_args_parser() -We first load the dataset and perform data partitioning, and the pre-processed data is stored in a :code:`list`. -After the simulation begins, the clients won't need to pre-process their partitions again. + # Load (HIGGS) dataset and conduct partitioning + partitioner = instantiate_partitioner( + partitioner_type=args.partitioner_type, num_partitions=args.pool_size + ) + fds = FederatedDataset( + dataset="jxie/higgs", + partitioners={"train": partitioner}, + resplitter=resplit, + ) + + # Load centralised test set + if args.centralised_eval or args.centralised_eval_client: + log(INFO, "Loading centralised test set...") + test_data = fds.load_split("test") + test_data.set_format("numpy") + num_test = test_data.shape[0] + test_dmatrix = transform_dataset_to_dmatrix(test_data) + + # Load partitions and reformat data to DMatrix for xgboost + log(INFO, "Loading client local partitions...") + train_data_list = [] + valid_data_list = [] + + # Load and process all client partitions. This upfront cost is amortized soon + # after the simulation begins since clients wont need to preprocess their partition. + for node_id in tqdm(range(args.pool_size), desc="Extracting client partition"): + # Extract partition for client with node_id + partition = fds.load_partition(node_id=node_id, split="train") + partition.set_format("numpy") + + if args.centralised_eval_client: + # Use centralised test set for evaluation + train_data = partition + num_train = train_data.shape[0] + x_test, y_test = separate_xy(test_data) + valid_data_list.append(((x_test, y_test), num_test)) + else: + # Train/test splitting + train_data, valid_data, num_train, num_val = train_test_split( + partition, test_fraction=args.test_fraction, seed=args.seed + ) + x_valid, y_valid = separate_xy(valid_data) + valid_data_list.append(((x_valid, y_valid), num_val)) + + x_train, y_train = separate_xy(train_data) + train_data_list.append(((x_train, y_train), num_train)) + +We first load the dataset and perform data partitioning, and the pre-processed data is +stored in a ``list``. After the simulation begins, the clients won't need to pre-process +their partitions again. Then, we define the strategies and other hyper-parameters: @@ -985,21 +1019,21 @@ Then, we define the strategies and other hyper-parameters: if args.train_method == "bagging": # Bagging training strategy = FedXgbBagging( - evaluate_function=get_evaluate_fn(test_dmatrix) - if args.centralised_eval - else None, + evaluate_function=( + get_evaluate_fn(test_dmatrix) if args.centralised_eval else None + ), fraction_fit=(float(args.num_clients_per_round) / args.pool_size), min_fit_clients=args.num_clients_per_round, min_available_clients=args.pool_size, - min_evaluate_clients=args.num_evaluate_clients - if not args.centralised_eval - else 0, + min_evaluate_clients=( + args.num_evaluate_clients if not args.centralised_eval else 0 + ), fraction_evaluate=1.0 if not args.centralised_eval else 0.0, on_evaluate_config_fn=eval_config, on_fit_config_fn=fit_config, - evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation - if not args.centralised_eval - else None, + evaluate_metrics_aggregation_fn=( + evaluate_metrics_aggregation if not args.centralised_eval else None + ), ) else: # Cyclic training @@ -1028,7 +1062,7 @@ Then, we define the strategies and other hyper-parameters: new_lr = params["eta"] / args.pool_size params.update({"eta": new_lr}) -After that, we start the simulation by calling :code:`fl.simulation.start_simulation`: +After that, we start the simulation by calling ``fl.simulation.start_simulation``: .. code-block:: python @@ -1048,53 +1082,52 @@ After that, we start the simulation by calling :code:`fl.simulation.start_simula client_manager=CyclicClientManager() if args.train_method == "cyclic" else None, ) -One of key parameters for :code:`start_simulation` is :code:`client_fn` which returns a function to construct a client. -We define it as follows: +One of key parameters for ``start_simulation`` is ``client_fn`` which returns a function +to construct a client. We define it as follows: .. code-block:: python - def get_client_fn( - train_data_list, valid_data_list, train_method, params, num_local_round - ): - """Return a function to construct a client. - - The VirtualClientEngine will execute this function whenever a client is sampled by - the strategy to participate. - """ - - def client_fn(cid: str) -> fl.client.Client: - """Construct a FlowerClient with its own dataset partition.""" - x_train, y_train = train_data_list[int(cid)][0] - x_valid, y_valid = valid_data_list[int(cid)][0] - - # Reformat data to DMatrix - train_dmatrix = xgb.DMatrix(x_train, label=y_train) - valid_dmatrix = xgb.DMatrix(x_valid, label=y_valid) - - # Fetch the number of examples - num_train = train_data_list[int(cid)][1] - num_val = valid_data_list[int(cid)][1] - - # Create and return client - return XgbClient( - train_dmatrix, - valid_dmatrix, - num_train, - num_val, - num_local_round, - params, - train_method, - ) - - return client_fn - + def get_client_fn( + train_data_list, valid_data_list, train_method, params, num_local_round + ): + """Return a function to construct a client. + + The VirtualClientEngine will execute this function whenever a client is sampled by + the strategy to participate. + """ + + def client_fn(cid: str) -> fl.client.Client: + """Construct a FlowerClient with its own dataset partition.""" + x_train, y_train = train_data_list[int(cid)][0] + x_valid, y_valid = valid_data_list[int(cid)][0] + + # Reformat data to DMatrix + train_dmatrix = xgb.DMatrix(x_train, label=y_train) + valid_dmatrix = xgb.DMatrix(x_valid, label=y_valid) + + # Fetch the number of examples + num_train = train_data_list[int(cid)][1] + num_val = valid_data_list[int(cid)][1] + + # Create and return client + return XgbClient( + train_dmatrix, + valid_dmatrix, + num_train, + num_val, + num_local_round, + params, + train_method, + ) + return client_fn Arguments parser -~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~ -In :code:`utils.py`, we define the arguments parsers for clients, server and simulation, allowing users to specify different experimental settings. -Let's first see the sever side: +In ``utils.py``, we define the arguments parsers for clients, server and simulation, +allowing users to specify different experimental settings. Let's first see the sever +side: .. code-block:: python @@ -1102,190 +1135,192 @@ Let's first see the sever side: def server_args_parser(): - """Parse arguments to define experimental settings on server side.""" - parser = argparse.ArgumentParser() - - parser.add_argument( - "--train-method", - default="bagging", - type=str, - choices=["bagging", "cyclic"], - help="Training methods selected from bagging aggregation or cyclic training.", - ) - parser.add_argument( - "--pool-size", default=2, type=int, help="Number of total clients." - ) - parser.add_argument( - "--num-rounds", default=5, type=int, help="Number of FL rounds." - ) - parser.add_argument( - "--num-clients-per-round", - default=2, - type=int, - help="Number of clients participate in training each round.", - ) - parser.add_argument( - "--num-evaluate-clients", - default=2, - type=int, - help="Number of clients selected for evaluation.", - ) - parser.add_argument( - "--centralised-eval", - action="store_true", - help="Conduct centralised evaluation (True), or client evaluation on hold-out data (False).", - ) - - args = parser.parse_args() - return args - -This allows user to specify training strategies / the number of total clients / FL rounds / participating clients / clients for evaluation, -and evaluation fashion. Note that with :code:`--centralised-eval`, the sever will do centralised evaluation -and all functionalities for client evaluation will be disabled. + """Parse arguments to define experimental settings on server side.""" + parser = argparse.ArgumentParser() + + parser.add_argument( + "--train-method", + default="bagging", + type=str, + choices=["bagging", "cyclic"], + help="Training methods selected from bagging aggregation or cyclic training.", + ) + parser.add_argument( + "--pool-size", default=2, type=int, help="Number of total clients." + ) + parser.add_argument( + "--num-rounds", default=5, type=int, help="Number of FL rounds." + ) + parser.add_argument( + "--num-clients-per-round", + default=2, + type=int, + help="Number of clients participate in training each round.", + ) + parser.add_argument( + "--num-evaluate-clients", + default=2, + type=int, + help="Number of clients selected for evaluation.", + ) + parser.add_argument( + "--centralised-eval", + action="store_true", + help="Conduct centralised evaluation (True), or client evaluation on hold-out data (False).", + ) + + args = parser.parse_args() + return args + +This allows user to specify training strategies / the number of total clients / FL +rounds / participating clients / clients for evaluation, and evaluation fashion. Note +that with ``--centralised-eval``, the sever will do centralised evaluation and all +functionalities for client evaluation will be disabled. Then, the argument parser on client side: .. code-block:: python def client_args_parser(): - """Parse arguments to define experimental settings on client side.""" - parser = argparse.ArgumentParser() - - parser.add_argument( - "--train-method", - default="bagging", - type=str, - choices=["bagging", "cyclic"], - help="Training methods selected from bagging aggregation or cyclic training.", - ) - parser.add_argument( - "--num-partitions", default=10, type=int, help="Number of partitions." - ) - parser.add_argument( - "--partitioner-type", - default="uniform", - type=str, - choices=["uniform", "linear", "square", "exponential"], - help="Partitioner types.", - ) - parser.add_argument( - "--node-id", - default=0, - type=int, - help="Node ID used for the current client.", - ) - parser.add_argument( - "--seed", default=42, type=int, help="Seed used for train/test splitting." - ) - parser.add_argument( - "--test-fraction", - default=0.2, - type=float, - help="Test fraction for train/test splitting.", - ) - parser.add_argument( - "--centralised-eval", - action="store_true", - help="Conduct evaluation on centralised test set (True), or on hold-out data (False).", - ) - parser.add_argument( - "--scaled-lr", - action="store_true", - help="Perform scaled learning rate based on the number of clients (True).", - ) - - args = parser.parse_args() - return args - -This defines various options for client data partitioning. -Besides, clients also have an option to conduct evaluation on centralised test set by setting :code:`--centralised-eval`, -as well as an option to perform scaled learning rate based on the number of clients by setting :code:`--scaled-lr`. + """Parse arguments to define experimental settings on client side.""" + parser = argparse.ArgumentParser() + + parser.add_argument( + "--train-method", + default="bagging", + type=str, + choices=["bagging", "cyclic"], + help="Training methods selected from bagging aggregation or cyclic training.", + ) + parser.add_argument( + "--num-partitions", default=10, type=int, help="Number of partitions." + ) + parser.add_argument( + "--partitioner-type", + default="uniform", + type=str, + choices=["uniform", "linear", "square", "exponential"], + help="Partitioner types.", + ) + parser.add_argument( + "--node-id", + default=0, + type=int, + help="Node ID used for the current client.", + ) + parser.add_argument( + "--seed", default=42, type=int, help="Seed used for train/test splitting." + ) + parser.add_argument( + "--test-fraction", + default=0.2, + type=float, + help="Test fraction for train/test splitting.", + ) + parser.add_argument( + "--centralised-eval", + action="store_true", + help="Conduct evaluation on centralised test set (True), or on hold-out data (False).", + ) + parser.add_argument( + "--scaled-lr", + action="store_true", + help="Perform scaled learning rate based on the number of clients (True).", + ) + + args = parser.parse_args() + return args + +This defines various options for client data partitioning. Besides, clients also have an +option to conduct evaluation on centralised test set by setting ``--centralised-eval``, +as well as an option to perform scaled learning rate based on the number of clients by +setting ``--scaled-lr``. We also have an argument parser for simulation: .. code-block:: python - def sim_args_parser(): - """Parse arguments to define experimental settings on server side.""" - parser = argparse.ArgumentParser() - - parser.add_argument( - "--train-method", - default="bagging", - type=str, - choices=["bagging", "cyclic"], - help="Training methods selected from bagging aggregation or cyclic training.", - ) - - # Server side - parser.add_argument( - "--pool-size", default=5, type=int, help="Number of total clients." - ) - parser.add_argument( - "--num-rounds", default=30, type=int, help="Number of FL rounds." - ) - parser.add_argument( - "--num-clients-per-round", - default=5, - type=int, - help="Number of clients participate in training each round.", - ) - parser.add_argument( - "--num-evaluate-clients", - default=5, - type=int, - help="Number of clients selected for evaluation.", - ) - parser.add_argument( - "--centralised-eval", - action="store_true", - help="Conduct centralised evaluation (True), or client evaluation on hold-out data (False).", - ) - parser.add_argument( - "--num-cpus-per-client", - default=2, - type=int, - help="Number of CPUs used for per client.", - ) - - # Client side - parser.add_argument( - "--partitioner-type", - default="uniform", - type=str, - choices=["uniform", "linear", "square", "exponential"], - help="Partitioner types.", - ) - parser.add_argument( - "--seed", default=42, type=int, help="Seed used for train/test splitting." - ) - parser.add_argument( - "--test-fraction", - default=0.2, - type=float, - help="Test fraction for train/test splitting.", - ) - parser.add_argument( - "--centralised-eval-client", - action="store_true", - help="Conduct evaluation on centralised test set (True), or on hold-out data (False).", - ) - parser.add_argument( - "--scaled-lr", - action="store_true", - help="Perform scaled learning rate based on the number of clients (True).", - ) - - args = parser.parse_args() - return args + def sim_args_parser(): + """Parse arguments to define experimental settings on server side.""" + parser = argparse.ArgumentParser() + + parser.add_argument( + "--train-method", + default="bagging", + type=str, + choices=["bagging", "cyclic"], + help="Training methods selected from bagging aggregation or cyclic training.", + ) + + # Server side + parser.add_argument( + "--pool-size", default=5, type=int, help="Number of total clients." + ) + parser.add_argument( + "--num-rounds", default=30, type=int, help="Number of FL rounds." + ) + parser.add_argument( + "--num-clients-per-round", + default=5, + type=int, + help="Number of clients participate in training each round.", + ) + parser.add_argument( + "--num-evaluate-clients", + default=5, + type=int, + help="Number of clients selected for evaluation.", + ) + parser.add_argument( + "--centralised-eval", + action="store_true", + help="Conduct centralised evaluation (True), or client evaluation on hold-out data (False).", + ) + parser.add_argument( + "--num-cpus-per-client", + default=2, + type=int, + help="Number of CPUs used for per client.", + ) + + # Client side + parser.add_argument( + "--partitioner-type", + default="uniform", + type=str, + choices=["uniform", "linear", "square", "exponential"], + help="Partitioner types.", + ) + parser.add_argument( + "--seed", default=42, type=int, help="Seed used for train/test splitting." + ) + parser.add_argument( + "--test-fraction", + default=0.2, + type=float, + help="Test fraction for train/test splitting.", + ) + parser.add_argument( + "--centralised-eval-client", + action="store_true", + help="Conduct evaluation on centralised test set (True), or on hold-out data (False).", + ) + parser.add_argument( + "--scaled-lr", + action="store_true", + help="Perform scaled learning rate based on the number of clients (True).", + ) + + args = parser.parse_args() + return args This integrates all arguments for both client and server sides. Example commands -~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~ -To run a centralised evaluated experiment with bagging strategy on 5 clients with exponential distribution for 50 rounds, -we first start the server as below: +To run a centralised evaluated experiment with bagging strategy on 5 clients with +exponential distribution for 50 rounds, we first start the server as below: .. code-block:: shell @@ -1303,4 +1338,6 @@ To run the same experiment with Flower simulation: $ python3 sim.py --train-method=bagging --pool-size=5 --num-rounds=50 --num-clients-per-round=5 --partitioner-type=exponential --centralised-eval -The full `code `_ for this comprehensive example can be found in :code:`examples/xgboost-comprehensive`. +The full `code +`_ for this +comprehensive example can be found in ``examples/xgboost-comprehensive``. diff --git a/pyproject.toml b/pyproject.toml index f4555a6f1762..81c1369f6552 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -136,6 +136,15 @@ licensecheck = "==2024" pre-commit = "==3.5.0" sphinx-substitution-extensions = "2022.02.16" sphinxext-opengraph = "==0.9.1" +docstrfmt = { git = "https://github.com/charlesbvll/docstrfmt.git", branch = "patch-1" } + +[tool.docstrfmt] +extend_exclude = [ + "doc/source/conf.py", + "doc/source/tutorial-quickstart-huggingface.rst", + "doc/source/_templates/autosummary/*", + "doc/source/ref-api/*", +] [tool.isort] profile = "black" From 3f1af9b8903339b3143362642f1e63c468e1f1e0 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Wed, 25 Sep 2024 13:30:59 +0000 Subject: [PATCH 13/23] docs(framework:skip) Update source for translations (automated) (#4256) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- doc/locales/fr/LC_MESSAGES/framework-docs.po | 405 +++++++++++++++--- doc/locales/ko/LC_MESSAGES/framework-docs.po | 403 ++++++++++++++--- .../pt_BR/LC_MESSAGES/framework-docs.po | 398 ++++++++++++++--- .../zh_Hans/LC_MESSAGES/framework-docs.po | 405 +++++++++++++++--- 4 files changed, 1339 insertions(+), 272 deletions(-) diff --git a/doc/locales/fr/LC_MESSAGES/framework-docs.po b/doc/locales/fr/LC_MESSAGES/framework-docs.po index 681916e78ed5..37daac8e95b7 100644 --- a/doc/locales/fr/LC_MESSAGES/framework-docs.po +++ b/doc/locales/fr/LC_MESSAGES/framework-docs.po @@ -3,7 +3,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower Docs\n" "Report-Msgid-Bugs-To: EMAIL@ADDRESS\n" -"POT-Creation-Date: 2024-09-24 00:29+0000\n" +"POT-Creation-Date: 2024-09-25 00:30+0000\n" "PO-Revision-Date: 2023-09-05 17:54+0000\n" "Last-Translator: Charles Beauville \n" "Language: fr\n" @@ -1522,7 +1522,7 @@ msgstr "" msgid "Setting up the repository" msgstr "Mise en place du référentiel" -#: ../../source/contributor-tutorial-contribute-on-github.rst:21 +#: ../../source/contributor-tutorial-contribute-on-github.rst:12 msgid "**Create a GitHub account and setup Git**" msgstr "**Créer un compte GitHub et configurer Git**" @@ -1571,7 +1571,7 @@ msgstr "" " des modifications localement et tu en gardes une trace à l'aide de Git, " "puis tu télécharges ton nouvel historique à nouveau sur GitHub." -#: ../../source/contributor-tutorial-contribute-on-github.rst:32 +#: ../../source/contributor-tutorial-contribute-on-github.rst:23 msgid "**Forking the Flower repository**" msgstr "**Fourche le dépôt de Flower**" @@ -1601,7 +1601,7 @@ msgstr "" " devrais voir dans le coin supérieur gauche que tu es en train de " "regarder ta propre version de Flower." -#: ../../source/contributor-tutorial-contribute-on-github.rst:47 +#: ../../source/contributor-tutorial-contribute-on-github.rst:34 msgid "**Cloning your forked repository**" msgstr "**Clonage de ton dépôt forké**" @@ -1635,7 +1635,7 @@ msgstr "" "Cela créera un dossier `flower/` (ou le nom de ta fourche si tu l'as " "renommée) dans le répertoire de travail actuel." -#: ../../source/contributor-tutorial-contribute-on-github.rst:66 +#: ../../source/contributor-tutorial-contribute-on-github.rst:49 msgid "**Add origin**" msgstr "**Ajouter l'origine**" @@ -1663,7 +1663,7 @@ msgstr "" "Une fois que le \\ est copié, nous pouvons taper la commande " "suivante dans notre terminal :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:90 +#: ../../source/contributor-tutorial-contribute-on-github.rst:68 msgid "**Add upstream**" msgstr "**Ajouter en amont**" @@ -1739,7 +1739,7 @@ msgstr "" msgid "And with Flower's repository:" msgstr "Et avec le référentiel de Flower :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:122 +#: ../../source/contributor-tutorial-contribute-on-github.rst:114 msgid "**Create a new branch**" msgstr "**Créer une nouvelle branche**" @@ -1761,7 +1761,7 @@ msgstr "" "Pour ce faire, il suffit d'exécuter la commande suivante dans le " "répertoire du référentiel :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:125 +#: ../../source/contributor-tutorial-contribute-on-github.rst:124 msgid "**Make changes**" msgstr "**Apporter des modifications**" @@ -1771,7 +1771,7 @@ msgstr "" "Écris du bon code et crée de merveilleuses modifications à l'aide de ton " "éditeur préféré !" -#: ../../source/contributor-tutorial-contribute-on-github.rst:138 +#: ../../source/contributor-tutorial-contribute-on-github.rst:127 msgid "**Test and format your code**" msgstr "**Teste et mets en forme ton code**" @@ -1789,7 +1789,7 @@ msgstr "" msgid "To do so, we have written a few scripts that you can execute:" msgstr "Pour ce faire, nous avons écrit quelques scripts que tu peux exécuter :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:150 +#: ../../source/contributor-tutorial-contribute-on-github.rst:140 msgid "**Stage changes**" msgstr "**Changements de scène**" @@ -1815,7 +1815,7 @@ msgstr "" "version (last commit) et pour voir quels fichiers sont mis à disposition " "pour le commit, tu peux utiliser la commande :code:`git status`." -#: ../../source/contributor-tutorial-contribute-on-github.rst:160 +#: ../../source/contributor-tutorial-contribute-on-github.rst:152 msgid "**Commit changes**" msgstr "**Commit changes**" @@ -1838,7 +1838,7 @@ msgstr "" "commit. Il doit être écrit dans un style impératif et être concis. Un " "exemple serait :code:`git commit -m \"Ajouter des images au README\"`." -#: ../../source/contributor-tutorial-contribute-on-github.rst:171 +#: ../../source/contributor-tutorial-contribute-on-github.rst:162 msgid "**Push the changes to the fork**" msgstr "**Pousser les changements vers la fourche**" @@ -1865,7 +1865,7 @@ msgstr "" msgid "Creating and merging a pull request (PR)" msgstr "Créer et fusionner une pull request (PR)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:206 +#: ../../source/contributor-tutorial-contribute-on-github.rst:176 msgid "**Create the PR**" msgstr "**Créer le PR**" @@ -1949,7 +1949,7 @@ msgstr "" " personne, tu as la possibilité de créer un brouillon de demande de " "traction :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:209 +#: ../../source/contributor-tutorial-contribute-on-github.rst:208 msgid "**Making new changes**" msgstr "**Faire de nouveaux changements**" @@ -1963,7 +1963,7 @@ msgstr "" "toujours y pousser de nouveaux commits de la même manière qu'auparavant, " "en apportant des modifications à la branche associée au PR." -#: ../../source/contributor-tutorial-contribute-on-github.rst:231 +#: ../../source/contributor-tutorial-contribute-on-github.rst:211 msgid "**Review the PR**" msgstr "**Review the PR**" @@ -2008,7 +2008,7 @@ msgstr "" "Une fois que toutes les conversations ont été résolues, tu peux " "redemander un examen." -#: ../../source/contributor-tutorial-contribute-on-github.rst:251 +#: ../../source/contributor-tutorial-contribute-on-github.rst:233 msgid "**Once the PR is merged**" msgstr "**Une fois que le PR est fusionné**" @@ -2341,6 +2341,7 @@ msgstr "Devenez un·e contributeur·ice" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 #: ../../source/docker/run-as-subprocess.rst:11 #: ../../source/docker/run-quickstart-examples-docker-compose.rst:15 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:22 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:12 #: ../../source/docker/tutorial-quickstart-docker.rst:11 msgid "Prerequisites" @@ -3128,6 +3129,7 @@ msgid "Before you start, make sure that:" msgstr "" #: ../../source/docker/run-quickstart-examples-docker-compose.rst:19 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:26 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 #: ../../source/docker/tutorial-quickstart-docker.rst:15 msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." @@ -3346,6 +3348,209 @@ msgid "" "environment variables for a container." msgstr "" +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:3 +#, fuzzy +msgid "Deploy Flower on Multiple Machines with Docker Compose" +msgstr "Démarrage rapide XGBoost" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:5 +msgid "" +"This guide will help you set up a Flower project on multiple machines " +"using Docker Compose." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:8 +msgid "" +"You will learn how to run the Flower client and server components on two " +"separate machines, with Flower configured to use TLS encryption and " +"persist SuperLink state across restarts. A server consists of a SuperLink" +" and ``SuperExec``. For more details about the Flower architecture, refer" +" to the :doc:`../explanation-flower-architecture` explainer page." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:15 +msgid "" +"This guide assumes you have completed the :doc:`tutorial-quickstart-" +"docker-compose` tutorial. It is highly recommended that you follow and " +"understand the contents of that tutorial before proceeding with this " +"guide." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:24 +msgid "Before you begin, make sure you have the following prerequisites:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:28 +msgid "The Docker daemon is running on your local machine and the remote machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:30 +msgid "" +"Docker Compose V2 is installed on both your local machine and the remote " +"machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:32 +msgid "You can connect to the remote machine from your local machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:33 +msgid "Ports ``9091`` and ``9093`` are accessible on the remote machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:37 +msgid "" +"The guide uses the |quickstart_sklearn_tabular|_ example as an example " +"project." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:40 +msgid "" +"If your project has a different name or location, please remember to " +"adjust the commands/paths accordingly." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:45 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 +#: ../../source/docker/tutorial-quickstart-docker.rst:19 +msgid "Step 1: Set Up" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:47 +msgid "Clone the Flower repository and change to the ``distributed`` directory:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:55 +msgid "Get the IP address from the remote machine and save it for later." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:57 +msgid "" +"Use the ``certs.yml`` Compose file to generate your own self-signed " +"certificates. If you have certificates, you can continue with Step 2." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:62 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:215 +msgid "These certificates should be used only for development purposes." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:64 +msgid "" +"For production environments, you may have to use dedicated services to " +"obtain your certificates." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:67 +msgid "" +"First, set the environment variables ``SUPERLINK_IP`` and " +"``SUPEREXEC_IP`` with the IP address from the remote machine. For " +"example, if the IP is ``192.168.2.33``, execute:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:76 +msgid "Next, generate the self-signed certificates:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:84 +msgid "Step 2: Copy the Server Compose Files" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:86 +msgid "" +"Use the method that works best for you to copy the ``server`` directory, " +"the certificates, and your Flower project to the remote machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:89 +msgid "For example, you can use ``scp`` to copy the directories:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:100 +#, fuzzy +msgid "Step 3: Start the Flower Server Components" +msgstr "Démarrer le serveur" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:102 +msgid "" +"Log into the remote machine using ``ssh`` and run the following command " +"to start the SuperLink and SuperExec services:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:115 +msgid "" +"The Path of the ``PROJECT_DIR`` should be relative to the location of the" +" ``server`` Docker Compose files." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:118 +msgid "Go back to your terminal on your local machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:122 +#, fuzzy +msgid "Step 4: Start the Flower Client Components" +msgstr "Démarrer le serveur" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:124 +msgid "" +"On your local machine, run the following command to start the client " +"components:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:135 +msgid "" +"The Path of the ``PROJECT_DIR`` should be relative to the location of the" +" ``client`` Docker Compose files." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:140 +#, fuzzy +msgid "Step 5: Run Your Flower Project" +msgstr "Serveur de Flower" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:142 +msgid "" +"Specify the remote SuperExec IP addresses and the path to the root " +"certificate in the ``[tool.flwr.federations.remote-superexec]`` table in " +"the ``pyproject.toml`` file. Here, we have named our remote federation " +"``remote-superexec``:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:147 +#, fuzzy +msgid "examples/quickstart-sklearn-tabular/pyproject.toml" +msgstr "Démarrage rapide de scikit-learn" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:156 +msgid "" +"The Path of the ``root-certificates`` should be relative to the location " +"of the ``pyproject.toml`` file." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:159 +msgid "To run the project, execute:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:165 +msgid "" +"That's it! With these steps, you've set up Flower on two separate " +"machines and are ready to start using it." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:170 +msgid "Step 6: Clean Up" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:172 +#, fuzzy +msgid "Shut down the Flower client components:" +msgstr "Client de Flower" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:179 +msgid "Shut down the Flower server components and delete the SuperLink state:" +msgstr "" + #: ../../source/docker/tutorial-quickstart-docker.rst:2 #, fuzzy msgid "Quickstart with Docker" @@ -3364,11 +3569,6 @@ msgid "" " understanding the basic workflow that uses the minimum configurations." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 -#: ../../source/docker/tutorial-quickstart-docker.rst:19 -msgid "Step 1: Set Up" -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:31 #: ../../source/docker/tutorial-quickstart-docker.rst:21 msgid "Create a new Flower project (PyTorch):" @@ -4096,10 +4296,6 @@ msgid "" " the ``certs.yml`` Compose file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:215 -msgid "These certificates should be used only for development purposes." -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:217 msgid "" "For production environments, use a service like `Let's Encrypt " @@ -4608,7 +4804,7 @@ msgstr "" "getting/setting model parameters, one method for training the model, and " "one method for testing the model:" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:219 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:218 msgid ":code:`set_parameters`" msgstr ":code:`set_parameters`" @@ -4644,9 +4840,9 @@ msgstr "" ":code:`ndarray` NumPy (ce qui correspond à ce que " ":code:`flwr.client.NumPyClient` attend)" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:225 -#: ../../source/tutorial-quickstart-jax.rst:173 -#: ../../source/tutorial-quickstart-scikitlearn.rst:125 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:223 +#: ../../source/tutorial-quickstart-jax.rst:171 +#: ../../source/tutorial-quickstart-scikitlearn.rst:123 msgid ":code:`fit`" msgstr ":code:`fit`" @@ -4670,9 +4866,9 @@ msgstr "entraîne le modèle sur l'ensemble d'apprentissage local" msgid "get the updated local model weights and return them to the server" msgstr "récupère les poids du modèle local mis à jour et les renvoie au serveur" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 -#: ../../source/tutorial-quickstart-jax.rst:178 -#: ../../source/tutorial-quickstart-scikitlearn.rst:128 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:227 +#: ../../source/tutorial-quickstart-jax.rst:175 +#: ../../source/tutorial-quickstart-scikitlearn.rst:127 msgid ":code:`evaluate`" msgstr ":code:`évaluer`" @@ -4791,7 +4987,7 @@ msgid "" " individual's information remains hidden in the crowd." msgstr "" -#: ../../source/explanation-differential-privacy.rst:-1 +#: ../../source/explanation-differential-privacy.rst:16 msgid "DP Intro" msgstr "" @@ -4904,8 +5100,8 @@ msgid "" "the client's data." msgstr "" -#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:68 +#: ../../source/explanation-differential-privacy.rst:71 #: ../../source/how-to-use-differential-privacy.rst:11 #, fuzzy msgid "Central Differential Privacy" @@ -4933,7 +5129,7 @@ msgid "" "that larger updates are scaled down to fit within the norm `S`." msgstr "" -#: ../../source/explanation-differential-privacy.rst:-1 +#: ../../source/explanation-differential-privacy.rst:84 msgid "clipping" msgstr "" @@ -4978,8 +5174,8 @@ msgid "" "others." msgstr "" -#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:105 +#: ../../source/explanation-differential-privacy.rst:110 #: ../../source/how-to-use-differential-privacy.rst:96 #, fuzzy msgid "Local Differential Privacy" @@ -5266,7 +5462,7 @@ msgstr "" msgid "This is sometimes called a hub-and-spoke topology:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:24 +#: ../../source/explanation-flower-architecture.rst:18 #, fuzzy msgid "Hub-and-spoke topology in federated learning" msgstr "Qu'est-ce que l'apprentissage fédéré ?" @@ -5339,7 +5535,7 @@ msgid "" "`missing link` between all those SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:71 +#: ../../source/explanation-flower-architecture.rst:65 #, fuzzy msgid "Basic Flower architecture" msgstr "Architecture florale" @@ -5377,7 +5573,7 @@ msgid "" "SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:97 +#: ../../source/explanation-flower-architecture.rst:91 #, fuzzy msgid "Multi-tenancy federated learning architecture" msgstr "Stratégie de moyenne fédérée." @@ -5401,7 +5597,7 @@ msgid "" "their corresponding ``ClientApp``\\s:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:113 +#: ../../source/explanation-flower-architecture.rst:107 #, fuzzy msgid "Multi-tenancy federated learning architecture - Run 1" msgstr "Stratégie de moyenne fédérée." @@ -5418,7 +5614,7 @@ msgid "" " to participate in the training:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:125 +#: ../../source/explanation-flower-architecture.rst:119 #, fuzzy msgid "Multi-tenancy federated learning architecture - Run 2" msgstr "Stratégie de moyenne fédérée." @@ -5455,7 +5651,7 @@ msgid "" "developer machine." msgstr "" -#: ../../source/explanation-flower-architecture.rst:151 +#: ../../source/explanation-flower-architecture.rst:145 msgid "Flower Deployment Engine with SuperExec" msgstr "" @@ -8745,7 +8941,7 @@ msgid "" "adaptive clipping." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:-1 +#: ../../source/how-to-use-differential-privacy.rst:25 #, fuzzy msgid "server side clipping" msgstr "Logique côté serveur" @@ -8776,7 +8972,7 @@ msgid "" ":code:`DifferentialPrivacyClientSideAdaptiveClipping`." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:-1 +#: ../../source/how-to-use-differential-privacy.rst:57 #, fuzzy msgid "client side clipping" msgstr "Logique côté client" @@ -8804,7 +9000,7 @@ msgid "" "clipping norm value, sensitivity, epsilon, and delta." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:-1 +#: ../../source/how-to-use-differential-privacy.rst:99 msgid "local DP mod" msgstr "" @@ -9271,6 +9467,10 @@ msgstr "Amélioration de la documentation" msgid "Optional argument" msgstr "Améliorations facultatives" +#: ../../flwr install:1 +msgid "The source FAB file to install." +msgstr "" + #: ../../flwr log:1 msgid "Get logs from a Flower project run." msgstr "" @@ -9293,6 +9493,19 @@ msgstr "" msgid "Required argument" msgstr "Amélioration de la documentation" +#: ../../flwr log:1 +#, fuzzy +msgid "The Flower run ID to query" +msgstr "Rejoignez la communauté de Flower" + +#: ../../flwr log:1 +msgid "Path of the Flower project to run" +msgstr "" + +#: ../../flwr log:1 +msgid "Name of the federation to run the app on" +msgstr "" + #: ../../flwr new:1 #, fuzzy msgid "Create new Flower App." @@ -9317,6 +9530,11 @@ msgstr "" msgid "The Flower username of the author" msgstr "" +#: ../../flwr new:1 +#, fuzzy +msgid "The name of the Flower App" +msgstr "Chargement des données" + #: ../../flwr run:1 #, fuzzy msgid "Run Flower App." @@ -9338,6 +9556,15 @@ msgid "" " the `pyproject.toml` in order to be properly overriden." msgstr "" +#: ../../flwr run:1 +#, fuzzy +msgid "Path of the Flower App to run." +msgstr "Chargement des données" + +#: ../../flwr run:1 +msgid "Name of the federation to run the app on." +msgstr "" + #: ../../source/ref-api-cli.rst:16 #, fuzzy msgid "flower-simulation" @@ -11905,7 +12132,7 @@ msgstr "" msgid "The encoding in which to encode the string." msgstr "" -#: flwr.common.EventType.encode:9 of +#: flwr.common.EventType.encode:5 of msgid "errors" msgstr "" @@ -12081,7 +12308,7 @@ msgid "" "string." msgstr "" -#: flwr.common.EventType.replace:5 of +#: flwr.common.EventType.replace:3 of msgid "count" msgstr "" @@ -12117,7 +12344,7 @@ msgid "" "strings and the original string." msgstr "" -#: flwr.common.EventType.rsplit:7 flwr.common.EventType.split:7 of +#: flwr.common.EventType.rsplit:3 flwr.common.EventType.split:3 of msgid "sep" msgstr "" @@ -12132,7 +12359,7 @@ msgid "" " empty strings from the result." msgstr "" -#: flwr.common.EventType.rsplit:11 flwr.common.EventType.split:11 of +#: flwr.common.EventType.rsplit:9 flwr.common.EventType.split:9 of msgid "maxsplit" msgstr "" @@ -12173,7 +12400,7 @@ msgid "" "remaining cased characters have lower case." msgstr "" -#: flwr.common.EventType.translate:5 of +#: flwr.common.EventType.translate:3 of #, fuzzy msgid "table" msgstr "Database" @@ -23895,7 +24122,7 @@ msgstr "" "Oui, bien sûr, une liste d'exemples disponibles utilisant Flower dans un " "environnement blockchain est disponible ici :" -#: ../../source/ref-faq.rst:29 +#: ../../source/ref-faq.rst:28 msgid "`FLock: A Decentralised AI Training Platform `_." msgstr "" @@ -25009,7 +25236,7 @@ msgstr "" "paramètres du modèle, une méthode pour former le modèle, et une méthode " "pour tester le modèle :" -#: ../../source/tutorial-quickstart-jax.rst:167 +#: ../../source/tutorial-quickstart-jax.rst:165 msgid ":code:`set_parameters (optional)`" msgstr ":code:`set_parameters (optional)`" @@ -25578,7 +25805,7 @@ msgstr ":code:`set_model_params()`" msgid "Sets the parameters of a :code:`sklearn` LogisticRegression model" msgstr "Définit les paramètres d'un modèle de régression logistique :code:`sklean`" -#: ../../source/tutorial-quickstart-scikitlearn.rst:50 +#: ../../source/tutorial-quickstart-scikitlearn.rst:49 msgid ":code:`set_initial_params()`" msgstr ":code:`set_initial_params()`" @@ -25653,7 +25880,7 @@ msgstr "" msgid "return the model weight as a list of NumPy ndarrays" msgstr "renvoie le poids du modèle sous la forme d'une liste de ndarrays NumPy" -#: ../../source/tutorial-quickstart-scikitlearn.rst:121 +#: ../../source/tutorial-quickstart-scikitlearn.rst:120 msgid ":code:`set_parameters` (optional)" msgstr ":code:`set_parameters` (optionnel)" @@ -28488,7 +28715,7 @@ msgstr "" "chose d'autre, comme la régression linéaire classique." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 -msgid "|e87b69b2ada74ea49412df16f4a0b9cc|" +msgid "|b8714c45b74b4d8fb008e2ebb3bc1d44|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 @@ -28507,7 +28734,7 @@ msgstr "" " Go." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 -msgid "|33cacb7d985c4906b348515c1a5cd993|" +msgid "|75f1561efcfd422ea67d28d1513120dc|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 @@ -28538,7 +28765,7 @@ msgstr "" "chanson." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 -msgid "|cc080a555947492fa66131dc3a967603|" +msgid "|6a1f51b235304558a9bdaaabfc93b8d2|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 @@ -28559,7 +28786,7 @@ msgstr "" " données pour la même tâche." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 -msgid "|085c3e0fb8664c6aa06246636524b20b|" +msgid "|35e70dab1fb544af9aa3a9c09c4f9797|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 @@ -28580,7 +28807,7 @@ msgstr "" "cloud." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 -msgid "|bfe69c74e48c45d49b50251c38c2a019|" +msgid "|d7efb5705dd3467f991ed23746824a07|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 @@ -28601,7 +28828,7 @@ msgstr "" "appuyés." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 -msgid "|ebbecd651f0348d99c6511ea859bf4ca|" +msgid "|94e7b021c7b540bfbedf7f082a41ff87|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 @@ -28626,7 +28853,7 @@ msgstr "" " sur un serveur centralisé." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 -msgid "|163117eb654a4273babba413cf8065f5|" +msgid "|a80714782dde439ab73936518f91fc3c|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 @@ -28645,7 +28872,7 @@ msgstr "" "suffisantes pour former un bon modèle." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 -msgid "|452ac3ba453b4cd1be27be1ba7560d64|" +msgid "|c62080ca6197473da57d191c8225a9d9|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 @@ -28867,7 +29094,7 @@ msgstr "" "partir d'un point de contrôle précédemment sauvegardé." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 -msgid "|f403fcd69e4e44409627e748b404c086|" +msgid "|21a8f1e6a5b14a7bbb8559979d0e8a2b|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 @@ -28902,7 +29129,7 @@ msgstr "" "rendements décroissants." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 -msgid "|4b00fe63870145968f8443619a792a42|" +msgid "|c310f2a22f7b4917bf42775aae7a1c09|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 @@ -28935,7 +29162,7 @@ msgstr "" "données locales, ou même de quelques étapes (mini-batchs)." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 -msgid "|368378731066486fa4397e89bc6b870c|" +msgid "|a0c5b43401194535a8460bcf02e65f9a|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 @@ -28966,7 +29193,7 @@ msgstr "" " l'entraînement local." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 -msgid "|a66aa83d85bf4ffba7ed660b718066da|" +msgid "|aabfdbd5564e41a790f8ea93cc21a444|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 @@ -29025,7 +29252,7 @@ msgstr "" "times as much as each of the 100 examples." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 -msgid "|82324b9af72a4582a81839d55caab767|" +msgid "|c9cc8f160fa647b09e742fe4dc8edb54|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 @@ -29168,7 +29395,7 @@ msgstr "" "quel cadre de ML et n'importe quel langage de programmation." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 -msgid "|fbf2da0da3cc4f8ab3b3eff852d80c41|" +msgid "|7e83aad011cd4907b2f02f907c6922e9|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 @@ -37531,3 +37758,45 @@ msgstr "" #~ msgid "|ed9aae51da70428eab7eef32f21e819e|" #~ msgstr "" +#~ msgid "|e87b69b2ada74ea49412df16f4a0b9cc|" +#~ msgstr "" + +#~ msgid "|33cacb7d985c4906b348515c1a5cd993|" +#~ msgstr "" + +#~ msgid "|cc080a555947492fa66131dc3a967603|" +#~ msgstr "" + +#~ msgid "|085c3e0fb8664c6aa06246636524b20b|" +#~ msgstr "" + +#~ msgid "|bfe69c74e48c45d49b50251c38c2a019|" +#~ msgstr "" + +#~ msgid "|ebbecd651f0348d99c6511ea859bf4ca|" +#~ msgstr "" + +#~ msgid "|163117eb654a4273babba413cf8065f5|" +#~ msgstr "" + +#~ msgid "|452ac3ba453b4cd1be27be1ba7560d64|" +#~ msgstr "" + +#~ msgid "|f403fcd69e4e44409627e748b404c086|" +#~ msgstr "" + +#~ msgid "|4b00fe63870145968f8443619a792a42|" +#~ msgstr "" + +#~ msgid "|368378731066486fa4397e89bc6b870c|" +#~ msgstr "" + +#~ msgid "|a66aa83d85bf4ffba7ed660b718066da|" +#~ msgstr "" + +#~ msgid "|82324b9af72a4582a81839d55caab767|" +#~ msgstr "" + +#~ msgid "|fbf2da0da3cc4f8ab3b3eff852d80c41|" +#~ msgstr "" + diff --git a/doc/locales/ko/LC_MESSAGES/framework-docs.po b/doc/locales/ko/LC_MESSAGES/framework-docs.po index 4c738e16b434..78c7bdd427a7 100644 --- a/doc/locales/ko/LC_MESSAGES/framework-docs.po +++ b/doc/locales/ko/LC_MESSAGES/framework-docs.po @@ -7,7 +7,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower main\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-09-24 00:29+0000\n" +"POT-Creation-Date: 2024-09-25 00:30+0000\n" "PO-Revision-Date: 2024-08-23 13:09+0000\n" "Last-Translator: Seulki Yun \n" "Language: ko\n" @@ -1468,7 +1468,7 @@ msgstr "" msgid "Setting up the repository" msgstr "레포지토리 설정하기" -#: ../../source/contributor-tutorial-contribute-on-github.rst:21 +#: ../../source/contributor-tutorial-contribute-on-github.rst:12 msgid "**Create a GitHub account and setup Git**" msgstr "**GitHub 계정을 만들고 Git을 설정합니다**" @@ -1509,7 +1509,7 @@ msgstr "" "일반적인 Git 및 GitHub 워크플로우의 기본 개념은 다음과 같이 요약됩니다. GitHub의 원격 레포지토리에서 코드를 " "다운로드하고 로컬에서 변경한 후 Git을 사용하여 추적한 다음 새 기록을 다시 GitHub에 업로드하는 것입니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:32 +#: ../../source/contributor-tutorial-contribute-on-github.rst:23 msgid "**Forking the Flower repository**" msgstr "**Flower 레포지토리 포크하기**" @@ -1534,7 +1534,7 @@ msgstr "" "원하는 경우 이름을 변경할 수 있지만, 이 버전의 Flower는 자신의 계정(즉, 자신의 리포지토리 목록)에 위치하게 되므로 변경할" " 필요는 없습니다. 만들기가 완료되면 왼쪽 상단에Flower 버전이 표시되는 것을 볼 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:47 +#: ../../source/contributor-tutorial-contribute-on-github.rst:34 msgid "**Cloning your forked repository**" msgstr "**포크된 레포지토리 클론하기**" @@ -1560,7 +1560,7 @@ msgid "" "it) folder in the current working directory." msgstr "현재 작업 디렉터리에``flower/``(또는 포크 이름을 변경한 경우 포크 이름) 폴더가 생성됩니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:66 +#: ../../source/contributor-tutorial-contribute-on-github.rst:49 msgid "**Add origin**" msgstr "**origin 추가**" @@ -1584,7 +1584,7 @@ msgid "" "terminal:" msgstr "\\ 이 복사되면 터미널에 다음 명령을 입력하면 됩니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:90 +#: ../../source/contributor-tutorial-contribute-on-github.rst:68 msgid "**Add upstream**" msgstr "**Upstream 추가하기**" @@ -1645,7 +1645,7 @@ msgstr "변경하기 전에 레포지토리를 최신 상태로 유지하세요: msgid "And with Flower's repository:" msgstr "Flower의 레포지토리도 있습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:122 +#: ../../source/contributor-tutorial-contribute-on-github.rst:114 msgid "**Create a new branch**" msgstr "**새 브랜치 만들기**" @@ -1662,7 +1662,7 @@ msgid "" "directory:" msgstr "이렇게 하려면 레포지토리 디렉토리에서 다음 명령을 실행하면 됩니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:125 +#: ../../source/contributor-tutorial-contribute-on-github.rst:124 msgid "**Make changes**" msgstr "**변경하기**" @@ -1670,7 +1670,7 @@ msgstr "**변경하기**" msgid "Write great code and create wonderful changes using your favorite editor!" msgstr "선호하는 편집기를 사용하여 멋진 코드를 작성하고 훌륭한 변화를 만들어 보세요!" -#: ../../source/contributor-tutorial-contribute-on-github.rst:138 +#: ../../source/contributor-tutorial-contribute-on-github.rst:127 msgid "**Test and format your code**" msgstr "**코드 테스트 및 서식 지정**" @@ -1687,7 +1687,7 @@ msgstr "" msgid "To do so, we have written a few scripts that you can execute:" msgstr "이를 위해 실행할 수 있는 몇 가지 스크립트를 작성했습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:150 +#: ../../source/contributor-tutorial-contribute-on-github.rst:140 msgid "**Stage changes**" msgstr "**변경사항 스테이징**" @@ -1710,7 +1710,7 @@ msgstr "" "마지막 버전(마지막 커밋)과 비교하여 수정된 파일을 확인하고 커밋을 위해 스테이징된 파일을 확인하려면 :code:`git " "status` 명령을 사용하면 됩니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:160 +#: ../../source/contributor-tutorial-contribute-on-github.rst:152 msgid "**Commit changes**" msgstr "**변경사항 커밋**" @@ -1729,7 +1729,7 @@ msgstr "" "커밋의 내용을 다른 사람에게 설명하기 위해 \\가 있습니다. 명령형 스타일로 작성해야 하며 간결해야" " 합니다. 예를 들면 :code:`git commit -m \"Add images to README\"`." -#: ../../source/contributor-tutorial-contribute-on-github.rst:171 +#: ../../source/contributor-tutorial-contribute-on-github.rst:162 msgid "**Push the changes to the fork**" msgstr "**변경 사항을 포크에 푸시**" @@ -1752,7 +1752,7 @@ msgstr "이 작업이 완료되면 변경한 내용으로 포크된 레포지토 msgid "Creating and merging a pull request (PR)" msgstr "pull request(PR) 만들기 및 병합하기" -#: ../../source/contributor-tutorial-contribute-on-github.rst:206 +#: ../../source/contributor-tutorial-contribute-on-github.rst:176 msgid "**Create the PR**" msgstr "**PR 만들기**" @@ -1823,7 +1823,7 @@ msgid "" "anyone, you have the option to create a draft pull request:" msgstr "PR이 아직 검토할 준비가 되지 않았고 다른 사람에게 알리고 싶지 않은 경우 pull request 초안을 만드는 옵션이 있습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:209 +#: ../../source/contributor-tutorial-contribute-on-github.rst:208 msgid "**Making new changes**" msgstr "**new changes 만들기**" @@ -1834,7 +1834,7 @@ msgid "" " associated with the PR." msgstr "PR이 초안으로 열렸든 아니든, PR과 연결된 브랜치를 변경하여 이전과 같은 방식으로 새 커밋을 푸시할 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:231 +#: ../../source/contributor-tutorial-contribute-on-github.rst:211 msgid "**Review the PR**" msgstr "**PR 검토하기**" @@ -1870,7 +1870,7 @@ msgid "" "review." msgstr "모든 대화가 해결되면 검토를 다시 요청할 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:251 +#: ../../source/contributor-tutorial-contribute-on-github.rst:233 msgid "**Once the PR is merged**" msgstr "**PR이 병합되면**" @@ -2158,6 +2158,7 @@ msgstr "기여자로 시작하기" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 #: ../../source/docker/run-as-subprocess.rst:11 #: ../../source/docker/run-quickstart-examples-docker-compose.rst:15 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:22 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:12 #: ../../source/docker/tutorial-quickstart-docker.rst:11 msgid "Prerequisites" @@ -2972,6 +2973,7 @@ msgid "Before you start, make sure that:" msgstr "시작하기 전에 Docker daemon이 실행 중인지 확인하세요:" #: ../../source/docker/run-quickstart-examples-docker-compose.rst:19 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:26 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 #: ../../source/docker/tutorial-quickstart-docker.rst:15 msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." @@ -3188,6 +3190,207 @@ msgid "" "environment variables for a container." msgstr "Docker 컨테이너 내에서 변수를 설정하려면 ``-e =`` 플래그를 사용하면 됩니다." +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:3 +#, fuzzy +msgid "Deploy Flower on Multiple Machines with Docker Compose" +msgstr "빠른 시작 튜토리얼" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:5 +msgid "" +"This guide will help you set up a Flower project on multiple machines " +"using Docker Compose." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:8 +msgid "" +"You will learn how to run the Flower client and server components on two " +"separate machines, with Flower configured to use TLS encryption and " +"persist SuperLink state across restarts. A server consists of a SuperLink" +" and ``SuperExec``. For more details about the Flower architecture, refer" +" to the :doc:`../explanation-flower-architecture` explainer page." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:15 +msgid "" +"This guide assumes you have completed the :doc:`tutorial-quickstart-" +"docker-compose` tutorial. It is highly recommended that you follow and " +"understand the contents of that tutorial before proceeding with this " +"guide." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:24 +msgid "Before you begin, make sure you have the following prerequisites:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:28 +msgid "The Docker daemon is running on your local machine and the remote machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:30 +msgid "" +"Docker Compose V2 is installed on both your local machine and the remote " +"machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:32 +msgid "You can connect to the remote machine from your local machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:33 +msgid "Ports ``9091`` and ``9093`` are accessible on the remote machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:37 +msgid "" +"The guide uses the |quickstart_sklearn_tabular|_ example as an example " +"project." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:40 +msgid "" +"If your project has a different name or location, please remember to " +"adjust the commands/paths accordingly." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:45 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 +#: ../../source/docker/tutorial-quickstart-docker.rst:19 +msgid "Step 1: Set Up" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:47 +msgid "Clone the Flower repository and change to the ``distributed`` directory:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:55 +msgid "Get the IP address from the remote machine and save it for later." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:57 +msgid "" +"Use the ``certs.yml`` Compose file to generate your own self-signed " +"certificates. If you have certificates, you can continue with Step 2." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:62 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:215 +msgid "These certificates should be used only for development purposes." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:64 +msgid "" +"For production environments, you may have to use dedicated services to " +"obtain your certificates." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:67 +msgid "" +"First, set the environment variables ``SUPERLINK_IP`` and " +"``SUPEREXEC_IP`` with the IP address from the remote machine. For " +"example, if the IP is ``192.168.2.33``, execute:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:76 +msgid "Next, generate the self-signed certificates:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:84 +msgid "Step 2: Copy the Server Compose Files" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:86 +msgid "" +"Use the method that works best for you to copy the ``server`` directory, " +"the certificates, and your Flower project to the remote machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:89 +msgid "For example, you can use ``scp`` to copy the directories:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:100 +msgid "Step 3: Start the Flower Server Components" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:102 +msgid "" +"Log into the remote machine using ``ssh`` and run the following command " +"to start the SuperLink and SuperExec services:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:115 +msgid "" +"The Path of the ``PROJECT_DIR`` should be relative to the location of the" +" ``server`` Docker Compose files." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:118 +msgid "Go back to your terminal on your local machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:122 +#, fuzzy +msgid "Step 4: Start the Flower Client Components" +msgstr "서버(SuperLink)" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:124 +msgid "" +"On your local machine, run the following command to start the client " +"components:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:135 +msgid "" +"The Path of the ``PROJECT_DIR`` should be relative to the location of the" +" ``client`` Docker Compose files." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:140 +#, fuzzy +msgid "Step 5: Run Your Flower Project" +msgstr "Flower SuperNode를 실행합니다." + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:142 +msgid "" +"Specify the remote SuperExec IP addresses and the path to the root " +"certificate in the ``[tool.flwr.federations.remote-superexec]`` table in " +"the ``pyproject.toml`` file. Here, we have named our remote federation " +"``remote-superexec``:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:147 +msgid "examples/quickstart-sklearn-tabular/pyproject.toml" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:156 +msgid "" +"The Path of the ``root-certificates`` should be relative to the location " +"of the ``pyproject.toml`` file." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:159 +msgid "To run the project, execute:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:165 +msgid "" +"That's it! With these steps, you've set up Flower on two separate " +"machines and are ready to start using it." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:170 +msgid "Step 6: Clean Up" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:172 +#, fuzzy +msgid "Shut down the Flower client components:" +msgstr "Flower 클라이언트 앱을 실행합니다." + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:179 +msgid "Shut down the Flower server components and delete the SuperLink state:" +msgstr "" + #: ../../source/docker/tutorial-quickstart-docker.rst:2 #, fuzzy msgid "Quickstart with Docker" @@ -3206,11 +3409,6 @@ msgid "" " understanding the basic workflow that uses the minimum configurations." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 -#: ../../source/docker/tutorial-quickstart-docker.rst:19 -msgid "Step 1: Set Up" -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:31 #: ../../source/docker/tutorial-quickstart-docker.rst:21 msgid "Create a new Flower project (PyTorch):" @@ -3942,10 +4140,6 @@ msgid "" " the ``certs.yml`` Compose file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:215 -msgid "These certificates should be used only for development purposes." -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:217 msgid "" "For production environments, use a service like `Let's Encrypt " @@ -4398,7 +4592,7 @@ msgstr "" "code:`CifarClient`는 모델 파라미터를 가져오거나 설정하는 메서드 2개, 모델 학습을 위한 메서드 1개, 모델 테스트를" " 위한 메서드 1개 등 네 가지 메서드를 구현해야 합니다:" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:219 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:218 msgid ":code:`set_parameters`" msgstr ":code:`set_parameters`" @@ -4431,9 +4625,9 @@ msgstr "" "모델 매개변수를 가져와서 NumPy :code:`ndarray`의 목록으로 반환합니다(이는 " ":code:`flwr.client.NumPyClient`가 기대하는 바와 같습니다)" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:225 -#: ../../source/tutorial-quickstart-jax.rst:173 -#: ../../source/tutorial-quickstart-scikitlearn.rst:125 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:223 +#: ../../source/tutorial-quickstart-jax.rst:171 +#: ../../source/tutorial-quickstart-scikitlearn.rst:123 msgid ":code:`fit`" msgstr ":code:`fit`" @@ -4455,9 +4649,9 @@ msgstr "로컬 훈련 세트에서 모델을 훈련합니다" msgid "get the updated local model weights and return them to the server" msgstr "업데이트된 로컬 모델 가중치를 가져와 서버로 반환합니다" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 -#: ../../source/tutorial-quickstart-jax.rst:178 -#: ../../source/tutorial-quickstart-scikitlearn.rst:128 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:227 +#: ../../source/tutorial-quickstart-jax.rst:175 +#: ../../source/tutorial-quickstart-scikitlearn.rst:127 msgid ":code:`evaluate`" msgstr ":code:`evaluate`" @@ -4568,7 +4762,7 @@ msgstr "" "평균 소득 계산과 같은 모든 분석(M)이 두 데이터 세트에 대해 거의 동일한 결과를 산출하도록 보장합니다(O와 O' 는 비슷할 " "것입니다). 이렇게 하면 그룹 패턴은 보존하면서 개별 세부 정보는 가려져 개인의 정보가 군중 속에 숨겨집니다." -#: ../../source/explanation-differential-privacy.rst:-1 +#: ../../source/explanation-differential-privacy.rst:16 msgid "DP Intro" msgstr "DP 소개" @@ -4707,8 +4901,8 @@ msgstr "" "**로컬 차등 프라이버시**: DP는 정보를 서버로 보내기 전에 클라이언트 측에서 적용되며, 서버로 전송되는 업데이트가 클라이언트 " "데이터에 대한 정보를 유출하는 것을 방지하는 것이 목표입니다." -#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:68 +#: ../../source/explanation-differential-privacy.rst:71 #: ../../source/how-to-use-differential-privacy.rst:11 msgid "Central Differential Privacy" msgstr "중앙 차등 프라이버시" @@ -4743,7 +4937,7 @@ msgstr "" "개인정보 보호에 중요하고 견고성에 도움이 되는 경우가 많습니다. 이를 달성하기 위한 일반적인 접근 방식은 클라이언트 모델 업데이트의" " `L2` 규범을 제한하여 더 큰 업데이트가 규범 `S`에 맞도록 축소되도록 하는 것입니다." -#: ../../source/explanation-differential-privacy.rst:-1 +#: ../../source/explanation-differential-privacy.rst:84 msgid "clipping" msgstr "클리핑" @@ -4795,8 +4989,8 @@ msgid "" "others." msgstr "고정 클리핑과 조정 클리핑 중 선택은 개인정보 보호 요구 사항, 데이터 배포, 모델 복잡성 등 다양한 요인에 따라 달라집니다." -#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:105 +#: ../../source/explanation-differential-privacy.rst:110 #: ../../source/how-to-use-differential-privacy.rst:96 msgid "Local Differential Privacy" msgstr "로컬 차등 프라이버시" @@ -5064,7 +5258,7 @@ msgstr "" msgid "This is sometimes called a hub-and-spoke topology:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:24 +#: ../../source/explanation-flower-architecture.rst:18 #, fuzzy msgid "Hub-and-spoke topology in federated learning" msgstr "연합 학습이란 무엇입니까?" @@ -5137,7 +5331,7 @@ msgid "" "`missing link` between all those SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:71 +#: ../../source/explanation-flower-architecture.rst:65 #, fuzzy msgid "Basic Flower architecture" msgstr "Flower 아키텍처" @@ -5174,7 +5368,7 @@ msgid "" "SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:97 +#: ../../source/explanation-flower-architecture.rst:91 msgid "Multi-tenancy federated learning architecture" msgstr "" @@ -5196,7 +5390,7 @@ msgid "" "their corresponding ``ClientApp``\\s:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:113 +#: ../../source/explanation-flower-architecture.rst:107 msgid "Multi-tenancy federated learning architecture - Run 1" msgstr "" @@ -5212,7 +5406,7 @@ msgid "" " to participate in the training:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:125 +#: ../../source/explanation-flower-architecture.rst:119 msgid "Multi-tenancy federated learning architecture - Run 2" msgstr "" @@ -5248,7 +5442,7 @@ msgid "" "developer machine." msgstr "" -#: ../../source/explanation-flower-architecture.rst:151 +#: ../../source/explanation-flower-architecture.rst:145 msgid "Flower Deployment Engine with SuperExec" msgstr "" @@ -8419,7 +8613,7 @@ msgstr "" " 위한 :code:`DifferentialPrivacyServerSideFixedClipping`과 " ":code:`DifferentialPrivacyServerSideAdaptiveClipping`입니다." -#: ../../source/how-to-use-differential-privacy.rst:-1 +#: ../../source/how-to-use-differential-privacy.rst:25 msgid "server side clipping" msgstr "서버 측 클리핑" @@ -8458,7 +8652,7 @@ msgstr "" " 해당 서버 측 래퍼 :code:`DifferentialPrivacyClientSideFixedClipping` 및 " ":code:`DifferentialPrivacyClientSideAdaptiveClipping`이 있습니다." -#: ../../source/how-to-use-differential-privacy.rst:-1 +#: ../../source/how-to-use-differential-privacy.rst:57 msgid "client side clipping" msgstr "클라이언트 측 클리핑" @@ -8492,7 +8686,7 @@ msgstr "" "로컬 차분 프라이버시(DP)를 활용하고 클라이언트 모델 파라미터를 서버로 전송하기 전에 노이즈를 추가하려면 `LocalDpMod`를" " 사용하면 됩니다. 클리핑 노멀 값, 감도, 엡실론, 델타 등의 하이퍼파라미터를 설정해야 합니다." -#: ../../source/how-to-use-differential-privacy.rst:-1 +#: ../../source/how-to-use-differential-privacy.rst:99 msgid "local DP mod" msgstr "로컬 DP mod" @@ -8923,6 +9117,10 @@ msgstr "빌드 전달인자" msgid "Optional argument" msgstr "선택적 개선 사항" +#: ../../flwr install:1 +msgid "The source FAB file to install." +msgstr "" + #: ../../flwr log:1 msgid "Get logs from a Flower project run." msgstr "" @@ -8945,6 +9143,19 @@ msgstr "``DISTRO``" msgid "Required argument" msgstr "빌드 전달인자" +#: ../../flwr log:1 +#, fuzzy +msgid "The Flower run ID to query" +msgstr "Flower 커뮤니티 가입하기" + +#: ../../flwr log:1 +msgid "Path of the Flower project to run" +msgstr "" + +#: ../../flwr log:1 +msgid "Name of the federation to run the app on" +msgstr "" + #: ../../flwr new:1 #, fuzzy msgid "Create new Flower App." @@ -8969,6 +9180,11 @@ msgstr "" msgid "The Flower username of the author" msgstr "" +#: ../../flwr new:1 +#, fuzzy +msgid "The name of the Flower App" +msgstr "Flower 기본 이미지의 태그." + #: ../../flwr run:1 #, fuzzy msgid "Run Flower App." @@ -8990,6 +9206,15 @@ msgid "" " the `pyproject.toml` in order to be properly overriden." msgstr "" +#: ../../flwr run:1 +#, fuzzy +msgid "Path of the Flower App to run." +msgstr "Flower 기본 이미지의 태그." + +#: ../../flwr run:1 +msgid "Name of the federation to run the app on." +msgstr "" + #: ../../source/ref-api-cli.rst:16 msgid "flower-simulation" msgstr "flower 시뮬레이션" @@ -11684,7 +11909,7 @@ msgstr "인코딩" msgid "The encoding in which to encode the string." msgstr "문자열을 인코딩합니다." -#: flwr.common.EventType.encode:9 of +#: flwr.common.EventType.encode:5 of msgid "errors" msgstr "오류" @@ -11878,7 +12103,7 @@ msgstr "" "문자열이 접미사 문자열로 끝나고 해당 접미사가 비어 있지 않으면 문자열[:-len(suffix)]을 반환합니다. 그렇지 않으면 원본" " 문자열의 복사본을 반환합니다." -#: flwr.common.EventType.replace:5 of +#: flwr.common.EventType.replace:3 of msgid "count" msgstr "카운트" @@ -11918,7 +12143,7 @@ msgid "" "strings and the original string." msgstr "구분 기호를 찾을 수 없는 경우 빈 문자열 2개와 원래 문자열을 포함하는 3-tuple을 반환합니다." -#: flwr.common.EventType.rsplit:7 flwr.common.EventType.split:7 of +#: flwr.common.EventType.rsplit:3 flwr.common.EventType.split:3 of msgid "sep" msgstr "sep" @@ -11935,7 +12160,7 @@ msgstr "" "None(기본값)으로 설정하면 모든 공백 문자(\\\\n \\\\r \\\\t \\\\f 및 공백 포함)를 분할하고 결과에서 빈 " "문자열을 삭제합니다." -#: flwr.common.EventType.rsplit:11 flwr.common.EventType.split:11 of +#: flwr.common.EventType.rsplit:9 flwr.common.EventType.split:9 of msgid "maxsplit" msgstr "maxsplit" @@ -11981,7 +12206,7 @@ msgid "" "remaining cased characters have lower case." msgstr "보다 구체적으로, 단어는 대문자로 시작하고 나머지 모든 대소문자는 소문자로 표기합니다." -#: flwr.common.EventType.translate:5 of +#: flwr.common.EventType.translate:3 of msgid "table" msgstr "table" @@ -21848,7 +22073,7 @@ msgid "" "blockchain environment is available here:" msgstr "" -#: ../../source/ref-faq.rst:29 +#: ../../source/ref-faq.rst:28 msgid "`FLock: A Decentralised AI Training Platform `_." msgstr "" @@ -22748,7 +22973,7 @@ msgstr "" "code:`FlowerClient`는 모델 매개변수를 가져오거나 설정하는 메서드 2개, 모델 학습을 위한 메서드 1개, 모델 " "테스트를 위한 메서드 1개 등 총 4개의 메서드를 구현해야 합니다:" -#: ../../source/tutorial-quickstart-jax.rst:167 +#: ../../source/tutorial-quickstart-jax.rst:165 msgid ":code:`set_parameters (optional)`" msgstr ":code:`set_parameters (선택사항)`" @@ -23243,7 +23468,7 @@ msgstr "" msgid "Sets the parameters of a :code:`sklearn` LogisticRegression model" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:50 +#: ../../source/tutorial-quickstart-scikitlearn.rst:49 msgid ":code:`set_initial_params()`" msgstr "" @@ -23299,7 +23524,7 @@ msgstr "" msgid "return the model weight as a list of NumPy ndarrays" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:121 +#: ../../source/tutorial-quickstart-scikitlearn.rst:120 msgid ":code:`set_parameters` (optional)" msgstr "" @@ -25394,7 +25619,7 @@ msgstr "" " 수도 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 -msgid "|e87b69b2ada74ea49412df16f4a0b9cc|" +msgid "|b8714c45b74b4d8fb008e2ebb3bc1d44|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 @@ -25411,7 +25636,7 @@ msgstr "" " 바둑과 같은 게임을 하는 것일 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 -msgid "|33cacb7d985c4906b348515c1a5cd993|" +msgid "|75f1561efcfd422ea67d28d1513120dc|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 @@ -25434,7 +25659,7 @@ msgstr "" "부르리는 것을 듣는 스마트 스피커에서 비롯됩니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 -msgid "|cc080a555947492fa66131dc3a967603|" +msgid "|6a1f51b235304558a9bdaaabfc93b8d2|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 @@ -25452,7 +25677,7 @@ msgstr "" "있습니다. 하지만 여러 조직이 모두 같은 작업을 위해 데이터를 생성하는 것일 수도 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 -msgid "|085c3e0fb8664c6aa06246636524b20b|" +msgid "|35e70dab1fb544af9aa3a9c09c4f9797|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 @@ -25470,7 +25695,7 @@ msgstr "" "서버는 데이터 센터 어딘가에 있을 수도 있고 클라우드 어딘가에 있을 수도 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 -msgid "|bfe69c74e48c45d49b50251c38c2a019|" +msgid "|d7efb5705dd3467f991ed23746824a07|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 @@ -25487,7 +25712,7 @@ msgstr "" " 우리가 기본적으로 사용해 온 머신러닝 방법입니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 -msgid "|ebbecd651f0348d99c6511ea859bf4ca|" +msgid "|94e7b021c7b540bfbedf7f082a41ff87|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 @@ -25509,7 +25734,7 @@ msgstr "" "트래픽을 분석하는 것이 있습니다. 이러한 사례에서 모든 데이터는 자연스럽게 중앙 서버에 존재합니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 -msgid "|163117eb654a4273babba413cf8065f5|" +msgid "|a80714782dde439ab73936518f91fc3c|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 @@ -25526,7 +25751,7 @@ msgstr "" "좋은 모델을 훈련하기에 충분하지 않을 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 -msgid "|452ac3ba453b4cd1be27be1ba7560d64|" +msgid "|c62080ca6197473da57d191c8225a9d9|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 @@ -25693,7 +25918,7 @@ msgstr "" "체크포인트에서 모델 매개변수를 초기화합니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 -msgid "|f403fcd69e4e44409627e748b404c086|" +msgid "|21a8f1e6a5b14a7bbb8559979d0e8a2b|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 @@ -25720,7 +25945,7 @@ msgstr "" "개의 연결 노드만 사용합니다. 그 이유는 점점 더 많은 클라이언트 노드를 선택하면 학습의 효율성이 감소하기 때문입니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 -msgid "|4b00fe63870145968f8443619a792a42|" +msgid "|c310f2a22f7b4917bf42775aae7a1c09|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 @@ -25747,7 +25972,7 @@ msgstr "" "데이터에서 한 단계 정도로 짧거나 몇 단계(mini-batches)에 불과할 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 -msgid "|368378731066486fa4397e89bc6b870c|" +msgid "|a0c5b43401194535a8460bcf02e65f9a|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 @@ -25773,7 +25998,7 @@ msgstr "" "보냅니다. 보내는 모델 업데이트는 전체 모델 파라미터거나 로컬 교육 중에 누적된 그레디언트(gradient)일 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 -msgid "|a66aa83d85bf4ffba7ed660b718066da|" +msgid "|aabfdbd5564e41a790f8ea93cc21a444|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 @@ -25822,7 +26047,7 @@ msgstr "" "많은 영향을 미칩니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 -msgid "|82324b9af72a4582a81839d55caab767|" +msgid "|c9cc8f160fa647b09e742fe4dc8edb54|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 @@ -25941,7 +26166,7 @@ msgstr "" "사용자는 모든 워크로드, 머신러닝 프레임워크 및 모든 프로그래밍 언어를 통합할 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 -msgid "|fbf2da0da3cc4f8ab3b3eff852d80c41|" +msgid "|7e83aad011cd4907b2f02f907c6922e9|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 @@ -28121,3 +28346,45 @@ msgstr "" #~ msgid "|ed9aae51da70428eab7eef32f21e819e|" #~ msgstr "" +#~ msgid "|e87b69b2ada74ea49412df16f4a0b9cc|" +#~ msgstr "" + +#~ msgid "|33cacb7d985c4906b348515c1a5cd993|" +#~ msgstr "" + +#~ msgid "|cc080a555947492fa66131dc3a967603|" +#~ msgstr "" + +#~ msgid "|085c3e0fb8664c6aa06246636524b20b|" +#~ msgstr "" + +#~ msgid "|bfe69c74e48c45d49b50251c38c2a019|" +#~ msgstr "" + +#~ msgid "|ebbecd651f0348d99c6511ea859bf4ca|" +#~ msgstr "" + +#~ msgid "|163117eb654a4273babba413cf8065f5|" +#~ msgstr "" + +#~ msgid "|452ac3ba453b4cd1be27be1ba7560d64|" +#~ msgstr "" + +#~ msgid "|f403fcd69e4e44409627e748b404c086|" +#~ msgstr "" + +#~ msgid "|4b00fe63870145968f8443619a792a42|" +#~ msgstr "" + +#~ msgid "|368378731066486fa4397e89bc6b870c|" +#~ msgstr "" + +#~ msgid "|a66aa83d85bf4ffba7ed660b718066da|" +#~ msgstr "" + +#~ msgid "|82324b9af72a4582a81839d55caab767|" +#~ msgstr "" + +#~ msgid "|fbf2da0da3cc4f8ab3b3eff852d80c41|" +#~ msgstr "" + diff --git a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po index 44223940cdce..1f8c40d304e7 100644 --- a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po +++ b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po @@ -7,7 +7,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower main\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-09-24 00:29+0000\n" +"POT-Creation-Date: 2024-09-25 00:30+0000\n" "PO-Revision-Date: 2024-05-25 11:09+0000\n" "Last-Translator: Gustavo Bertoli \n" "Language: pt_BR\n" @@ -1401,7 +1401,7 @@ msgstr "" msgid "Setting up the repository" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:21 +#: ../../source/contributor-tutorial-contribute-on-github.rst:12 msgid "**Create a GitHub account and setup Git**" msgstr "" @@ -1435,7 +1435,7 @@ msgid "" "history back to GitHub." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:32 +#: ../../source/contributor-tutorial-contribute-on-github.rst:23 msgid "**Forking the Flower repository**" msgstr "" @@ -1455,7 +1455,7 @@ msgid "" " the top left corner that you are looking at your own version of Flower." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:47 +#: ../../source/contributor-tutorial-contribute-on-github.rst:34 msgid "**Cloning your forked repository**" msgstr "" @@ -1479,7 +1479,7 @@ msgid "" "it) folder in the current working directory." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:66 +#: ../../source/contributor-tutorial-contribute-on-github.rst:49 msgid "**Add origin**" msgstr "" @@ -1501,7 +1501,7 @@ msgid "" "terminal:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:90 +#: ../../source/contributor-tutorial-contribute-on-github.rst:68 msgid "**Add upstream**" msgstr "" @@ -1556,7 +1556,7 @@ msgstr "" msgid "And with Flower's repository:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:122 +#: ../../source/contributor-tutorial-contribute-on-github.rst:114 msgid "**Create a new branch**" msgstr "" @@ -1573,7 +1573,7 @@ msgid "" "directory:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:125 +#: ../../source/contributor-tutorial-contribute-on-github.rst:124 msgid "**Make changes**" msgstr "" @@ -1581,7 +1581,7 @@ msgstr "" msgid "Write great code and create wonderful changes using your favorite editor!" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:138 +#: ../../source/contributor-tutorial-contribute-on-github.rst:127 msgid "**Test and format your code**" msgstr "" @@ -1596,7 +1596,7 @@ msgstr "" msgid "To do so, we have written a few scripts that you can execute:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:150 +#: ../../source/contributor-tutorial-contribute-on-github.rst:140 msgid "**Stage changes**" msgstr "" @@ -1617,7 +1617,7 @@ msgid "" "the :code:`git status` command." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:160 +#: ../../source/contributor-tutorial-contribute-on-github.rst:152 msgid "**Commit changes**" msgstr "" @@ -1634,7 +1634,7 @@ msgid "" "example would be :code:`git commit -m \"Add images to README\"`." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:171 +#: ../../source/contributor-tutorial-contribute-on-github.rst:162 msgid "**Push the changes to the fork**" msgstr "" @@ -1655,7 +1655,7 @@ msgstr "" msgid "Creating and merging a pull request (PR)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:206 +#: ../../source/contributor-tutorial-contribute-on-github.rst:176 msgid "**Create the PR**" msgstr "" @@ -1718,7 +1718,7 @@ msgid "" "anyone, you have the option to create a draft pull request:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:209 +#: ../../source/contributor-tutorial-contribute-on-github.rst:208 msgid "**Making new changes**" msgstr "" @@ -1729,7 +1729,7 @@ msgid "" " associated with the PR." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:231 +#: ../../source/contributor-tutorial-contribute-on-github.rst:211 msgid "**Review the PR**" msgstr "" @@ -1765,7 +1765,7 @@ msgid "" "review." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:251 +#: ../../source/contributor-tutorial-contribute-on-github.rst:233 msgid "**Once the PR is merged**" msgstr "" @@ -2034,6 +2034,7 @@ msgstr "" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 #: ../../source/docker/run-as-subprocess.rst:11 #: ../../source/docker/run-quickstart-examples-docker-compose.rst:15 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:22 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:12 #: ../../source/docker/tutorial-quickstart-docker.rst:11 msgid "Prerequisites" @@ -2766,6 +2767,7 @@ msgid "Before you start, make sure that:" msgstr "" #: ../../source/docker/run-quickstart-examples-docker-compose.rst:19 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:26 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 #: ../../source/docker/tutorial-quickstart-docker.rst:15 msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." @@ -2964,6 +2966,203 @@ msgid "" "environment variables for a container." msgstr "" +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:3 +msgid "Deploy Flower on Multiple Machines with Docker Compose" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:5 +msgid "" +"This guide will help you set up a Flower project on multiple machines " +"using Docker Compose." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:8 +msgid "" +"You will learn how to run the Flower client and server components on two " +"separate machines, with Flower configured to use TLS encryption and " +"persist SuperLink state across restarts. A server consists of a SuperLink" +" and ``SuperExec``. For more details about the Flower architecture, refer" +" to the :doc:`../explanation-flower-architecture` explainer page." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:15 +msgid "" +"This guide assumes you have completed the :doc:`tutorial-quickstart-" +"docker-compose` tutorial. It is highly recommended that you follow and " +"understand the contents of that tutorial before proceeding with this " +"guide." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:24 +msgid "Before you begin, make sure you have the following prerequisites:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:28 +msgid "The Docker daemon is running on your local machine and the remote machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:30 +msgid "" +"Docker Compose V2 is installed on both your local machine and the remote " +"machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:32 +msgid "You can connect to the remote machine from your local machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:33 +msgid "Ports ``9091`` and ``9093`` are accessible on the remote machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:37 +msgid "" +"The guide uses the |quickstart_sklearn_tabular|_ example as an example " +"project." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:40 +msgid "" +"If your project has a different name or location, please remember to " +"adjust the commands/paths accordingly." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:45 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 +#: ../../source/docker/tutorial-quickstart-docker.rst:19 +msgid "Step 1: Set Up" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:47 +msgid "Clone the Flower repository and change to the ``distributed`` directory:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:55 +msgid "Get the IP address from the remote machine and save it for later." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:57 +msgid "" +"Use the ``certs.yml`` Compose file to generate your own self-signed " +"certificates. If you have certificates, you can continue with Step 2." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:62 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:215 +msgid "These certificates should be used only for development purposes." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:64 +msgid "" +"For production environments, you may have to use dedicated services to " +"obtain your certificates." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:67 +msgid "" +"First, set the environment variables ``SUPERLINK_IP`` and " +"``SUPEREXEC_IP`` with the IP address from the remote machine. For " +"example, if the IP is ``192.168.2.33``, execute:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:76 +msgid "Next, generate the self-signed certificates:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:84 +msgid "Step 2: Copy the Server Compose Files" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:86 +msgid "" +"Use the method that works best for you to copy the ``server`` directory, " +"the certificates, and your Flower project to the remote machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:89 +msgid "For example, you can use ``scp`` to copy the directories:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:100 +msgid "Step 3: Start the Flower Server Components" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:102 +msgid "" +"Log into the remote machine using ``ssh`` and run the following command " +"to start the SuperLink and SuperExec services:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:115 +msgid "" +"The Path of the ``PROJECT_DIR`` should be relative to the location of the" +" ``server`` Docker Compose files." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:118 +msgid "Go back to your terminal on your local machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:122 +msgid "Step 4: Start the Flower Client Components" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:124 +msgid "" +"On your local machine, run the following command to start the client " +"components:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:135 +msgid "" +"The Path of the ``PROJECT_DIR`` should be relative to the location of the" +" ``client`` Docker Compose files." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:140 +msgid "Step 5: Run Your Flower Project" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:142 +msgid "" +"Specify the remote SuperExec IP addresses and the path to the root " +"certificate in the ``[tool.flwr.federations.remote-superexec]`` table in " +"the ``pyproject.toml`` file. Here, we have named our remote federation " +"``remote-superexec``:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:147 +msgid "examples/quickstart-sklearn-tabular/pyproject.toml" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:156 +msgid "" +"The Path of the ``root-certificates`` should be relative to the location " +"of the ``pyproject.toml`` file." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:159 +msgid "To run the project, execute:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:165 +msgid "" +"That's it! With these steps, you've set up Flower on two separate " +"machines and are ready to start using it." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:170 +msgid "Step 6: Clean Up" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:172 +msgid "Shut down the Flower client components:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:179 +msgid "Shut down the Flower server components and delete the SuperLink state:" +msgstr "" + #: ../../source/docker/tutorial-quickstart-docker.rst:2 msgid "Quickstart with Docker" msgstr "" @@ -2981,11 +3180,6 @@ msgid "" " understanding the basic workflow that uses the minimum configurations." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 -#: ../../source/docker/tutorial-quickstart-docker.rst:19 -msgid "Step 1: Set Up" -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:31 #: ../../source/docker/tutorial-quickstart-docker.rst:21 msgid "Create a new Flower project (PyTorch):" @@ -3697,10 +3891,6 @@ msgid "" " the ``certs.yml`` Compose file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:215 -msgid "These certificates should be used only for development purposes." -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:217 msgid "" "For production environments, use a service like `Let's Encrypt " @@ -4070,7 +4260,7 @@ msgid "" "one method for testing the model:" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:219 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:218 msgid ":code:`set_parameters`" msgstr "" @@ -4101,9 +4291,9 @@ msgid "" ":code:`ndarray`'s (which is what :code:`flwr.client.NumPyClient` expects)" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:225 -#: ../../source/tutorial-quickstart-jax.rst:173 -#: ../../source/tutorial-quickstart-scikitlearn.rst:125 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:223 +#: ../../source/tutorial-quickstart-jax.rst:171 +#: ../../source/tutorial-quickstart-scikitlearn.rst:123 msgid ":code:`fit`" msgstr "" @@ -4125,9 +4315,9 @@ msgstr "" msgid "get the updated local model weights and return them to the server" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 -#: ../../source/tutorial-quickstart-jax.rst:178 -#: ../../source/tutorial-quickstart-scikitlearn.rst:128 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:227 +#: ../../source/tutorial-quickstart-jax.rst:175 +#: ../../source/tutorial-quickstart-scikitlearn.rst:127 msgid ":code:`evaluate`" msgstr "" @@ -4216,7 +4406,7 @@ msgid "" " individual's information remains hidden in the crowd." msgstr "" -#: ../../source/explanation-differential-privacy.rst:-1 +#: ../../source/explanation-differential-privacy.rst:16 msgid "DP Intro" msgstr "" @@ -4326,8 +4516,8 @@ msgid "" "the client's data." msgstr "" -#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:68 +#: ../../source/explanation-differential-privacy.rst:71 #: ../../source/how-to-use-differential-privacy.rst:11 msgid "Central Differential Privacy" msgstr "" @@ -4354,7 +4544,7 @@ msgid "" "that larger updates are scaled down to fit within the norm `S`." msgstr "" -#: ../../source/explanation-differential-privacy.rst:-1 +#: ../../source/explanation-differential-privacy.rst:84 msgid "clipping" msgstr "" @@ -4399,8 +4589,8 @@ msgid "" "others." msgstr "" -#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:105 +#: ../../source/explanation-differential-privacy.rst:110 #: ../../source/how-to-use-differential-privacy.rst:96 msgid "Local Differential Privacy" msgstr "" @@ -4631,7 +4821,7 @@ msgstr "" msgid "This is sometimes called a hub-and-spoke topology:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:24 +#: ../../source/explanation-flower-architecture.rst:18 msgid "Hub-and-spoke topology in federated learning" msgstr "" @@ -4703,7 +4893,7 @@ msgid "" "`missing link` between all those SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:71 +#: ../../source/explanation-flower-architecture.rst:65 #, fuzzy msgid "Basic Flower architecture" msgstr "Arquitetura do Flower" @@ -4740,7 +4930,7 @@ msgid "" "SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:97 +#: ../../source/explanation-flower-architecture.rst:91 msgid "Multi-tenancy federated learning architecture" msgstr "" @@ -4762,7 +4952,7 @@ msgid "" "their corresponding ``ClientApp``\\s:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:113 +#: ../../source/explanation-flower-architecture.rst:107 msgid "Multi-tenancy federated learning architecture - Run 1" msgstr "" @@ -4778,7 +4968,7 @@ msgid "" " to participate in the training:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:125 +#: ../../source/explanation-flower-architecture.rst:119 msgid "Multi-tenancy federated learning architecture - Run 2" msgstr "" @@ -4814,7 +5004,7 @@ msgid "" "developer machine." msgstr "" -#: ../../source/explanation-flower-architecture.rst:151 +#: ../../source/explanation-flower-architecture.rst:145 msgid "Flower Deployment Engine with SuperExec" msgstr "" @@ -7483,7 +7673,7 @@ msgid "" "adaptive clipping." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:-1 +#: ../../source/how-to-use-differential-privacy.rst:25 msgid "server side clipping" msgstr "" @@ -7512,7 +7702,7 @@ msgid "" ":code:`DifferentialPrivacyClientSideAdaptiveClipping`." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:-1 +#: ../../source/how-to-use-differential-privacy.rst:57 msgid "client side clipping" msgstr "" @@ -7539,7 +7729,7 @@ msgid "" "clipping norm value, sensitivity, epsilon, and delta." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:-1 +#: ../../source/how-to-use-differential-privacy.rst:99 msgid "local DP mod" msgstr "" @@ -7927,6 +8117,10 @@ msgstr "Argumento de compilação" msgid "Optional argument" msgstr "Argumento de compilação" +#: ../../flwr install:1 +msgid "The source FAB file to install." +msgstr "" + #: ../../flwr log:1 msgid "Get logs from a Flower project run." msgstr "" @@ -7948,6 +8142,18 @@ msgstr "" msgid "Required argument" msgstr "Argumento de compilação" +#: ../../flwr log:1 +msgid "The Flower run ID to query" +msgstr "" + +#: ../../flwr log:1 +msgid "Path of the Flower project to run" +msgstr "" + +#: ../../flwr log:1 +msgid "Name of the federation to run the app on" +msgstr "" + #: ../../flwr new:1 msgid "Create new Flower App." msgstr "" @@ -7970,6 +8176,11 @@ msgstr "" msgid "The Flower username of the author" msgstr "" +#: ../../flwr new:1 +#, fuzzy +msgid "The name of the Flower App" +msgstr "O nome do repositório da imagem base." + #: ../../flwr run:1 msgid "Run Flower App." msgstr "" @@ -7990,6 +8201,15 @@ msgid "" " the `pyproject.toml` in order to be properly overriden." msgstr "" +#: ../../flwr run:1 +#, fuzzy +msgid "Path of the Flower App to run." +msgstr "O nome do repositório da imagem base." + +#: ../../flwr run:1 +msgid "Name of the federation to run the app on." +msgstr "" + #: ../../source/ref-api-cli.rst:16 msgid "flower-simulation" msgstr "" @@ -10438,7 +10658,7 @@ msgstr "" msgid "The encoding in which to encode the string." msgstr "" -#: flwr.common.EventType.encode:9 of +#: flwr.common.EventType.encode:5 of msgid "errors" msgstr "" @@ -10614,7 +10834,7 @@ msgid "" "string." msgstr "" -#: flwr.common.EventType.replace:5 of +#: flwr.common.EventType.replace:3 of msgid "count" msgstr "" @@ -10650,7 +10870,7 @@ msgid "" "strings and the original string." msgstr "" -#: flwr.common.EventType.rsplit:7 flwr.common.EventType.split:7 of +#: flwr.common.EventType.rsplit:3 flwr.common.EventType.split:3 of msgid "sep" msgstr "" @@ -10665,7 +10885,7 @@ msgid "" " empty strings from the result." msgstr "" -#: flwr.common.EventType.rsplit:11 flwr.common.EventType.split:11 of +#: flwr.common.EventType.rsplit:9 flwr.common.EventType.split:9 of msgid "maxsplit" msgstr "" @@ -10706,7 +10926,7 @@ msgid "" "remaining cased characters have lower case." msgstr "" -#: flwr.common.EventType.translate:5 of +#: flwr.common.EventType.translate:3 of msgid "table" msgstr "" @@ -20506,7 +20726,7 @@ msgid "" "blockchain environment is available here:" msgstr "" -#: ../../source/ref-faq.rst:29 +#: ../../source/ref-faq.rst:28 msgid "`FLock: A Decentralised AI Training Platform `_." msgstr "" @@ -21362,7 +21582,7 @@ msgid "" " the model:" msgstr "" -#: ../../source/tutorial-quickstart-jax.rst:167 +#: ../../source/tutorial-quickstart-jax.rst:165 msgid ":code:`set_parameters (optional)`" msgstr "" @@ -21841,7 +22061,7 @@ msgstr "" msgid "Sets the parameters of a :code:`sklearn` LogisticRegression model" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:50 +#: ../../source/tutorial-quickstart-scikitlearn.rst:49 msgid ":code:`set_initial_params()`" msgstr "" @@ -21897,7 +22117,7 @@ msgstr "" msgid "return the model weight as a list of NumPy ndarrays" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:121 +#: ../../source/tutorial-quickstart-scikitlearn.rst:120 msgid ":code:`set_parameters` (optional)" msgstr "" @@ -23969,7 +24189,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 -msgid "|e87b69b2ada74ea49412df16f4a0b9cc|" +msgid "|b8714c45b74b4d8fb008e2ebb3bc1d44|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 @@ -23984,7 +24204,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 -msgid "|33cacb7d985c4906b348515c1a5cd993|" +msgid "|75f1561efcfd422ea67d28d1513120dc|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 @@ -24005,7 +24225,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 -msgid "|cc080a555947492fa66131dc3a967603|" +msgid "|6a1f51b235304558a9bdaaabfc93b8d2|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 @@ -24021,7 +24241,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 -msgid "|085c3e0fb8664c6aa06246636524b20b|" +msgid "|35e70dab1fb544af9aa3a9c09c4f9797|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 @@ -24037,7 +24257,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 -msgid "|bfe69c74e48c45d49b50251c38c2a019|" +msgid "|d7efb5705dd3467f991ed23746824a07|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 @@ -24052,7 +24272,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 -msgid "|ebbecd651f0348d99c6511ea859bf4ca|" +msgid "|94e7b021c7b540bfbedf7f082a41ff87|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 @@ -24072,7 +24292,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 -msgid "|163117eb654a4273babba413cf8065f5|" +msgid "|a80714782dde439ab73936518f91fc3c|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 @@ -24087,7 +24307,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 -msgid "|452ac3ba453b4cd1be27be1ba7560d64|" +msgid "|c62080ca6197473da57d191c8225a9d9|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 @@ -24227,7 +24447,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 -msgid "|f403fcd69e4e44409627e748b404c086|" +msgid "|21a8f1e6a5b14a7bbb8559979d0e8a2b|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 @@ -24251,7 +24471,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 -msgid "|4b00fe63870145968f8443619a792a42|" +msgid "|c310f2a22f7b4917bf42775aae7a1c09|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 @@ -24275,7 +24495,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 -msgid "|368378731066486fa4397e89bc6b870c|" +msgid "|a0c5b43401194535a8460bcf02e65f9a|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 @@ -24298,7 +24518,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 -msgid "|a66aa83d85bf4ffba7ed660b718066da|" +msgid "|aabfdbd5564e41a790f8ea93cc21a444|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 @@ -24336,7 +24556,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 -msgid "|82324b9af72a4582a81839d55caab767|" +msgid "|c9cc8f160fa647b09e742fe4dc8edb54|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 @@ -24430,7 +24650,7 @@ msgid "" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 -msgid "|fbf2da0da3cc4f8ab3b3eff852d80c41|" +msgid "|7e83aad011cd4907b2f02f907c6922e9|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 @@ -30186,3 +30406,45 @@ msgstr "" #~ msgid "|ed9aae51da70428eab7eef32f21e819e|" #~ msgstr "" +#~ msgid "|e87b69b2ada74ea49412df16f4a0b9cc|" +#~ msgstr "" + +#~ msgid "|33cacb7d985c4906b348515c1a5cd993|" +#~ msgstr "" + +#~ msgid "|cc080a555947492fa66131dc3a967603|" +#~ msgstr "" + +#~ msgid "|085c3e0fb8664c6aa06246636524b20b|" +#~ msgstr "" + +#~ msgid "|bfe69c74e48c45d49b50251c38c2a019|" +#~ msgstr "" + +#~ msgid "|ebbecd651f0348d99c6511ea859bf4ca|" +#~ msgstr "" + +#~ msgid "|163117eb654a4273babba413cf8065f5|" +#~ msgstr "" + +#~ msgid "|452ac3ba453b4cd1be27be1ba7560d64|" +#~ msgstr "" + +#~ msgid "|f403fcd69e4e44409627e748b404c086|" +#~ msgstr "" + +#~ msgid "|4b00fe63870145968f8443619a792a42|" +#~ msgstr "" + +#~ msgid "|368378731066486fa4397e89bc6b870c|" +#~ msgstr "" + +#~ msgid "|a66aa83d85bf4ffba7ed660b718066da|" +#~ msgstr "" + +#~ msgid "|82324b9af72a4582a81839d55caab767|" +#~ msgstr "" + +#~ msgid "|fbf2da0da3cc4f8ab3b3eff852d80c41|" +#~ msgstr "" + diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po index 9af452fb0be2..edb7f09f4f15 100644 --- a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po +++ b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po @@ -7,7 +7,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower main\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-09-24 00:29+0000\n" +"POT-Creation-Date: 2024-09-25 00:30+0000\n" "PO-Revision-Date: 2024-06-12 10:09+0000\n" "Last-Translator: Yan Gao \n" "Language: zh_Hans\n" @@ -1476,7 +1476,7 @@ msgstr "" msgid "Setting up the repository" msgstr "建立资源库" -#: ../../source/contributor-tutorial-contribute-on-github.rst:21 +#: ../../source/contributor-tutorial-contribute-on-github.rst:12 msgid "**Create a GitHub account and setup Git**" msgstr "**创建 GitHub 账户并设置 Git**" @@ -1515,7 +1515,7 @@ msgstr "" "通用的 Git 和 GitHub 工作流程背后的理念可以归结为：从 GitHub 上的远程仓库下载代码，在本地进行修改并使用 Git " "进行跟踪，然后将新的历史记录上传回 GitHub。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:32 +#: ../../source/contributor-tutorial-contribute-on-github.rst:23 msgid "**Forking the Flower repository**" msgstr "**叉花仓库**" @@ -1540,7 +1540,7 @@ msgstr "" "您可以更改名称，但没有必要，因为这个版本的 Flower " "将是您自己的，并位于您自己的账户中（即，在您自己的版本库列表中）。创建完成后，您会在左上角看到自己的 Flower 版本。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:47 +#: ../../source/contributor-tutorial-contribute-on-github.rst:34 msgid "**Cloning your forked repository**" msgstr "**克隆你的分叉仓库**" @@ -1567,7 +1567,7 @@ msgid "" "it) folder in the current working directory." msgstr "这将在当前工作目录下创建一个 `flower/`（如果重命名了，则使用 fork 的名称）文件夹。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:66 +#: ../../source/contributor-tutorial-contribute-on-github.rst:49 msgid "**Add origin**" msgstr "**添加原产地**" @@ -1591,7 +1591,7 @@ msgid "" "terminal:" msgstr "一旦复制了 \\ ，我们就可以在终端中键入以下命令：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:90 +#: ../../source/contributor-tutorial-contribute-on-github.rst:68 msgid "**Add upstream**" msgstr "**增加上游**" @@ -1650,7 +1650,7 @@ msgstr "在进行任何更改之前，请确保您的版本库是最新的：" msgid "And with Flower's repository:" msgstr "还有Flower的存储库：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:122 +#: ../../source/contributor-tutorial-contribute-on-github.rst:114 msgid "**Create a new branch**" msgstr "**创建一个新分支**" @@ -1667,7 +1667,7 @@ msgid "" "directory:" msgstr "为此，只需在版本库目录下运行以下命令即可：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:125 +#: ../../source/contributor-tutorial-contribute-on-github.rst:124 msgid "**Make changes**" msgstr "**进行修改**" @@ -1675,7 +1675,7 @@ msgstr "**进行修改**" msgid "Write great code and create wonderful changes using your favorite editor!" msgstr "使用您最喜欢的编辑器编写优秀的代码并创建精彩的更改！" -#: ../../source/contributor-tutorial-contribute-on-github.rst:138 +#: ../../source/contributor-tutorial-contribute-on-github.rst:127 msgid "**Test and format your code**" msgstr "**测试并格式化您的代码**" @@ -1690,7 +1690,7 @@ msgstr "不要忘记测试和格式化您的代码！否则您的代码将无法 msgid "To do so, we have written a few scripts that you can execute:" msgstr "为此，我们编写了一些脚本供您执行：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:150 +#: ../../source/contributor-tutorial-contribute-on-github.rst:140 msgid "**Stage changes**" msgstr "**舞台变化**" @@ -1711,7 +1711,7 @@ msgid "" "the :code:`git status` command." msgstr "要查看与上一版本（上次提交）相比哪些文件已被修改，以及哪些文件处于提交阶段，可以使用 :code:`git status` 命令。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:160 +#: ../../source/contributor-tutorial-contribute-on-github.rst:152 msgid "**Commit changes**" msgstr "**提交更改**" @@ -1730,7 +1730,7 @@ msgstr "" " 用于向他人解释提交的作用。它应该以命令式风格书写，并且简明扼要。例如 :code:`git commit " "-m \"Add images to README\"`。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:171 +#: ../../source/contributor-tutorial-contribute-on-github.rst:162 msgid "**Push the changes to the fork**" msgstr "**将更改推送到分叉**" @@ -1751,7 +1751,7 @@ msgstr "完成此操作后，您将在 GitHub 上看到您的分叉仓库已根 msgid "Creating and merging a pull request (PR)" msgstr "创建和合并拉取请求 (PR)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:206 +#: ../../source/contributor-tutorial-contribute-on-github.rst:176 msgid "**Create the PR**" msgstr "**创建 PR**" @@ -1820,7 +1820,7 @@ msgid "" "anyone, you have the option to create a draft pull request:" msgstr "如果您的 PR 尚未准备好接受审核，而且您不想通知任何人，您可以选择创建一个草案拉取请求：" -#: ../../source/contributor-tutorial-contribute-on-github.rst:209 +#: ../../source/contributor-tutorial-contribute-on-github.rst:208 msgid "**Making new changes**" msgstr "**作出新的改变**" @@ -1831,7 +1831,7 @@ msgid "" " associated with the PR." msgstr "一旦 PR 被打开（无论是否作为草案），你仍然可以像以前一样，通过修改与 PR 关联的分支来推送新的提交。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:231 +#: ../../source/contributor-tutorial-contribute-on-github.rst:211 msgid "**Review the PR**" msgstr "**审查 PR**" @@ -1867,7 +1867,7 @@ msgid "" "review." msgstr "一旦所有对话都得到解决，您就可以重新申请审核。" -#: ../../source/contributor-tutorial-contribute-on-github.rst:251 +#: ../../source/contributor-tutorial-contribute-on-github.rst:233 msgid "**Once the PR is merged**" msgstr "**一旦 PR 被合并**" @@ -2180,6 +2180,7 @@ msgstr "成为贡献者" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 #: ../../source/docker/run-as-subprocess.rst:11 #: ../../source/docker/run-quickstart-examples-docker-compose.rst:15 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:22 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:12 #: ../../source/docker/tutorial-quickstart-docker.rst:11 msgid "Prerequisites" @@ -2991,6 +2992,7 @@ msgid "Before you start, make sure that:" msgstr "开始之前，请确保 Docker 守护进程正在运行：" #: ../../source/docker/run-quickstart-examples-docker-compose.rst:19 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:26 #: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 #: ../../source/docker/tutorial-quickstart-docker.rst:15 msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." @@ -3210,6 +3212,209 @@ msgid "" "environment variables for a container." msgstr "要在 Docker 容器内设置变量，可以使用 ``-e =`` 标志。" +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:3 +#, fuzzy +msgid "Deploy Flower on Multiple Machines with Docker Compose" +msgstr "快速入门 iOS" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:5 +msgid "" +"This guide will help you set up a Flower project on multiple machines " +"using Docker Compose." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:8 +msgid "" +"You will learn how to run the Flower client and server components on two " +"separate machines, with Flower configured to use TLS encryption and " +"persist SuperLink state across restarts. A server consists of a SuperLink" +" and ``SuperExec``. For more details about the Flower architecture, refer" +" to the :doc:`../explanation-flower-architecture` explainer page." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:15 +msgid "" +"This guide assumes you have completed the :doc:`tutorial-quickstart-" +"docker-compose` tutorial. It is highly recommended that you follow and " +"understand the contents of that tutorial before proceeding with this " +"guide." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:24 +msgid "Before you begin, make sure you have the following prerequisites:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:28 +msgid "The Docker daemon is running on your local machine and the remote machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:30 +msgid "" +"Docker Compose V2 is installed on both your local machine and the remote " +"machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:32 +msgid "You can connect to the remote machine from your local machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:33 +msgid "Ports ``9091`` and ``9093`` are accessible on the remote machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:37 +msgid "" +"The guide uses the |quickstart_sklearn_tabular|_ example as an example " +"project." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:40 +msgid "" +"If your project has a different name or location, please remember to " +"adjust the commands/paths accordingly." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:45 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 +#: ../../source/docker/tutorial-quickstart-docker.rst:19 +msgid "Step 1: Set Up" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:47 +msgid "Clone the Flower repository and change to the ``distributed`` directory:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:55 +msgid "Get the IP address from the remote machine and save it for later." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:57 +msgid "" +"Use the ``certs.yml`` Compose file to generate your own self-signed " +"certificates. If you have certificates, you can continue with Step 2." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:62 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:215 +msgid "These certificates should be used only for development purposes." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:64 +msgid "" +"For production environments, you may have to use dedicated services to " +"obtain your certificates." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:67 +msgid "" +"First, set the environment variables ``SUPERLINK_IP`` and " +"``SUPEREXEC_IP`` with the IP address from the remote machine. For " +"example, if the IP is ``192.168.2.33``, execute:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:76 +msgid "Next, generate the self-signed certificates:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:84 +msgid "Step 2: Copy the Server Compose Files" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:86 +msgid "" +"Use the method that works best for you to copy the ``server`` directory, " +"the certificates, and your Flower project to the remote machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:89 +msgid "For example, you can use ``scp`` to copy the directories:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:100 +#, fuzzy +msgid "Step 3: Start the Flower Server Components" +msgstr "然后，我们启动服务器：" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:102 +msgid "" +"Log into the remote machine using ``ssh`` and run the following command " +"to start the SuperLink and SuperExec services:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:115 +msgid "" +"The Path of the ``PROJECT_DIR`` should be relative to the location of the" +" ``server`` Docker Compose files." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:118 +msgid "Go back to your terminal on your local machine." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:122 +#, fuzzy +msgid "Step 4: Start the Flower Client Components" +msgstr "然后，我们启动服务器：" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:124 +msgid "" +"On your local machine, run the following command to start the client " +"components:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:135 +msgid "" +"The Path of the ``PROJECT_DIR`` should be relative to the location of the" +" ``client`` Docker Compose files." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:140 +#, fuzzy +msgid "Step 5: Run Your Flower Project" +msgstr "Flower 服务器。" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:142 +msgid "" +"Specify the remote SuperExec IP addresses and the path to the root " +"certificate in the ``[tool.flwr.federations.remote-superexec]`` table in " +"the ``pyproject.toml`` file. Here, we have named our remote federation " +"``remote-superexec``:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:147 +#, fuzzy +msgid "examples/quickstart-sklearn-tabular/pyproject.toml" +msgstr "scikit-learn快速入门" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:156 +msgid "" +"The Path of the ``root-certificates`` should be relative to the location " +"of the ``pyproject.toml`` file." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:159 +msgid "To run the project, execute:" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:165 +msgid "" +"That's it! With these steps, you've set up Flower on two separate " +"machines and are ready to start using it." +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:170 +msgid "Step 6: Clean Up" +msgstr "" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:172 +#, fuzzy +msgid "Shut down the Flower client components:" +msgstr "Flower 客户端。" + +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:179 +msgid "Shut down the Flower server components and delete the SuperLink state:" +msgstr "" + #: ../../source/docker/tutorial-quickstart-docker.rst:2 #, fuzzy msgid "Quickstart with Docker" @@ -3228,11 +3433,6 @@ msgid "" " understanding the basic workflow that uses the minimum configurations." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 -#: ../../source/docker/tutorial-quickstart-docker.rst:19 -msgid "Step 1: Set Up" -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:31 #: ../../source/docker/tutorial-quickstart-docker.rst:21 msgid "Create a new Flower project (PyTorch):" @@ -3969,10 +4169,6 @@ msgid "" " the ``certs.yml`` Compose file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:215 -msgid "These certificates should be used only for development purposes." -msgstr "" - #: ../../source/docker/tutorial-quickstart-docker-compose.rst:217 msgid "" "For production environments, use a service like `Let's Encrypt " @@ -4406,7 +4602,7 @@ msgstr "" ":code:`Client`略微容易一些，因为它避免了一些不必要的操作。:code:`CifarClient` " "需要实现四个方法，两个用于获取/设置模型参数，一个用于训练模型，一个用于测试模型：" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:219 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:218 msgid ":code:`set_parameters`" msgstr ":code:`set_parameters`" @@ -4439,9 +4635,9 @@ msgstr "" "获取模型参数，并以 NumPy :code:`ndarray`的列表形式返回（这正是 " ":code:`flwr.client.NumPyClient`所匹配的格式）" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:225 -#: ../../source/tutorial-quickstart-jax.rst:173 -#: ../../source/tutorial-quickstart-scikitlearn.rst:125 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:223 +#: ../../source/tutorial-quickstart-jax.rst:171 +#: ../../source/tutorial-quickstart-scikitlearn.rst:123 msgid ":code:`fit`" msgstr ":code:`fit`" @@ -4463,9 +4659,9 @@ msgstr "在本地训练集上训练模型" msgid "get the updated local model weights and return them to the server" msgstr "获取更新后的本地模型参数并发送回服务器" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 -#: ../../source/tutorial-quickstart-jax.rst:178 -#: ../../source/tutorial-quickstart-scikitlearn.rst:128 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:227 +#: ../../source/tutorial-quickstart-jax.rst:175 +#: ../../source/tutorial-quickstart-scikitlearn.rst:127 msgid ":code:`evaluate`" msgstr ":code:`evaluate`" @@ -4569,7 +4765,7 @@ msgstr "" "的数据）之外完全相同。差分隐私（DP）可以保证任何分析（M），比如计算平均收入，对两个数据集都会产生几乎相同的结果（O 和 O' " "将是相似的）。这既保留了群体模式，又掩盖了个人细节，确保个人的信息隐藏在人群中。" -#: ../../source/explanation-differential-privacy.rst:-1 +#: ../../source/explanation-differential-privacy.rst:16 #, fuzzy msgid "DP Intro" msgstr "DP 介绍" @@ -4707,8 +4903,8 @@ msgid "" "the client's data." msgstr "**本地差分隐私**： 在向服务器发送任何信息之前，在客户端应用 DP，目的是防止向服务器发送的更新泄露任何有关客户端数据的信息。" -#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:68 +#: ../../source/explanation-differential-privacy.rst:71 #: ../../source/how-to-use-differential-privacy.rst:11 #, fuzzy msgid "Central Differential Privacy" @@ -4740,7 +4936,7 @@ msgstr "" "虽然在联合学习中实现中央数据处理的方法有很多种，但我们将重点放在[2]和[3]提出的算法上。总体方法是剪辑客户端发送的模型更新，并在聚合模型中添加一定量的噪声。在每次迭代中，以特定概率随机选择一组客户端进行训练。每个客户端对自己的数据进行局部训练。然后，每个客户端的更新会被某个值`S`（灵敏度`S`）剪切。这将限制任何单个客户端的影响，这对隐私至关重要，通常也有利于稳健性。实现这一点的常用方法是限制客户机模型更新的" " `L2` 准则，确保较大的更新被缩减以适应 `S` 准则。" -#: ../../source/explanation-differential-privacy.rst:-1 +#: ../../source/explanation-differential-privacy.rst:84 #, fuzzy msgid "clipping" msgstr "剪贴" @@ -4794,8 +4990,8 @@ msgid "" "others." msgstr "在固定剪切和自适应剪切之间做出选择取决于各种因素，如隐私要求、数据分布、模型复杂性等。" -#: ../../source/explanation-differential-privacy.rst:-1 #: ../../source/explanation-differential-privacy.rst:105 +#: ../../source/explanation-differential-privacy.rst:110 #: ../../source/how-to-use-differential-privacy.rst:96 #, fuzzy msgid "Local Differential Privacy" @@ -5066,7 +5262,7 @@ msgstr "" msgid "This is sometimes called a hub-and-spoke topology:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:24 +#: ../../source/explanation-flower-architecture.rst:18 #, fuzzy msgid "Hub-and-spoke topology in federated learning" msgstr "什么是联邦学习？" @@ -5139,7 +5335,7 @@ msgid "" "`missing link` between all those SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:71 +#: ../../source/explanation-flower-architecture.rst:65 #, fuzzy msgid "Basic Flower architecture" msgstr "Flower的架构" @@ -5177,7 +5373,7 @@ msgid "" "SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:97 +#: ../../source/explanation-flower-architecture.rst:91 #, fuzzy msgid "Multi-tenancy federated learning architecture" msgstr "使用联邦学习策略" @@ -5201,7 +5397,7 @@ msgid "" "their corresponding ``ClientApp``\\s:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:113 +#: ../../source/explanation-flower-architecture.rst:107 #, fuzzy msgid "Multi-tenancy federated learning architecture - Run 1" msgstr "使用联邦学习策略" @@ -5218,7 +5414,7 @@ msgid "" " to participate in the training:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:125 +#: ../../source/explanation-flower-architecture.rst:119 #, fuzzy msgid "Multi-tenancy federated learning architecture - Run 2" msgstr "使用联邦学习策略" @@ -5255,7 +5451,7 @@ msgid "" "developer machine." msgstr "" -#: ../../source/explanation-flower-architecture.rst:151 +#: ../../source/explanation-flower-architecture.rst:145 msgid "Flower Deployment Engine with SuperExec" msgstr "" @@ -8370,7 +8566,7 @@ msgstr "" ":code:`DifferentialPrivacyServerSideFixedClipping` 和 " ":code:`DifferentialPrivacyServerSideAdaptiveClipping` ，用于固定剪辑和自适应剪辑。" -#: ../../source/how-to-use-differential-privacy.rst:-1 +#: ../../source/how-to-use-differential-privacy.rst:25 #, fuzzy msgid "server side clipping" msgstr "服务器端逻辑" @@ -8411,7 +8607,7 @@ msgstr "" ":code:`DifferentialPrivacyClientSideFixedClipping` 和 " ":code:`DifferentialPrivacyClientSideAdaptiveClipping`。" -#: ../../source/how-to-use-differential-privacy.rst:-1 +#: ../../source/how-to-use-differential-privacy.rst:57 #, fuzzy msgid "client side clipping" msgstr "客户端逻辑" @@ -8448,7 +8644,7 @@ msgstr "" "要利用本地差分隐私（DP）并在将客户端模型参数传输到 Flower 服务器之前为其添加噪声，可以使用 " "`LocalDpMod`。需要设置以下超参数：剪切规范值、灵敏度、ε 和 delta。" -#: ../../source/how-to-use-differential-privacy.rst:-1 +#: ../../source/how-to-use-differential-privacy.rst:99 #, fuzzy msgid "local DP mod" msgstr "本地 DP 模式" @@ -8875,6 +9071,10 @@ msgstr "参数解析器" msgid "Optional argument" msgstr "可选的改进措施" +#: ../../flwr install:1 +msgid "The source FAB file to install." +msgstr "" + #: ../../flwr log:1 msgid "Get logs from a Flower project run." msgstr "" @@ -8897,6 +9097,19 @@ msgstr "" msgid "Required argument" msgstr "构建文档" +#: ../../flwr log:1 +#, fuzzy +msgid "The Flower run ID to query" +msgstr "加入 Flower 社区" + +#: ../../flwr log:1 +msgid "Path of the Flower project to run" +msgstr "" + +#: ../../flwr log:1 +msgid "Name of the federation to run the app on" +msgstr "" + #: ../../flwr new:1 #, fuzzy msgid "Create new Flower App." @@ -8921,6 +9134,11 @@ msgstr "" msgid "The Flower username of the author" msgstr "" +#: ../../flwr new:1 +#, fuzzy +msgid "The name of the Flower App" +msgstr "基础镜像的存储库名称。" + #: ../../flwr run:1 #, fuzzy msgid "Run Flower App." @@ -8942,6 +9160,15 @@ msgid "" " the `pyproject.toml` in order to be properly overriden." msgstr "" +#: ../../flwr run:1 +#, fuzzy +msgid "Path of the Flower App to run." +msgstr "基础镜像的存储库名称。" + +#: ../../flwr run:1 +msgid "Name of the federation to run the app on." +msgstr "" + #: ../../source/ref-api-cli.rst:16 #, fuzzy msgid "flower-simulation" @@ -11859,7 +12086,7 @@ msgstr "编码" msgid "The encoding in which to encode the string." msgstr "字符串的编码。" -#: flwr.common.EventType.encode:9 of +#: flwr.common.EventType.encode:5 of #, fuzzy msgid "errors" msgstr "错误" @@ -12071,7 +12298,7 @@ msgid "" "string." msgstr "如果字符串以后缀字符串结尾，且后缀不为空，则返回 string[:-len(suffix)]。否则，返回原始字符串的副本。" -#: flwr.common.EventType.replace:5 of +#: flwr.common.EventType.replace:3 of #, fuzzy msgid "count" msgstr "背景" @@ -12113,7 +12340,7 @@ msgid "" "strings and the original string." msgstr "如果找不到分隔符，则返回一个包含两个空字符串和原始字符串的 3 元组。" -#: flwr.common.EventType.rsplit:7 flwr.common.EventType.split:7 of +#: flwr.common.EventType.rsplit:3 flwr.common.EventType.split:3 of #, fuzzy msgid "sep" msgstr "sep" @@ -12131,7 +12358,7 @@ msgid "" " empty strings from the result." msgstr "当设置为 \"无\"（默认值）时，将对任何空白字符（包括 \\n \\r \\t \\f 和空格）进行分割，并从结果中剔除空字符串。" -#: flwr.common.EventType.rsplit:11 flwr.common.EventType.split:11 of +#: flwr.common.EventType.rsplit:9 flwr.common.EventType.split:9 of #, fuzzy msgid "maxsplit" msgstr "最大分割" @@ -12181,7 +12408,7 @@ msgid "" "remaining cased characters have lower case." msgstr "更具体地说，单词以大写字母开头，其余所有大小写字符均为小写。" -#: flwr.common.EventType.translate:5 of +#: flwr.common.EventType.translate:3 of #, fuzzy msgid "table" msgstr "数据库" @@ -24959,7 +25186,7 @@ msgid "" "blockchain environment is available here:" msgstr "当然可以。有关在区块链环境中使用 Flower 的可用示例列表，请点击此处：" -#: ../../source/ref-faq.rst:29 +#: ../../source/ref-faq.rst:28 msgid "`FLock: A Decentralised AI Training Platform `_." msgstr "" @@ -25935,7 +26162,7 @@ msgstr "" ":code:`Client`更容易实现，因为它避免了一些不必要的操作。:code:`FlowerClient` " "需要实现四个方法，两个用于获取/设置模型参数，一个用于训练模型，一个用于测试模型：" -#: ../../source/tutorial-quickstart-jax.rst:167 +#: ../../source/tutorial-quickstart-jax.rst:165 msgid ":code:`set_parameters (optional)`" msgstr ":code:`set_parameters (可选)`" @@ -26455,7 +26682,7 @@ msgstr ":code:`set_model_params()`" msgid "Sets the parameters of a :code:`sklearn` LogisticRegression model" msgstr "设置:code:`sklean`的LogisticRegression模型的参数" -#: ../../source/tutorial-quickstart-scikitlearn.rst:50 +#: ../../source/tutorial-quickstart-scikitlearn.rst:49 msgid ":code:`set_initial_params()`" msgstr ":code:`set_initial_params()`" @@ -26526,7 +26753,7 @@ msgstr "" msgid "return the model weight as a list of NumPy ndarrays" msgstr "以 NumPy ndarrays 列表形式返回模型参数" -#: ../../source/tutorial-quickstart-scikitlearn.rst:121 +#: ../../source/tutorial-quickstart-scikitlearn.rst:120 msgid ":code:`set_parameters` (optional)" msgstr ":code:`set_parameters` （可选）" @@ -28994,7 +29221,7 @@ msgid "" msgstr "在机器学习中，我们有一个模型和数据。模型可以是一个神经网络（如图所示），也可以是其他东西，比如经典的线性回归。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 -msgid "|e87b69b2ada74ea49412df16f4a0b9cc|" +msgid "|b8714c45b74b4d8fb008e2ebb3bc1d44|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 @@ -29009,7 +29236,7 @@ msgid "" msgstr "我们使用数据来训练模型，以完成一项有用的任务。任务可以是检测图像中的物体、转录音频或玩围棋等游戏。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 -msgid "|33cacb7d985c4906b348515c1a5cd993|" +msgid "|75f1561efcfd422ea67d28d1513120dc|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 @@ -29030,7 +29257,7 @@ msgid "" msgstr "它源于智能手机上用户与应用程序的交互、汽车上传感器数据的收集、笔记本电脑上键盘输入的接收，或者智能扬声器上某人试着唱的歌。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 -msgid "|cc080a555947492fa66131dc3a967603|" +msgid "|6a1f51b235304558a9bdaaabfc93b8d2|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 @@ -29048,7 +29275,7 @@ msgstr "" "\"通常不只是一个地方，而是很多地方。它可能是多个运行同一应用程序的设备。但也可能是多个组织，都在为同一任务生成数据。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 -msgid "|085c3e0fb8664c6aa06246636524b20b|" +msgid "|35e70dab1fb544af9aa3a9c09c4f9797|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 @@ -29064,7 +29291,7 @@ msgid "" msgstr "因此，要使用机器学习或任何类型的数据分析，过去使用的方法是在中央服务器上收集所有数据。这个服务器可以在数据中心的某个地方，也可以在云端的某个地方。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 -msgid "|bfe69c74e48c45d49b50251c38c2a019|" +msgid "|d7efb5705dd3467f991ed23746824a07|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 @@ -29079,7 +29306,7 @@ msgid "" msgstr "一旦所有数据都收集到一处，我们最终就可以使用机器学习算法在数据上训练我们的模型。这就是我们基本上一直依赖的机器学习方法。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 -msgid "|ebbecd651f0348d99c6511ea859bf4ca|" +msgid "|94e7b021c7b540bfbedf7f082a41ff87|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 @@ -29099,7 +29326,7 @@ msgid "" msgstr "我们刚刚看到的经典机器学习方法可以在某些情况下使用。很好的例子包括对假日照片进行分类或分析网络流量。在这些案例中，所有数据自然都可以在中央服务器上获得。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 -msgid "|163117eb654a4273babba413cf8065f5|" +msgid "|a80714782dde439ab73936518f91fc3c|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 @@ -29114,7 +29341,7 @@ msgid "" msgstr "但这种方法并不适用于许多其他情况。例如，集中服务器上没有数据，或者一台服务器上的数据不足以训练出一个好的模型。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 -msgid "|452ac3ba453b4cd1be27be1ba7560d64|" +msgid "|c62080ca6197473da57d191c8225a9d9|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 @@ -29262,7 +29489,7 @@ msgid "" msgstr "我们首先在服务器上初始化模型。这与经典的集中式学习完全相同：我们随机或从先前保存的检查点初始化模型参数。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 -msgid "|f403fcd69e4e44409627e748b404c086|" +msgid "|21a8f1e6a5b14a7bbb8559979d0e8a2b|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 @@ -29286,7 +29513,7 @@ msgid "" msgstr "接下来，我们会将全局模型的参数发送到连接的客户端节点（如智能手机等边缘设备或企业的服务器）。这是为了确保每个参与节点都使用相同的模型参数开始本地训练。我们通常只使用几个连接节点，而不是所有节点。这样做的原因是，选择越来越多的客户端节点会导致收益递减。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 -msgid "|4b00fe63870145968f8443619a792a42|" +msgid "|c310f2a22f7b4917bf42775aae7a1c09|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 @@ -29312,7 +29539,7 @@ msgstr "" "（mini-batches）。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 -msgid "|368378731066486fa4397e89bc6b870c|" +msgid "|a0c5b43401194535a8460bcf02e65f9a|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 @@ -29335,7 +29562,7 @@ msgid "" msgstr "经过本地训练后，每个客户节点最初收到的模型参数都会略有不同。参数之所以不同，是因为每个客户端节点的本地数据集中都有不同的数据。然后，客户端节点将这些模型更新发回服务器。它们发送的模型更新既可以是完整的模型参数，也可以只是本地训练过程中积累的梯度。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 -msgid "|a66aa83d85bf4ffba7ed660b718066da|" +msgid "|aabfdbd5564e41a790f8ea93cc21a444|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 @@ -29381,7 +29608,7 @@ msgstr "" " 100 个示例的 10 倍。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 -msgid "|82324b9af72a4582a81839d55caab767|" +msgid "|c9cc8f160fa647b09e742fe4dc8edb54|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 @@ -29488,7 +29715,7 @@ msgstr "" "为联邦学习、分析和评估提供了一种统一的方法。它允许用户联邦化任何工作负载、任何 ML 框架和任何编程语言。" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 -msgid "|fbf2da0da3cc4f8ab3b3eff852d80c41|" +msgid "|7e83aad011cd4907b2f02f907c6922e9|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 @@ -33370,3 +33597,45 @@ msgstr "" #~ msgid "|ed9aae51da70428eab7eef32f21e819e|" #~ msgstr "" +#~ msgid "|e87b69b2ada74ea49412df16f4a0b9cc|" +#~ msgstr "" + +#~ msgid "|33cacb7d985c4906b348515c1a5cd993|" +#~ msgstr "" + +#~ msgid "|cc080a555947492fa66131dc3a967603|" +#~ msgstr "" + +#~ msgid "|085c3e0fb8664c6aa06246636524b20b|" +#~ msgstr "" + +#~ msgid "|bfe69c74e48c45d49b50251c38c2a019|" +#~ msgstr "" + +#~ msgid "|ebbecd651f0348d99c6511ea859bf4ca|" +#~ msgstr "" + +#~ msgid "|163117eb654a4273babba413cf8065f5|" +#~ msgstr "" + +#~ msgid "|452ac3ba453b4cd1be27be1ba7560d64|" +#~ msgstr "" + +#~ msgid "|f403fcd69e4e44409627e748b404c086|" +#~ msgstr "" + +#~ msgid "|4b00fe63870145968f8443619a792a42|" +#~ msgstr "" + +#~ msgid "|368378731066486fa4397e89bc6b870c|" +#~ msgstr "" + +#~ msgid "|a66aa83d85bf4ffba7ed660b718066da|" +#~ msgstr "" + +#~ msgid "|82324b9af72a4582a81839d55caab767|" +#~ msgstr "" + +#~ msgid "|fbf2da0da3cc4f8ab3b3eff852d80c41|" +#~ msgstr "" + From 30bfcbab236b0c588526c1697c43e7c4b2c76d4b Mon Sep 17 00:00:00 2001 From: Robert Steiner Date: Thu, 26 Sep 2024 11:00:41 +0200 Subject: [PATCH 14/23] feat(framework:skip) Add Docker Hub badge (#4192) Signed-off-by: Robert Steiner --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 9f2604ad37b0..1be37ed391f7 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,7 @@ [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/adap/flower/blob/main/CONTRIBUTING.md) ![Build](https://github.com/adap/flower/actions/workflows/framework.yml/badge.svg) [![Downloads](https://static.pepy.tech/badge/flwr)](https://pepy.tech/project/flwr) +[![Docker Hub](https://img.shields.io/badge/Docker%20Hub-flwr-blue)](https://hub.docker.com/u/flwr) [![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.ai/join-slack) Flower (`flwr`) is a framework for building federated learning systems. The From e6fb60d21f2f0a2bd1516d3e904b4212048746d0 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Thu, 26 Sep 2024 09:08:24 +0000 Subject: [PATCH 15/23] docs(framework:skip) Update source for translations (automated) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- doc/locales/fr/LC_MESSAGES/framework-docs.po | 4998 +++++++------ doc/locales/ko/LC_MESSAGES/framework-docs.po | 5065 +++++++------ .../pt_BR/LC_MESSAGES/framework-docs.po | 6451 +++++++++++------ .../zh_Hans/LC_MESSAGES/framework-docs.po | 4456 ++++++------ 4 files changed, 12388 insertions(+), 8582 deletions(-) diff --git a/doc/locales/fr/LC_MESSAGES/framework-docs.po b/doc/locales/fr/LC_MESSAGES/framework-docs.po index 37daac8e95b7..ec6618bb4c75 100644 --- a/doc/locales/fr/LC_MESSAGES/framework-docs.po +++ b/doc/locales/fr/LC_MESSAGES/framework-docs.po @@ -3,7 +3,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower Docs\n" "Report-Msgid-Bugs-To: EMAIL@ADDRESS\n" -"POT-Creation-Date: 2024-09-25 00:30+0000\n" +"POT-Creation-Date: 2024-09-26 00:29+0000\n" "PO-Revision-Date: 2023-09-05 17:54+0000\n" "Last-Translator: Charles Beauville \n" "Language: fr\n" @@ -58,23 +58,23 @@ msgid "" "or not by reading the Flower source code." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:22 +#: ../../source/contributor-explanation-public-and-private-apis.rst:23 #, fuzzy msgid "Flower public API" msgstr "Flower ClientApp." -#: ../../source/contributor-explanation-public-and-private-apis.rst:24 +#: ../../source/contributor-explanation-public-and-private-apis.rst:25 msgid "Flower has a well-defined public API. Let's look at this in more detail." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:28 +#: ../../source/contributor-explanation-public-and-private-apis.rst:29 msgid "" "Every component that is reachable by recursively following " "``__init__.__all__`` starting from the root package (``flwr``) is part of" " the public API." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:30 +#: ../../source/contributor-explanation-public-and-private-apis.rst:32 msgid "" "If you want to determine whether a component " "(class/function/generator/...) is part of the public API or not, you need" @@ -82,13 +82,13 @@ msgid "" "src/py/flwr`` to look at the Python sub-packages contained ``flwr``:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:43 +#: ../../source/contributor-explanation-public-and-private-apis.rst:46 msgid "" "Contrast this with the definition of ``__all__`` in the root " "``src/py/flwr/__init__.py``:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:55 +#: ../../source/contributor-explanation-public-and-private-apis.rst:59 msgid "" "You can see that ``flwr`` has six subpackages (``cli``, ``client``, " "``common``, ``proto``, ``server``, ``simulation``), but only four of them" @@ -96,7 +96,7 @@ msgid "" "``simulation``)." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:57 +#: ../../source/contributor-explanation-public-and-private-apis.rst:63 msgid "" "What does this mean? It means that ``client``, ``common``, ``server`` and" " ``simulation`` are part of the public API, but ``cli`` and ``proto`` are" @@ -107,21 +107,21 @@ msgid "" "even be removed completely." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:62 +#: ../../source/contributor-explanation-public-and-private-apis.rst:70 msgid "Therefore, as a Flower user:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:64 +#: ../../source/contributor-explanation-public-and-private-apis.rst:72 msgid "``from flwr import client`` ✅ Ok, you're importing a public API." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:65 +#: ../../source/contributor-explanation-public-and-private-apis.rst:73 msgid "" "``from flwr import proto`` ❌ Not recommended, you're importing a private " "API." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:67 +#: ../../source/contributor-explanation-public-and-private-apis.rst:75 msgid "" "What about components that are nested deeper in the hierarchy? Let's look" " at Flower strategies to see another typical pattern. Flower strategies " @@ -130,7 +130,7 @@ msgid "" "``src/py/flwr/server/strategy/__init__.py``:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:81 +#: ../../source/contributor-explanation-public-and-private-apis.rst:91 msgid "" "What's notable here is that all strategies are implemented in dedicated " "modules (e.g., ``fedavg.py``). In ``__init__.py``, we *import* the " @@ -142,33 +142,33 @@ msgid "" "the public API (as long as we update the import path in ``__init__.py``)." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:86 +#: ../../source/contributor-explanation-public-and-private-apis.rst:99 msgid "Therefore:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:88 +#: ../../source/contributor-explanation-public-and-private-apis.rst:101 msgid "" "``from flwr.server.strategy import FedAvg`` ✅ Ok, you're importing a " "class that is part of the public API." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:89 +#: ../../source/contributor-explanation-public-and-private-apis.rst:103 msgid "" "``from flwr.server.strategy import fedavg`` ❌ Not recommended, you're " "importing a private module." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:91 +#: ../../source/contributor-explanation-public-and-private-apis.rst:106 msgid "" "This approach is also implemented in the tooling that automatically " "builds API reference docs." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:94 +#: ../../source/contributor-explanation-public-and-private-apis.rst:110 msgid "Flower public API of private packages" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:96 +#: ../../source/contributor-explanation-public-and-private-apis.rst:112 msgid "" "We also use this to define the public API of private subpackages. Public," " in this context, means the API that other ``flwr`` subpackages should " @@ -176,14 +176,14 @@ msgid "" "not exported via ``src/py/flwr/server/__init__.py``'s ``__all__``)." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:100 +#: ../../source/contributor-explanation-public-and-private-apis.rst:117 msgid "" "Still, the private sub-package ``flwr.server.driver`` defines a " "\"public\" API using ``__all__`` in " "``src/py/flwr/server/driver/__init__.py``:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:114 +#: ../../source/contributor-explanation-public-and-private-apis.rst:132 msgid "" "The interesting part is that both ``GrpcDriver`` and ``InMemoryDriver`` " "are never used by Flower framework users, only by other parts of the " @@ -195,7 +195,7 @@ msgid "" "``InMemoryDriver`` class definition)." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:117 +#: ../../source/contributor-explanation-public-and-private-apis.rst:140 msgid "" "This is because ``flwr.server.driver`` defines a public interface for " "other ``flwr`` subpackages. This allows codeowners of " @@ -224,23 +224,23 @@ msgid "" "development environment." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:12 +#: ../../source/contributor-how-to-build-docker-images.rst:13 #, fuzzy msgid "Clone the ``flower`` repository." msgstr "**Fourche le dépôt de Flower**" -#: ../../source/contributor-how-to-build-docker-images.rst:18 +#: ../../source/contributor-how-to-build-docker-images.rst:19 msgid "Verify the Docker daemon is running." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:20 +#: ../../source/contributor-how-to-build-docker-images.rst:21 msgid "" "The build instructions that assemble the images are located in the " "respective Dockerfiles. You can find them in the subdirectories of " "``src/docker``." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:23 +#: ../../source/contributor-how-to-build-docker-images.rst:24 msgid "" "Flower Docker images are configured via build arguments. Through build " "arguments, we can make the creation of images more flexible. For example," @@ -251,149 +251,149 @@ msgid "" "below." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:30 +#: ../../source/contributor-how-to-build-docker-images.rst:32 #, fuzzy msgid "Building the Base Image" msgstr "Chargement des données" -#: ../../source/contributor-how-to-build-docker-images.rst:36 -#: ../../source/contributor-how-to-build-docker-images.rst:98 +#: ../../source/contributor-how-to-build-docker-images.rst:38 +#: ../../source/contributor-how-to-build-docker-images.rst:104 #, fuzzy msgid "Build argument" msgstr "Amélioration de la documentation" -#: ../../source/contributor-how-to-build-docker-images.rst:37 -#: ../../source/contributor-how-to-build-docker-images.rst:99 +#: ../../source/contributor-how-to-build-docker-images.rst:39 +#: ../../source/contributor-how-to-build-docker-images.rst:105 #, fuzzy msgid "Description" msgstr "Dépréciations" -#: ../../source/contributor-how-to-build-docker-images.rst:38 -#: ../../source/contributor-how-to-build-docker-images.rst:100 +#: ../../source/contributor-how-to-build-docker-images.rst:40 +#: ../../source/contributor-how-to-build-docker-images.rst:106 #, fuzzy msgid "Required" msgstr "Changements nécessaires" -#: ../../source/contributor-how-to-build-docker-images.rst:39 -#: ../../source/contributor-how-to-build-docker-images.rst:101 -#: ../../source/docker/persist-superlink-state.rst:18 -#: ../../source/docker/pin-version.rst:11 +#: ../../source/contributor-how-to-build-docker-images.rst:41 +#: ../../source/contributor-how-to-build-docker-images.rst:107 +#: ../../source/docker/persist-superlink-state.rst:19 +#: ../../source/docker/pin-version.rst:12 #: ../../source/docker/set-environment-variables.rst:8 #, fuzzy msgid "Example" msgstr "Exemples de PyTorch" -#: ../../source/contributor-how-to-build-docker-images.rst:40 +#: ../../source/contributor-how-to-build-docker-images.rst:42 msgid "``DISTRO``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:41 +#: ../../source/contributor-how-to-build-docker-images.rst:43 #, fuzzy msgid "The Linux distribution to use as the base image." msgstr "Chargement des données" -#: ../../source/contributor-how-to-build-docker-images.rst:42 -#: ../../source/contributor-how-to-build-docker-images.rst:46 -#: ../../source/contributor-how-to-build-docker-images.rst:50 -#: ../../source/contributor-how-to-build-docker-images.rst:66 -#: ../../source/contributor-how-to-build-docker-images.rst:70 -#: ../../source/contributor-how-to-build-docker-images.rst:104 +#: ../../source/contributor-how-to-build-docker-images.rst:44 +#: ../../source/contributor-how-to-build-docker-images.rst:48 +#: ../../source/contributor-how-to-build-docker-images.rst:52 +#: ../../source/contributor-how-to-build-docker-images.rst:68 +#: ../../source/contributor-how-to-build-docker-images.rst:75 +#: ../../source/contributor-how-to-build-docker-images.rst:110 #, fuzzy msgid "No" msgstr "Aucun" -#: ../../source/contributor-how-to-build-docker-images.rst:43 +#: ../../source/contributor-how-to-build-docker-images.rst:45 msgid "``ubuntu``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:44 +#: ../../source/contributor-how-to-build-docker-images.rst:46 #, fuzzy msgid "``DISTRO_VERSION``" msgstr "Version Python" -#: ../../source/contributor-how-to-build-docker-images.rst:45 +#: ../../source/contributor-how-to-build-docker-images.rst:47 msgid "Version of the Linux distribution." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:47 +#: ../../source/contributor-how-to-build-docker-images.rst:49 msgid ":substitution-code:`|ubuntu_version|`" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:48 +#: ../../source/contributor-how-to-build-docker-images.rst:50 #, fuzzy msgid "``PYTHON_VERSION``" msgstr "Version Python" -#: ../../source/contributor-how-to-build-docker-images.rst:49 +#: ../../source/contributor-how-to-build-docker-images.rst:51 msgid "Version of ``python`` to be installed." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:51 +#: ../../source/contributor-how-to-build-docker-images.rst:53 msgid "``3.11`` or ``3.11.1``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:52 +#: ../../source/contributor-how-to-build-docker-images.rst:54 msgid "``PIP_VERSION``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:53 +#: ../../source/contributor-how-to-build-docker-images.rst:55 msgid "Version of ``pip`` to be installed." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:54 -#: ../../source/contributor-how-to-build-docker-images.rst:58 -#: ../../source/contributor-how-to-build-docker-images.rst:62 -#: ../../source/contributor-how-to-build-docker-images.rst:108 +#: ../../source/contributor-how-to-build-docker-images.rst:56 +#: ../../source/contributor-how-to-build-docker-images.rst:60 +#: ../../source/contributor-how-to-build-docker-images.rst:64 +#: ../../source/contributor-how-to-build-docker-images.rst:114 #, fuzzy msgid "Yes" msgstr "Types" -#: ../../source/contributor-how-to-build-docker-images.rst:55 +#: ../../source/contributor-how-to-build-docker-images.rst:57 msgid ":substitution-code:`|pip_version|`" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:56 +#: ../../source/contributor-how-to-build-docker-images.rst:58 msgid "``SETUPTOOLS_VERSION``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:57 +#: ../../source/contributor-how-to-build-docker-images.rst:59 msgid "Version of ``setuptools`` to be installed." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:59 +#: ../../source/contributor-how-to-build-docker-images.rst:61 msgid ":substitution-code:`|setuptools_version|`" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:60 +#: ../../source/contributor-how-to-build-docker-images.rst:62 msgid "``FLWR_VERSION``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:61 +#: ../../source/contributor-how-to-build-docker-images.rst:63 msgid "Version of Flower to be installed." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:63 +#: ../../source/contributor-how-to-build-docker-images.rst:65 msgid ":substitution-code:`|stable_flwr_version|`" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:64 +#: ../../source/contributor-how-to-build-docker-images.rst:66 msgid "``FLWR_PACKAGE``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:65 +#: ../../source/contributor-how-to-build-docker-images.rst:67 msgid "The Flower package to be installed." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:67 +#: ../../source/contributor-how-to-build-docker-images.rst:69 msgid "``flwr`` or ``flwr-nightly``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:68 +#: ../../source/contributor-how-to-build-docker-images.rst:70 #, fuzzy msgid "``FLWR_VERSION_REF``" msgstr "Version Python" -#: ../../source/contributor-how-to-build-docker-images.rst:69 +#: ../../source/contributor-how-to-build-docker-images.rst:71 msgid "" "A `direct reference " "`_." msgstr "" -#: ../../source/contributor-how-to-contribute-translations.rst:29 +#: ../../source/contributor-how-to-contribute-translations.rst:28 msgid "" "Once you are signed in to Weblate, you can navigate to the `Flower " "Framework project `_." msgstr "" -#: ../../source/contributor-how-to-contribute-translations.rst:67 +#: ../../source/contributor-how-to-contribute-translations.rst:64 msgid "Add new languages" msgstr "" -#: ../../source/contributor-how-to-contribute-translations.rst:69 +#: ../../source/contributor-how-to-contribute-translations.rst:66 msgid "" "If you want to add a new language, you will first have to contact us, " "either on `Slack `_, or by opening an issue" @@ -598,17 +598,17 @@ msgstr "" "formater le code ou exécuter des tests. À cette fin, nous utilisons " "l'extension VSCode Remote Containers. Qu'est-ce que c'est ?" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:7 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:8 +#, fuzzy msgid "" "The Visual Studio Code Remote - Containers extension lets you use a " "Docker container as a fully-featured development environment. It allows " "you to open any folder inside (or mounted into) a container and take " "advantage of Visual Studio Code's full feature set. A " -":code:`devcontainer.json` file in your project tells VS Code how to " -"access (or create) a development container with a well-defined tool and " -"runtime stack. This container can be used to run an application or to " -"separate tools, libraries, or runtimes needed for working with a " -"codebase." +"``devcontainer.json`` file in your project tells VS Code how to access " +"(or create) a development container with a well-defined tool and runtime " +"stack. This container can be used to run an application or to separate " +"tools, libraries, or runtimes needed for working with a codebase." msgstr "" "L'extension Visual Studio Code Remote - Containers te permet d'utiliser " "un conteneur Docker comme environnement de développement complet. Elle te" @@ -621,7 +621,7 @@ msgstr "" " les outils, les bibliothèques ou les exécutions nécessaires pour " "travailler avec une base de code." -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:9 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:16 msgid "" "Workspace files are mounted from the local file system or copied or " "cloned into the container. Extensions are installed and run inside the " @@ -637,7 +637,7 @@ msgstr "" "environnement de développement simplement en te connectant à un autre " "conteneur." -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:11 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:22 #, fuzzy msgid "" "Source: `Official VSCode documentation " @@ -646,19 +646,19 @@ msgstr "" "Source : `Documentation officielle de VSCode " "`_" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:15 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:26 msgid "Getting started" msgstr "Pour commencer" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:17 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:28 #, fuzzy msgid "" -"Configuring and setting up the :code:`Dockerfile` as well the " -"configuration for the devcontainer can be a bit more involved. The good " -"thing is you don't have to do it. Usually it should be enough to install " -"`Docker `_ on your system and " -"ensure its available on your command line. Additionally, install the " -"`VSCode Containers Extension `_ on your system and ensure its" +" available on your command line. Additionally, install the `VSCode " +"Containers Extension `_." msgstr "" "La configuration et le paramétrage du :code:`Dockerfile` ainsi que la " @@ -669,7 +669,7 @@ msgstr "" "`VSCode Containers Extension `_." -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:19 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:35 msgid "" "Now you should be good to go. When starting VSCode, it will ask you to " "run in the container environment and - if you confirm - automatically " @@ -686,7 +686,7 @@ msgstr "" "inférieur gauche de ta fenêtre VSCode et sélectionner l'option " "*(Re)Ouvrir le dossier dans le conteneur*." -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:21 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:41 msgid "" "In some cases your setup might be more involved. For those cases consult " "the following sources:" @@ -694,7 +694,7 @@ msgstr "" "Dans certains cas, ton installation peut être plus complexe. Pour ces " "cas-là, consulte les sources suivantes :" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:23 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:44 #, fuzzy msgid "" "`Developing inside a Container " @@ -705,7 +705,7 @@ msgstr "" "`_" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:24 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:46 #, fuzzy msgid "" "`Remote development in Containers " @@ -737,7 +737,7 @@ msgstr "" "supprimer ``poetry.lock`` (``rm poetry.lock``) avant d'exécuter ``poetry " "install``)." -#: ../../source/contributor-how-to-install-development-versions.rst:12 +#: ../../source/contributor-how-to-install-development-versions.rst:14 msgid "" "``flwr = { version = \"1.0.0a0\", allow-prereleases = true }`` (without " "extras)" @@ -745,7 +745,7 @@ msgstr "" "``flwr = { version = \"1.0.0a0\", allow-prereleases = true }`` (sans " "extras)" -#: ../../source/contributor-how-to-install-development-versions.rst:13 +#: ../../source/contributor-how-to-install-development-versions.rst:15 msgid "" "``flwr = { version = \"1.0.0a0\", allow-prereleases = true, extras = " "[\"simulation\"] }`` (with extras)" @@ -753,7 +753,7 @@ msgstr "" "``flwr = { version = \"1.0.0a0\", allow-prereleases = true, extras = " "[\"simulation\"] }`` (avec extras)" -#: ../../source/contributor-how-to-install-development-versions.rst:15 +#: ../../source/contributor-how-to-install-development-versions.rst:18 msgid "" "Install ``flwr`` from a local copy of the Flower source code via " "``pyproject.toml``:" @@ -761,11 +761,11 @@ msgstr "" "Installez ``flwr`` à partir d'une copie locale du code source de Flower " "via ``pyproject.toml`` :" -#: ../../source/contributor-how-to-install-development-versions.rst:17 +#: ../../source/contributor-how-to-install-development-versions.rst:20 msgid "``flwr = { path = \"../../\", develop = true }`` (without extras)" msgstr "``flwr = { path = \"../../\", develop = true }`` (sans extras)" -#: ../../source/contributor-how-to-install-development-versions.rst:18 +#: ../../source/contributor-how-to-install-development-versions.rst:21 msgid "" "``flwr = { path = \"../../\", develop = true, extras = [\"simulation\"] " "}`` (with extras)" @@ -773,11 +773,11 @@ msgstr "" "``flwr = { path = \"../../\", develop = true, extras = [\"simulation\"] " "}`` (avec extras)" -#: ../../source/contributor-how-to-install-development-versions.rst:20 +#: ../../source/contributor-how-to-install-development-versions.rst:23 msgid "Install ``flwr`` from a local wheel file via ``pyproject.toml``:" msgstr "Installez ``flwr`` à partir d'un fichier local via ``pyproject.toml`` :" -#: ../../source/contributor-how-to-install-development-versions.rst:22 +#: ../../source/contributor-how-to-install-development-versions.rst:25 #, fuzzy msgid "" "``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\" }`` (without" @@ -786,7 +786,7 @@ msgstr "" "``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\" }`` (sans " "extras)" -#: ../../source/contributor-how-to-install-development-versions.rst:23 +#: ../../source/contributor-how-to-install-development-versions.rst:26 #, fuzzy msgid "" "``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\", extras = " @@ -795,7 +795,7 @@ msgstr "" "``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\", extras = " "[\"simulation\"] }`` (avec extras)" -#: ../../source/contributor-how-to-install-development-versions.rst:25 +#: ../../source/contributor-how-to-install-development-versions.rst:29 msgid "" "Please refer to the Poetry documentation for further details: `Poetry " "Dependency Specification `_" -#: ../../source/contributor-how-to-install-development-versions.rst:28 +#: ../../source/contributor-how-to-install-development-versions.rst:33 msgid "Using pip (recommended on Colab)" msgstr "Utiliser pip (recommandé sur Colab)" -#: ../../source/contributor-how-to-install-development-versions.rst:30 +#: ../../source/contributor-how-to-install-development-versions.rst:35 msgid "Install a ``flwr`` pre-release from PyPI:" msgstr "Installe une pré-version de ``flwr`` depuis PyPI :" -#: ../../source/contributor-how-to-install-development-versions.rst:32 +#: ../../source/contributor-how-to-install-development-versions.rst:37 msgid "``pip install -U --pre flwr`` (without extras)" msgstr "``pip install -U --pre flwr`` (sans les extras)" -#: ../../source/contributor-how-to-install-development-versions.rst:33 +#: ../../source/contributor-how-to-install-development-versions.rst:38 msgid "``pip install -U --pre 'flwr[simulation]'`` (with extras)" msgstr "``pip install -U --pre 'flwr[simulation]'`` (avec les extras)" -#: ../../source/contributor-how-to-install-development-versions.rst:35 +#: ../../source/contributor-how-to-install-development-versions.rst:40 msgid "" "Python packages can be installed from git repositories. Use one of the " "following commands to install the Flower directly from GitHub." @@ -830,11 +830,11 @@ msgstr "" "Utilise l'une des commandes suivantes pour installer Flower directement à" " partir de GitHub." -#: ../../source/contributor-how-to-install-development-versions.rst:37 +#: ../../source/contributor-how-to-install-development-versions.rst:43 msgid "Install ``flwr`` from the default GitHub branch (``main``):" msgstr "Installez ``flwr`` à partir de la branche GitHub par défaut (``main``) :" -#: ../../source/contributor-how-to-install-development-versions.rst:39 +#: ../../source/contributor-how-to-install-development-versions.rst:45 msgid "" "``pip install flwr@git+https://github.com/adap/flower.git`` (without " "extras)" @@ -842,7 +842,7 @@ msgstr "" "``pip install flwr@git+https://github.com/adap/flower.git`` (sans les " "extras)" -#: ../../source/contributor-how-to-install-development-versions.rst:40 +#: ../../source/contributor-how-to-install-development-versions.rst:46 msgid "" "``pip install 'flwr[simulation]@git+https://github.com/adap/flower.git'``" " (with extras)" @@ -850,13 +850,13 @@ msgstr "" "``pip install 'flwr[simulation]@git+https://github.com/adap/flower.git'``" " (avec les extras)" -#: ../../source/contributor-how-to-install-development-versions.rst:42 +#: ../../source/contributor-how-to-install-development-versions.rst:49 msgid "Install ``flwr`` from a specific GitHub branch (``branch-name``):" msgstr "" "Installez ``flwr`` à partir d'une branche GitHub spécifique (``nom-" "branche``) :" -#: ../../source/contributor-how-to-install-development-versions.rst:44 +#: ../../source/contributor-how-to-install-development-versions.rst:51 msgid "" "``pip install flwr@git+https://github.com/adap/flower.git@branch-name`` " "(without extras)" @@ -864,7 +864,7 @@ msgstr "" "``pip install flwr@git+https://github.com/adap/flower.git@nom-branche`` " "(sans les extras)" -#: ../../source/contributor-how-to-install-development-versions.rst:45 +#: ../../source/contributor-how-to-install-development-versions.rst:53 #, fuzzy msgid "" "``pip install 'flwr[simulation]@git+https://github.com/adap/flower.git" @@ -873,11 +873,11 @@ msgstr "" "``pip install 'flwr[simulation]@git+https://github.com/adap/flower.git" "``@nom-de-la-branche'`` (avec des extras)" -#: ../../source/contributor-how-to-install-development-versions.rst:49 +#: ../../source/contributor-how-to-install-development-versions.rst:57 msgid "Open Jupyter Notebooks on Google Colab" msgstr "Ouvre les carnets Jupyter sur Google Colab" -#: ../../source/contributor-how-to-install-development-versions.rst:51 +#: ../../source/contributor-how-to-install-development-versions.rst:59 #, fuzzy msgid "" "Open the notebook ``doc/source/tutorial-series-get-started-with-flower-" @@ -886,7 +886,7 @@ msgstr "" "Ouvrir le notebook ``doc/source/tutorial/Flower-1-Intro-to-FL-" "PyTorch.ipynb`` :" -#: ../../source/contributor-how-to-install-development-versions.rst:53 +#: ../../source/contributor-how-to-install-development-versions.rst:61 #, fuzzy msgid "" "https://colab.research.google.com/github/adap/flower/blob/main/doc/source" @@ -895,7 +895,7 @@ msgstr "" "https://colab.research.google.com/github/adap/flower/blob/main/doc/source" "/tutorial-get-started-with-flower-pytorch.ipynb" -#: ../../source/contributor-how-to-install-development-versions.rst:55 +#: ../../source/contributor-how-to-install-development-versions.rst:63 msgid "" "Open a development version of the same notebook from branch `branch-name`" " by changing ``main`` to ``branch-name`` (right after ``blob``):" @@ -904,7 +904,7 @@ msgstr "" "`nom-branche` en remplaçant `main` par `nom-branche` (juste après `blob`)" " :" -#: ../../source/contributor-how-to-install-development-versions.rst:57 +#: ../../source/contributor-how-to-install-development-versions.rst:66 #, fuzzy msgid "" "https://colab.research.google.com/github/adap/flower/blob/branch-" @@ -913,21 +913,21 @@ msgstr "" "https://colab.research.google.com/github/adap/flower/blob/branch-" "name/doc/source/tutorial-get-started-with-flower-pytorch.ipynb" -#: ../../source/contributor-how-to-install-development-versions.rst:59 +#: ../../source/contributor-how-to-install-development-versions.rst:68 msgid "Install a `whl` on Google Colab:" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:61 +#: ../../source/contributor-how-to-install-development-versions.rst:70 msgid "" "In the vertical icon grid on the left hand side, select ``Files`` > " "``Upload to session storage``" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:62 +#: ../../source/contributor-how-to-install-development-versions.rst:72 msgid "Upload the whl (e.g., ``flwr-1.8.0-py3-none-any.whl``)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:63 +#: ../../source/contributor-how-to-install-development-versions.rst:73 msgid "" "Change ``!pip install -q 'flwr[simulation]' torch torchvision " "matplotlib`` to ``!pip install -q 'flwr-1.8.0-py3-none-" @@ -946,11 +946,11 @@ msgstr "" "Ce document décrit le processus de diffusion actuel, qui peut ou non " "changer à l'avenir." -#: ../../source/contributor-how-to-release-flower.rst:7 +#: ../../source/contributor-how-to-release-flower.rst:8 msgid "During the release" msgstr "Lors de la sortie" -#: ../../source/contributor-how-to-release-flower.rst:9 +#: ../../source/contributor-how-to-release-flower.rst:10 msgid "" "The version number of a release is stated in ``pyproject.toml``. To " "release a new version of Flower, the following things need to happen (in " @@ -960,14 +960,14 @@ msgstr "" "Pour publier une nouvelle version de Flower, les choses suivantes doivent" " se produire (dans cet ordre) :" -#: ../../source/contributor-how-to-release-flower.rst:11 +#: ../../source/contributor-how-to-release-flower.rst:13 msgid "" "Run ``python3 src/py/flwr_tool/update_changelog.py `` in " "order to add every new change to the changelog (feel free to make manual " "changes to the changelog afterwards until it looks good)." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:12 +#: ../../source/contributor-how-to-release-flower.rst:16 msgid "" "Once the changelog has been updated with all the changes, run ``./dev" "/prepare-release-changelog.sh v``, where ```` " @@ -977,7 +977,7 @@ msgid "" "the contributors. Open a pull request with those changes." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:13 +#: ../../source/contributor-how-to-release-flower.rst:22 msgid "" "Once the pull request is merged, tag the release commit with the version " "number as soon as the PR is merged: ``git tag v`` (notice " @@ -986,33 +986,33 @@ msgid "" "artifacts and the relevant part of the changelog." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:14 +#: ../../source/contributor-how-to-release-flower.rst:26 msgid "Check the draft release on GitHub, and if everything is good, publish it." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:17 +#: ../../source/contributor-how-to-release-flower.rst:29 msgid "After the release" msgstr "Après la publication" -#: ../../source/contributor-how-to-release-flower.rst:19 +#: ../../source/contributor-how-to-release-flower.rst:31 msgid "Create a pull request which contains the following changes:" msgstr "Crée une demande de pull qui contient les modifications suivantes :" -#: ../../source/contributor-how-to-release-flower.rst:21 +#: ../../source/contributor-how-to-release-flower.rst:33 msgid "Increase the minor version in ``pyproject.toml`` by one." msgstr "Augmente la version mineure de ``pyproject.toml`` d'une unité." -#: ../../source/contributor-how-to-release-flower.rst:22 +#: ../../source/contributor-how-to-release-flower.rst:34 msgid "Update all files which contain the current version number if necessary." msgstr "" "Mets à jour tous les fichiers qui contiennent le numéro de version actuel" " si nécessaire." -#: ../../source/contributor-how-to-release-flower.rst:23 +#: ../../source/contributor-how-to-release-flower.rst:35 msgid "Add a new ``Unreleased`` section in ``changelog.md``." msgstr "Ajoute une nouvelle section ``Unreleased`` dans ``changelog.md``." -#: ../../source/contributor-how-to-release-flower.rst:25 +#: ../../source/contributor-how-to-release-flower.rst:37 msgid "" "Merge the pull request on the same day (i.e., before a new nightly " "release gets published to PyPI)." @@ -1020,15 +1020,15 @@ msgstr "" "Fusionne la pull request le jour même (c'est-à-dire avant qu'une nouvelle" " version nightly ne soit publiée sur PyPI)." -#: ../../source/contributor-how-to-release-flower.rst:28 +#: ../../source/contributor-how-to-release-flower.rst:41 msgid "Publishing a pre-release" msgstr "Publier une pré-version" -#: ../../source/contributor-how-to-release-flower.rst:31 +#: ../../source/contributor-how-to-release-flower.rst:44 msgid "Pre-release naming" msgstr "Nom de la pré-version" -#: ../../source/contributor-how-to-release-flower.rst:33 +#: ../../source/contributor-how-to-release-flower.rst:46 msgid "" "PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases" " MUST use one of the following naming patterns:" @@ -1037,39 +1037,39 @@ msgstr "" "Les préversions DOIVENT utiliser l'un des modèles de dénomination " "suivants :" -#: ../../source/contributor-how-to-release-flower.rst:35 +#: ../../source/contributor-how-to-release-flower.rst:49 msgid "Alpha: ``MAJOR.MINOR.PATCHaN``" msgstr "Alpha : ``MAJOR.MINOR.PATCHaN``" -#: ../../source/contributor-how-to-release-flower.rst:36 +#: ../../source/contributor-how-to-release-flower.rst:50 msgid "Beta: ``MAJOR.MINOR.PATCHbN``" msgstr "Bêta : ``MAJOR.MINOR.PATCHbN``" -#: ../../source/contributor-how-to-release-flower.rst:37 +#: ../../source/contributor-how-to-release-flower.rst:51 msgid "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``" msgstr "Candidat à la publication (RC) : ``MAJOR.MINOR.PATCHrcN``" -#: ../../source/contributor-how-to-release-flower.rst:39 +#: ../../source/contributor-how-to-release-flower.rst:53 msgid "Examples include:" msgstr "Voici quelques exemples :" -#: ../../source/contributor-how-to-release-flower.rst:41 +#: ../../source/contributor-how-to-release-flower.rst:55 msgid "``1.0.0a0``" msgstr "``1.0.0a0``" -#: ../../source/contributor-how-to-release-flower.rst:42 +#: ../../source/contributor-how-to-release-flower.rst:56 msgid "``1.0.0b0``" msgstr "``1.0.0b0``" -#: ../../source/contributor-how-to-release-flower.rst:43 +#: ../../source/contributor-how-to-release-flower.rst:57 msgid "``1.0.0rc0``" msgstr "``1.0.0rc0``" -#: ../../source/contributor-how-to-release-flower.rst:44 +#: ../../source/contributor-how-to-release-flower.rst:58 msgid "``1.0.0rc1``" msgstr "1.0.0rc1" -#: ../../source/contributor-how-to-release-flower.rst:46 +#: ../../source/contributor-how-to-release-flower.rst:60 msgid "" "This is in line with PEP-440 and the recommendations from the Python " "Packaging Authority (PyPA):" @@ -1077,11 +1077,11 @@ msgstr "" "Ceci est conforme au PEP-440 et aux recommandations de l'Autorité de " "l'emballage Python (PyPA) :" -#: ../../source/contributor-how-to-release-flower.rst:49 +#: ../../source/contributor-how-to-release-flower.rst:63 msgid "`PEP-440 `_" msgstr "`PEP-440 `_" -#: ../../source/contributor-how-to-release-flower.rst:50 +#: ../../source/contributor-how-to-release-flower.rst:64 msgid "" "`PyPA Choosing a versioning scheme " "`_" -#: ../../source/contributor-how-to-release-flower.rst:52 +#: ../../source/contributor-how-to-release-flower.rst:67 msgid "" "Note that the approach defined by PyPA is not compatible with SemVer " "2.0.0 spec, for details consult the `Semantic Versioning Specification " @@ -1103,17 +1103,17 @@ msgstr "" "Versioning Specification `_ (en particulier le point 11 sur la préséance)." -#: ../../source/contributor-how-to-release-flower.rst:55 +#: ../../source/contributor-how-to-release-flower.rst:73 msgid "Pre-release classification" msgstr "Classification avant publication" -#: ../../source/contributor-how-to-release-flower.rst:57 +#: ../../source/contributor-how-to-release-flower.rst:75 msgid "Should the next pre-release be called alpha, beta, or release candidate?" msgstr "" "La prochaine préversion doit-elle être appelée alpha, bêta ou release " "candidate ?" -#: ../../source/contributor-how-to-release-flower.rst:59 +#: ../../source/contributor-how-to-release-flower.rst:77 msgid "" "RC: feature complete, no known issues (apart from issues that are " "classified as \"won't fix\" for the next stable release) - if no issues " @@ -1124,11 +1124,11 @@ msgstr "" "version stable) - si aucun problème n'apparaît, cette version deviendra " "la prochaine version stable" -#: ../../source/contributor-how-to-release-flower.rst:60 +#: ../../source/contributor-how-to-release-flower.rst:80 msgid "Beta: feature complete, allowed to have known issues" msgstr "Bêta : fonctionnalité complète, autorisée à avoir des problèmes connus" -#: ../../source/contributor-how-to-release-flower.rst:61 +#: ../../source/contributor-how-to-release-flower.rst:81 msgid "Alpha: not feature complete, allowed to have known issues" msgstr "" "Alpha : les fonctionnalités ne sont pas complètes, les problèmes connus " @@ -1151,12 +1151,12 @@ msgstr "" "Anaconda. Tu peux suivre les instructions ou choisir la configuration que" " tu préfères." -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:9 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:10 msgid "Python Version" msgstr "Version Python" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:11 -#: ../../source/how-to-install-flower.rst:8 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:12 +#: ../../source/how-to-install-flower.rst:7 #, fuzzy msgid "" "Flower requires at least `Python 3.9 `_, " @@ -1166,7 +1166,7 @@ msgstr "" "Flower nécessite `Python 3.9 `_ ou plus, " "nous recommandons `Python 3.10 `_." -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:14 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:17 msgid "" "Due to a known incompatibility with `ray " "`_, we currently recommend utilizing at " @@ -1174,12 +1174,12 @@ msgid "" "simulations." msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:19 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:22 #, fuzzy msgid "Virtualenv with Pyenv/Virtualenv" msgstr "Virutualenv avec Pyenv/Virtualenv" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:21 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:24 msgid "" "One of the recommended virtual environment is `pyenv " "`_/`virtualenv `_ pour plus de " "détails." -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:23 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:29 #, fuzzy msgid "" "Once Pyenv is set up, you can use it to install `Python Version 3.10 " @@ -1201,19 +1201,19 @@ msgstr "" "Une fois Pyenv mis en place, tu peux l'utiliser pour installer `Python " "Version 3.7 `_ ou supérieure :" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:29 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:36 msgid "Create the virtualenv with:" msgstr "Crée le virtualenv avec :" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:36 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:42 msgid "Activate the virtualenv by running the following command:" msgstr "Active la virtualenv en exécutant la commande suivante :" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:44 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:49 msgid "Virtualenv with Poetry" msgstr "Virtualenv et la poésie" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:46 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:51 msgid "" "The Flower examples are based on `Poetry `_ to manage dependencies. After installing Poetry you " @@ -1223,7 +1223,7 @@ msgstr "" "poetry.org/docs/>`_ pour gérer les dépendances. Après l'installation de " "Poetry, il te suffit de créer un environnement virtuel avec :" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:52 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:58 msgid "" "If you open a new terminal you can activate the previously created " "virtual environment with the following command:" @@ -1231,15 +1231,16 @@ msgstr "" "Si tu ouvres un nouveau terminal, tu peux activer l'environnement virtuel" " précédemment créé avec la commande suivante :" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:60 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:66 msgid "Virtualenv with Anaconda" msgstr "Virtualenv avec Anaconda" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:62 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:68 +#, fuzzy msgid "" "If you prefer to use Anaconda for your virtual environment then install " "and setup the `conda `_ package. After setting it up you can " +"/user-guide/install/index.html>`_ package. After setting it up you can " "create a virtual environment with:" msgstr "" "Si tu préfères utiliser Anaconda pour ton environnement virtuel, installe" @@ -1248,15 +1249,15 @@ msgstr "" "guide/install/index.html>`_. Après l'avoir configuré, tu peux créer un " "environnement virtuel avec :" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:68 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:76 msgid "and activate the virtual environment with:" msgstr "et active l'environnement virtuel avec :" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:76 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:83 msgid "And then?" msgstr "Et ensuite ?" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:78 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:85 msgid "" "As soon as you created your virtual environment you clone one of the " "`Flower examples `_." @@ -1268,11 +1269,11 @@ msgstr "" msgid "Write documentation" msgstr "Rédiger de la documentation" -#: ../../source/contributor-how-to-write-documentation.rst:6 +#: ../../source/contributor-how-to-write-documentation.rst:5 msgid "Project layout" msgstr "Schéma du projet" -#: ../../source/contributor-how-to-write-documentation.rst:8 +#: ../../source/contributor-how-to-write-documentation.rst:7 msgid "" "The Flower documentation lives in the ``doc`` directory. The Sphinx-based" " documentation system supports both reStructuredText (``.rst`` files) and" @@ -1283,7 +1284,7 @@ msgstr "" "reStructuredText (fichiers `.rst`) et Markdown (fichiers `.md`)." #: ../../source/contributor-how-to-write-documentation.rst:10 -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:169 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:193 #, fuzzy msgid "" "Note that, in order to build the documentation locally (with ``poetry run" @@ -1295,20 +1296,20 @@ msgstr "" "make html``, comme décrit plus bas), `Pandoc " "_` doit être installé sur le système." -#: ../../source/contributor-how-to-write-documentation.rst:14 +#: ../../source/contributor-how-to-write-documentation.rst:15 msgid "Edit an existing page" msgstr "Modifier une page existante" -#: ../../source/contributor-how-to-write-documentation.rst:16 +#: ../../source/contributor-how-to-write-documentation.rst:17 msgid "Edit an existing ``.rst`` (or ``.md``) file under ``doc/source/``" msgstr "Modifier un fichier ``.rst`` (ou ``.md``) existant sous ``doc/source/``" -#: ../../source/contributor-how-to-write-documentation.rst:17 +#: ../../source/contributor-how-to-write-documentation.rst:18 #: ../../source/contributor-how-to-write-documentation.rst:27 msgid "Compile the docs: ``cd doc``, then ``poetry run make html``" msgstr "Compilez les documents : ``cd doc``, puis ``poetry run make html``" -#: ../../source/contributor-how-to-write-documentation.rst:18 +#: ../../source/contributor-how-to-write-documentation.rst:19 #: ../../source/contributor-how-to-write-documentation.rst:28 msgid "Open ``doc/build/html/index.html`` in the browser to check the result" msgstr "" @@ -1347,11 +1348,11 @@ msgstr "" "quelques recommandations sur les points de départ pour augmenter tes " "chances de voir ton PR accepté dans la base de code de Flower." -#: ../../source/contributor-ref-good-first-contributions.rst:11 +#: ../../source/contributor-ref-good-first-contributions.rst:9 msgid "Where to start" msgstr "Par où commencer" -#: ../../source/contributor-ref-good-first-contributions.rst:13 +#: ../../source/contributor-ref-good-first-contributions.rst:11 msgid "" "Until the Flower core library matures it will be easier to get PR's " "accepted if they only touch non-core areas of the codebase. Good " @@ -1362,25 +1363,25 @@ msgstr "" " non essentielles de la base de code. Les bons candidats pour commencer " "sont :" -#: ../../source/contributor-ref-good-first-contributions.rst:17 +#: ../../source/contributor-ref-good-first-contributions.rst:14 msgid "Documentation: What's missing? What could be expressed more clearly?" msgstr "" "Documentation : Qu'est-ce qui manque ? Qu'est-ce qui pourrait être " "exprimé plus clairement ?" -#: ../../source/contributor-ref-good-first-contributions.rst:18 +#: ../../source/contributor-ref-good-first-contributions.rst:15 msgid "Baselines: See below." msgstr "Références : voir ci-dessous." -#: ../../source/contributor-ref-good-first-contributions.rst:19 +#: ../../source/contributor-ref-good-first-contributions.rst:16 msgid "Examples: See below." msgstr "Exemples : voir ci-dessous." -#: ../../source/contributor-ref-good-first-contributions.rst:23 +#: ../../source/contributor-ref-good-first-contributions.rst:19 msgid "Request for Flower Baselines" msgstr "Demande pour une nouvelle Flower Baseline" -#: ../../source/contributor-ref-good-first-contributions.rst:25 +#: ../../source/contributor-ref-good-first-contributions.rst:21 #, fuzzy msgid "" "If you are not familiar with Flower Baselines, you should probably check-" @@ -1391,7 +1392,7 @@ msgstr "" "probablement consulter notre `guide de contribution pour les baselines " "`_." -#: ../../source/contributor-ref-good-first-contributions.rst:27 +#: ../../source/contributor-ref-good-first-contributions.rst:25 #, fuzzy msgid "" "You should then check out the open `issues " @@ -1406,7 +1407,7 @@ msgstr "" " laquelle tu aimerais travailler et qui n'a pas d'assignés, n'hésite pas " "à te l'attribuer et à commencer à travailler dessus !" -#: ../../source/contributor-ref-good-first-contributions.rst:31 +#: ../../source/contributor-ref-good-first-contributions.rst:30 msgid "" "Otherwise, if you don't find a baseline you'd like to work on, be sure to" " open a new issue with the baseline request template!" @@ -1459,12 +1460,13 @@ msgstr "" "protocole SecAgg peut être considéré comme un cas particulier du " "protocole SecAgg+." -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:8 -msgid "The :code:`SecAgg+` abstraction" +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:9 +#, fuzzy +msgid "The ``SecAgg+`` abstraction" msgstr "L'abstraction :code:`SecAgg+`" -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:10 -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:161 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:11 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:163 msgid "" "In this implementation, each client will be assigned with a unique index " "(int) for secure aggregation, and thus many python dictionaries used have" @@ -1475,8 +1477,8 @@ msgstr "" "dictionnaires python utilisés ont des clés de type int plutôt que de type" " ClientProxy." -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:65 -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:198 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:67 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:204 msgid "" "The Flower server will execute and process received results in the " "following order:" @@ -1484,11 +1486,12 @@ msgstr "" "Le serveur Flower exécutera et traitera les résultats reçus dans l'ordre " "suivant :" -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:159 -msgid "The :code:`LightSecAgg` abstraction" +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:161 +#, fuzzy +msgid "The ``LightSecAgg`` abstraction" msgstr "L'abstraction :code:`LightSecAgg`" -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:271 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:277 msgid "Types" msgstr "Types" @@ -1505,7 +1508,7 @@ msgstr "" "de Flower mais qui n'ont pas l'habitude de contribuer à des projets " "GitHub." -#: ../../source/contributor-tutorial-contribute-on-github.rst:6 +#: ../../source/contributor-tutorial-contribute-on-github.rst:7 #, fuzzy msgid "" "If you're familiar with how contributing on GitHub works, you can " @@ -1518,15 +1521,15 @@ msgstr "" "contributors.html>`_ et des exemples de `bonnes premières contributions " "`_." -#: ../../source/contributor-tutorial-contribute-on-github.rst:10 +#: ../../source/contributor-tutorial-contribute-on-github.rst:12 msgid "Setting up the repository" msgstr "Mise en place du référentiel" -#: ../../source/contributor-tutorial-contribute-on-github.rst:12 +#: ../../source/contributor-tutorial-contribute-on-github.rst:29 msgid "**Create a GitHub account and setup Git**" msgstr "**Créer un compte GitHub et configurer Git**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:13 +#: ../../source/contributor-tutorial-contribute-on-github.rst:15 #, fuzzy msgid "" "Git is a distributed version control tool. This allows for an entire " @@ -1541,7 +1544,7 @@ msgstr "" "locale, tu peux suivre ce `guide `_ pour le mettre en place." -#: ../../source/contributor-tutorial-contribute-on-github.rst:16 +#: ../../source/contributor-tutorial-contribute-on-github.rst:21 msgid "" "GitHub, itself, is a code hosting platform for version control and " "collaboration. It allows for everyone to collaborate and work from " @@ -1551,7 +1554,7 @@ msgstr "" "contrôle des versions et la collaboration. Il permet à chacun de " "collaborer et de travailler de n'importe où sur des dépôts à distance." -#: ../../source/contributor-tutorial-contribute-on-github.rst:18 +#: ../../source/contributor-tutorial-contribute-on-github.rst:25 msgid "" "If you haven't already, you will need to create an account on `GitHub " "`_." @@ -1559,7 +1562,7 @@ msgstr "" "Si ce n'est pas déjà fait, tu devras créer un compte sur `GitHub " "`_." -#: ../../source/contributor-tutorial-contribute-on-github.rst:20 +#: ../../source/contributor-tutorial-contribute-on-github.rst:28 msgid "" "The idea behind the generic Git and GitHub workflow boils down to this: " "you download code from a remote repository on GitHub, make changes " @@ -1571,15 +1574,15 @@ msgstr "" " des modifications localement et tu en gardes une trace à l'aide de Git, " "puis tu télécharges ton nouvel historique à nouveau sur GitHub." -#: ../../source/contributor-tutorial-contribute-on-github.rst:23 +#: ../../source/contributor-tutorial-contribute-on-github.rst:42 msgid "**Forking the Flower repository**" msgstr "**Fourche le dépôt de Flower**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:24 +#: ../../source/contributor-tutorial-contribute-on-github.rst:32 #, fuzzy msgid "" "A fork is a personal copy of a GitHub repository. To create one for " -"Flower, you must navigate to ``_ (while " +"Flower, you must navigate to https://github.com/adap/flower (while " "connected to your GitHub account) and click the ``Fork`` button situated " "on the top right of the page." msgstr "" @@ -1588,7 +1591,7 @@ msgstr "" "étant connecté à ton compte GitHub) et cliquer sur le bouton ``Fork`` " "situé en haut à droite de la page." -#: ../../source/contributor-tutorial-contribute-on-github.rst:29 +#: ../../source/contributor-tutorial-contribute-on-github.rst:38 msgid "" "You can change the name if you want, but this is not necessary as this " "version of Flower will be yours and will sit inside your own account " @@ -1601,11 +1604,11 @@ msgstr "" " devrais voir dans le coin supérieur gauche que tu es en train de " "regarder ta propre version de Flower." -#: ../../source/contributor-tutorial-contribute-on-github.rst:34 +#: ../../source/contributor-tutorial-contribute-on-github.rst:59 msgid "**Cloning your forked repository**" msgstr "**Clonage de ton dépôt forké**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:35 +#: ../../source/contributor-tutorial-contribute-on-github.rst:45 msgid "" "The next step is to download the forked repository on your machine to be " "able to make changes to it. On your forked repository page, you should " @@ -1617,7 +1620,7 @@ msgstr "" "forké, tu dois d'abord cliquer sur le bouton ``Code`` à droite, ce qui te" " permettra de copier le lien HTTPS du dépôt." -#: ../../source/contributor-tutorial-contribute-on-github.rst:41 +#: ../../source/contributor-tutorial-contribute-on-github.rst:52 msgid "" "Once you copied the \\, you can open a terminal on your machine, " "navigate to the place you want to download the repository to and type:" @@ -1626,7 +1629,7 @@ msgstr "" "machine, naviguer jusqu'à l'endroit où tu veux télécharger le référentiel" " et taper :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:47 +#: ../../source/contributor-tutorial-contribute-on-github.rst:59 #, fuzzy msgid "" "This will create a ``flower/`` (or the name of your fork if you renamed " @@ -1635,15 +1638,15 @@ msgstr "" "Cela créera un dossier `flower/` (ou le nom de ta fourche si tu l'as " "renommée) dans le répertoire de travail actuel." -#: ../../source/contributor-tutorial-contribute-on-github.rst:49 +#: ../../source/contributor-tutorial-contribute-on-github.rst:78 msgid "**Add origin**" msgstr "**Ajouter l'origine**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:50 +#: ../../source/contributor-tutorial-contribute-on-github.rst:62 msgid "You can then go into the repository folder:" msgstr "Tu peux ensuite aller dans le dossier du référentiel :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:56 +#: ../../source/contributor-tutorial-contribute-on-github.rst:68 msgid "" "And here we will need to add an origin to our repository. The origin is " "the \\ of the remote fork repository. To obtain it, we can do as " @@ -1655,7 +1658,7 @@ msgstr "" "indiqué précédemment en allant sur notre dépôt fork sur notre compte " "GitHub et en copiant le lien." -#: ../../source/contributor-tutorial-contribute-on-github.rst:61 +#: ../../source/contributor-tutorial-contribute-on-github.rst:75 msgid "" "Once the \\ is copied, we can type the following command in our " "terminal:" @@ -1663,11 +1666,11 @@ msgstr "" "Une fois que le \\ est copié, nous pouvons taper la commande " "suivante dans notre terminal :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:68 +#: ../../source/contributor-tutorial-contribute-on-github.rst:102 msgid "**Add upstream**" msgstr "**Ajouter en amont**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:69 +#: ../../source/contributor-tutorial-contribute-on-github.rst:81 #, fuzzy msgid "" "Now we will add an upstream address to our repository. Still in the same " @@ -1677,13 +1680,13 @@ msgstr "" "Toujours dans le même directroy, nous devons exécuter la commande " "suivante :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:76 +#: ../../source/contributor-tutorial-contribute-on-github.rst:88 msgid "The following diagram visually explains what we did in the previous steps:" msgstr "" "Le schéma suivant explique visuellement ce que nous avons fait dans les " "étapes précédentes :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:80 +#: ../../source/contributor-tutorial-contribute-on-github.rst:92 msgid "" "The upstream is the GitHub remote address of the parent repository (in " "this case Flower), i.e. the one we eventually want to contribute to and " @@ -1697,7 +1700,7 @@ msgstr "" "simplement l'adresse distante GitHub du dépôt forké que nous avons créé, " "c'est-à-dire la copie (fork) dans notre propre compte." -#: ../../source/contributor-tutorial-contribute-on-github.rst:84 +#: ../../source/contributor-tutorial-contribute-on-github.rst:97 msgid "" "To make sure our local version of the fork is up-to-date with the latest " "changes from the Flower repository, we can execute the following command:" @@ -1706,11 +1709,11 @@ msgstr "" "dernières modifications du dépôt Flower, nous pouvons exécuter la " "commande suivante :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:93 +#: ../../source/contributor-tutorial-contribute-on-github.rst:105 msgid "Setting up the coding environment" msgstr "Mise en place de l'environnement de codage" -#: ../../source/contributor-tutorial-contribute-on-github.rst:95 +#: ../../source/contributor-tutorial-contribute-on-github.rst:107 #, fuzzy msgid "" "This can be achieved by following this :doc:`getting started guide for " @@ -1723,11 +1726,11 @@ msgstr "" "fois que tu es capable d'écrire du code et de le tester, tu peux enfin " "commencer à faire des changements !" -#: ../../source/contributor-tutorial-contribute-on-github.rst:100 +#: ../../source/contributor-tutorial-contribute-on-github.rst:113 msgid "Making changes" msgstr "Apporter des changements" -#: ../../source/contributor-tutorial-contribute-on-github.rst:102 +#: ../../source/contributor-tutorial-contribute-on-github.rst:115 msgid "" "Before making any changes make sure you are up-to-date with your " "repository:" @@ -1735,15 +1738,15 @@ msgstr "" "Avant de faire des changements, assure-toi que tu es à jour avec ton " "référentiel :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:108 +#: ../../source/contributor-tutorial-contribute-on-github.rst:121 msgid "And with Flower's repository:" msgstr "Et avec le référentiel de Flower :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:114 +#: ../../source/contributor-tutorial-contribute-on-github.rst:134 msgid "**Create a new branch**" msgstr "**Créer une nouvelle branche**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:115 +#: ../../source/contributor-tutorial-contribute-on-github.rst:128 msgid "" "To make the history cleaner and easier to work with, it is good practice " "to create a new branch for each feature/project that needs to be " @@ -1753,7 +1756,7 @@ msgstr "" "une bonne pratique de créer une nouvelle branche pour chaque " "fonctionnalité/projet qui doit être mis en œuvre." -#: ../../source/contributor-tutorial-contribute-on-github.rst:118 +#: ../../source/contributor-tutorial-contribute-on-github.rst:131 msgid "" "To do so, just run the following command inside the repository's " "directory:" @@ -1761,21 +1764,21 @@ msgstr "" "Pour ce faire, il suffit d'exécuter la commande suivante dans le " "répertoire du référentiel :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:124 +#: ../../source/contributor-tutorial-contribute-on-github.rst:136 msgid "**Make changes**" msgstr "**Apporter des modifications**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:125 +#: ../../source/contributor-tutorial-contribute-on-github.rst:137 msgid "Write great code and create wonderful changes using your favorite editor!" msgstr "" "Écris du bon code et crée de merveilleuses modifications à l'aide de ton " "éditeur préféré !" -#: ../../source/contributor-tutorial-contribute-on-github.rst:127 +#: ../../source/contributor-tutorial-contribute-on-github.rst:149 msgid "**Test and format your code**" msgstr "**Teste et mets en forme ton code**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:128 +#: ../../source/contributor-tutorial-contribute-on-github.rst:139 msgid "" "Don't forget to test and format your code! Otherwise your code won't be " "able to be merged into the Flower repository. This is done so the " @@ -1785,15 +1788,15 @@ msgstr "" "pourra pas être fusionné dans le dépôt Flower, et ce, afin que la base de" " code reste cohérente et facile à comprendre." -#: ../../source/contributor-tutorial-contribute-on-github.rst:131 +#: ../../source/contributor-tutorial-contribute-on-github.rst:143 msgid "To do so, we have written a few scripts that you can execute:" msgstr "Pour ce faire, nous avons écrit quelques scripts que tu peux exécuter :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:140 +#: ../../source/contributor-tutorial-contribute-on-github.rst:162 msgid "**Stage changes**" msgstr "**Changements de scène**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:141 +#: ../../source/contributor-tutorial-contribute-on-github.rst:152 msgid "" "Before creating a commit that will update your history, you must specify " "to Git which files it needs to take into account." @@ -1801,48 +1804,51 @@ msgstr "" "Avant de créer un commit qui mettra à jour ton historique, tu dois " "spécifier à Git les fichiers qu'il doit prendre en compte." -#: ../../source/contributor-tutorial-contribute-on-github.rst:143 +#: ../../source/contributor-tutorial-contribute-on-github.rst:155 msgid "This can be done with:" msgstr "Cela peut se faire avec :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:149 +#: ../../source/contributor-tutorial-contribute-on-github.rst:161 +#, fuzzy msgid "" "To check which files have been modified compared to the last version " "(last commit) and to see which files are staged for commit, you can use " -"the :code:`git status` command." +"the ``git status`` command." msgstr "" "Pour vérifier quels fichiers ont été modifiés par rapport à la dernière " "version (last commit) et pour voir quels fichiers sont mis à disposition " "pour le commit, tu peux utiliser la commande :code:`git status`." -#: ../../source/contributor-tutorial-contribute-on-github.rst:152 +#: ../../source/contributor-tutorial-contribute-on-github.rst:173 msgid "**Commit changes**" msgstr "**Commit changes**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:153 +#: ../../source/contributor-tutorial-contribute-on-github.rst:165 +#, fuzzy msgid "" -"Once you have added all the files you wanted to commit using :code:`git " -"add`, you can finally create your commit using this command:" +"Once you have added all the files you wanted to commit using ``git add``," +" you can finally create your commit using this command:" msgstr "" "Une fois que tu as ajouté tous les fichiers que tu voulais livrer à " "l'aide de :code:`git add`, tu peux enfin créer ta livraison à l'aide de " "cette commande :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:159 +#: ../../source/contributor-tutorial-contribute-on-github.rst:172 +#, fuzzy msgid "" "The \\ is there to explain to others what the commit " "does. It should be written in an imperative style and be concise. An " -"example would be :code:`git commit -m \"Add images to README\"`." +"example would be ``git commit -m \"Add images to README\"``." msgstr "" "Le ``commit_message`` est là pour expliquer aux autres ce que fait le " "commit. Il doit être écrit dans un style impératif et être concis. Un " "exemple serait :code:`git commit -m \"Ajouter des images au README\"`." -#: ../../source/contributor-tutorial-contribute-on-github.rst:162 +#: ../../source/contributor-tutorial-contribute-on-github.rst:185 msgid "**Push the changes to the fork**" msgstr "**Pousser les changements vers la fourche**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:163 +#: ../../source/contributor-tutorial-contribute-on-github.rst:176 msgid "" "Once we have committed our changes, we have effectively updated our local" " history, but GitHub has no way of knowing this unless we push our " @@ -1853,7 +1859,7 @@ msgstr "" "moyen de le savoir à moins que nous ne poussions nos modifications vers " "l'adresse distante de notre origine :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:170 +#: ../../source/contributor-tutorial-contribute-on-github.rst:184 msgid "" "Once this is done, you will see on the GitHub that your forked repo was " "updated with the changes you have made." @@ -1861,15 +1867,15 @@ msgstr "" "Une fois que c'est fait, tu verras sur GitHub que ton repo forké a été " "mis à jour avec les modifications que tu as apportées." -#: ../../source/contributor-tutorial-contribute-on-github.rst:174 +#: ../../source/contributor-tutorial-contribute-on-github.rst:188 msgid "Creating and merging a pull request (PR)" msgstr "Créer et fusionner une pull request (PR)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:176 +#: ../../source/contributor-tutorial-contribute-on-github.rst:226 msgid "**Create the PR**" msgstr "**Créer le PR**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:177 +#: ../../source/contributor-tutorial-contribute-on-github.rst:191 msgid "" "Once you have pushed changes, on the GitHub webpage of your repository " "you should see the following message:" @@ -1877,12 +1883,12 @@ msgstr "" "Une fois que tu as poussé les modifications, sur la page web GitHub de " "ton dépôt, tu devrais voir le message suivant :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:181 +#: ../../source/contributor-tutorial-contribute-on-github.rst:196 #, fuzzy msgid "Otherwise you can always find this option in the ``Branches`` page." msgstr "Sinon, tu peux toujours trouver cette option dans la page `Branches`." -#: ../../source/contributor-tutorial-contribute-on-github.rst:183 +#: ../../source/contributor-tutorial-contribute-on-github.rst:198 #, fuzzy msgid "" "Once you click the ``Compare & pull request`` button, you should see " @@ -1891,13 +1897,13 @@ msgstr "" "Une fois que tu as cliqué sur le bouton `Compare & pull request`, tu " "devrais voir quelque chose de similaire à ceci :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:187 +#: ../../source/contributor-tutorial-contribute-on-github.rst:203 msgid "At the top you have an explanation of which branch will be merged where:" msgstr "" "En haut, tu as une explication de quelle branche sera fusionnée à quel " "endroit :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:191 +#: ../../source/contributor-tutorial-contribute-on-github.rst:207 msgid "" "In this example you can see that the request is to merge the branch " "``doc-fixes`` from my forked repository to branch ``main`` from the " @@ -1907,14 +1913,14 @@ msgstr "" "branche ``doc-fixes`` de mon dépôt forké à la branche ``main`` du dépôt " "Flower." -#: ../../source/contributor-tutorial-contribute-on-github.rst:193 +#: ../../source/contributor-tutorial-contribute-on-github.rst:210 msgid "" "The title should be changed to adhere to the :ref:`pr_title_format` " "guidelines, otherwise it won't be possible to merge the PR. So in this " "case, a correct title might be ``docs(framework:skip) Fix typos``." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:196 +#: ../../source/contributor-tutorial-contribute-on-github.rst:214 msgid "" "The input box in the middle is there for you to describe what your PR " "does and to link it to existing issues. We have placed comments (that " @@ -1926,11 +1932,11 @@ msgstr "" "commentaires (qui ne seront pas rendus une fois le PR ouvert) pour te " "guider tout au long du processus." -#: ../../source/contributor-tutorial-contribute-on-github.rst:199 +#: ../../source/contributor-tutorial-contribute-on-github.rst:218 msgid "It is important to follow the instructions described in comments." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:201 +#: ../../source/contributor-tutorial-contribute-on-github.rst:220 msgid "" "At the bottom you will find the button to open the PR. This will notify " "reviewers that a new PR has been opened and that they should look over it" @@ -1940,7 +1946,7 @@ msgstr "" "qui informera les réviseurs qu'un nouveau PR a été ouvert et qu'ils " "doivent le consulter pour le fusionner ou demander des modifications." -#: ../../source/contributor-tutorial-contribute-on-github.rst:204 +#: ../../source/contributor-tutorial-contribute-on-github.rst:224 msgid "" "If your PR is not yet ready for review, and you don't want to notify " "anyone, you have the option to create a draft pull request:" @@ -1949,11 +1955,11 @@ msgstr "" " personne, tu as la possibilité de créer un brouillon de demande de " "traction :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:230 msgid "**Making new changes**" msgstr "**Faire de nouveaux changements**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:209 +#: ../../source/contributor-tutorial-contribute-on-github.rst:229 msgid "" "Once the PR has been opened (as draft or not), you can still push new " "commits to it the same way we did before, by making changes to the branch" @@ -1963,11 +1969,11 @@ msgstr "" "toujours y pousser de nouveaux commits de la même manière qu'auparavant, " "en apportant des modifications à la branche associée au PR." -#: ../../source/contributor-tutorial-contribute-on-github.rst:211 +#: ../../source/contributor-tutorial-contribute-on-github.rst:253 msgid "**Review the PR**" msgstr "**Review the PR**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:212 +#: ../../source/contributor-tutorial-contribute-on-github.rst:233 msgid "" "Once the PR has been opened or once the draft PR has been marked as " "ready, a review from code owners will be automatically requested:" @@ -1976,7 +1982,7 @@ msgstr "" " étant prêt, une révision des propriétaires de code sera automatiquement " "demandée :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:216 +#: ../../source/contributor-tutorial-contribute-on-github.rst:238 msgid "" "Code owners will then look into the code, ask questions, request changes " "or validate the PR." @@ -1984,11 +1990,11 @@ msgstr "" "Les propriétaires du code vont alors se pencher sur le code, poser des " "questions, demander des modifications ou valider le RP." -#: ../../source/contributor-tutorial-contribute-on-github.rst:218 +#: ../../source/contributor-tutorial-contribute-on-github.rst:241 msgid "Merging will be blocked if there are ongoing requested changes." msgstr "La fusion sera bloquée s'il y a des changements demandés en cours." -#: ../../source/contributor-tutorial-contribute-on-github.rst:222 +#: ../../source/contributor-tutorial-contribute-on-github.rst:245 msgid "" "To resolve them, just push the necessary changes to the branch associated" " with the PR:" @@ -1996,11 +2002,11 @@ msgstr "" "Pour les résoudre, il suffit de pousser les changements nécessaires vers " "la branche associée au PR :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:226 +#: ../../source/contributor-tutorial-contribute-on-github.rst:250 msgid "And resolve the conversation:" msgstr "Et résous la conversation :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:230 +#: ../../source/contributor-tutorial-contribute-on-github.rst:254 msgid "" "Once all the conversations have been resolved, you can re-request a " "review." @@ -2008,11 +2014,11 @@ msgstr "" "Une fois que toutes les conversations ont été résolues, tu peux " "redemander un examen." -#: ../../source/contributor-tutorial-contribute-on-github.rst:233 +#: ../../source/contributor-tutorial-contribute-on-github.rst:274 msgid "**Once the PR is merged**" msgstr "**Une fois que le PR est fusionné**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:234 +#: ../../source/contributor-tutorial-contribute-on-github.rst:256 msgid "" "If all the automatic tests have passed and reviewers have no more changes" " to request, they can approve the PR and merge it." @@ -2021,7 +2027,7 @@ msgstr "" " de modifications à demander, ils peuvent approuver le PR et le " "fusionner." -#: ../../source/contributor-tutorial-contribute-on-github.rst:238 +#: ../../source/contributor-tutorial-contribute-on-github.rst:261 msgid "" "Once it is merged, you can delete the branch on GitHub (a button should " "appear to do so) and also delete it locally by doing:" @@ -2030,19 +2036,19 @@ msgstr "" "(un bouton devrait apparaître pour le faire) et aussi la supprimer " "localement en faisant :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:245 +#: ../../source/contributor-tutorial-contribute-on-github.rst:269 msgid "Then you should update your forked repository by doing:" msgstr "Ensuite, tu dois mettre à jour ton dépôt forké en faisant :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:254 +#: ../../source/contributor-tutorial-contribute-on-github.rst:277 msgid "Example of first contribution" msgstr "Exemple de première contribution" -#: ../../source/contributor-tutorial-contribute-on-github.rst:257 +#: ../../source/contributor-tutorial-contribute-on-github.rst:280 msgid "Problem" msgstr "Problème" -#: ../../source/contributor-tutorial-contribute-on-github.rst:259 +#: ../../source/contributor-tutorial-contribute-on-github.rst:282 #, fuzzy msgid "" "For our documentation, we've started to use the `Diàtaxis framework " @@ -2051,7 +2057,7 @@ msgstr "" "Pour notre documentation, nous avons commencé à utiliser le cadre " "`Diàtaxis `_." -#: ../../source/contributor-tutorial-contribute-on-github.rst:261 +#: ../../source/contributor-tutorial-contribute-on-github.rst:285 #, fuzzy msgid "" "Our \"How to\" guides should have titles that continue the sentence \"How" @@ -2061,7 +2067,7 @@ msgstr "" "la phrase \"Comment faire pour...\", par exemple, \"Comment passer à " "Flower 1.0\"." -#: ../../source/contributor-tutorial-contribute-on-github.rst:263 +#: ../../source/contributor-tutorial-contribute-on-github.rst:288 msgid "" "Most of our guides do not follow this new format yet, and changing their " "title is (unfortunately) more involved than one might think." @@ -2070,7 +2076,7 @@ msgstr "" "changer leur titre est (malheureusement) plus compliqué qu'on ne le " "pense." -#: ../../source/contributor-tutorial-contribute-on-github.rst:265 +#: ../../source/contributor-tutorial-contribute-on-github.rst:291 #, fuzzy msgid "" "This issue is about changing the title of a doc from present continuous " @@ -2079,7 +2085,7 @@ msgstr "" "Cette question porte sur le changement du titre d'un document du présent " "continu au présent simple." -#: ../../source/contributor-tutorial-contribute-on-github.rst:267 +#: ../../source/contributor-tutorial-contribute-on-github.rst:294 #, fuzzy msgid "" "Let's take the example of \"Saving Progress\" which we changed to \"Save " @@ -2089,21 +2095,21 @@ msgstr "" "remplacé par \"Sauvegarder la progression\". Est-ce que cela passe notre " "contrôle ?" -#: ../../source/contributor-tutorial-contribute-on-github.rst:269 +#: ../../source/contributor-tutorial-contribute-on-github.rst:297 #, fuzzy msgid "Before: \"How to saving progress\" ❌" msgstr "Avant : \"Comment sauvegarder les progrès\" ❌" -#: ../../source/contributor-tutorial-contribute-on-github.rst:271 +#: ../../source/contributor-tutorial-contribute-on-github.rst:299 #, fuzzy msgid "After: \"How to save progress\" ✅" msgstr "Après : \"Comment sauvegarder la progression\" ✅" -#: ../../source/contributor-tutorial-contribute-on-github.rst:274 +#: ../../source/contributor-tutorial-contribute-on-github.rst:302 msgid "Solution" msgstr "Solution" -#: ../../source/contributor-tutorial-contribute-on-github.rst:276 +#: ../../source/contributor-tutorial-contribute-on-github.rst:304 #, fuzzy msgid "" "This is a tiny change, but it'll allow us to test your end-to-end setup. " @@ -2113,12 +2119,12 @@ msgstr "" "configuration de bout en bout. Après avoir cloné et configuré le repo " "Flower, voici ce que tu dois faire :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:278 +#: ../../source/contributor-tutorial-contribute-on-github.rst:307 #, fuzzy msgid "Find the source file in ``doc/source``" msgstr "Trouve le fichier source dans `doc/source`" -#: ../../source/contributor-tutorial-contribute-on-github.rst:279 +#: ../../source/contributor-tutorial-contribute-on-github.rst:308 #, fuzzy msgid "" "Make the change in the ``.rst`` file (beware, the dashes under the title " @@ -2127,7 +2133,7 @@ msgstr "" "Effectue la modification dans le fichier `.rst` (attention, les tirets " "sous le titre doivent être de la même longueur que le titre lui-même)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:280 +#: ../../source/contributor-tutorial-contribute-on-github.rst:310 #, fuzzy msgid "" "Build the docs and `check the result `_" -#: ../../source/contributor-tutorial-contribute-on-github.rst:283 +#: ../../source/contributor-tutorial-contribute-on-github.rst:314 msgid "Rename file" msgstr "Renommer le fichier" -#: ../../source/contributor-tutorial-contribute-on-github.rst:285 +#: ../../source/contributor-tutorial-contribute-on-github.rst:316 msgid "" "You might have noticed that the file name still reflects the old wording." " If we just change the file, then we break all existing links to it - it " @@ -2153,22 +2159,22 @@ msgstr "" "important** d'éviter cela, car briser des liens peut nuire à notre " "classement dans les moteurs de recherche." -#: ../../source/contributor-tutorial-contribute-on-github.rst:288 +#: ../../source/contributor-tutorial-contribute-on-github.rst:320 #, fuzzy msgid "Here's how to change the file name:" msgstr "Voici comment changer le nom du fichier :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:290 +#: ../../source/contributor-tutorial-contribute-on-github.rst:322 #, fuzzy msgid "Change the file name to ``save-progress.rst``" msgstr "Change le nom du fichier en `save-progress.rst`" -#: ../../source/contributor-tutorial-contribute-on-github.rst:291 +#: ../../source/contributor-tutorial-contribute-on-github.rst:323 #, fuzzy msgid "Add a redirect rule to ``doc/source/conf.py``" msgstr "Ajouter une règle de redirection à `doc/source/conf.py`" -#: ../../source/contributor-tutorial-contribute-on-github.rst:293 +#: ../../source/contributor-tutorial-contribute-on-github.rst:325 #, fuzzy msgid "" "This will cause a redirect from ``saving-progress.html`` to ``save-" @@ -2177,11 +2183,11 @@ msgstr "" "Cela entraînera une redirection de `saving-progress.html` vers `save-" "progress.html`, les anciens liens continueront à fonctionner." -#: ../../source/contributor-tutorial-contribute-on-github.rst:296 +#: ../../source/contributor-tutorial-contribute-on-github.rst:329 msgid "Apply changes in the index file" msgstr "Applique les changements dans le fichier d'index" -#: ../../source/contributor-tutorial-contribute-on-github.rst:298 +#: ../../source/contributor-tutorial-contribute-on-github.rst:331 #, fuzzy msgid "" "For the lateral navigation bar to work properly, it is very important to " @@ -2192,16 +2198,16 @@ msgstr "" "très important de mettre également à jour le fichier `index.rst`. C'est " "là que nous définissons toute l'arborescence de la barre de navigation." -#: ../../source/contributor-tutorial-contribute-on-github.rst:301 +#: ../../source/contributor-tutorial-contribute-on-github.rst:335 #, fuzzy msgid "Find and modify the file name in ``index.rst``" msgstr "Trouve et modifie le nom du fichier dans `index.rst`" -#: ../../source/contributor-tutorial-contribute-on-github.rst:304 +#: ../../source/contributor-tutorial-contribute-on-github.rst:338 msgid "Open PR" msgstr "Open PR" -#: ../../source/contributor-tutorial-contribute-on-github.rst:306 +#: ../../source/contributor-tutorial-contribute-on-github.rst:340 #, fuzzy msgid "" "Commit the changes (commit messages are always imperative: \"Do " @@ -2210,27 +2216,27 @@ msgstr "" "Valide les modifications (les messages de validation sont toujours " "impératifs : \"Fais quelque chose\", dans ce cas \"Modifie...\")" -#: ../../source/contributor-tutorial-contribute-on-github.rst:307 +#: ../../source/contributor-tutorial-contribute-on-github.rst:342 msgid "Push the changes to your fork" msgstr "Transmets les changements à ta fourchette" -#: ../../source/contributor-tutorial-contribute-on-github.rst:308 +#: ../../source/contributor-tutorial-contribute-on-github.rst:343 msgid "" "Open a PR (as shown above) with title ``docs(framework) Update how-to " "guide title``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:309 +#: ../../source/contributor-tutorial-contribute-on-github.rst:344 msgid "Wait for it to be approved!" msgstr "Attends qu'elle soit approuvée !" -#: ../../source/contributor-tutorial-contribute-on-github.rst:310 +#: ../../source/contributor-tutorial-contribute-on-github.rst:345 msgid "Congrats! 🥳 You're now officially a Flower contributor!" msgstr "" "Félicitations 🥳 Tu es désormais officiellement une contributrice de " "Flower !" -#: ../../source/contributor-tutorial-contribute-on-github.rst:314 +#: ../../source/contributor-tutorial-contribute-on-github.rst:348 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:573 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:1012 #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:811 @@ -2239,7 +2245,7 @@ msgstr "" msgid "Next steps" msgstr "Prochaines étapes" -#: ../../source/contributor-tutorial-contribute-on-github.rst:316 +#: ../../source/contributor-tutorial-contribute-on-github.rst:350 msgid "" "Once you have made your first PR, and want to contribute more, be sure to" " check out the following :" @@ -2247,37 +2253,37 @@ msgstr "" "Une fois que tu auras fait ton premier RP, et que tu voudras contribuer " "davantage, ne manque pas de consulter les sites suivants :" -#: ../../source/contributor-tutorial-contribute-on-github.rst:318 +#: ../../source/contributor-tutorial-contribute-on-github.rst:353 #, fuzzy msgid "" ":doc:`Good first contributions `, where you should particularly look into the " -":code:`baselines` contributions." +"``baselines`` contributions." msgstr "" "`Bonnes premières contributions `_, où vous devriez " "particulièrement regarder les contributions :code:`baselines`." -#: ../../source/contributor-tutorial-contribute-on-github.rst:322 +#: ../../source/contributor-tutorial-contribute-on-github.rst:357 #: ../../source/fed/0000-20200102-fed-template.md:60 msgid "Appendix" msgstr "Annexe" -#: ../../source/contributor-tutorial-contribute-on-github.rst:327 +#: ../../source/contributor-tutorial-contribute-on-github.rst:362 msgid "PR title format" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:329 +#: ../../source/contributor-tutorial-contribute-on-github.rst:364 msgid "We enforce the following PR title format:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:335 +#: ../../source/contributor-tutorial-contribute-on-github.rst:370 msgid "" "(or ``(:skip) `` to ignore the PR in the " "changelog)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:337 +#: ../../source/contributor-tutorial-contribute-on-github.rst:372 msgid "" "Where ```` needs to be in ``{ci, fix, feat, docs, refactor, " "break}``, ```` should be in ``{framework, baselines, datasets, " @@ -2286,51 +2292,51 @@ msgid "" "verb in the imperative mood." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:341 +#: ../../source/contributor-tutorial-contribute-on-github.rst:377 #, fuzzy msgid "Valid examples:" msgstr "Exemples de PyTorch" -#: ../../source/contributor-tutorial-contribute-on-github.rst:343 +#: ../../source/contributor-tutorial-contribute-on-github.rst:379 msgid "``feat(framework) Add flwr build CLI command``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:344 +#: ../../source/contributor-tutorial-contribute-on-github.rst:380 msgid "``refactor(examples:skip) Improve quickstart-pytorch logging``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:345 +#: ../../source/contributor-tutorial-contribute-on-github.rst:381 msgid "``ci(*:skip) Enforce PR title format``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:347 +#: ../../source/contributor-tutorial-contribute-on-github.rst:383 #, fuzzy msgid "Invalid examples:" msgstr "Exemples de PyTorch" -#: ../../source/contributor-tutorial-contribute-on-github.rst:349 +#: ../../source/contributor-tutorial-contribute-on-github.rst:385 msgid "``feat(framework): Add flwr build CLI command`` (extra ``:``)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:350 +#: ../../source/contributor-tutorial-contribute-on-github.rst:386 msgid "" "``feat(*) Add flwr build CLI command`` (missing ``skip`` flag along with " "``*``)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:351 +#: ../../source/contributor-tutorial-contribute-on-github.rst:387 msgid "``feat(skip) Add flwr build CLI command`` (missing ````)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:352 +#: ../../source/contributor-tutorial-contribute-on-github.rst:388 msgid "``feat(framework) add flwr build CLI command`` (non capitalised verb)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:353 +#: ../../source/contributor-tutorial-contribute-on-github.rst:389 msgid "``feat(framework) Add flwr build CLI command.`` (dot at the end)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:354 +#: ../../source/contributor-tutorial-contribute-on-github.rst:390 msgid "``Add flwr build CLI command.`` (missing ``()``)" msgstr "" @@ -2340,9 +2346,9 @@ msgstr "Devenez un·e contributeur·ice" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 #: ../../source/docker/run-as-subprocess.rst:11 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:15 -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:22 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:12 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:16 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:18 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:13 #: ../../source/docker/tutorial-quickstart-docker.rst:11 msgid "Prerequisites" msgstr "Prérequis" @@ -2369,7 +2375,7 @@ msgstr "" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:12 #, fuzzy msgid "" -"Flower uses :code:`pyproject.toml` to manage dependencies and configure " +"Flower uses ``pyproject.toml`` to manage dependencies and configure " "development tools (the ones which support it). Poetry is a build tool " "which supports `PEP 517 `_." msgstr "" @@ -2378,11 +2384,11 @@ msgstr "" "le supportent). Poetry est un outil qui support `PEP 517 " "`_." -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:18 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:17 msgid "Developer Machine Setup" msgstr "Setup de la machine" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:21 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:20 #, fuzzy msgid "Preliminaries" msgstr "Principes" @@ -2401,113 +2407,113 @@ msgid "" "installation actions to add `brew` to your PATH." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:28 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:29 msgid "" "Install `xz` (to install different Python versions) and `pandoc` to build" -" the docs::" +" the docs:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:34 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:36 msgid "For Ubuntu" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:35 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:38 msgid "" "Ensure you system (Ubuntu 22.04+) is up-to-date, and you have all " -"necessary packages::" +"necessary packages:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:44 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:47 #, fuzzy msgid "Create Flower Dev Environment" msgstr "Créer/Supprimer l'environment virtuel" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:46 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:49 #, fuzzy msgid "" -"1. Clone the `Flower repository `_ from " -"GitHub::" +"Clone the `Flower repository `_ from " +"GitHub:" msgstr "" "Pour commencer, cloner la `repo Flower `_" " depuis GitHub::" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:52 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:56 msgid "" "Let's create the Python environment for all-things Flower. If you wish to" -" use :code:`pyenv`, we provide two convenience scripts that you can use. " -"If you prefer using something else than :code:`pyenv`, create a new " -"environment, activate and skip to the last point where all packages are " -"installed." +" use ``pyenv``, we provide two convenience scripts that you can use. If " +"you prefer using something else than ``pyenv``, create a new environment," +" activate and skip to the last point where all packages are installed." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:54 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:61 #, fuzzy msgid "" -"If you don't have :code:`pyenv` installed, the following script that will" -" install it, set it up, and create the virtual environment (with " -":code:`Python 3.9.20` by default)::" +"If you don't have ``pyenv`` installed, the following script that will " +"install it, set it up, and create the virtual environment (with ``Python " +"3.9.20`` by default):" msgstr "" "Si vous n'avez pas :code:`pyenv` installé, vous pouvez utiliser le script" " suivant qui l'installera, le configurera et créera l'environnement " "virtuel (avec :code:`Python 3.9.20` par défaut)::" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:58 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:68 #, fuzzy msgid "" -"If you already have :code:`pyenv` installed (along with the :code:`pyenv-" -"virtualenv` plugin), you can use the following convenience script (with " -":code:`Python 3.9.20` by default)::" +"If you already have ``pyenv`` installed (along with the ``pyenv-" +"virtualenv`` plugin), you can use the following convenience script (with " +"``Python 3.9.20`` by default):" msgstr "" "Si vous n'avez pas :code:`pyenv` installé, vous pouvez utiliser le script" " suivant qui l'installera, le configurera et créera l'environnement " "virtuel (avec :code:`Python 3.9.20` par défaut)::" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:62 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:75 #, fuzzy msgid "" -"3. Install the Flower package in development mode (think :code:`pip " -"install -e`) along with all necessary dependencies::" +"3. Install the Flower package in development mode (think ``pip install " +"-e``) along with all necessary dependencies:" msgstr "" "Troisièmement, installez le paquet Flower en mode de développement ( " ":code :`pip install -e`) avec toutes les dépendances nécessaires :" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:69 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:83 msgid "Convenience Scripts" msgstr "Scripts pratiques" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:71 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:85 +#, fuzzy msgid "" "The Flower repository contains a number of convenience scripts to make " -"recurring development tasks easier and less error-prone. See the " -":code:`/dev` subdirectory for a full list. The following scripts are " -"amongst the most important ones:" +"recurring development tasks easier and less error-prone. See the ``/dev``" +" subdirectory for a full list. The following scripts are amongst the most" +" important ones:" msgstr "" "La repo de Flower contient un certain nombre de scripts de commodité pour" " rendre les tâches de développement récurrentes plus faciles et moins " "problématiques. Voir le sous-répertoire :code :`/dev` pour une liste " "complète. Les scripts suivants sont parmis les plus importants :" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:77 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:90 msgid "Create/Delete Virtual Environment" msgstr "Créer/Supprimer l'environment virtuel" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:85 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:98 msgid "Compile ProtoBuf Definitions" msgstr "Compiler les définitions ProtoBuf" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:92 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:105 msgid "Auto-Format Code" msgstr "Formatter le code" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:99 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:112 msgid "Run Linters and Tests" msgstr "Vérifier le format et tester le code" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:106 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:119 msgid "Add a pre-commit hook" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:108 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:121 msgid "" "Developers may integrate a pre-commit hook into their workflow utilizing " "the `pre-commit `_ library. The pre-" @@ -2515,50 +2521,50 @@ msgid "" "``./dev/format.sh`` and ``./dev/test.sh`` scripts." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:110 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:125 msgid "There are multiple ways developers can use this:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:112 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:127 msgid "Install the pre-commit hook to your local git directory by simply running:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:118 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:133 msgid "" "Each ``git commit`` will trigger the execution of formatting and " "linting/test scripts." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:119 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:135 msgid "" "If in a hurry, bypass the hook using ``--no-verify`` with the ``git " -"commit`` command. ::" +"commit`` command." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:124 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142 msgid "" "For developers who prefer not to install the hook permanently, it is " "possible to execute a one-time check prior to committing changes by using" " the following command:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:130 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:149 msgid "" "This executes the formatting and linting checks/tests on all the files " "without modifying the default behavior of ``git commit``." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:133 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:153 msgid "Run Github Actions (CI) locally" msgstr "Exécuter les GitHub Actions (CI) localement" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:135 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:155 #, fuzzy msgid "" "Developers could run the full set of Github Actions workflows under their" " local environment by using `Act `_. " "Please refer to the installation instructions under the linked repository" -" and run the next command under Flower main cloned repository folder::" +" and run the next command under Flower main cloned repository folder:" msgstr "" "Il est possible d'exécuter l'ensemble des Github Actions sous leur " "environnement local en utilisant `Act _`." @@ -2566,7 +2572,7 @@ msgstr "" "fois installé, exécuter la commande suivante dans le dossier principale " "de Flower :" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:164 msgid "" "The Flower default workflow would run by setting up the required Docker " "machines underneath." @@ -2574,40 +2580,42 @@ msgstr "" "Le workflow par défaut de Flower sera exécuté en configurant les machines" " Docker requises en arrière plan." -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:147 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:168 #, fuzzy msgid "Build Release" msgstr "Inédit" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:149 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:170 +#, fuzzy msgid "" "Flower uses Poetry to build releases. The necessary command is wrapped in" -" a simple script::" +" a simple script:" msgstr "" "Flower utilise Poetry pour construire les nouvelles versions. La commande" " nécessaire est comprise dans un script simple ::" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:154 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:177 +#, fuzzy msgid "" -"The resulting :code:`.whl` and :code:`.tar.gz` releases will be stored in" -" the :code:`/dist` subdirectory." +"The resulting ``.whl`` and ``.tar.gz`` releases will be stored in the " +"``/dist`` subdirectory." msgstr "" "Les versions résultantes :code:`.whl` et :code:`.tar.gz` seront stockées " "dans le sous-répertoire:code:`/dist`." -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:159 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:181 #, fuzzy msgid "Build Documentation" msgstr "Amélioration de la documentation" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:161 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:183 msgid "" "Flower's documentation uses `Sphinx `_. " "There's no convenience script to re-build the documentation yet, but it's" -" pretty easy::" +" pretty easy:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:167 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:191 msgid "This will generate HTML documentation in ``doc/build/html``." msgstr "" @@ -2651,7 +2659,7 @@ msgid "" msgstr "" #: ../../source/docker/enable-tls.rst:23 -#: ../../source/docker/persist-superlink-state.rst:14 +#: ../../source/docker/persist-superlink-state.rst:15 msgid "" "If you later want to delete the directory, you can change the user ID " "back to the current user ID by running ``sudo chown -R $USER:$(id -gn) " @@ -2675,21 +2683,21 @@ msgstr "" msgid "Understanding the command" msgstr "Entraîne le modèle" -#: ../../source/docker/enable-tls.rst:44 ../../source/docker/enable-tls.rst:91 +#: ../../source/docker/enable-tls.rst:45 ../../source/docker/enable-tls.rst:92 #: ../../source/docker/enable-tls.rst:125 #: ../../source/docker/tutorial-quickstart-docker.rst:66 #: ../../source/docker/tutorial-quickstart-docker.rst:103 -#: ../../source/docker/tutorial-quickstart-docker.rst:213 -#: ../../source/docker/tutorial-quickstart-docker.rst:300 +#: ../../source/docker/tutorial-quickstart-docker.rst:217 +#: ../../source/docker/tutorial-quickstart-docker.rst:305 msgid "``docker run``: This tells Docker to run a container from an image." msgstr "" -#: ../../source/docker/enable-tls.rst:45 ../../source/docker/enable-tls.rst:92 +#: ../../source/docker/enable-tls.rst:46 ../../source/docker/enable-tls.rst:93 #: ../../source/docker/enable-tls.rst:126 #: ../../source/docker/tutorial-quickstart-docker.rst:67 #: ../../source/docker/tutorial-quickstart-docker.rst:104 -#: ../../source/docker/tutorial-quickstart-docker.rst:214 -#: ../../source/docker/tutorial-quickstart-docker.rst:301 +#: ../../source/docker/tutorial-quickstart-docker.rst:218 +#: ../../source/docker/tutorial-quickstart-docker.rst:306 msgid "``--rm``: Remove the container once it is stopped or the command exits." msgstr "" @@ -2794,19 +2802,19 @@ msgstr "" msgid "the network." msgstr "" -#: ../../source/docker/enable-tls.rst:71 +#: ../../source/docker/enable-tls.rst:72 #, fuzzy msgid "SuperNode" msgstr "flower-superlink" -#: ../../source/docker/enable-tls.rst:73 +#: ../../source/docker/enable-tls.rst:74 msgid "" "Assuming that the ``ca.crt`` certificate already exists locally, we can " "use the flag ``--volume`` to mount the local certificate into the " "container's ``/app/`` directory." msgstr "" -#: ../../source/docker/enable-tls.rst:78 +#: ../../source/docker/enable-tls.rst:79 msgid "" "If you're generating self-signed certificates and the ``ca.crt`` " "certificate doesn't exist on the SuperNode, you can copy it over after " @@ -2921,16 +2929,16 @@ msgstr "" msgid "Getting Started" msgstr "Pour commencer" -#: ../../source/docker/index.rst:20 +#: ../../source/docker/index.rst:19 msgid "Running in Production" msgstr "" -#: ../../source/docker/index.rst:29 +#: ../../source/docker/index.rst:28 #, fuzzy msgid "Advanced Options" msgstr "Options d'installation avancées" -#: ../../source/docker/index.rst:41 +#: ../../source/docker/index.rst:40 #, fuzzy msgid "Run Flower using Docker Compose" msgstr "Serveur de Flower" @@ -2953,7 +2961,7 @@ msgid "" " on your host system and a name for the database file." msgstr "" -#: ../../source/docker/persist-superlink-state.rst:10 +#: ../../source/docker/persist-superlink-state.rst:11 msgid "" "By default, the SuperLink container runs with a non-root user called " "``app`` with the user ID ``49999``. It is recommended to create a new " @@ -2961,7 +2969,7 @@ msgid "" "the mounted directory has the proper permissions." msgstr "" -#: ../../source/docker/persist-superlink-state.rst:20 +#: ../../source/docker/persist-superlink-state.rst:21 msgid "" "In the example below, we create a new directory called ``state``, change " "the user ID and tell Docker via the flag ``--volume`` to mount the local " @@ -2970,7 +2978,7 @@ msgid "" "database file." msgstr "" -#: ../../source/docker/persist-superlink-state.rst:35 +#: ../../source/docker/persist-superlink-state.rst:36 msgid "" "As soon as the SuperLink starts, the file ``state.db`` is created in the " "``state`` directory on your host system. If the file already exists, the " @@ -2995,17 +3003,17 @@ msgid "" "by-digest-immutable-identifier>`_ of the image instead of the tag." msgstr "" -#: ../../source/docker/pin-version.rst:13 +#: ../../source/docker/pin-version.rst:14 msgid "" "The following command returns the current image digest referenced by the " ":substitution-code:`superlink:|stable_flwr_version|` tag:" msgstr "" -#: ../../source/docker/pin-version.rst:22 +#: ../../source/docker/pin-version.rst:23 msgid "This will output" msgstr "" -#: ../../source/docker/pin-version.rst:29 +#: ../../source/docker/pin-version.rst:30 msgid "Next, we can pin the digest when running a new SuperLink container:" msgstr "" @@ -3052,7 +3060,7 @@ msgid "" "``USER root`` directive within your Dockerfile." msgstr "" -#: ../../source/docker/run-as-root-user.rst:29 +#: ../../source/docker/run-as-root-user.rst:30 #, fuzzy msgid "SuperNode Dockerfile" msgstr "Démarrer le serveur" @@ -3079,12 +3087,12 @@ msgid "" "done by extending the SuperNode image:" msgstr "" -#: ../../source/docker/run-as-subprocess.rst:16 +#: ../../source/docker/run-as-subprocess.rst:17 #, fuzzy msgid "Dockerfile.supernode" msgstr "Serveur de Flower" -#: ../../source/docker/run-as-subprocess.rst:30 +#: ../../source/docker/run-as-subprocess.rst:31 msgid "" "Next, build the SuperNode Docker image by running the following command " "in the directory where Dockerfile is located:" @@ -3115,80 +3123,80 @@ msgid "" " Engine via Docker Compose." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:11 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:12 msgid "" "Some quickstart examples may have limitations or requirements that " "prevent them from running on every environment. For more information, " -"please see `Limitations`_." +"please see Limitations_." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:17 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:14 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:18 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:15 #: ../../source/docker/tutorial-quickstart-docker.rst:13 msgid "Before you start, make sure that:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:19 -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:26 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:20 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:22 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:17 #: ../../source/docker/tutorial-quickstart-docker.rst:15 msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:20 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:17 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:21 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:18 #: ../../source/docker/tutorial-quickstart-docker.rst:16 msgid "The Docker daemon is running." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:21 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:18 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:22 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:19 msgid "Docker Compose is `installed `_." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:24 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:25 #, fuzzy msgid "Run the Quickstart Example" msgstr "Demande pour un nouveau Flower Example" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:26 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:27 msgid "" "Clone the quickstart example you like to run. For example, ``quickstart-" "pytorch``:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:34 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:35 msgid "" "Download the `compose.yml " "`_" " file into the example directory:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:41 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:44 #, fuzzy msgid "Build and start the services using the following command:" msgstr "Active la virtualenv en exécutant la commande suivante :" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:47 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:50 #, fuzzy msgid "" "Append the following lines to the end of the ``pyproject.toml`` file and " "save it:" msgstr "Augmente la version mineure de ``pyproject.toml`` d'une unité." -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:49 -#: ../../source/docker/tutorial-quickstart-docker.rst:319 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:52 +#: ../../source/docker/tutorial-quickstart-docker.rst:324 msgid "pyproject.toml" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:58 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:61 msgid "" "You can customize the string that follows ``tool.flwr.federations.`` to " "fit your needs. However, please note that the string cannot contain a dot" " (``.``)." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:61 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:64 msgid "" "In this example, ``local-deployment`` has been used. Just remember to " "replace ``local-deployment`` with your chosen name in both the " @@ -3196,57 +3204,57 @@ msgid "" "command." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:65 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:68 #, fuzzy msgid "Run the example:" msgstr "Fédérer l'exemple" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:71 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:74 msgid "Follow the logs of the SuperExec service:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:77 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:80 msgid "" "That is all it takes! You can monitor the progress of the run through the" " logs of the SuperExec." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:80 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:84 msgid "Run a Different Quickstart Example" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:82 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:86 msgid "" "To run a different quickstart example, such as ``quickstart-tensorflow``," " first, shut down the Docker Compose services of the current example:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:89 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:93 msgid "After that, you can repeat the steps above." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:92 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:98 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:96 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:102 #, fuzzy msgid "Limitations" msgstr "Simulation de moniteur" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:97 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:101 #, fuzzy msgid "Quickstart Example" msgstr "Démarrage rapide de JAX" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:99 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:103 #, fuzzy msgid "quickstart-fastai" msgstr "Démarrage rapide fastai" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:100 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:102 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:110 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:112 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:116 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:118 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:104 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:106 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:115 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:117 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:121 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:123 #: ../../source/ref-changelog.md:33 ../../source/ref-changelog.md:399 #: ../../source/ref-changelog.md:676 ../../source/ref-changelog.md:740 #: ../../source/ref-changelog.md:798 ../../source/ref-changelog.md:867 @@ -3254,19 +3262,19 @@ msgstr "Démarrage rapide fastai" msgid "None" msgstr "Aucun" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:101 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:105 #, fuzzy msgid "quickstart-huggingface" msgstr "Quickstart tutorials" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:103 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:107 #, fuzzy msgid "quickstart-jax" msgstr "Démarrage rapide de JAX" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:104 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:106 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:120 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:108 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:110 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:125 #, fuzzy msgid "" "The example has not yet been updated to work with the latest ``flwr`` " @@ -3275,64 +3283,64 @@ msgstr "" "Les exemples de code couvrant scikit-learn et PyTorch Lightning ont été " "mis à jour pour fonctionner avec la dernière version de Flower." -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:105 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:109 #, fuzzy msgid "quickstart-mlcube" msgstr "Démarrage rapide de JAX" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:107 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:111 #, fuzzy msgid "quickstart-mlx" msgstr "Démarrage rapide de JAX" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:108 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:112 msgid "" "`Requires to run on macOS with Apple Silicon `_." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:109 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:114 #, fuzzy msgid "quickstart-monai" msgstr "Démarrage rapide de JAX" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:111 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:116 #, fuzzy msgid "quickstart-pandas" msgstr "Démarrage rapide des Pandas" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:113 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:118 #, fuzzy msgid "quickstart-pytorch-lightning" msgstr "Démarrage rapide de PyTorch Lightning" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:114 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:119 msgid "" "Requires an older pip version that is not supported by the Flower Docker " "images." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:115 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:120 #, fuzzy msgid "quickstart-pytorch" msgstr "Démarrage rapide de PyTorch" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:117 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:122 #, fuzzy msgid "quickstart-sklearn-tabular" msgstr "Démarrage rapide de scikit-learn" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:119 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:124 #, fuzzy msgid "quickstart-tabnet" msgstr "Démarrage rapide de JAX" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:121 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:126 #, fuzzy msgid "quickstart-tensorflow" msgstr "Démarrage rapide de TensorFlow" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:122 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:127 msgid "Only runs on AMD64." msgstr "" @@ -3348,18 +3356,18 @@ msgid "" "environment variables for a container." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:3 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:2 #, fuzzy msgid "Deploy Flower on Multiple Machines with Docker Compose" msgstr "Démarrage rapide XGBoost" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:5 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:4 msgid "" "This guide will help you set up a Flower project on multiple machines " "using Docker Compose." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:8 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:7 msgid "" "You will learn how to run the Flower client and server components on two " "separate machines, with Flower configured to use TLS encryption and " @@ -3368,7 +3376,7 @@ msgid "" " to the :doc:`../explanation-flower-architecture` explainer page." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:15 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:13 msgid "" "This guide assumes you have completed the :doc:`tutorial-quickstart-" "docker-compose` tutorial. It is highly recommended that you follow and " @@ -3376,140 +3384,140 @@ msgid "" "guide." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:24 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:20 msgid "Before you begin, make sure you have the following prerequisites:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:28 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:23 msgid "The Docker daemon is running on your local machine and the remote machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:30 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:24 msgid "" "Docker Compose V2 is installed on both your local machine and the remote " "machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:32 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:25 msgid "You can connect to the remote machine from your local machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:33 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:26 msgid "Ports ``9091`` and ``9093`` are accessible on the remote machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:37 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:30 msgid "" "The guide uses the |quickstart_sklearn_tabular|_ example as an example " "project." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:40 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:32 msgid "" "If your project has a different name or location, please remember to " "adjust the commands/paths accordingly." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:45 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:36 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:22 #: ../../source/docker/tutorial-quickstart-docker.rst:19 msgid "Step 1: Set Up" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:47 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:38 msgid "Clone the Flower repository and change to the ``distributed`` directory:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:55 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:45 msgid "Get the IP address from the remote machine and save it for later." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:57 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:46 msgid "" "Use the ``certs.yml`` Compose file to generate your own self-signed " "certificates. If you have certificates, you can continue with Step 2." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:62 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:215 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:51 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:221 msgid "These certificates should be used only for development purposes." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:64 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:53 msgid "" "For production environments, you may have to use dedicated services to " "obtain your certificates." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:67 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:56 msgid "" "First, set the environment variables ``SUPERLINK_IP`` and " "``SUPEREXEC_IP`` with the IP address from the remote machine. For " "example, if the IP is ``192.168.2.33``, execute:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:76 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:65 msgid "Next, generate the self-signed certificates:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:84 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:72 msgid "Step 2: Copy the Server Compose Files" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:86 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:74 msgid "" "Use the method that works best for you to copy the ``server`` directory, " "the certificates, and your Flower project to the remote machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:89 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:77 msgid "For example, you can use ``scp`` to copy the directories:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:100 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:87 #, fuzzy msgid "Step 3: Start the Flower Server Components" msgstr "Démarrer le serveur" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:102 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:89 msgid "" "Log into the remote machine using ``ssh`` and run the following command " "to start the SuperLink and SuperExec services:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:115 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:102 msgid "" "The Path of the ``PROJECT_DIR`` should be relative to the location of the" " ``server`` Docker Compose files." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:118 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:105 msgid "Go back to your terminal on your local machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:122 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:108 #, fuzzy msgid "Step 4: Start the Flower Client Components" msgstr "Démarrer le serveur" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:124 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:110 msgid "" "On your local machine, run the following command to start the client " "components:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:135 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:120 msgid "" "The Path of the ``PROJECT_DIR`` should be relative to the location of the" " ``client`` Docker Compose files." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:140 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:124 #, fuzzy msgid "Step 5: Run Your Flower Project" msgstr "Serveur de Flower" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:142 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:126 msgid "" "Specify the remote SuperExec IP addresses and the path to the root " "certificate in the ``[tool.flwr.federations.remote-superexec]`` table in " @@ -3517,37 +3525,37 @@ msgid "" "``remote-superexec``:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:147 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:130 #, fuzzy msgid "examples/quickstart-sklearn-tabular/pyproject.toml" msgstr "Démarrage rapide de scikit-learn" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:156 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:139 msgid "" "The Path of the ``root-certificates`` should be relative to the location " "of the ``pyproject.toml`` file." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:159 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:142 msgid "To run the project, execute:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:165 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:148 msgid "" "That's it! With these steps, you've set up Flower on two separate " "machines and are ready to start using it." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:170 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:152 msgid "Step 6: Clean Up" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:172 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:154 #, fuzzy msgid "Shut down the Flower client components:" msgstr "Client de Flower" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:179 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:161 msgid "Shut down the Flower server components and delete the SuperLink state:" msgstr "" @@ -3569,7 +3577,7 @@ msgid "" " understanding the basic workflow that uses the minimum configurations." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:31 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:32 #: ../../source/docker/tutorial-quickstart-docker.rst:21 msgid "Create a new Flower project (PyTorch):" msgstr "" @@ -3591,7 +3599,7 @@ msgstr "" msgid "Step 2: Start the SuperLink" msgstr "Démarrer le serveur" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:60 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:62 #: ../../source/docker/tutorial-quickstart-docker.rst:52 #, fuzzy msgid "Open your terminal and run:" @@ -3618,8 +3626,8 @@ msgstr "" #: ../../source/docker/tutorial-quickstart-docker.rst:71 #: ../../source/docker/tutorial-quickstart-docker.rst:108 -#: ../../source/docker/tutorial-quickstart-docker.rst:215 -#: ../../source/docker/tutorial-quickstart-docker.rst:304 +#: ../../source/docker/tutorial-quickstart-docker.rst:219 +#: ../../source/docker/tutorial-quickstart-docker.rst:309 msgid "" "``--network flwr-network``: Make the container join the network named " "``flwr-network``." @@ -3631,8 +3639,8 @@ msgstr "" #: ../../source/docker/tutorial-quickstart-docker.rst:73 #: ../../source/docker/tutorial-quickstart-docker.rst:110 -#: ../../source/docker/tutorial-quickstart-docker.rst:216 -#: ../../source/docker/tutorial-quickstart-docker.rst:306 +#: ../../source/docker/tutorial-quickstart-docker.rst:220 +#: ../../source/docker/tutorial-quickstart-docker.rst:311 msgid "" "``--detach``: Run the container in the background, freeing up the " "terminal." @@ -3750,13 +3758,13 @@ msgid "" "extends the ClientApp image and installs the required dependencies." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:148 +#: ../../source/docker/tutorial-quickstart-docker.rst:149 msgid "" "Create a ClientApp Dockerfile called ``Dockerfile.clientapp`` and paste " "the following code into it:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:150 +#: ../../source/docker/tutorial-quickstart-docker.rst:152 #, fuzzy msgid "Dockerfile.clientapp" msgstr "Flower ClientApp." @@ -3839,7 +3847,7 @@ msgstr "" msgid "the default command run when the container is started." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:184 +#: ../../source/docker/tutorial-quickstart-docker.rst:186 msgid "" "Note that `flwr `__ is already installed " "in the ``flwr/clientapp`` base image, so only other package dependencies " @@ -3848,20 +3856,20 @@ msgid "" "after it has been copied into the Docker image (see line 5)." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:189 +#: ../../source/docker/tutorial-quickstart-docker.rst:192 msgid "" "Next, build the ClientApp Docker image by running the following command " "in the directory where the Dockerfile is located:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:198 +#: ../../source/docker/tutorial-quickstart-docker.rst:201 msgid "" "The image name was set as ``flwr_clientapp`` with the tag ``0.0.1``. " "Remember that these values are merely examples, and you can customize " "them according to your requirements." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:201 +#: ../../source/docker/tutorial-quickstart-docker.rst:205 #, fuzzy msgid "Start the first ClientApp container:" msgstr "Utilisation du moteur du client virtuel" @@ -3882,34 +3890,34 @@ msgstr "" msgid "``supernode-1:9094``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:222 +#: ../../source/docker/tutorial-quickstart-docker.rst:226 msgid "Start the second ClientApp container:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:233 +#: ../../source/docker/tutorial-quickstart-docker.rst:237 #, fuzzy msgid "Step 5: Start the SuperExec" msgstr "Démarrer le serveur" -#: ../../source/docker/tutorial-quickstart-docker.rst:235 +#: ../../source/docker/tutorial-quickstart-docker.rst:239 msgid "" "The procedure for building and running a SuperExec image is almost " "identical to the ClientApp image." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:237 +#: ../../source/docker/tutorial-quickstart-docker.rst:242 msgid "" "Similar to the ClientApp image, you will need to create a Dockerfile that" " extends the SuperExec image and installs the required FAB dependencies." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:240 +#: ../../source/docker/tutorial-quickstart-docker.rst:245 msgid "" "Create a SuperExec Dockerfile called ``Dockerfile.superexec`` and paste " "the following code in:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:242 +#: ../../source/docker/tutorial-quickstart-docker.rst:248 msgid "Dockerfile.superexec" msgstr "" @@ -3939,13 +3947,13 @@ msgstr "" msgid "``flwr.superexec.deployment:executor`` executor to run the ServerApps." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:277 +#: ../../source/docker/tutorial-quickstart-docker.rst:283 msgid "" "Afterward, in the directory that holds the Dockerfile, execute this " "Docker command to build the SuperExec image:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:285 +#: ../../source/docker/tutorial-quickstart-docker.rst:290 #, fuzzy msgid "Start the SuperExec container:" msgstr "Démarrer le serveur" @@ -3960,7 +3968,7 @@ msgid "" "``http://localhost:9093``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:305 +#: ../../source/docker/tutorial-quickstart-docker.rst:310 msgid "``--name superexec``: Assign the name ``superexec`` to the container." msgstr "" @@ -3980,79 +3988,79 @@ msgstr "" msgid "connect to the SuperLink running on port ``9091``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:315 +#: ../../source/docker/tutorial-quickstart-docker.rst:320 msgid "Step 6: Run the Quickstart Project" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:317 +#: ../../source/docker/tutorial-quickstart-docker.rst:322 #, fuzzy msgid "Add the following lines to the ``pyproject.toml``:" msgstr "Augmente la version mineure de ``pyproject.toml`` d'une unité." -#: ../../source/docker/tutorial-quickstart-docker.rst:326 +#: ../../source/docker/tutorial-quickstart-docker.rst:331 msgid "Run the ``quickstart-docker`` project by executing the command:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:332 +#: ../../source/docker/tutorial-quickstart-docker.rst:337 msgid "Follow the SuperExec logs to track the execution of the run:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:339 +#: ../../source/docker/tutorial-quickstart-docker.rst:344 #, fuzzy msgid "Step 7: Update the Application" msgstr "Étape 3 : Sérialisation personnalisée" -#: ../../source/docker/tutorial-quickstart-docker.rst:341 +#: ../../source/docker/tutorial-quickstart-docker.rst:346 msgid "" -"Change the application code. For example, change the ``seed`` in " +"Change the application code. For example, change the ``seed`` in " "``quickstart_docker/task.py`` to ``43`` and save it:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:344 +#: ../../source/docker/tutorial-quickstart-docker.rst:349 #, fuzzy msgid "quickstart_docker/task.py" msgstr "Démarrage rapide des Pandas" -#: ../../source/docker/tutorial-quickstart-docker.rst:351 +#: ../../source/docker/tutorial-quickstart-docker.rst:356 msgid "Stop the current ClientApp containers:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:357 +#: ../../source/docker/tutorial-quickstart-docker.rst:362 #, fuzzy msgid "Rebuild the FAB and ClientApp image:" msgstr "Chargement des données" -#: ../../source/docker/tutorial-quickstart-docker.rst:363 +#: ../../source/docker/tutorial-quickstart-docker.rst:368 msgid "Launch two new ClientApp containers based on the newly built image:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:378 +#: ../../source/docker/tutorial-quickstart-docker.rst:383 msgid "Run the updated project:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:385 +#: ../../source/docker/tutorial-quickstart-docker.rst:390 msgid "Step 8: Clean Up" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:387 +#: ../../source/docker/tutorial-quickstart-docker.rst:392 msgid "Remove the containers and the bridge network:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:401 -#: ../../source/docker/tutorial-quickstart-docker.rst:399 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:408 +#: ../../source/docker/tutorial-quickstart-docker.rst:404 #, fuzzy msgid "Where to Go Next" msgstr "Par où commencer" -#: ../../source/docker/tutorial-quickstart-docker.rst:401 +#: ../../source/docker/tutorial-quickstart-docker.rst:406 msgid ":doc:`enable-tls`" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:402 +#: ../../source/docker/tutorial-quickstart-docker.rst:407 msgid ":doc:`persist-superlink-state`" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:403 +#: ../../source/docker/tutorial-quickstart-docker.rst:408 msgid ":doc:`tutorial-quickstart-docker-compose`" msgstr "" @@ -4075,179 +4083,179 @@ msgid "" "configuration that best suits your project's needs." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:23 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:24 msgid "Clone the Docker Compose ``complete`` directory:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:37 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:38 msgid "" "Export the path of the newly created project. The path should be relative" " to the location of the Docker Compose files:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:44 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:45 msgid "" "Setting the ``PROJECT_DIR`` helps Docker Compose locate the " "``pyproject.toml`` file, allowing it to install dependencies in the " "SuperExec and SuperNode images correctly." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:48 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:49 #, fuzzy msgid "Step 2: Run Flower in Insecure Mode" msgstr "Serveur de Flower" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:50 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:51 msgid "" "To begin, start Flower with the most basic configuration. In this setup, " "Flower will run without TLS and without persisting the state." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:55 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:56 msgid "" "Without TLS, the data sent between the services remains **unencrypted**. " "Use it only for development purposes." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:58 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:59 msgid "" "For production-oriented use cases, :ref:`enable TLS` for secure data" " transmission." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:68 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:179 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:70 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:184 msgid "``docker compose``: The Docker command to run the Docker Compose tool." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:69 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:180 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:71 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:185 msgid "" "``-f compose.yml``: Specify the YAML file that contains the basic Flower " "service definitions." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:70 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:185 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:72 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:190 msgid "" "``--build``: Rebuild the images for each service if they don't already " "exist." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:71 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:186 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:73 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:191 msgid "" "``-d``: Detach the containers from the terminal and run them in the " "background." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:74 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:76 msgid "Step 3: Run the Quickstart Project" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:76 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:78 msgid "" "Now that the Flower services have been started via Docker Compose, it is " "time to run the quickstart example." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:79 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:81 msgid "" "To ensure the ``flwr`` CLI connects to the SuperExec, you need to specify" " the SuperExec addresses in the ``pyproject.toml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:82 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:226 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:84 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:232 msgid "Add the following lines to the ``quickstart-compose/pyproject.toml``:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:84 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:228 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:86 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:234 msgid "quickstart-compose/pyproject.toml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:91 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:93 msgid "Execute the command to run the quickstart example:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:97 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:99 msgid "Monitor the SuperExec logs and wait for the summary to appear:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:104 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:106 #, fuzzy msgid "Step 4: Update the Application" msgstr "Étape 3 : Sérialisation personnalisée" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:106 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:108 msgid "In the next step, change the application code." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:108 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:110 msgid "" "For example, go to the ``task.py`` file in the ``quickstart-" "compose/quickstart_compose/`` directory and add a ``print`` call in the " "``get_weights`` function:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:111 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:114 msgid "quickstart-compose/quickstart_compose/task.py" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:120 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:125 #, fuzzy msgid "Rebuild and restart the services." msgstr "Nous pouvons déjà démarrer le *serveur* :" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:124 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:129 msgid "" "If you have modified the dependencies listed in your ``pyproject.toml`` " "file, it is essential to rebuild images." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:127 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:132 msgid "If you haven't made any changes, you can skip this step." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:129 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:134 msgid "Run the following command to rebuild and restart the services:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:135 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:140 msgid "Run the updated quickstart example:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:142 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:147 msgid "In the SuperExec logs, you should find the ``Get weights`` line:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:159 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:164 msgid "Step 5: Persisting the SuperLink State" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:161 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:166 msgid "" "In this step, Flower services are configured to persist the state of the " "SuperLink service, ensuring that it maintains its state even after a " "restart." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:166 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:171 msgid "" "When working with Docker Compose on Linux, you may need to create the " "``state`` directory first and change its ownership to ensure proper " "access and permissions." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:169 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:174 msgid "" "For more information, consult the following page: :doc:`persist-" "superlink-state`." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:171 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:220 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:176 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:226 msgid "Run the command:" msgstr "" @@ -4268,17 +4276,17 @@ msgid "" "rules>`_." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:188 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:241 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:369 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:193 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:247 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:375 msgid "Rerun the ``quickstart-compose`` project:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:194 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:199 msgid "Check the content of the ``state`` directory:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:201 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:206 msgid "" "You should see a ``state.db`` file in the ``state`` directory. If you " "restart the service, the state file will be used to restore the state " @@ -4286,120 +4294,120 @@ msgid "" "if the containers are stopped and started again." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:208 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:214 msgid "Step 6: Run Flower with TLS" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:210 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:216 msgid "" "To demonstrate how to enable TLS, generate self-signed certificates using" " the ``certs.yml`` Compose file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:217 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:223 msgid "" "For production environments, use a service like `Let's Encrypt " "`_ to obtain your certificates." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:235 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:241 msgid "Restart the services with TLS enabled:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:249 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:255 msgid "Step 7: Add another SuperNode" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:251 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:257 msgid "" "You can add more SuperNodes and ClientApps by duplicating their " "definitions in the ``compose.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:254 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:260 msgid "" "Just give each new SuperNode and ClientApp service a unique service name " "like ``supernode-3``, ``clientapp-3``, etc." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:257 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:263 msgid "In ``compose.yml``, add the following:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:259 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:265 msgid "compose.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:310 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:316 msgid "" "If you also want to enable TLS for the new SuperNodes, duplicate the " "SuperNode definition for each new SuperNode service in the ``with-" "tls.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:313 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:319 msgid "" "Make sure that the names of the services match with the one in the " "``compose.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:315 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:321 msgid "In ``with-tls.yml``, add the following:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:317 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:323 msgid "with-tls.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:339 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:345 msgid "Step 8: Persisting the SuperLink State and Enabling TLS" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:341 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:347 msgid "" "To run Flower with persisted SuperLink state and enabled TLS, a slight " "change in the ``with-state.yml`` file is required:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:344 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:350 msgid "Comment out the lines 2-4 and uncomment the lines 5-9:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:346 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:352 msgid "with-state.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:363 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:369 #, fuzzy msgid "Restart the services:" msgstr "Démarrer le serveur" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:377 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:383 msgid "Step 9: Merge Multiple Compose Files" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:379 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:385 msgid "" "You can merge multiple Compose files into a single file. For instance, if" " you wish to combine the basic configuration with the TLS configuration, " "execute the following command:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:387 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:394 msgid "" "This will merge the contents of ``compose.yml`` and ``with-tls.yml`` into" " a new file called ``my_compose.yml``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:391 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:398 msgid "Step 10: Clean Up" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:393 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:400 msgid "Remove all services and volumes:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:403 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:410 #, fuzzy msgid ":doc:`run-quickstart-examples-docker-compose`" msgstr "Démarrage rapide XGBoost" @@ -4415,7 +4423,7 @@ msgid "" " `Docker Hub `__." msgstr "" -#: ../../source/docker/use-a-different-version.rst:9 +#: ../../source/docker/use-a-different-version.rst:10 msgid "" "When using Flower nightly, the SuperLink nightly image must be paired " "with the corresponding SuperNode and ServerApp nightly images released on" @@ -4450,18 +4458,18 @@ msgstr "" "fédération `_." -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:9 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:10 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:12 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:12 msgid "Centralized Training" msgstr "Formation centralisée" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:10 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:14 #, fuzzy msgid "" "All files are revised based on :doc:`Example: PyTorch - From Centralized " "To Federated `. The only " -"thing to do is modifying the file called :code:`cifar.py`, revised part " -"is shown below:" +"thing to do is modifying the file called ``cifar.py``, revised part is " +"shown below:" msgstr "" "Tous les fichiers sont révisés sur la base de `Exemple : PyTorch - From " "Centralized To Federated `, the following parts are" -" easy to follow, only :code:`get_parameters` and :code:`set_parameters` " -"function in :code:`client.py` needed to revise. If not, please read the " -":doc:`Example: PyTorch - From Centralized To Federated `. first." +" easy to follow, only ``get_parameters`` and ``set_parameters`` function " +"in ``client.py`` needed to revise. If not, please read the :doc:`Example:" +" PyTorch - From Centralized To Federated `. first." msgstr "" "Si vous avez lu `Exemple : PyTorch - From Centralized To Federated " "`. d'abord." -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:56 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:66 +#, fuzzy msgid "" "Our example consists of one *server* and two *clients*. In FedBN, " -":code:`server.py` keeps unchanged, we can start the server directly." +"``server.py`` keeps unchanged, we can start the server directly." msgstr "" "Notre exemple consiste en un *serveur* et deux *clients*. Dans FedBN, " ":code:`server.py` reste inchangé, nous pouvons démarrer le serveur " "directement." -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:62 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:73 +#, fuzzy msgid "" -"Finally, we will revise our *client* logic by changing " -":code:`get_parameters` and :code:`set_parameters` in :code:`client.py`, " -"we will exclude batch normalization parameters from model parameter list " -"when sending to or receiving from the server." +"Finally, we will revise our *client* logic by changing ``get_parameters``" +" and ``set_parameters`` in ``client.py``, we will exclude batch " +"normalization parameters from model parameter list when sending to or " +"receiving from the server." msgstr "" "Enfin, nous allons réviser notre logique *client* en modifiant " ":code:`get_parameters` et :code:`set_parameters` dans :code:`client.py`, " @@ -4543,11 +4553,11 @@ msgstr "" "des paramètres du modèle lors de l'envoi ou de la réception depuis le " "serveur." -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:85 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:102 msgid "Now, you can now open two additional terminal windows and run" msgstr "Tu peux maintenant ouvrir deux autres fenêtres de terminal et lancer" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:91 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:108 msgid "" "in each window (make sure that the server is still running before you do " "so) and see your (previously centralized) PyTorch project run federated " @@ -4558,13 +4568,13 @@ msgstr "" "(auparavant centralisé) exécuter l'apprentissage fédéré avec la stratégie" " FedBN sur deux clients. Félicitations !" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:94 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:310 -#: ../../source/tutorial-quickstart-jax.rst:283 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:113 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:349 +#: ../../source/tutorial-quickstart-jax.rst:319 msgid "Next Steps" msgstr "Prochaines étapes" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:96 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:115 #, fuzzy msgid "" "The full source code for this example can be found `here " @@ -4610,7 +4620,7 @@ msgstr "" "Ensuite, nous nous appuyons sur le code d'entraînement centralisé pour " "exécuter l'entraînement de manière fédérée." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:12 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:14 msgid "" "We begin with a brief description of the centralized CNN training code. " "If you want a more in-depth explanation of what's going on then have a " @@ -4622,14 +4632,15 @@ msgstr "" "passe, jette un coup d'œil au tutoriel officiel `PyTorch " "`_." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:15 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:18 +#, fuzzy msgid "" -"Let's create a new file called :code:`cifar.py` with all the components " +"Let's create a new file called ``cifar.py`` with all the components " "required for a traditional (centralized) training on CIFAR-10. First, all" -" required packages (such as :code:`torch` and :code:`torchvision`) need " -"to be imported. You can see that we do not import any package for " -"federated learning. You can keep all these imports as they are even when " -"we add the federated learning components at a later point." +" required packages (such as ``torch`` and ``torchvision``) need to be " +"imported. You can see that we do not import any package for federated " +"learning. You can keep all these imports as they are even when we add the" +" federated learning components at a later point." msgstr "" "Créons un nouveau fichier appelé :code:`cifar.py` avec tous les " "composants requis pour une formation traditionnelle (centralisée) sur le " @@ -4639,30 +4650,33 @@ msgstr "" "toutes ces importations telles quelles même lorsque nous ajouterons les " "composants d'apprentissage fédéré à un moment ultérieur." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:32 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:36 +#, fuzzy msgid "" "As already mentioned we will use the CIFAR-10 dataset for this machine " "learning workload. The model architecture (a very simple Convolutional " -"Neural Network) is defined in :code:`class Net()`." +"Neural Network) is defined in ``class Net()``." msgstr "" "Comme nous l'avons déjà mentionné, nous utiliserons l'ensemble de données" " CIFAR-10 pour cette charge de travail d'apprentissage automatique. " "L'architecture du modèle (un réseau neuronal convolutif très simple) est " "définie dans :code:`class Net()`." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:56 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:62 +#, fuzzy msgid "" -"The :code:`load_data()` function loads the CIFAR-10 training and test " -"sets. The :code:`transform` normalized the data after loading." +"The ``load_data()`` function loads the CIFAR-10 training and test sets. " +"The ``transform`` normalized the data after loading." msgstr "" "La fonction :code:`load_data()` charge les ensembles d'entraînement et de" " test CIFAR-10. La fonction :code:`transform` normalise les données après" " leur chargement." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:74 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:84 +#, fuzzy msgid "" -"We now need to define the training (function :code:`train()`) which loops" -" over the training set, measures the loss, backpropagates it, and then " +"We now need to define the training (function ``train()``) which loops " +"over the training set, measures the loss, backpropagates it, and then " "takes one optimizer step for each batch of training examples." msgstr "" "Nous devons maintenant définir la formation (fonction :code:`train()`) " @@ -4670,17 +4684,18 @@ msgstr "" "rétropropage, puis effectue une étape d'optimisation pour chaque lot " "d'exemples de formation." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:76 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:88 +#, fuzzy msgid "" -"The evaluation of the model is defined in the function :code:`test()`. " -"The function loops over all test samples and measures the loss of the " -"model based on the test dataset." +"The evaluation of the model is defined in the function ``test()``. The " +"function loops over all test samples and measures the loss of the model " +"based on the test dataset." msgstr "" "L'évaluation du modèle est définie dans la fonction :code:`test()`. La " "fonction boucle sur tous les échantillons de test et mesure la perte du " "modèle en fonction de l'ensemble des données de test." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:136 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:149 msgid "" "Having defined the data loading, model architecture, training, and " "evaluation we can put everything together and train our CNN on CIFAR-10." @@ -4689,7 +4704,7 @@ msgstr "" "la formation et l'évaluation, nous pouvons tout mettre ensemble et former" " notre CNN sur CIFAR-10." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:163 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:177 msgid "" "So far, this should all look fairly familiar if you've used PyTorch " "before. Let's take the next step and use what we've built to create a " @@ -4701,7 +4716,7 @@ msgstr "" " avons construit pour créer un simple système d'apprentissage fédéré " "composé d'un serveur et de deux clients." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:169 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:184 msgid "" "The simple machine learning project discussed in the previous section " "trains the model on a single dataset (CIFAR-10), we call this centralized" @@ -4721,7 +4736,7 @@ msgstr "" "changer la plupart de ton code et tout mettre en place à partir de zéro, " "ce qui peut représenter un effort considérable." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:173 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:191 msgid "" "However, with Flower you can evolve your pre-existing code into a " "federated learning setup without the need for a major rewrite." @@ -4730,11 +4745,12 @@ msgstr "" "une configuration d'apprentissage fédéré sans avoir besoin d'une " "réécriture majeure." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:175 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:194 +#, fuzzy msgid "" "The concept is easy to understand. We have to start a *server* and then " -"use the code in :code:`cifar.py` for the *clients* that are connected to " -"the *server*. The *server* sends model parameters to the clients. The " +"use the code in ``cifar.py`` for the *clients* that are connected to the " +"*server*. The *server* sends model parameters to the clients. The " "*clients* run the training and update the parameters. The updated " "parameters are sent back to the *server* which averages all received " "parameter updates. This describes one round of the federated learning " @@ -4749,13 +4765,14 @@ msgstr "" "un tour du processus d'apprentissage fédéré et nous répétons cette " "opération pour plusieurs tours." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:181 -#: ../../source/tutorial-quickstart-jax.rst:129 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:201 +#: ../../source/tutorial-quickstart-jax.rst:147 +#, fuzzy msgid "" "Our example consists of one *server* and two *clients*. Let's set up " -":code:`server.py` first. The *server* needs to import the Flower package " -":code:`flwr`. Next, we use the :code:`start_server` function to start a " -"server and tell it to perform three rounds of federated learning." +"``server.py`` first. The *server* needs to import the Flower package " +"``flwr``. Next, we use the ``start_server`` function to start a server " +"and tell it to perform three rounds of federated learning." msgstr "" "Notre exemple consiste en un *serveur* et deux *clients*. Commençons par " "configurer :code:`server.py`. Le *serveur* doit importer le paquet Flower" @@ -4763,35 +4780,36 @@ msgstr "" "pour démarrer un serveur et lui demander d'effectuer trois cycles " "d'apprentissage fédéré." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:191 -#: ../../source/tutorial-quickstart-jax.rst:139 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:215 +#: ../../source/tutorial-quickstart-jax.rst:161 msgid "We can already start the *server*:" msgstr "Nous pouvons déjà démarrer le *serveur* :" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:197 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:221 +#, fuzzy msgid "" -"Finally, we will define our *client* logic in :code:`client.py` and build" -" upon the previously defined centralized training in :code:`cifar.py`. " -"Our *client* needs to import :code:`flwr`, but also :code:`torch` to " -"update the parameters on our PyTorch model:" +"Finally, we will define our *client* logic in ``client.py`` and build " +"upon the previously defined centralized training in ``cifar.py``. Our " +"*client* needs to import ``flwr``, but also ``torch`` to update the " +"parameters on our PyTorch model:" msgstr "" "Enfin, nous allons définir notre logique *client* dans :code:`client.py` " "et nous appuyer sur la formation centralisée définie précédemment dans " ":code:`cifar.py`. Notre *client* doit importer :code:`flwr`, mais aussi " ":code:`torch` pour mettre à jour les paramètres de notre modèle PyTorch :" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:213 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:238 +#, fuzzy msgid "" "Implementing a Flower *client* basically means implementing a subclass of" -" either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. " -"Our implementation will be based on :code:`flwr.client.NumPyClient` and " -"we'll call it :code:`CifarClient`. :code:`NumPyClient` is slightly easier" -" to implement than :code:`Client` if you use a framework with good NumPy " -"interoperability (like PyTorch or TensorFlow/Keras) because it avoids " -"some of the boilerplate that would otherwise be necessary. " -":code:`CifarClient` needs to implement four methods, two methods for " -"getting/setting model parameters, one method for training the model, and " -"one method for testing the model:" +" either ``flwr.client.Client`` or ``flwr.client.NumPyClient``. Our " +"implementation will be based on ``flwr.client.NumPyClient`` and we'll " +"call it ``CifarClient``. ``NumPyClient`` is slightly easier to implement " +"than ``Client`` if you use a framework with good NumPy interoperability " +"(like PyTorch or TensorFlow/Keras) because it avoids some of the " +"boilerplate that would otherwise be necessary. ``CifarClient`` needs to " +"implement four methods, two methods for getting/setting model parameters," +" one method for training the model, and one method for testing the model:" msgstr "" "Implementing a Flower *client* basically means implementing a subclass of" " either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. " @@ -4804,52 +4822,56 @@ msgstr "" "getting/setting model parameters, one method for training the model, and " "one method for testing the model:" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:218 -msgid ":code:`set_parameters`" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:249 +#, fuzzy +msgid "``set_parameters``" msgstr ":code:`set_parameters`" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:219 -#: ../../source/tutorial-quickstart-jax.rst:166 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:248 +#: ../../source/tutorial-quickstart-jax.rst:192 msgid "" "set the model parameters on the local model that are received from the " "server" msgstr "règle les paramètres du modèle local reçus du serveur" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:220 -#: ../../source/tutorial-quickstart-jax.rst:168 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:249 +#: ../../source/tutorial-quickstart-jax.rst:194 +#, fuzzy msgid "" -"loop over the list of model parameters received as NumPy " -":code:`ndarray`'s (think list of neural network layers)" +"loop over the list of model parameters received as NumPy ``ndarray``'s " +"(think list of neural network layers)" msgstr "" "boucle sur la liste des paramètres du modèle reçus sous forme de NumPy " ":code:`ndarray`'s (pensez à la liste des couches du réseau neuronal)" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:221 -#: ../../source/tutorial-quickstart-jax.rst:169 -#: ../../source/tutorial-quickstart-scikitlearn.rst:118 -msgid ":code:`get_parameters`" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:252 +#: ../../source/tutorial-quickstart-jax.rst:197 +#: ../../source/tutorial-quickstart-scikitlearn.rst:129 +#, fuzzy +msgid "``get_parameters``" msgstr ":code:`get_parameters`" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:222 -#: ../../source/tutorial-quickstart-jax.rst:170 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:252 +#: ../../source/tutorial-quickstart-jax.rst:197 +#, fuzzy msgid "" -"get the model parameters and return them as a list of NumPy " -":code:`ndarray`'s (which is what :code:`flwr.client.NumPyClient` expects)" +"get the model parameters and return them as a list of NumPy ``ndarray``'s" +" (which is what ``flwr.client.NumPyClient`` expects)" msgstr "" "récupère les paramètres du modèle et les renvoie sous forme de liste de " ":code:`ndarray` NumPy (ce qui correspond à ce que " ":code:`flwr.client.NumPyClient` attend)" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:223 -#: ../../source/tutorial-quickstart-jax.rst:171 -#: ../../source/tutorial-quickstart-scikitlearn.rst:123 -msgid ":code:`fit`" -msgstr ":code:`fit`" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:257 +#: ../../source/tutorial-quickstart-jax.rst:202 +#: ../../source/tutorial-quickstart-scikitlearn.rst:136 +msgid "``fit``" +msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:224 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:228 -#: ../../source/tutorial-quickstart-jax.rst:172 -#: ../../source/tutorial-quickstart-jax.rst:176 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:255 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:260 +#: ../../source/tutorial-quickstart-jax.rst:200 +#: ../../source/tutorial-quickstart-jax.rst:205 msgid "" "update the parameters of the local model with the parameters received " "from the server" @@ -4857,39 +4879,40 @@ msgstr "" "mettre à jour les paramètres du modèle local avec les paramètres reçus du" " serveur" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:225 -#: ../../source/tutorial-quickstart-jax.rst:173 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:257 +#: ../../source/tutorial-quickstart-jax.rst:202 msgid "train the model on the local training set" msgstr "entraîne le modèle sur l'ensemble d'apprentissage local" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:226 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:258 msgid "get the updated local model weights and return them to the server" msgstr "récupère les poids du modèle local mis à jour et les renvoie au serveur" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:227 -#: ../../source/tutorial-quickstart-jax.rst:175 -#: ../../source/tutorial-quickstart-scikitlearn.rst:127 -msgid ":code:`evaluate`" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:263 +#: ../../source/tutorial-quickstart-jax.rst:208 +#: ../../source/tutorial-quickstart-scikitlearn.rst:139 +#, fuzzy +msgid "``evaluate``" msgstr ":code:`évaluer`" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:229 -#: ../../source/tutorial-quickstart-jax.rst:177 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:262 +#: ../../source/tutorial-quickstart-jax.rst:207 msgid "evaluate the updated model on the local test set" msgstr "évaluer le modèle mis à jour sur l'ensemble de test local" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:263 msgid "return the local loss and accuracy to the server" msgstr "renvoie la perte locale et la précision au serveur" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:232 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:265 +#, fuzzy msgid "" -"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make" -" use of the functions :code:`train()` and :code:`test()` previously " -"defined in :code:`cifar.py`. So what we really do here is we tell Flower " -"through our :code:`NumPyClient` subclass which of our already defined " -"functions to call for training and evaluation. We included type " -"annotations to give you a better understanding of the data types that get" -" passed around." +"The two ``NumPyClient`` methods ``fit`` and ``evaluate`` make use of the " +"functions ``train()`` and ``test()`` previously defined in ``cifar.py``. " +"So what we really do here is we tell Flower through our ``NumPyClient`` " +"subclass which of our already defined functions to call for training and " +"evaluation. We included type annotations to give you a better " +"understanding of the data types that get passed around." msgstr "" "Les deux méthodes :code:`NumPyClient` :code:`fit` et :code:`evaluate` " "utilisent les fonctions :code:`train()` et :code:`test()` définies " @@ -4900,14 +4923,14 @@ msgstr "" "annotations de type pour te donner une meilleure compréhension des types " "de données qui sont transmis." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:280 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:315 #, fuzzy msgid "" "All that's left to do it to define a function that loads both model and " -"data, creates a :code:`CifarClient`, and starts this client. You load " -"your data and model by using :code:`cifar.py`. Start :code:`CifarClient` " -"with the function :code:`fl.client.start_client()` by pointing it at the " -"same IP address we used in :code:`server.py`:" +"data, creates a ``CifarClient``, and starts this client. You load your " +"data and model by using ``cifar.py``. Start ``CifarClient`` with the " +"function ``fl.client.start_client()`` by pointing it at the same IP " +"address we used in ``server.py``:" msgstr "" "Il ne reste plus qu'à définir une fonction qui charge le modèle et les " "données, crée un :code:`CifarClient` et démarre ce client. Tu charges tes" @@ -4916,14 +4939,14 @@ msgstr "" "la faisant pointer sur la même adresse IP que celle que nous avons " "utilisée dans :code:`server.py` :" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:301 -#: ../../source/tutorial-quickstart-jax.rst:274 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:338 +#: ../../source/tutorial-quickstart-jax.rst:309 msgid "And that's it. You can now open two additional terminal windows and run" msgstr "" "Tu peux maintenant ouvrir deux autres fenêtres de terminal et exécuter " "les commandes suivantes" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:307 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:344 msgid "" "in each window (make sure that the server is running before you do so) " "and see your (previously centralized) PyTorch project run federated " @@ -4933,7 +4956,7 @@ msgstr "" "faire) et tu verras ton projet PyTorch (auparavant centralisé) exécuter " "l'apprentissage fédéré sur deux clients. Félicitations !" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:312 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:351 #, fuzzy msgid "" "The full source code for this example: `PyTorch: From Centralized To " @@ -4954,13 +4977,13 @@ msgstr "" "d'autres clients ?" #: ../../source/explanation-differential-privacy.rst:2 -#: ../../source/explanation-differential-privacy.rst:11 +#: ../../source/explanation-differential-privacy.rst:14 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:303 #, fuzzy msgid "Differential Privacy" msgstr "Confidentialité différentielle" -#: ../../source/explanation-differential-privacy.rst:3 +#: ../../source/explanation-differential-privacy.rst:4 msgid "" "The information in datasets like healthcare, financial transactions, user" " preferences, etc., is valuable and has the potential for scientific " @@ -4969,7 +4992,7 @@ msgid "" "privacy." msgstr "" -#: ../../source/explanation-differential-privacy.rst:6 +#: ../../source/explanation-differential-privacy.rst:9 msgid "" "Traditional methods like anonymization alone would not work because of " "attacks like Re-identification and Data Linkage. That's where " @@ -4977,7 +5000,7 @@ msgid "" "data while ensuring the privacy of individuals." msgstr "" -#: ../../source/explanation-differential-privacy.rst:12 +#: ../../source/explanation-differential-privacy.rst:16 msgid "" "Imagine two datasets that are identical except for a single record (for " "instance, Alice's data). Differential Privacy (DP) guarantees that any " @@ -4987,11 +5010,11 @@ msgid "" " individual's information remains hidden in the crowd." msgstr "" -#: ../../source/explanation-differential-privacy.rst:16 +#: ../../source/explanation-differential-privacy.rst:-1 msgid "DP Intro" msgstr "" -#: ../../source/explanation-differential-privacy.rst:22 +#: ../../source/explanation-differential-privacy.rst:27 msgid "" "One of the most commonly used mechanisms to achieve DP is adding enough " "noise to the output of the analysis to mask the contribution of each " @@ -4999,12 +5022,12 @@ msgid "" "analysis." msgstr "" -#: ../../source/explanation-differential-privacy.rst:25 +#: ../../source/explanation-differential-privacy.rst:32 #, fuzzy msgid "Formal Definition" msgstr "Compiler les définitions ProtoBuf" -#: ../../source/explanation-differential-privacy.rst:26 +#: ../../source/explanation-differential-privacy.rst:34 msgid "" "Differential Privacy (DP) provides statistical guarantees against the " "information an adversary can infer through the output of a randomized " @@ -5016,13 +5039,13 @@ msgid "" "record, and for all possible outputs S ⊆ Range(A):" msgstr "" -#: ../../source/explanation-differential-privacy.rst:32 +#: ../../source/explanation-differential-privacy.rst:42 msgid "" "\\small\n" "P[M(D_{1} \\in A)] \\leq e^{\\epsilon} P[M(D_{2} \\in A)] + \\delta" msgstr "" -#: ../../source/explanation-differential-privacy.rst:38 +#: ../../source/explanation-differential-privacy.rst:47 msgid "" "The :math:`\\epsilon` parameter, also known as the privacy budget, is a " "metric of privacy loss. It also controls the privacy-utility trade-off; " @@ -5034,12 +5057,12 @@ msgid "" " change in the output due to the inclusion or removal of a single record." msgstr "" -#: ../../source/explanation-differential-privacy.rst:45 +#: ../../source/explanation-differential-privacy.rst:56 #, fuzzy msgid "Differential Privacy in Machine Learning" msgstr "Confidentialité différentielle" -#: ../../source/explanation-differential-privacy.rst:46 +#: ../../source/explanation-differential-privacy.rst:58 msgid "" "DP can be utilized in machine learning to preserve the privacy of the " "training data. Differentially private machine learning algorithms are " @@ -5054,12 +5077,12 @@ msgid "" "model's output." msgstr "" -#: ../../source/explanation-differential-privacy.rst:53 +#: ../../source/explanation-differential-privacy.rst:69 #, fuzzy msgid "Differential Privacy in Federated Learning" msgstr "Mise à l'échelle de l'apprentissage fédéré" -#: ../../source/explanation-differential-privacy.rst:54 +#: ../../source/explanation-differential-privacy.rst:71 msgid "" "Federated learning is a data minimization approach that allows multiple " "parties to collaboratively train a model without sharing their raw data. " @@ -5070,13 +5093,13 @@ msgid "" "attacks." msgstr "" -#: ../../source/explanation-differential-privacy.rst:58 +#: ../../source/explanation-differential-privacy.rst:78 msgid "" "DP can play a crucial role in federated learning to provide privacy for " "the clients' data." msgstr "" -#: ../../source/explanation-differential-privacy.rst:60 +#: ../../source/explanation-differential-privacy.rst:81 msgid "" "Depending on the granularity of privacy provision or the location of " "noise addition, different forms of DP exist in federated learning. In " @@ -5085,14 +5108,14 @@ msgid "" " the center) or at the client (also known as the local)." msgstr "" -#: ../../source/explanation-differential-privacy.rst:63 +#: ../../source/explanation-differential-privacy.rst:86 msgid "" "**Central Differential Privacy**: DP is applied by the server and the " "goal is to prevent the aggregated model from leaking information about " "each client's data." msgstr "" -#: ../../source/explanation-differential-privacy.rst:65 +#: ../../source/explanation-differential-privacy.rst:88 msgid "" "**Local Differential Privacy**: DP is applied on the client side before " "sending any information to the server and the goal is to prevent the " @@ -5100,21 +5123,21 @@ msgid "" "the client's data." msgstr "" -#: ../../source/explanation-differential-privacy.rst:68 -#: ../../source/explanation-differential-privacy.rst:71 -#: ../../source/how-to-use-differential-privacy.rst:11 +#: ../../source/explanation-differential-privacy.rst:-1 +#: ../../source/explanation-differential-privacy.rst:93 +#: ../../source/how-to-use-differential-privacy.rst:15 #, fuzzy msgid "Central Differential Privacy" msgstr "Confidentialité différentielle" -#: ../../source/explanation-differential-privacy.rst:69 +#: ../../source/explanation-differential-privacy.rst:95 msgid "" "In this approach, which is also known as user-level DP, the central " "server is responsible for adding noise to the globally aggregated " "parameters. It should be noted that trust in the server is required." msgstr "" -#: ../../source/explanation-differential-privacy.rst:76 +#: ../../source/explanation-differential-privacy.rst:104 msgid "" "While there are various ways to implement central DP in federated " "learning, we concentrate on the algorithms proposed by [2] and [3]. The " @@ -5129,11 +5152,11 @@ msgid "" "that larger updates are scaled down to fit within the norm `S`." msgstr "" -#: ../../source/explanation-differential-privacy.rst:84 +#: ../../source/explanation-differential-privacy.rst:-1 msgid "clipping" msgstr "" -#: ../../source/explanation-differential-privacy.rst:89 +#: ../../source/explanation-differential-privacy.rst:120 msgid "" "Afterwards, the Gaussian mechanism is used to add noise in order to " "distort the sum of all clients' updates. The amount of noise is scaled to" @@ -5142,24 +5165,24 @@ msgid "" "noise_scale * S ) / (number of sampled clients)`." msgstr "" -#: ../../source/explanation-differential-privacy.rst:94 +#: ../../source/explanation-differential-privacy.rst:126 msgid "Clipping" msgstr "" -#: ../../source/explanation-differential-privacy.rst:96 +#: ../../source/explanation-differential-privacy.rst:128 msgid "" "There are two forms of clipping commonly used in Central DP: Fixed " "Clipping and Adaptive Clipping." msgstr "" -#: ../../source/explanation-differential-privacy.rst:98 +#: ../../source/explanation-differential-privacy.rst:131 msgid "" "**Fixed Clipping** : A predefined fix threshold is set for the magnitude " "of clients' updates. Any update exceeding this threshold is clipped back " "to the threshold value." msgstr "" -#: ../../source/explanation-differential-privacy.rst:100 +#: ../../source/explanation-differential-privacy.rst:133 msgid "" "**Adaptive Clipping** : The clipping threshold dynamically adjusts based " "on the observed update distribution [4]. It means that the clipping value" @@ -5167,21 +5190,21 @@ msgid "" "norm distribution." msgstr "" -#: ../../source/explanation-differential-privacy.rst:102 +#: ../../source/explanation-differential-privacy.rst:137 msgid "" "The choice between fixed and adaptive clipping depends on various factors" " such as privacy requirements, data distribution, model complexity, and " "others." msgstr "" -#: ../../source/explanation-differential-privacy.rst:105 -#: ../../source/explanation-differential-privacy.rst:110 -#: ../../source/how-to-use-differential-privacy.rst:96 +#: ../../source/explanation-differential-privacy.rst:-1 +#: ../../source/explanation-differential-privacy.rst:141 +#: ../../source/how-to-use-differential-privacy.rst:113 #, fuzzy msgid "Local Differential Privacy" msgstr "Confidentialité différentielle" -#: ../../source/explanation-differential-privacy.rst:107 +#: ../../source/explanation-differential-privacy.rst:143 msgid "" "In this approach, each client is responsible for performing DP. Local DP " "avoids the need for a fully trusted aggregator, but it should be noted " @@ -5189,11 +5212,11 @@ msgid "" "comparison to central DP." msgstr "" -#: ../../source/explanation-differential-privacy.rst:116 +#: ../../source/explanation-differential-privacy.rst:152 msgid "In this explainer, we focus on two forms of achieving Local DP:" msgstr "" -#: ../../source/explanation-differential-privacy.rst:118 +#: ../../source/explanation-differential-privacy.rst:154 msgid "" "Each client adds noise to the local updates before sending them to the " "server. To achieve (:math:`\\epsilon`, :math:`\\delta`)-DP, considering " @@ -5201,37 +5224,36 @@ msgid "" "with a noise scale of σ where:" msgstr "" -#: ../../source/explanation-differential-privacy.rst:120 +#: ../../source/explanation-differential-privacy.rst:158 msgid "" "\\small\n" "\\frac{∆ \\times \\sqrt{2 \\times " -"\\log\\left(\\frac{1.25}{\\delta}\\right)}}{\\epsilon}\n" -"\n" +"\\log\\left(\\frac{1.25}{\\delta}\\right)}}{\\epsilon}" msgstr "" -#: ../../source/explanation-differential-privacy.rst:125 +#: ../../source/explanation-differential-privacy.rst:163 msgid "" "Each client adds noise to the gradients of the model during the local " "training (DP-SGD). More specifically, in this approach, gradients are " "clipped and an amount of calibrated noise is injected into the gradients." msgstr "" -#: ../../source/explanation-differential-privacy.rst:128 +#: ../../source/explanation-differential-privacy.rst:167 msgid "" "Please note that these two approaches are providing privacy at different " "levels." msgstr "" -#: ../../source/explanation-differential-privacy.rst:131 +#: ../../source/explanation-differential-privacy.rst:169 #, fuzzy msgid "**References:**" msgstr "Référence" -#: ../../source/explanation-differential-privacy.rst:133 +#: ../../source/explanation-differential-privacy.rst:171 msgid "[1] Dwork et al. The Algorithmic Foundations of Differential Privacy." msgstr "" -#: ../../source/explanation-differential-privacy.rst:135 +#: ../../source/explanation-differential-privacy.rst:173 #, fuzzy msgid "" "[2] McMahan et al. Learning Differentially Private Recurrent Language " @@ -5240,13 +5262,13 @@ msgstr "" "McMahan, H. Brendan, et al. \"Learning differentially private recurrent " "language models\", arXiv preprint arXiv:1710.06963 (2017)." -#: ../../source/explanation-differential-privacy.rst:137 +#: ../../source/explanation-differential-privacy.rst:175 msgid "" "[3] Geyer et al. Differentially Private Federated Learning: A Client " "Level Perspective." msgstr "" -#: ../../source/explanation-differential-privacy.rst:139 +#: ../../source/explanation-differential-privacy.rst:177 #, fuzzy msgid "[4] Galen et al. Differentially Private Learning with Adaptive Clipping." msgstr "" @@ -5290,17 +5312,17 @@ msgstr "" "prendre les paramètres du modèle global actuel comme entrée et renvoyer " "les résultats de l'évaluation :" -#: ../../source/explanation-federated-evaluation.rst:58 +#: ../../source/explanation-federated-evaluation.rst:61 msgid "Custom Strategies" msgstr "Stratégies personnalisées" -#: ../../source/explanation-federated-evaluation.rst:60 +#: ../../source/explanation-federated-evaluation.rst:63 +#, fuzzy msgid "" -"The :code:`Strategy` abstraction provides a method called " -":code:`evaluate` that can directly be used to evaluate the current global" -" model parameters. The current server implementation calls " -":code:`evaluate` after parameter aggregation and before federated " -"evaluation (see next paragraph)." +"The ``Strategy`` abstraction provides a method called ``evaluate`` that " +"can directly be used to evaluate the current global model parameters. The" +" current server implementation calls ``evaluate`` after parameter " +"aggregation and before federated evaluation (see next paragraph)." msgstr "" "L'abstraction :code:`Strategy` fournit une méthode appelée " ":code:`evaluate` qui peut être directement utilisée pour évaluer les " @@ -5308,27 +5330,28 @@ msgstr "" "appelle :code:`evaluate` après l'agrégation des paramètres et avant " "l'évaluation fédérée (voir le paragraphe suivant)." -#: ../../source/explanation-federated-evaluation.rst:65 +#: ../../source/explanation-federated-evaluation.rst:69 msgid "Federated Evaluation" msgstr "Évaluation fédérée" -#: ../../source/explanation-federated-evaluation.rst:68 +#: ../../source/explanation-federated-evaluation.rst:72 msgid "Implementing Federated Evaluation" msgstr "Mise en œuvre de l'évaluation fédérée" -#: ../../source/explanation-federated-evaluation.rst:70 +#: ../../source/explanation-federated-evaluation.rst:74 +#, fuzzy msgid "" -"Client-side evaluation happens in the :code:`Client.evaluate` method and " -"can be configured from the server side." +"Client-side evaluation happens in the ``Client.evaluate`` method and can " +"be configured from the server side." msgstr "" "L'évaluation côté client se fait dans la méthode :code:`Client.evaluate` " "et peut être configurée côté serveur." -#: ../../source/explanation-federated-evaluation.rst:101 +#: ../../source/explanation-federated-evaluation.rst:108 msgid "Configuring Federated Evaluation" msgstr "Configuration de l'évaluation fédérée" -#: ../../source/explanation-federated-evaluation.rst:103 +#: ../../source/explanation-federated-evaluation.rst:110 msgid "" "Federated evaluation can be configured from the server side. Built-in " "strategies support the following arguments:" @@ -5336,14 +5359,14 @@ msgstr "" "L'évaluation fédérée peut être configurée du côté du serveur. Les " "stratégies intégrées prennent en charge les arguments suivants :" -#: ../../source/explanation-federated-evaluation.rst:105 +#: ../../source/explanation-federated-evaluation.rst:113 +#, fuzzy msgid "" -":code:`fraction_evaluate`: a :code:`float` defining the fraction of " -"clients that will be selected for evaluation. If " -":code:`fraction_evaluate` is set to :code:`0.1` and :code:`100` clients " -"are connected to the server, then :code:`10` will be randomly selected " -"for evaluation. If :code:`fraction_evaluate` is set to :code:`0.0`, " -"federated evaluation will be disabled." +"``fraction_evaluate``: a ``float`` defining the fraction of clients that " +"will be selected for evaluation. If ``fraction_evaluate`` is set to " +"``0.1`` and ``100`` clients are connected to the server, then ``10`` will" +" be randomly selected for evaluation. If ``fraction_evaluate`` is set to " +"``0.0``, federated evaluation will be disabled." msgstr "" ":code:`fraction_evaluate` : un :code:`float` définissant la fraction de " "clients qui sera sélectionnée pour l'évaluation. Si " @@ -5352,27 +5375,27 @@ msgstr "" "aléatoirement pour l'évaluation. Si :code:`fraction_evaluate` est défini " "à :code:`0.0`, l'évaluation fédérée sera désactivée." -#: ../../source/explanation-federated-evaluation.rst:106 +#: ../../source/explanation-federated-evaluation.rst:118 +#, fuzzy msgid "" -":code:`min_evaluate_clients`: an :code:`int`: the minimum number of " -"clients to be selected for evaluation. If :code:`fraction_evaluate` is " -"set to :code:`0.1`, :code:`min_evaluate_clients` is set to 20, and " -":code:`100` clients are connected to the server, then :code:`20` clients " -"will be selected for evaluation." +"``min_evaluate_clients``: an ``int``: the minimum number of clients to be" +" selected for evaluation. If ``fraction_evaluate`` is set to ``0.1``, " +"``min_evaluate_clients`` is set to 20, and ``100`` clients are connected " +"to the server, then ``20`` clients will be selected for evaluation." msgstr "" "si :code:`fraction_evaluate` est réglé sur :code:`0.1`, " ":code:`min_evaluate_clients` est réglé sur 20, et que :code:`100` clients" " sont connectés au serveur, alors :code:`20` clients seront sélectionnés " "pour l'évaluation." -#: ../../source/explanation-federated-evaluation.rst:107 +#: ../../source/explanation-federated-evaluation.rst:122 +#, fuzzy msgid "" -":code:`min_available_clients`: an :code:`int` that defines the minimum " -"number of clients which need to be connected to the server before a round" -" of federated evaluation can start. If fewer than " -":code:`min_available_clients` are connected to the server, the server " -"will wait until more clients are connected before it continues to sample " -"clients for evaluation." +"``min_available_clients``: an ``int`` that defines the minimum number of " +"clients which need to be connected to the server before a round of " +"federated evaluation can start. If fewer than ``min_available_clients`` " +"are connected to the server, the server will wait until more clients are " +"connected before it continues to sample clients for evaluation." msgstr "" ":code:`min_available_clients` : un :code:`int` qui définit le nombre " "minimum de clients qui doivent être connectés au serveur avant qu'un " @@ -5381,9 +5404,10 @@ msgstr "" "attendra que d'autres clients soient connectés avant de continuer à " "échantillonner des clients pour l'évaluation." -#: ../../source/explanation-federated-evaluation.rst:108 +#: ../../source/explanation-federated-evaluation.rst:127 +#, fuzzy msgid "" -":code:`on_evaluate_config_fn`: a function that returns a configuration " +"``on_evaluate_config_fn``: a function that returns a configuration " "dictionary which will be sent to the selected clients. The function will " "be called during each round and provides a convenient way to customize " "client-side evaluation from the server side, for example, to configure " @@ -5395,25 +5419,25 @@ msgstr "" "l'évaluation côté client depuis le côté serveur, par exemple pour " "configurer le nombre d'étapes de validation effectuées." -#: ../../source/explanation-federated-evaluation.rst:135 +#: ../../source/explanation-federated-evaluation.rst:157 msgid "Evaluating Local Model Updates During Training" msgstr "Évaluer les mises à jour du modèle local pendant la formation" -#: ../../source/explanation-federated-evaluation.rst:137 +#: ../../source/explanation-federated-evaluation.rst:159 +#, fuzzy msgid "" -"Model parameters can also be evaluated during training. " -":code:`Client.fit` can return arbitrary evaluation results as a " -"dictionary:" +"Model parameters can also be evaluated during training. ``Client.fit`` " +"can return arbitrary evaluation results as a dictionary:" msgstr "" "Les paramètres du modèle peuvent également être évalués pendant la " "formation. :code:`Client.fit` peut renvoyer des résultats d'évaluation " "arbitraires sous forme de dictionnaire :" -#: ../../source/explanation-federated-evaluation.rst:177 +#: ../../source/explanation-federated-evaluation.rst:201 msgid "Full Code Example" msgstr "Exemple de code complet" -#: ../../source/explanation-federated-evaluation.rst:179 +#: ../../source/explanation-federated-evaluation.rst:203 #, fuzzy msgid "" "For a full code example that uses both centralized and federated " @@ -5434,46 +5458,46 @@ msgid "" "learning while preserving data privacy." msgstr "" -#: ../../source/explanation-flower-architecture.rst:3 +#: ../../source/explanation-flower-architecture.rst:2 msgid "Flower Architecture" msgstr "Architecture florale" -#: ../../source/explanation-flower-architecture.rst:5 +#: ../../source/explanation-flower-architecture.rst:4 msgid "" "This page explains the architecture of deployed Flower federated learning" " system." msgstr "" -#: ../../source/explanation-flower-architecture.rst:8 +#: ../../source/explanation-flower-architecture.rst:6 msgid "" "In federated learning (FL), there is typically one server and a number of" " clients that are connected to the server. This is often called a " "federation." msgstr "" -#: ../../source/explanation-flower-architecture.rst:12 +#: ../../source/explanation-flower-architecture.rst:9 msgid "" "The role of the server is to coordinate the training process. The role of" " each client is to receive tasks from the server, execute those tasks and" " return the results back to the server." msgstr "" -#: ../../source/explanation-flower-architecture.rst:16 +#: ../../source/explanation-flower-architecture.rst:13 msgid "This is sometimes called a hub-and-spoke topology:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:18 +#: ../../source/explanation-flower-architecture.rst:21 #, fuzzy msgid "Hub-and-spoke topology in federated learning" msgstr "Qu'est-ce que l'apprentissage fédéré ?" -#: ../../source/explanation-flower-architecture.rst:24 +#: ../../source/explanation-flower-architecture.rst:21 msgid "" "Hub-and-spoke topology in federated learning (one server, multiple " "clients)." msgstr "" -#: ../../source/explanation-flower-architecture.rst:26 +#: ../../source/explanation-flower-architecture.rst:23 msgid "" "In a real-world deployment, we typically want to run different projects " "on such a federation. Each project could use different hyperparameters, " @@ -5481,7 +5505,7 @@ msgid "" "different machine learning frameworks like PyTorch and TensorFlow." msgstr "" -#: ../../source/explanation-flower-architecture.rst:31 +#: ../../source/explanation-flower-architecture.rst:28 msgid "" "This is why, in Flower, both the server side and the client side are " "split into two parts. One part is long-lived and responsible for " @@ -5489,17 +5513,17 @@ msgid "" "executes task-specific code." msgstr "" -#: ../../source/explanation-flower-architecture.rst:36 +#: ../../source/explanation-flower-architecture.rst:32 msgid "A Flower `server` consists of **SuperLink** and ``ServerApp``:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:38 +#: ../../source/explanation-flower-architecture.rst:34 msgid "" "**SuperLink**: a long-running process that forwards task instructions to " "clients (SuperNodes) and receives task results back." msgstr "" -#: ../../source/explanation-flower-architecture.rst:41 +#: ../../source/explanation-flower-architecture.rst:36 msgid "" "``ServerApp``: a short-lived process with project-spcific code that " "customizes all server-side aspects of federated learning systems (client " @@ -5507,18 +5531,18 @@ msgid "" "researchers and AI engineers write when they build Flower apps." msgstr "" -#: ../../source/explanation-flower-architecture.rst:47 +#: ../../source/explanation-flower-architecture.rst:41 msgid "A Flower `client` consists of **SuperNode** and ``ClientApp``:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:49 +#: ../../source/explanation-flower-architecture.rst:43 msgid "" "**SuperNode**: a long-running process that connects to the SuperLink, " "asks for tasks, executes tasks (for example, \"train this model on your " "local data\") and returns task results back to the SuperLink." msgstr "" -#: ../../source/explanation-flower-architecture.rst:53 +#: ../../source/explanation-flower-architecture.rst:46 msgid "" "``ClientApp``: a short-lived process with project-specific code that " "customizes all client-side aspects of federated learning systems (local " @@ -5526,7 +5550,7 @@ msgid "" " researchers and AI engineers write when they build Flower apps." msgstr "" -#: ../../source/explanation-flower-architecture.rst:59 +#: ../../source/explanation-flower-architecture.rst:51 msgid "" "Why SuperNode and SuperLink? Well, in federated learning, the clients are" " the actual stars of the show. They hold the training data and they run " @@ -5535,30 +5559,30 @@ msgid "" "`missing link` between all those SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:65 +#: ../../source/explanation-flower-architecture.rst:62 #, fuzzy msgid "Basic Flower architecture" msgstr "Architecture florale" -#: ../../source/explanation-flower-architecture.rst:71 +#: ../../source/explanation-flower-architecture.rst:62 #, fuzzy msgid "The basic Flower architecture for federated learning." msgstr "Qu'est-ce que l'apprentissage fédéré ?" -#: ../../source/explanation-flower-architecture.rst:73 +#: ../../source/explanation-flower-architecture.rst:64 msgid "" "In a Flower app project, users will typically develop the ``ServerApp`` " "and the ``ClientApp``. All the network communication between `server` and" " `clients` is taken care of by the SuperLink and SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:79 +#: ../../source/explanation-flower-architecture.rst:70 msgid "" "For more details, please refer to the |serverapp_link|_ and " "|clientapp_link|_ documentation." msgstr "" -#: ../../source/explanation-flower-architecture.rst:82 +#: ../../source/explanation-flower-architecture.rst:73 msgid "" "With *multi-run*, multiple ``ServerApp``\\s and ``ClientApp``\\s are now " "capable of running on the same federation consisting of a single long-" @@ -5566,24 +5590,24 @@ msgid "" " referred to as `multi-tenancy` or `multi-job`." msgstr "" -#: ../../source/explanation-flower-architecture.rst:87 +#: ../../source/explanation-flower-architecture.rst:78 msgid "" "As shown in the figure below, two projects, each consisting of a " "``ServerApp`` and a ``ClientApp``, could share the same SuperLink and " "SuperNodes." msgstr "" -#: ../../source/explanation-flower-architecture.rst:91 +#: ../../source/explanation-flower-architecture.rst:87 #, fuzzy msgid "Multi-tenancy federated learning architecture" msgstr "Stratégie de moyenne fédérée." -#: ../../source/explanation-flower-architecture.rst:97 +#: ../../source/explanation-flower-architecture.rst:87 #, fuzzy msgid "Multi-tenancy federated learning architecture with Flower" msgstr "Étape 2 : Apprentissage fédéré avec Flower" -#: ../../source/explanation-flower-architecture.rst:99 +#: ../../source/explanation-flower-architecture.rst:89 msgid "" "To illustrate how multi-run works, consider one federated learning " "training run where a ``ServerApp`` and a ``ClientApp`` are participating " @@ -5591,48 +5615,48 @@ msgid "" " is selected to participate in the training run." msgstr "" -#: ../../source/explanation-flower-architecture.rst:104 +#: ../../source/explanation-flower-architecture.rst:94 msgid "" "In ``[run 1]`` below, all the SuperNodes are selected and therefore run " "their corresponding ``ClientApp``\\s:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:107 +#: ../../source/explanation-flower-architecture.rst:103 #, fuzzy msgid "Multi-tenancy federated learning architecture - Run 1" msgstr "Stratégie de moyenne fédérée." -#: ../../source/explanation-flower-architecture.rst:113 +#: ../../source/explanation-flower-architecture.rst:103 msgid "" "Run 1 in a multi-run federated learning architecture with Flower. All " "SuperNodes participate in the training round." msgstr "" -#: ../../source/explanation-flower-architecture.rst:116 +#: ../../source/explanation-flower-architecture.rst:106 msgid "" "However, in ``[run 2]``, only the first and third SuperNodes are selected" " to participate in the training:" msgstr "" -#: ../../source/explanation-flower-architecture.rst:119 +#: ../../source/explanation-flower-architecture.rst:115 #, fuzzy msgid "Multi-tenancy federated learning architecture - Run 2" msgstr "Stratégie de moyenne fédérée." -#: ../../source/explanation-flower-architecture.rst:125 +#: ../../source/explanation-flower-architecture.rst:115 msgid "" "Run 2 in a multi-run federated learning architecture with Flower. Only " "the first and third SuperNodes are selected to participate in the " "training round." msgstr "" -#: ../../source/explanation-flower-architecture.rst:129 +#: ../../source/explanation-flower-architecture.rst:118 msgid "" "Therefore, with Flower multi-run, different projects (each consisting of " "a ``ServerApp`` and ``ClientApp``) can run on different sets of clients." msgstr "" -#: ../../source/explanation-flower-architecture.rst:132 +#: ../../source/explanation-flower-architecture.rst:121 msgid "" "To help you start and manage all of the concurrently executing training " "runs, Flower offers one additional long-running server-side service " @@ -5644,28 +5668,28 @@ msgid "" "``ClientApp``." msgstr "" -#: ../../source/explanation-flower-architecture.rst:141 +#: ../../source/explanation-flower-architecture.rst:128 msgid "" "This architecture allows many users to (concurrently) run their projects " "on the same federation, simply by typing ``flwr run`` on their local " "developer machine." msgstr "" -#: ../../source/explanation-flower-architecture.rst:145 +#: ../../source/explanation-flower-architecture.rst:137 msgid "Flower Deployment Engine with SuperExec" msgstr "" -#: ../../source/explanation-flower-architecture.rst:151 +#: ../../source/explanation-flower-architecture.rst:137 msgid "The SuperExec service for managing concurrent training runs in Flower." msgstr "" -#: ../../source/explanation-flower-architecture.rst:156 +#: ../../source/explanation-flower-architecture.rst:141 msgid "" "This explanation covers the Flower Deployment Engine. An explanation " "covering the Flower Simulation Engine will follow." msgstr "" -#: ../../source/explanation-flower-architecture.rst:161 +#: ../../source/explanation-flower-architecture.rst:146 msgid "" "As we continue to enhance Flower at a rapid pace, we'll periodically " "update this explainer document. Feel free to share any feedback with us." @@ -6267,9 +6291,10 @@ msgid "Aggregate Custom Evaluation Results" msgstr "Agréger les résultats de l'évaluation personnalisée" #: ../../source/how-to-aggregate-evaluation-results.rst:10 +#, fuzzy msgid "" -"The same :code:`Strategy`-customization approach can be used to aggregate" -" custom evaluation results coming from individual clients. Clients can " +"The same ``Strategy``-customization approach can be used to aggregate " +"custom evaluation results coming from individual clients. Clients can " "return custom metrics to the server by returning a dictionary:" msgstr "" "La même approche de personnalisation :code:`Stratégie` peut être utilisée" @@ -6277,7 +6302,7 @@ msgstr "" "clients individuels. Les clients peuvent renvoyer des mesures " "personnalisées au serveur en renvoyant un dictionnaire :" -#: ../../source/how-to-aggregate-evaluation-results.rst:36 +#: ../../source/how-to-aggregate-evaluation-results.rst:39 msgid "" "The server can then use a customized strategy to aggregate the metrics " "provided in these dictionaries:" @@ -6297,27 +6322,27 @@ msgid "" " works:" msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:7 +#: ../../source/how-to-authenticate-supernodes.rst:8 msgid "SuperLink (server) stores a list of known (client) node public keys" msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:8 +#: ../../source/how-to-authenticate-supernodes.rst:9 msgid "" "Using ECDH, both SuperNode and SuperLink independently derive a shared " "secret" msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:9 +#: ../../source/how-to-authenticate-supernodes.rst:10 msgid "" "Shared secret is used to compute the HMAC value of the message sent from " "SuperNode to SuperLink as a token" msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:10 +#: ../../source/how-to-authenticate-supernodes.rst:12 msgid "SuperLink verifies the token" msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:12 +#: ../../source/how-to-authenticate-supernodes.rst:14 #, fuzzy msgid "" "We recommend you to check out the complete `code example " @@ -6329,47 +6354,46 @@ msgstr "" "`_ " "pour en savoir plus." -#: ../../source/how-to-authenticate-supernodes.rst:15 +#: ../../source/how-to-authenticate-supernodes.rst:20 msgid "" "This guide covers a preview feature that might change in future versions " "of Flower." msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:18 +#: ../../source/how-to-authenticate-supernodes.rst:24 msgid "" "For increased security, node authentication can only be used when " "encrypted connections (SSL/TLS) are enabled." msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:21 -msgid "Enable node authentication in :code:`SuperLink`" +#: ../../source/how-to-authenticate-supernodes.rst:28 +msgid "Enable node authentication in ``SuperLink``" msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:23 +#: ../../source/how-to-authenticate-supernodes.rst:30 msgid "" "To enable node authentication, first you need to configure SSL/TLS " "connections to secure the SuperLink<>SuperNode communication. You can " "find the complete guide `here `_. After configuring secure connections, you" -" can enable client authentication in a long-running Flower " -":code:`SuperLink`. Use the following terminal command to start a Flower " -":code:`SuperNode` that has both secure connections and node " -"authentication enabled:" +" can enable client authentication in a long-running Flower ``SuperLink``." +" Use the following terminal command to start a Flower ``SuperNode`` that " +"has both secure connections and node authentication enabled:" msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:38 +#: ../../source/how-to-authenticate-supernodes.rst:47 msgid "Let's break down the authentication flags:" msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:40 +#: ../../source/how-to-authenticate-supernodes.rst:49 msgid "" -"The first flag :code:`--auth-list-public-keys` expects a path to a CSV " -"file storing all known node public keys. You need to store all known node" -" public keys that are allowed to participate in a federation in one CSV " -"file (:code:`.csv`)." +"The first flag ``--auth-list-public-keys`` expects a path to a CSV file " +"storing all known node public keys. You need to store all known node " +"public keys that are allowed to participate in a federation in one CSV " +"file (``.csv``)." msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:42 +#: ../../source/how-to-authenticate-supernodes.rst:53 msgid "" "A valid CSV file storing known node public keys should list the keys in " "OpenSSH format, separated by commas and without any comments. For an " @@ -6377,15 +6401,15 @@ msgid "" "known node public keys." msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:44 +#: ../../source/how-to-authenticate-supernodes.rst:57 msgid "" -"The second and third flags :code:`--auth-superlink-private-key` and :code" -":`--auth-superlink-public-key` expect paths to the server's private and " -"public keys. For development purposes, you can generate a private and " -"public key pair using :code:`ssh-keygen -t ecdsa -b 384`." +"The second and third flags ``--auth-superlink-private-key`` and ``--auth-" +"superlink-public-key`` expect paths to the server's private and public " +"keys. For development purposes, you can generate a private and public key" +" pair using ``ssh-keygen -t ecdsa -b 384``." msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:47 +#: ../../source/how-to-authenticate-supernodes.rst:64 msgid "" "In Flower 1.9, there is no support for dynamically removing, editing, or " "adding known node public keys to the SuperLink. To change the set of " @@ -6394,32 +6418,32 @@ msgid "" " nodes is on the roadmap to be released in Flower 1.10 (ETA: June)." msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:53 -msgid "Enable node authentication in :code:`SuperNode`" +#: ../../source/how-to-authenticate-supernodes.rst:71 +msgid "Enable node authentication in ``SuperNode``" msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:55 +#: ../../source/how-to-authenticate-supernodes.rst:73 msgid "" -"Similar to the long-running Flower server (:code:`SuperLink`), you can " -"easily enable node authentication in the long-running Flower client " -"(:code:`SuperNode`). Use the following terminal command to start an " -"authenticated :code:`SuperNode`:" +"Similar to the long-running Flower server (``SuperLink``), you can easily" +" enable node authentication in the long-running Flower client " +"(``SuperNode``). Use the following terminal command to start an " +"authenticated ``SuperNode``:" msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:66 +#: ../../source/how-to-authenticate-supernodes.rst:85 msgid "" -"The :code:`--auth-supernode-private-key` flag expects a path to the " -"node's private key file and the :code:`--auth-supernode-public-key` flag " -"expects a path to the node's public key file. For development purposes, " -"you can generate a private and public key pair using :code:`ssh-keygen -t" -" ecdsa -b 384`." +"The ``--auth-supernode-private-key`` flag expects a path to the node's " +"private key file and the ``--auth-supernode-public-key`` flag expects a " +"path to the node's public key file. For development purposes, you can " +"generate a private and public key pair using ``ssh-keygen -t ecdsa -b " +"384``." msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:70 +#: ../../source/how-to-authenticate-supernodes.rst:91 msgid "Security notice" msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:72 +#: ../../source/how-to-authenticate-supernodes.rst:93 msgid "" "The system's security relies on the credentials of the SuperLink and each" " SuperNode. Therefore, it is imperative to safeguard and safely store the" @@ -6430,19 +6454,19 @@ msgid "" "methods." msgstr "" -#: ../../source/how-to-authenticate-supernodes.rst:77 -#: ../../source/how-to-enable-ssl-connections.rst:68 -#: ../../source/how-to-use-built-in-mods.rst:85 +#: ../../source/how-to-authenticate-supernodes.rst:100 +#: ../../source/how-to-enable-ssl-connections.rst:71 +#: ../../source/how-to-use-built-in-mods.rst:95 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:287 msgid "Conclusion" msgstr "Conclusion" -#: ../../source/how-to-authenticate-supernodes.rst:79 +#: ../../source/how-to-authenticate-supernodes.rst:102 msgid "" "You should now have learned how to start a long-running Flower server " -"(:code:`SuperLink`) and client (:code:`SuperNode`) with node " -"authentication enabled. You should also know the significance of the " -"private key and store it safely to minimize security risks." +"(``SuperLink``) and client (``SuperNode``) with node authentication " +"enabled. You should also know the significance of the private key and " +"store it safely to minimize security risks." msgstr "" #: ../../source/how-to-configure-clients.rst:2 @@ -6463,11 +6487,11 @@ msgstr "" "populaire de contrôler les hyperparamètres côté client à partir du " "serveur." -#: ../../source/how-to-configure-clients.rst:7 +#: ../../source/how-to-configure-clients.rst:9 msgid "Configuration values" msgstr "Valeurs de configuration" -#: ../../source/how-to-configure-clients.rst:9 +#: ../../source/how-to-configure-clients.rst:11 msgid "" "Configuration values are represented as a dictionary with ``str`` keys " "and values of type ``bool``, ``bytes``, ``double`` (64-bit precision " @@ -6480,7 +6504,7 @@ msgstr "" "dans d'autres langages). Voici un exemple de dictionnaire de " "configuration en Python :" -#: ../../source/how-to-configure-clients.rst:20 +#: ../../source/how-to-configure-clients.rst:25 msgid "" "Flower serializes these configuration dictionaries (or *config dict* for " "short) to their ProtoBuf representation, transports them to the client " @@ -6490,7 +6514,7 @@ msgstr "" "abrégé) dans leur représentation ProtoBuf, les transporte vers le client " "à l'aide de gRPC, puis les désérialise à nouveau en dictionnaires Python." -#: ../../source/how-to-configure-clients.rst:24 +#: ../../source/how-to-configure-clients.rst:31 msgid "" "Currently, there is no support for directly sending collection types " "(e.g., ``Set``, ``List``, ``Map``) as values in configuration " @@ -6505,7 +6529,7 @@ msgstr "" "l'un des types de valeurs pris en charge (et en les reconvertissant du " "côté client)." -#: ../../source/how-to-configure-clients.rst:26 +#: ../../source/how-to-configure-clients.rst:36 msgid "" "One can, for example, convert a list of floating-point numbers to a JSON " "string, then send the JSON string using the configuration dictionary, and" @@ -6517,18 +6541,19 @@ msgstr "" "de configuration, et enfin reconvertir la chaîne JSON en une liste de " "nombres à virgule flottante sur le client." -#: ../../source/how-to-configure-clients.rst:30 +#: ../../source/how-to-configure-clients.rst:41 msgid "Configuration through built-in strategies" msgstr "Configuration par le biais de stratégies intégrées" -#: ../../source/how-to-configure-clients.rst:32 +#: ../../source/how-to-configure-clients.rst:43 +#, fuzzy msgid "" "The easiest way to send configuration values to clients is to use a " -"built-in strategy like :code:`FedAvg`. Built-in strategies support so-" -"called configuration functions. A configuration function is a function " -"that the built-in strategy calls to get the configuration dictionary for " -"the current round. It then forwards the configuration dictionary to all " -"the clients selected during that round." +"built-in strategy like ``FedAvg``. Built-in strategies support so-called " +"configuration functions. A configuration function is a function that the " +"built-in strategy calls to get the configuration dictionary for the " +"current round. It then forwards the configuration dictionary to all the " +"clients selected during that round." msgstr "" "La façon la plus simple d'envoyer des valeurs de configuration aux " "clients est d'utiliser une stratégie intégrée comme :code:`FedAvg`. Les " @@ -6538,7 +6563,7 @@ msgstr "" "pour le tour en cours. Elle transmet ensuite le dictionnaire de " "configuration à tous les clients sélectionnés au cours de ce tour." -#: ../../source/how-to-configure-clients.rst:34 +#: ../../source/how-to-configure-clients.rst:49 msgid "" "Let's start with a simple example. Imagine we want to send (a) the batch " "size that the client should use, (b) the current global round of " @@ -6550,21 +6575,22 @@ msgstr "" " de l'apprentissage fédéré et (c) le nombre d'époques à former du côté " "client. Notre fonction de configuration pourrait ressembler à ceci :" -#: ../../source/how-to-configure-clients.rst:47 +#: ../../source/how-to-configure-clients.rst:65 +#, fuzzy msgid "" "To make the built-in strategies use this function, we can pass it to " "``FedAvg`` during initialization using the parameter " -":code:`on_fit_config_fn`:" +"``on_fit_config_fn``:" msgstr "" "Pour que les stratégies intégrées utilisent cette fonction, nous pouvons " "la passer à ``FedAvg`` lors de l'initialisation en utilisant le paramètre" " :code:`on_fit_config_fn` :" -#: ../../source/how-to-configure-clients.rst:56 +#: ../../source/how-to-configure-clients.rst:75 msgid "One the client side, we receive the configuration dictionary in ``fit``:" msgstr "Côté client, nous recevons le dictionnaire de configuration dans ``fit`` :" -#: ../../source/how-to-configure-clients.rst:67 +#: ../../source/how-to-configure-clients.rst:86 msgid "" "There is also an `on_evaluate_config_fn` to configure evaluation, which " "works the same way. They are separate functions because one might want to" @@ -6576,7 +6602,7 @@ msgstr "" "séparées car on peut vouloir envoyer différentes valeurs de configuration" " à `evaluate` (par exemple, pour utiliser une taille de lot différente)." -#: ../../source/how-to-configure-clients.rst:69 +#: ../../source/how-to-configure-clients.rst:90 msgid "" "The built-in strategies call this function every round (that is, every " "time `Strategy.configure_fit` or `Strategy.configure_evaluate` runs). " @@ -6593,15 +6619,16 @@ msgstr "" "d'hyperparamètres, par exemple, pour augmenter le nombre d'époques " "locales au cours des derniers tours, nous pourrions faire ce qui suit :" -#: ../../source/how-to-configure-clients.rst:82 -msgid "The :code:`FedAvg` strategy will call this function *every round*." +#: ../../source/how-to-configure-clients.rst:107 +#, fuzzy +msgid "The ``FedAvg`` strategy will call this function *every round*." msgstr "La stratégie :code:`FedAvg` appellera cette fonction *à chaque tour*." -#: ../../source/how-to-configure-clients.rst:85 +#: ../../source/how-to-configure-clients.rst:110 msgid "Configuring individual clients" msgstr "Configuration des clients individuels" -#: ../../source/how-to-configure-clients.rst:87 +#: ../../source/how-to-configure-clients.rst:112 msgid "" "In some cases, it is necessary to send different configuration values to " "different clients." @@ -6609,16 +6636,16 @@ msgstr "" "Dans certains cas, il est nécessaire d'envoyer des valeurs de " "configuration différentes à des clients différents." -#: ../../source/how-to-configure-clients.rst:89 +#: ../../source/how-to-configure-clients.rst:115 #, fuzzy msgid "" "This can be achieved by customizing an existing strategy or by " ":doc:`implementing a custom strategy from scratch `. Here's a nonsensical example that customizes :code:`FedAvg`" -" by adding a custom ``\"hello\": \"world\"`` configuration key/value pair" -" to the config dict of a *single client* (only the first client in the " -"list, the other clients in this round to not receive this \"special\" " -"config value):" +"strategies>`. Here's a nonsensical example that customizes ``FedAvg`` by " +"adding a custom ``\"hello\": \"world\"`` configuration key/value pair to " +"the config dict of a *single client* (only the first client in the list, " +"the other clients in this round to not receive this \"special\" config " +"value):" msgstr "" "Ceci peut être réalisé en personnalisant une stratégie existante ou en " "`mettant en œuvre une stratégie personnalisée à partir de zéro " @@ -6644,34 +6671,35 @@ msgstr "" #: ../../source/how-to-configure-logging.rst:13 msgid "" "containing relevant information including: log message level (e.g. " -":code:`INFO`, :code:`DEBUG`), a timestamp, the line where the logging " -"took place from, as well as the log message itself. In this way, the " -"logger would typically display information on your terminal as follows:" +"``INFO``, ``DEBUG``), a timestamp, the line where the logging took place " +"from, as well as the log message itself. In this way, the logger would " +"typically display information on your terminal as follows:" msgstr "" -#: ../../source/how-to-configure-logging.rst:34 +#: ../../source/how-to-configure-logging.rst:35 msgid "Saving log to file" msgstr "" -#: ../../source/how-to-configure-logging.rst:36 +#: ../../source/how-to-configure-logging.rst:37 msgid "" "By default, the Flower log is outputted to the terminal where you launch " "your Federated Learning workload from. This applies for both gRPC-based " -"federation (i.e. when you do :code:`fl.server.start_server`) and when " -"using the :code:`VirtualClientEngine` (i.e. when you do " -":code:`fl.simulation.start_simulation`). In some situations you might " -"want to save this log to disk. You can do so by calling the " +"federation (i.e. when you do ``fl.server.start_server``) and when using " +"the ``VirtualClientEngine`` (i.e. when you do " +"``fl.simulation.start_simulation``). In some situations you might want to" +" save this log to disk. You can do so by calling the " "`fl.common.logger.configure() " "`_" " function. For example:" msgstr "" -#: ../../source/how-to-configure-logging.rst:53 +#: ../../source/how-to-configure-logging.rst:59 +#, fuzzy msgid "" "With the above, Flower will record the log you see on your terminal to " -":code:`log.txt`. This file will be created in the same directory as were " -"you are running the code from. If we inspect we see the log above is also" -" recorded but prefixing with :code:`identifier` each line:" +"``log.txt``. This file will be created in the same directory as were you " +"are running the code from. If we inspect we see the log above is also " +"recorded but prefixing with ``identifier`` each line:" msgstr "" "Avec ce qui précède, Flower enregistrera le log que vous voyez sur votre " "terminal dans :code:`log.txt`. Ce fichier sera créé dans le répertoire " @@ -6679,38 +6707,37 @@ msgstr "" "log ci-dessous est également enregistré mais préfixé avec " ":code:`identifier` sur chaque ligne :" -#: ../../source/how-to-configure-logging.rst:74 +#: ../../source/how-to-configure-logging.rst:81 msgid "Log your own messages" msgstr "Loggez vos propres messages" -#: ../../source/how-to-configure-logging.rst:76 +#: ../../source/how-to-configure-logging.rst:83 msgid "" "You might expand the information shown by default with the Flower logger " "by adding more messages relevant to your application. You can achieve " "this easily as follows." msgstr "" -#: ../../source/how-to-configure-logging.rst:102 +#: ../../source/how-to-configure-logging.rst:114 msgid "" "In this way your logger will show, in addition to the default messages, " "the ones introduced by the clients as specified above." msgstr "" -#: ../../source/how-to-configure-logging.rst:128 +#: ../../source/how-to-configure-logging.rst:140 msgid "Log to a remote service" msgstr "" -#: ../../source/how-to-configure-logging.rst:130 +#: ../../source/how-to-configure-logging.rst:142 msgid "" -"The :code:`fl.common.logger.configure` function, also allows specifying a" -" host to which logs can be pushed (via :code:`POST`) through a native " -"Python :code:`logging.handler.HTTPHandler`. This is a particularly useful" -" feature in :code:`gRPC`-based Federated Learning workloads where " -"otherwise gathering logs from all entities (i.e. the server and the " -"clients) might be cumbersome. Note that in Flower simulation, the server " -"automatically displays all logs. You can still specify a " -":code:`HTTPHandler` should you wish to backup or analyze the logs " -"somewhere else." +"The ``fl.common.logger.configure`` function, also allows specifying a " +"host to which logs can be pushed (via ``POST``) through a native Python " +"``logging.handler.HTTPHandler``. This is a particularly useful feature in" +" ``gRPC``-based Federated Learning workloads where otherwise gathering " +"logs from all entities (i.e. the server and the clients) might be " +"cumbersome. Note that in Flower simulation, the server automatically " +"displays all logs. You can still specify a ``HTTPHandler`` should you " +"wish to backup or analyze the logs somewhere else." msgstr "" #: ../../source/how-to-enable-ssl-connections.rst:2 @@ -6722,13 +6749,13 @@ msgstr "Collecte centralisée des données" #, fuzzy msgid "" "This guide describes how to a SSL-enabled secure Flower server " -"(:code:`SuperLink`) can be started and how a Flower client " -"(:code:`SuperNode`) can establish a secure connections to it." +"(``SuperLink``) can be started and how a Flower client (``SuperNode``) " +"can establish a secure connections to it." msgstr "" "Ce guide décrit comment démarrer un serveur Flower sécurisé par SSL et " "comment un client Flower peut établir une connexion sécurisée avec lui." -#: ../../source/how-to-enable-ssl-connections.rst:7 +#: ../../source/how-to-enable-ssl-connections.rst:8 #, fuzzy msgid "" "A complete code example demonstrating a secure connection can be found " @@ -6739,11 +6766,11 @@ msgstr "" "trouvé ici `_." -#: ../../source/how-to-enable-ssl-connections.rst:10 +#: ../../source/how-to-enable-ssl-connections.rst:11 #, fuzzy msgid "" -"The code example comes with a :code:`README.md` file which explains how " -"to start it. Although it is already SSL-enabled, it might be less " +"The code example comes with a ``README.md`` file which explains how to " +"start it. Although it is already SSL-enabled, it might be less " "descriptive on how it does so. Stick to this guide for a deeper " "introduction to the topic." msgstr "" @@ -6762,8 +6789,9 @@ msgid "" "Using SSL-enabled connections requires certificates to be passed to the " "server and client. For the purpose of this guide we are going to generate" " self-signed certificates. As this can become quite complex we are going " -"to ask you to run the script in :code:`examples/advanced-" -"tensorflow/certificates/generate.sh` with the following command sequence:" +"to ask you to run the script in ``examples/advanced-" +"tensorflow/certificates/generate.sh`` with the following command " +"sequence:" msgstr "" "L'utilisation de connexions compatibles avec le protocole SSL nécessite " "que des certificats soient transmis au serveur et au client. Pour les " @@ -6775,13 +6803,13 @@ msgstr "" #: ../../source/how-to-enable-ssl-connections.rst:29 #, fuzzy msgid "" -"This will generate the certificates in :code:`examples/advanced-" -"tensorflow/.cache/certificates`." +"This will generate the certificates in ``examples/advanced-" +"tensorflow/.cache/certificates``." msgstr "" "Cela générera les certificats dans :code:`examples/advanced-" "tensorflow/.cache/certificates`." -#: ../../source/how-to-enable-ssl-connections.rst:31 +#: ../../source/how-to-enable-ssl-connections.rst:32 #, fuzzy msgid "" "The approach for generating SSL certificates in the context of this " @@ -6796,12 +6824,12 @@ msgstr "" "servir d'inspiration et de point de départ, mais ne doit pas être " "considérée comme complète pour les environnements de production." -#: ../../source/how-to-enable-ssl-connections.rst:39 +#: ../../source/how-to-enable-ssl-connections.rst:40 #, fuzzy msgid "Server (SuperLink)" msgstr "flower-superlink" -#: ../../source/how-to-enable-ssl-connections.rst:41 +#: ../../source/how-to-enable-ssl-connections.rst:42 #, fuzzy msgid "" "Use the following terminal command to start a sever (SuperLink) that uses" @@ -6810,19 +6838,19 @@ msgstr "" "Nous allons maintenant montrer comment écrire un client qui utilise les " "scripts générés précédemment :" -#: ../../source/how-to-enable-ssl-connections.rst:50 +#: ../../source/how-to-enable-ssl-connections.rst:52 msgid "" "When providing certificates, the server expects a tuple of three " "certificates paths: CA certificate, server certificate and server private" " key." msgstr "" -#: ../../source/how-to-enable-ssl-connections.rst:54 +#: ../../source/how-to-enable-ssl-connections.rst:56 #, fuzzy msgid "Client (SuperNode)" msgstr "Codes d'état du client." -#: ../../source/how-to-enable-ssl-connections.rst:56 +#: ../../source/how-to-enable-ssl-connections.rst:58 #, fuzzy msgid "" "Use the following terminal command to start a client (SuperNode) that " @@ -6831,18 +6859,18 @@ msgstr "" "Nous allons maintenant montrer comment écrire un client qui utilise les " "scripts générés précédemment :" -#: ../../source/how-to-enable-ssl-connections.rst:64 +#: ../../source/how-to-enable-ssl-connections.rst:67 #, fuzzy msgid "" -"When setting :code:`root_certificates`, the client expects a file path to" -" PEM-encoded root certificates." +"When setting ``root_certificates``, the client expects a file path to " +"PEM-encoded root certificates." msgstr "" "En définissant :code:`root_certificates`, le client s'attend à recevoir " "les certificats racine codés en PEM sous forme de chaîne d'octets. Nous " "utilisons à nouveau :code:`Path` pour simplifier la lecture de ces " "certificats sous forme de chaînes d'octets." -#: ../../source/how-to-enable-ssl-connections.rst:70 +#: ../../source/how-to-enable-ssl-connections.rst:73 #, fuzzy msgid "" "You should now have learned how to generate self-signed certificates " @@ -6853,12 +6881,12 @@ msgstr "" "à l'aide du script donné, à démarrer un serveur compatible SSL et à " "demander à un client d'établir une connexion sécurisée avec lui." -#: ../../source/how-to-enable-ssl-connections.rst:75 +#: ../../source/how-to-enable-ssl-connections.rst:78 #, fuzzy msgid "Additional resources" msgstr "Ressources supplémentaires" -#: ../../source/how-to-enable-ssl-connections.rst:77 +#: ../../source/how-to-enable-ssl-connections.rst:80 msgid "" "These additional sources might be relevant if you would like to dive " "deeper into the topic of certificates:" @@ -6866,11 +6894,11 @@ msgstr "" "Ces sources supplémentaires peuvent être pertinentes si tu souhaites " "approfondir le sujet des certificats :" -#: ../../source/how-to-enable-ssl-connections.rst:79 +#: ../../source/how-to-enable-ssl-connections.rst:83 msgid "`Let's Encrypt `_" msgstr "`Let's Encrypt `_" -#: ../../source/how-to-enable-ssl-connections.rst:80 +#: ../../source/how-to-enable-ssl-connections.rst:84 msgid "`certbot `_" msgstr "`certbot `_" @@ -6897,13 +6925,15 @@ msgstr "" "intégrées qui sont basées sur la même API que celle décrite ci-dessous." #: ../../source/how-to-implement-strategies.rst:11 -msgid "The :code:`Strategy` abstraction" +#, fuzzy +msgid "The ``Strategy`` abstraction" msgstr "L'abstraction :code:`Stratégie`" #: ../../source/how-to-implement-strategies.rst:13 +#, fuzzy msgid "" "All strategy implementation are derived from the abstract base class " -":code:`flwr.server.strategy.Strategy`, both built-in implementations and " +"``flwr.server.strategy.Strategy``, both built-in implementations and " "third party implementations. This means that custom strategy " "implementations have the exact same capabilities at their disposal as " "built-in ones." @@ -6922,60 +6952,65 @@ msgstr "" "L'abstraction de la stratégie définit quelques méthodes abstraites qui " "doivent être mises en œuvre :" -#: ../../source/how-to-implement-strategies.rst:75 +#: ../../source/how-to-implement-strategies.rst:67 +#, fuzzy msgid "" -"Creating a new strategy means implementing a new :code:`class` (derived " -"from the abstract base class :code:`Strategy`) that implements for the " -"previously shown abstract methods:" +"Creating a new strategy means implementing a new ``class`` (derived from " +"the abstract base class ``Strategy``) that implements for the previously " +"shown abstract methods:" msgstr "" "La création d'une nouvelle stratégie implique la mise en œuvre d'une " "nouvelle :code:`classe` (dérivée de la classe de base abstraite " ":code:`Stratégie`) qui met en œuvre les méthodes abstraites présentées " "précédemment :" -#: ../../source/how-to-implement-strategies.rst:100 +#: ../../source/how-to-implement-strategies.rst:97 msgid "The Flower server calls these methods in the following order:" msgstr "Le serveur Flower appelle ces méthodes dans l'ordre suivant :" -#: ../../source/how-to-implement-strategies.rst:177 +#: ../../source/how-to-implement-strategies.rst:174 msgid "The following sections describe each of those methods in more detail." msgstr "Les sections suivantes décrivent chacune de ces méthodes plus en détail." -#: ../../source/how-to-implement-strategies.rst:180 -msgid "The :code:`initialize_parameters` method" +#: ../../source/how-to-implement-strategies.rst:177 +#, fuzzy +msgid "The ``initialize_parameters`` method" msgstr "La méthode :code:`initialize_parameters` (initialisation des paramètres)" -#: ../../source/how-to-implement-strategies.rst:182 +#: ../../source/how-to-implement-strategies.rst:179 +#, fuzzy msgid "" -":code:`initialize_parameters` is called only once, at the very beginning " -"of an execution. It is responsible for providing the initial global model" -" parameters in a serialized form (i.e., as a :code:`Parameters` object)." +"``initialize_parameters`` is called only once, at the very beginning of " +"an execution. It is responsible for providing the initial global model " +"parameters in a serialized form (i.e., as a ``Parameters`` object)." msgstr "" ":code:`initialize_parameters` n'est appelé qu'une seule fois, au tout " "début d'une exécution. Il est chargé de fournir les paramètres initiaux " "du modèle global sous une forme sérialisée (c'est-à-dire sous la forme " "d'un objet :code:`Parameters`)." -#: ../../source/how-to-implement-strategies.rst:184 +#: ../../source/how-to-implement-strategies.rst:183 +#, fuzzy msgid "" "Built-in strategies return user-provided initial parameters. The " "following example shows how initial parameters can be passed to " -":code:`FedAvg`:" +"``FedAvg``:" msgstr "" "Les stratégies intégrées renvoient les paramètres initiaux fournis par " "l'utilisateur. L'exemple suivant montre comment les paramètres initiaux " "peuvent être transmis à :code:`FedAvg` :" #: ../../source/how-to-implement-strategies.rst:209 +#, fuzzy msgid "" -"The Flower server will call :code:`initialize_parameters`, which either " -"returns the parameters that were passed to :code:`initial_parameters`, or" -" :code:`None`. If no parameters are returned from " -":code:`initialize_parameters` (i.e., :code:`None`), the server will " -"randomly select one client and ask it to provide its parameters. This is " -"a convenience feature and not recommended in practice, but it can be " -"useful for prototyping. In practice, it is recommended to always use " -"server-side parameter initialization." +"The Flower server will call ``initialize_parameters``, which either " +"returns the parameters that were passed to ``initial_parameters``, or " +"``None``. If no parameters are returned from ``initialize_parameters`` " +"(i.e., ``None``), the server will randomly select one client and ask it " +"to provide its parameters. This is a convenience feature and not " +"recommended in practice, but it can be useful for prototyping. In " +"practice, it is recommended to always use server-side parameter " +"initialization." msgstr "" "Le serveur Flower appelle :code:`initialize_parameters`, qui renvoie les " "paramètres passés à :code:`initial_parameters`, ou :code:`None`. Si aucun" @@ -6986,7 +7021,7 @@ msgstr "" "prototypage. Dans la pratique, il est recommandé de toujours utiliser " "l'initialisation des paramètres du côté du serveur." -#: ../../source/how-to-implement-strategies.rst:213 +#: ../../source/how-to-implement-strategies.rst:218 msgid "" "Server-side parameter initialization is a powerful mechanism. It can be " "used, for example, to resume training from a previously saved checkpoint." @@ -7001,58 +7036,63 @@ msgstr "" "hybrides, par exemple, pour affiner un modèle pré-entraîné à l'aide de " "l'apprentissage fédéré." -#: ../../source/how-to-implement-strategies.rst:216 -msgid "The :code:`configure_fit` method" +#: ../../source/how-to-implement-strategies.rst:224 +#, fuzzy +msgid "The ``configure_fit`` method" msgstr "La méthode :code:`configure_fit`" -#: ../../source/how-to-implement-strategies.rst:218 +#: ../../source/how-to-implement-strategies.rst:226 +#, fuzzy msgid "" -":code:`configure_fit` is responsible for configuring the upcoming round " -"of training. What does *configure* mean in this context? Configuring a " -"round means selecting clients and deciding what instructions to send to " -"these clients. The signature of :code:`configure_fit` makes this clear:" +"``configure_fit`` is responsible for configuring the upcoming round of " +"training. What does *configure* mean in this context? Configuring a round" +" means selecting clients and deciding what instructions to send to these " +"clients. The signature of ``configure_fit`` makes this clear:" msgstr "" ":code:`configure_fit` est chargé de configurer le prochain tour de " "formation. Que signifie *configurer* dans ce contexte ? Configurer un " "tour signifie sélectionner des clients et décider des instructions à leur" " envoyer. La signature de :code:`configure_fit` l'indique clairement :" -#: ../../source/how-to-implement-strategies.rst:231 +#: ../../source/how-to-implement-strategies.rst:239 +#, fuzzy msgid "" "The return value is a list of tuples, each representing the instructions " "that will be sent to a particular client. Strategy implementations " -"usually perform the following steps in :code:`configure_fit`:" +"usually perform the following steps in ``configure_fit``:" msgstr "" "La valeur de retour est une liste de tuples, chacun représentant les " "instructions qui seront envoyées à un client particulier. Les " "implémentations de stratégies effectuent généralement les étapes " "suivantes dans :code:`configure_fit` :" -#: ../../source/how-to-implement-strategies.rst:233 -#: ../../source/how-to-implement-strategies.rst:280 +#: ../../source/how-to-implement-strategies.rst:243 +#: ../../source/how-to-implement-strategies.rst:307 +#, fuzzy msgid "" -"Use the :code:`client_manager` to randomly sample all (or a subset of) " -"available clients (each represented as a :code:`ClientProxy` object)" +"Use the ``client_manager`` to randomly sample all (or a subset of) " +"available clients (each represented as a ``ClientProxy`` object)" msgstr "" "Utilise le :code:`client_manager` pour échantillonner au hasard tous les " "clients disponibles (ou un sous-ensemble d'entre eux) (chacun représenté " "par un objet :code:`ClientProxy`)" -#: ../../source/how-to-implement-strategies.rst:234 +#: ../../source/how-to-implement-strategies.rst:245 +#, fuzzy msgid "" -"Pair each :code:`ClientProxy` with the same :code:`FitIns` holding the " -"current global model :code:`parameters` and :code:`config` dict" +"Pair each ``ClientProxy`` with the same ``FitIns`` holding the current " +"global model ``parameters`` and ``config`` dict" msgstr "" "Associe chaque :code:`ClientProxy` au même :code:`FitIns` contenant le " "modèle global actuel :code:`parameters` et :code:`config` dict" -#: ../../source/how-to-implement-strategies.rst:236 +#: ../../source/how-to-implement-strategies.rst:248 #, fuzzy msgid "" -"More sophisticated implementations can use :code:`configure_fit` to " -"implement custom client selection logic. A client will only participate " -"in a round if the corresponding :code:`ClientProxy` is included in the " -"list returned from :code:`configure_fit`." +"More sophisticated implementations can use ``configure_fit`` to implement" +" custom client selection logic. A client will only participate in a round" +" if the corresponding ``ClientProxy`` is included in the list returned " +"from ``configure_fit``." msgstr "" "Les implémentations plus sophistiquées peuvent utiliser " ":code:`configure_fit` pour mettre en œuvre une logique de sélection des " @@ -7060,14 +7100,14 @@ msgstr "" ":code:`ClientProxy` correspondant est inclus dans la liste renvoyée par " ":code:`configure_fit`." -#: ../../source/how-to-implement-strategies.rst:240 +#: ../../source/how-to-implement-strategies.rst:254 +#, fuzzy msgid "" "The structure of this return value provides a lot of flexibility to the " "user. Since instructions are defined on a per-client basis, different " "instructions can be sent to each client. This enables custom strategies " "to train, for example, different models on different clients, or use " -"different hyperparameters on different clients (via the :code:`config` " -"dict)." +"different hyperparameters on different clients (via the ``config`` dict)." msgstr "" "La structure de cette valeur de retour offre beaucoup de souplesse à " "l'utilisateur. Comme les instructions sont définies par client, des " @@ -7076,26 +7116,28 @@ msgstr "" " différents modèles sur différents clients, ou utiliser différents " "hyperparamètres sur différents clients (via le dict :code:`config`)." -#: ../../source/how-to-implement-strategies.rst:243 -msgid "The :code:`aggregate_fit` method" +#: ../../source/how-to-implement-strategies.rst:261 +#, fuzzy +msgid "The ``aggregate_fit`` method" msgstr "La méthode :code:`aggregate_fit` (agrégation)" -#: ../../source/how-to-implement-strategies.rst:245 +#: ../../source/how-to-implement-strategies.rst:263 +#, fuzzy msgid "" -":code:`aggregate_fit` is responsible for aggregating the results returned" -" by the clients that were selected and asked to train in " -":code:`configure_fit`." +"``aggregate_fit`` is responsible for aggregating the results returned by " +"the clients that were selected and asked to train in ``configure_fit``." msgstr "" ":code:`aggregate_fit` est chargé d'agréger les résultats renvoyés par les" " clients qui ont été sélectionnés et à qui on a demandé de s'entraîner " "dans :code:`configure_fit`." -#: ../../source/how-to-implement-strategies.rst:258 +#: ../../source/how-to-implement-strategies.rst:277 +#, fuzzy msgid "" "Of course, failures can happen, so there is no guarantee that the server " "will get results from all the clients it sent instructions to (via " -":code:`configure_fit`). :code:`aggregate_fit` therefore receives a list " -"of :code:`results`, but also a list of :code:`failures`." +"``configure_fit``). ``aggregate_fit`` therefore receives a list of " +"``results``, but also a list of ``failures``." msgstr "" "Bien sûr, des échecs peuvent se produire, il n'y a donc aucune garantie " "que le serveur obtienne des résultats de tous les clients auxquels il a " @@ -7103,12 +7145,13 @@ msgstr "" ":code:`aggregate_fit` reçoit donc une liste de :code:`résultats`, mais " "aussi une liste de :code:`échecs`." -#: ../../source/how-to-implement-strategies.rst:260 +#: ../../source/how-to-implement-strategies.rst:282 +#, fuzzy msgid "" -":code:`aggregate_fit` returns an optional :code:`Parameters` object and a" -" dictionary of aggregated metrics. The :code:`Parameters` return value is" -" optional because :code:`aggregate_fit` might decide that the results " -"provided are not sufficient for aggregation (e.g., too many failures)." +"``aggregate_fit`` returns an optional ``Parameters`` object and a " +"dictionary of aggregated metrics. The ``Parameters`` return value is " +"optional because ``aggregate_fit`` might decide that the results provided" +" are not sufficient for aggregation (e.g., too many failures)." msgstr "" ":code:`aggregate_fit` renvoie un objet :code:`Parameters` facultatif et " "un dictionnaire de métriques agrégées. La valeur de retour " @@ -7116,17 +7159,18 @@ msgstr "" " que les résultats fournis ne sont pas suffisants pour l'agrégation (par " "exemple, trop d'échecs)." -#: ../../source/how-to-implement-strategies.rst:263 -msgid "The :code:`configure_evaluate` method" +#: ../../source/how-to-implement-strategies.rst:288 +#, fuzzy +msgid "The ``configure_evaluate`` method" msgstr "La méthode :code:`configure_evaluate` (en anglais)" -#: ../../source/how-to-implement-strategies.rst:265 +#: ../../source/how-to-implement-strategies.rst:290 +#, fuzzy msgid "" -":code:`configure_evaluate` is responsible for configuring the upcoming " -"round of evaluation. What does *configure* mean in this context? " -"Configuring a round means selecting clients and deciding what " -"instructions to send to these clients. The signature of " -":code:`configure_evaluate` makes this clear:" +"``configure_evaluate`` is responsible for configuring the upcoming round " +"of evaluation. What does *configure* mean in this context? Configuring a " +"round means selecting clients and deciding what instructions to send to " +"these clients. The signature of ``configure_evaluate`` makes this clear:" msgstr "" ":code:`configure_evaluate` est chargé de configurer le prochain tour " "d'évaluation. Que signifie *configurer* dans ce contexte ? Configurer un " @@ -7134,32 +7178,34 @@ msgstr "" " envoyer. La signature de :code:`configure_evaluate` l'indique clairement" " :" -#: ../../source/how-to-implement-strategies.rst:278 +#: ../../source/how-to-implement-strategies.rst:303 +#, fuzzy msgid "" "The return value is a list of tuples, each representing the instructions " "that will be sent to a particular client. Strategy implementations " -"usually perform the following steps in :code:`configure_evaluate`:" +"usually perform the following steps in ``configure_evaluate``:" msgstr "" "La valeur de retour est une liste de tuples, chacun représentant les " "instructions qui seront envoyées à un client particulier. Les " "implémentations de stratégies effectuent généralement les étapes " "suivantes dans :code:`configure_evaluate` :" -#: ../../source/how-to-implement-strategies.rst:281 +#: ../../source/how-to-implement-strategies.rst:309 +#, fuzzy msgid "" -"Pair each :code:`ClientProxy` with the same :code:`EvaluateIns` holding " -"the current global model :code:`parameters` and :code:`config` dict" +"Pair each ``ClientProxy`` with the same ``EvaluateIns`` holding the " +"current global model ``parameters`` and ``config`` dict" msgstr "" "Associe chaque :code:`ClientProxy` au même :code:`EvaluateIns` contenant " "le modèle global actuel :code:`parameters` et :code:`config` dict" -#: ../../source/how-to-implement-strategies.rst:283 +#: ../../source/how-to-implement-strategies.rst:312 #, fuzzy msgid "" -"More sophisticated implementations can use :code:`configure_evaluate` to " +"More sophisticated implementations can use ``configure_evaluate`` to " "implement custom client selection logic. A client will only participate " -"in a round if the corresponding :code:`ClientProxy` is included in the " -"list returned from :code:`configure_evaluate`." +"in a round if the corresponding ``ClientProxy`` is included in the list " +"returned from ``configure_evaluate``." msgstr "" "Les implémentations plus sophistiquées peuvent utiliser " ":code:`configure_evaluate` pour mettre en œuvre une logique de sélection " @@ -7167,14 +7213,14 @@ msgstr "" ":code:`ClientProxy` correspondant est inclus dans la liste renvoyée par " ":code:`configure_evaluate`." -#: ../../source/how-to-implement-strategies.rst:287 +#: ../../source/how-to-implement-strategies.rst:318 +#, fuzzy msgid "" "The structure of this return value provides a lot of flexibility to the " "user. Since instructions are defined on a per-client basis, different " "instructions can be sent to each client. This enables custom strategies " "to evaluate, for example, different models on different clients, or use " -"different hyperparameters on different clients (via the :code:`config` " -"dict)." +"different hyperparameters on different clients (via the ``config`` dict)." msgstr "" "La structure de cette valeur de retour offre beaucoup de souplesse à " "l'utilisateur. Comme les instructions sont définies par client, des " @@ -7183,26 +7229,29 @@ msgstr "" "modèles sur différents clients, ou d'utiliser différents hyperparamètres " "sur différents clients (via le dict :code:`config`)." -#: ../../source/how-to-implement-strategies.rst:291 -msgid "The :code:`aggregate_evaluate` method" +#: ../../source/how-to-implement-strategies.rst:325 +#, fuzzy +msgid "The ``aggregate_evaluate`` method" msgstr "La méthode :code:`aggregate_evaluate` (agréger_évaluer)" -#: ../../source/how-to-implement-strategies.rst:293 +#: ../../source/how-to-implement-strategies.rst:327 +#, fuzzy msgid "" -":code:`aggregate_evaluate` is responsible for aggregating the results " +"``aggregate_evaluate`` is responsible for aggregating the results " "returned by the clients that were selected and asked to evaluate in " -":code:`configure_evaluate`." +"``configure_evaluate``." msgstr "" ":code:`aggregate_evaluate` est chargé d'agréger les résultats renvoyés " "par les clients qui ont été sélectionnés et à qui l'on a demandé " "d'évaluer dans :code:`configure_evaluate`." -#: ../../source/how-to-implement-strategies.rst:306 +#: ../../source/how-to-implement-strategies.rst:341 +#, fuzzy msgid "" "Of course, failures can happen, so there is no guarantee that the server " "will get results from all the clients it sent instructions to (via " -":code:`configure_evaluate`). :code:`aggregate_evaluate` therefore " -"receives a list of :code:`results`, but also a list of :code:`failures`." +"``configure_evaluate``). ``aggregate_evaluate`` therefore receives a list" +" of ``results``, but also a list of ``failures``." msgstr "" "Bien sûr, des échecs peuvent se produire, il n'y a donc aucune garantie " "que le serveur obtienne des résultats de tous les clients auxquels il a " @@ -7210,12 +7259,13 @@ msgstr "" ":code:`aggregate_evaluate` reçoit donc une liste de :code:`résultats`, " "mais aussi une liste d' :code:`échecs`." -#: ../../source/how-to-implement-strategies.rst:308 +#: ../../source/how-to-implement-strategies.rst:346 +#, fuzzy msgid "" -":code:`aggregate_evaluate` returns an optional :code:`float` (loss) and a" -" dictionary of aggregated metrics. The :code:`float` return value is " -"optional because :code:`aggregate_evaluate` might decide that the results" -" provided are not sufficient for aggregation (e.g., too many failures)." +"``aggregate_evaluate`` returns an optional ``float`` (loss) and a " +"dictionary of aggregated metrics. The ``float`` return value is optional " +"because ``aggregate_evaluate`` might decide that the results provided are" +" not sufficient for aggregation (e.g., too many failures)." msgstr "" ":code:`aggregate_evaluate` renvoie un :code:`float` facultatif (perte) et" " un dictionnaire de mesures agrégées. La valeur de retour :code:`float` " @@ -7223,28 +7273,31 @@ msgstr "" "résultats fournis ne sont pas suffisants pour l'agrégation (par exemple, " "trop d'échecs)." -#: ../../source/how-to-implement-strategies.rst:311 -msgid "The :code:`evaluate` method" +#: ../../source/how-to-implement-strategies.rst:352 +#, fuzzy +msgid "The ``evaluate`` method" msgstr "La méthode :code:`évaluer`" -#: ../../source/how-to-implement-strategies.rst:313 +#: ../../source/how-to-implement-strategies.rst:354 +#, fuzzy msgid "" -":code:`evaluate` is responsible for evaluating model parameters on the " -"server-side. Having :code:`evaluate` in addition to " -":code:`configure_evaluate`/:code:`aggregate_evaluate` enables strategies " -"to perform both servers-side and client-side (federated) evaluation." +"``evaluate`` is responsible for evaluating model parameters on the " +"server-side. Having ``evaluate`` in addition to " +"``configure_evaluate``/``aggregate_evaluate`` enables strategies to " +"perform both servers-side and client-side (federated) evaluation." msgstr "" "le fait d'avoir :code:`evaluate` en plus de " ":code:`configure_evaluate`/:code:`aggregate_evaluate` permet aux " "stratégies d'effectuer des évaluations à la fois côté serveur et côté " "client (fédéré)." -#: ../../source/how-to-implement-strategies.rst:323 +#: ../../source/how-to-implement-strategies.rst:364 +#, fuzzy msgid "" "The return value is again optional because the strategy might not need to" " implement server-side evaluation or because the user-defined " -":code:`evaluate` method might not complete successfully (e.g., it might " -"fail to load the server-side evaluation data)." +"``evaluate`` method might not complete successfully (e.g., it might fail " +"to load the server-side evaluation data)." msgstr "" "La valeur de retour est à nouveau facultative parce que la stratégie peut" " ne pas avoir besoin de mettre en œuvre l'évaluation côté serveur ou " @@ -7257,65 +7310,65 @@ msgstr "" msgid "Install Flower" msgstr "Installer Flower" -#: ../../source/how-to-install-flower.rst:6 +#: ../../source/how-to-install-flower.rst:5 #, fuzzy msgid "Python version" msgstr "Version Python" -#: ../../source/how-to-install-flower.rst:12 +#: ../../source/how-to-install-flower.rst:11 msgid "Install stable release" msgstr "Installe la version stable" -#: ../../source/how-to-install-flower.rst:15 -#: ../../source/how-to-upgrade-to-flower-next.rst:46 +#: ../../source/how-to-install-flower.rst:14 +#: ../../source/how-to-upgrade-to-flower-next.rst:66 msgid "Using pip" msgstr "" -#: ../../source/how-to-install-flower.rst:17 -msgid "" -"Stable releases are available on `PyPI " -"`_::" +#: ../../source/how-to-install-flower.rst:16 +#, fuzzy +msgid "Stable releases are available on `PyPI `_:" msgstr "" "Les versions stables sont disponibles sur `PyPI " "`_: :" -#: ../../source/how-to-install-flower.rst:21 +#: ../../source/how-to-install-flower.rst:22 +#, fuzzy msgid "" "For simulations that use the Virtual Client Engine, ``flwr`` should be " -"installed with the ``simulation`` extra::" +"installed with the ``simulation`` extra:" msgstr "" "Pour les simulations qui utilisent le moteur de client virtuel, ``flwr`` " "doit être installé avec l'option ``simulation``: :" -#: ../../source/how-to-install-flower.rst:27 +#: ../../source/how-to-install-flower.rst:30 msgid "Using conda (or mamba)" msgstr "" -#: ../../source/how-to-install-flower.rst:29 +#: ../../source/how-to-install-flower.rst:32 msgid "Flower can also be installed from the ``conda-forge`` channel." msgstr "" -#: ../../source/how-to-install-flower.rst:31 +#: ../../source/how-to-install-flower.rst:34 msgid "" "If you have not added ``conda-forge`` to your channels, you will first " -"need to run the following::" +"need to run the following:" msgstr "" -#: ../../source/how-to-install-flower.rst:36 +#: ../../source/how-to-install-flower.rst:42 msgid "" "Once the ``conda-forge`` channel has been enabled, ``flwr`` can be " -"installed with ``conda``::" +"installed with ``conda``:" msgstr "" -#: ../../source/how-to-install-flower.rst:40 -msgid "or with ``mamba``::" +#: ../../source/how-to-install-flower.rst:49 +msgid "or with ``mamba``:" msgstr "" -#: ../../source/how-to-install-flower.rst:46 +#: ../../source/how-to-install-flower.rst:56 msgid "Verify installation" msgstr "Vérifie l'installation" -#: ../../source/how-to-install-flower.rst:48 +#: ../../source/how-to-install-flower.rst:58 #, fuzzy msgid "" "The following command can be used to verify if Flower was successfully " @@ -7326,58 +7379,62 @@ msgstr "" "installé avec succès. Si tout a fonctionné, la version de Flower devrait " "être imprimée sur la ligne de commande: :" -#: ../../source/how-to-install-flower.rst:58 +#: ../../source/how-to-install-flower.rst:68 msgid "Advanced installation options" msgstr "Options d'installation avancées" -#: ../../source/how-to-install-flower.rst:61 +#: ../../source/how-to-install-flower.rst:71 #, fuzzy msgid "Install via Docker" msgstr "Installer Flower" -#: ../../source/how-to-install-flower.rst:63 +#: ../../source/how-to-install-flower.rst:73 msgid ":doc:`Run Flower using Docker `" msgstr "" -#: ../../source/how-to-install-flower.rst:66 +#: ../../source/how-to-install-flower.rst:76 msgid "Install pre-release" msgstr "Installer la version pre-release" -#: ../../source/how-to-install-flower.rst:68 +#: ../../source/how-to-install-flower.rst:78 +#, fuzzy msgid "" "New (possibly unstable) versions of Flower are sometimes available as " "pre-release versions (alpha, beta, release candidate) before the stable " -"release happens::" +"release happens:" msgstr "" "Les nouvelles versions (éventuellement instables) de Flower sont parfois " "disponibles en tant que versions préliminaires (alpha, bêta, release " "candidate) avant que la version stable n'arrive : :" -#: ../../source/how-to-install-flower.rst:72 +#: ../../source/how-to-install-flower.rst:85 +#, fuzzy msgid "" "For simulations that use the Virtual Client Engine, ``flwr`` pre-releases" -" should be installed with the ``simulation`` extra::" +" should be installed with the ``simulation`` extra:" msgstr "" "Pour les simulations qui utilisent le moteur de client virtuel, les " "versions de ``flwr`` doivent être installées avec l'option " "``simulation``: :" -#: ../../source/how-to-install-flower.rst:77 +#: ../../source/how-to-install-flower.rst:93 msgid "Install nightly release" msgstr "Installer la version nightly" -#: ../../source/how-to-install-flower.rst:79 +#: ../../source/how-to-install-flower.rst:95 +#, fuzzy msgid "" "The latest (potentially unstable) changes in Flower are available as " -"nightly releases::" +"nightly releases:" msgstr "" "Les dernières modifications (potentiellement instables) de Flower sont " "disponibles sous forme de versions nocturnes: :" -#: ../../source/how-to-install-flower.rst:83 +#: ../../source/how-to-install-flower.rst:101 +#, fuzzy msgid "" "For simulations that use the Virtual Client Engine, ``flwr-nightly`` " -"should be installed with the ``simulation`` extra::" +"should be installed with the ``simulation`` extra:" msgstr "" "Pour les simulations qui utilisent le moteur de client virtuel, ``flwr-" "nightly`` doit être installé avec l'option ``simulation``: :" @@ -7402,7 +7459,7 @@ msgstr "" "sur la consommation des ressources peuvent t'aider à prendre des " "décisions plus intelligentes et à accélérer le temps d'exécution." -#: ../../source/how-to-monitor-simulation.rst:6 +#: ../../source/how-to-monitor-simulation.rst:9 msgid "" "The specific instructions assume you are using macOS and have the " "`Homebrew `_ package manager installed." @@ -7410,11 +7467,11 @@ msgstr "" "Les instructions spécifiques supposent que tu utilises macOS et que le " "gestionnaire de paquets `Homebrew `_ est installé." -#: ../../source/how-to-monitor-simulation.rst:10 +#: ../../source/how-to-monitor-simulation.rst:13 msgid "Downloads" msgstr "Téléchargements" -#: ../../source/how-to-monitor-simulation.rst:16 +#: ../../source/how-to-monitor-simulation.rst:19 msgid "" "`Prometheus `_ is used for data collection, while" " `Grafana `_ will enable you to visualize the " @@ -7426,7 +7483,7 @@ msgstr "" "visualiser les données collectées. Ils sont tous deux bien intégrés à " "`Ray `_ que Flower utilise sous le capot." -#: ../../source/how-to-monitor-simulation.rst:18 +#: ../../source/how-to-monitor-simulation.rst:23 msgid "" "Overwrite the configuration files (depending on your device, it might be " "installed on a different path)." @@ -7434,17 +7491,17 @@ msgstr "" "Écrase les fichiers de configuration (selon ton appareil, il se peut " "qu'il soit installé sur un chemin différent)." -#: ../../source/how-to-monitor-simulation.rst:20 +#: ../../source/how-to-monitor-simulation.rst:26 msgid "If you are on an M1 Mac, it should be:" msgstr "Si tu es sur un Mac M1, il devrait l'être :" -#: ../../source/how-to-monitor-simulation.rst:27 +#: ../../source/how-to-monitor-simulation.rst:33 msgid "On the previous generation Intel Mac devices, it should be:" msgstr "" "Sur les appareils Mac Intel de la génération précédente, ce devrait être " "le cas :" -#: ../../source/how-to-monitor-simulation.rst:34 +#: ../../source/how-to-monitor-simulation.rst:40 msgid "" "Open the respective configuration files and change them. Depending on " "your device, use one of the two following commands:" @@ -7452,7 +7509,7 @@ msgstr "" "Ouvre les fichiers de configuration respectifs et modifie-les. Selon ton " "appareil, utilise l'une des deux commandes suivantes :" -#: ../../source/how-to-monitor-simulation.rst:44 +#: ../../source/how-to-monitor-simulation.rst:51 msgid "" "and then delete all the text in the file and paste a new Prometheus " "config you see below. You may adjust the time intervals to your " @@ -7462,7 +7519,7 @@ msgstr "" "configuration Prometheus que tu vois ci-dessous. Tu peux adapter les " "intervalles de temps à tes besoins :" -#: ../../source/how-to-monitor-simulation.rst:59 +#: ../../source/how-to-monitor-simulation.rst:67 msgid "" "Now after you have edited the Prometheus configuration, do the same with " "the Grafana configuration files. Open those using one of the following " @@ -7472,7 +7529,7 @@ msgstr "" "même avec les fichiers de configuration de Grafana. Ouvre ces derniers à " "l'aide de l'une des commandes suivantes, comme précédemment :" -#: ../../source/how-to-monitor-simulation.rst:69 +#: ../../source/how-to-monitor-simulation.rst:78 msgid "" "Your terminal editor should open and allow you to apply the following " "configuration as before." @@ -7480,7 +7537,7 @@ msgstr "" "Ton éditeur de terminal devrait s'ouvrir et te permettre d'appliquer la " "configuration suivante comme précédemment." -#: ../../source/how-to-monitor-simulation.rst:84 +#: ../../source/how-to-monitor-simulation.rst:94 msgid "" "Congratulations, you just downloaded all the necessary software needed " "for metrics tracking. Now, let’s start it." @@ -7488,11 +7545,11 @@ msgstr "" "Félicitations, tu viens de télécharger tous les logiciels nécessaires au " "suivi des métriques, maintenant, démarrons-le." -#: ../../source/how-to-monitor-simulation.rst:88 +#: ../../source/how-to-monitor-simulation.rst:98 msgid "Tracking metrics" msgstr "Suivi des mesures" -#: ../../source/how-to-monitor-simulation.rst:90 +#: ../../source/how-to-monitor-simulation.rst:100 msgid "" "Before running your Flower simulation, you have to start the monitoring " "tools you have just installed and configured." @@ -7500,7 +7557,7 @@ msgstr "" "Avant de lancer ta simulation Flower, tu dois démarrer les outils de " "surveillance que tu viens d'installer et de configurer." -#: ../../source/how-to-monitor-simulation.rst:97 +#: ../../source/how-to-monitor-simulation.rst:108 msgid "" "Please include the following argument in your Python code when starting a" " simulation." @@ -7508,11 +7565,11 @@ msgstr "" "Tu dois inclure l'argument suivant dans ton code Python lorsque tu " "démarres une simulation." -#: ../../source/how-to-monitor-simulation.rst:108 +#: ../../source/how-to-monitor-simulation.rst:119 msgid "Now, you are ready to start your workload." msgstr "Maintenant, tu es prêt à commencer ta charge de travail." -#: ../../source/how-to-monitor-simulation.rst:110 +#: ../../source/how-to-monitor-simulation.rst:121 msgid "" "Shortly after the simulation starts, you should see the following logs in" " your terminal:" @@ -7520,11 +7577,12 @@ msgstr "" "Peu de temps après le début de la simulation, tu devrais voir les " "journaux suivants dans ton terminal :" -#: ../../source/how-to-monitor-simulation.rst:117 -msgid "You can look at everything at ``_ ." +#: ../../source/how-to-monitor-simulation.rst:127 +#, fuzzy +msgid "You can look at everything at http://127.0.0.1:8265 ." msgstr "Tu peux tout regarder sur ``_ ." -#: ../../source/how-to-monitor-simulation.rst:119 +#: ../../source/how-to-monitor-simulation.rst:129 msgid "" "It's a Ray Dashboard. You can navigate to Metrics (on the left panel, the" " lowest option)." @@ -7532,7 +7590,7 @@ msgstr "" "Il s'agit d'un tableau de bord Ray. Tu peux naviguer vers Metrics (sur le" " panneau de gauche, l'option la plus basse)." -#: ../../source/how-to-monitor-simulation.rst:121 +#: ../../source/how-to-monitor-simulation.rst:132 msgid "" "Or alternatively, you can just see them in Grafana by clicking on the " "right-up corner, “View in Grafana”. Please note that the Ray dashboard is" @@ -7546,21 +7604,22 @@ msgstr "" "terminée, tu ne peux utiliser Grafana que pour explorer les métriques. Tu" " peux démarrer Grafana en te rendant sur `http://localhost:3000/``." -#: ../../source/how-to-monitor-simulation.rst:123 +#: ../../source/how-to-monitor-simulation.rst:137 +#, fuzzy msgid "" "After you finish the visualization, stop Prometheus and Grafana. This is " -"important as they will otherwise block, for example port :code:`3000` on " -"your machine as long as they are running." +"important as they will otherwise block, for example port ``3000`` on your" +" machine as long as they are running." msgstr "" "Après avoir terminé la visualisation, arrête Prometheus et Grafana. C'est" " important car sinon ils bloqueront, par exemple, le port :code:`3000` " "sur ta machine tant qu'ils seront en cours d'exécution." -#: ../../source/how-to-monitor-simulation.rst:132 +#: ../../source/how-to-monitor-simulation.rst:147 msgid "Resource allocation" msgstr "Allocation des ressources" -#: ../../source/how-to-monitor-simulation.rst:134 +#: ../../source/how-to-monitor-simulation.rst:149 msgid "" "You must understand how the Ray library works to efficiently allocate " "system resources to simulation clients on your own." @@ -7569,7 +7628,7 @@ msgstr "" "efficacement les ressources du système aux clients de simulation de ton " "côté." -#: ../../source/how-to-monitor-simulation.rst:136 +#: ../../source/how-to-monitor-simulation.rst:152 msgid "" "Initially, the simulation (which Ray handles under the hood) starts by " "default with all the available resources on the system, which it shares " @@ -7586,11 +7645,11 @@ msgstr "" "ce blog. Tu peux vérifier les ressources du système en exécutant ce qui " "suit :" -#: ../../source/how-to-monitor-simulation.rst:143 +#: ../../source/how-to-monitor-simulation.rst:164 msgid "In Google Colab, the result you see might be similar to this:" msgstr "Dans Google Colab, le résultat que tu obtiens peut ressembler à ceci :" -#: ../../source/how-to-monitor-simulation.rst:155 +#: ../../source/how-to-monitor-simulation.rst:175 msgid "" "However, you can overwrite the defaults. When starting a simulation, do " "the following (you don't need to overwrite all of them):" @@ -7599,11 +7658,11 @@ msgstr "" "une simulation, fais ce qui suit (tu n'as pas besoin de les écraser " "toutes) :" -#: ../../source/how-to-monitor-simulation.rst:175 +#: ../../source/how-to-monitor-simulation.rst:195 msgid "Let’s also specify the resource for a single client." msgstr "Spécifions également la ressource pour un seul client." -#: ../../source/how-to-monitor-simulation.rst:205 +#: ../../source/how-to-monitor-simulation.rst:225 msgid "" "Now comes the crucial part. Ray will start a new client only when it has " "all the required resources (such that they run in parallel) when the " @@ -7613,14 +7672,15 @@ msgstr "" "ressources nécessaires (de manière à ce qu'ils fonctionnent en parallèle)" " lorsque les ressources le permettront." -#: ../../source/how-to-monitor-simulation.rst:207 +#: ../../source/how-to-monitor-simulation.rst:228 +#, fuzzy msgid "" "In the example above, only one client will be run, so your clients won't " -"run concurrently. Setting :code:`client_num_gpus = 0.5` would allow " -"running two clients and therefore enable them to run concurrently. Be " -"careful not to require more resources than available. If you specified " -":code:`client_num_gpus = 2`, the simulation wouldn't start (even if you " -"had 2 GPUs but decided to set 1 in :code:`ray_init_args`)." +"run concurrently. Setting ``client_num_gpus = 0.5`` would allow running " +"two clients and therefore enable them to run concurrently. Be careful not" +" to require more resources than available. If you specified " +"``client_num_gpus = 2``, the simulation wouldn't start (even if you had 2" +" GPUs but decided to set 1 in ``ray_init_args``)." msgstr "" "Dans l'exemple ci-dessus, un seul client sera exécuté, donc tes clients " "ne fonctionneront pas simultanément. En définissant " @@ -7631,15 +7691,15 @@ msgstr "" "as 2 GPU mais que tu as décidé d'en définir 1 dans " ":code:`ray_init_args`)." -#: ../../source/how-to-monitor-simulation.rst:212 ../../source/ref-faq.rst:2 +#: ../../source/how-to-monitor-simulation.rst:235 ../../source/ref-faq.rst:2 msgid "FAQ" msgstr "FAQ" -#: ../../source/how-to-monitor-simulation.rst:214 +#: ../../source/how-to-monitor-simulation.rst:237 msgid "Q: I don't see any metrics logged." msgstr "Q : Je ne vois aucune mesure enregistrée." -#: ../../source/how-to-monitor-simulation.rst:216 +#: ../../source/how-to-monitor-simulation.rst:239 msgid "" "A: The timeframe might not be properly set. The setting is in the top " "right corner (\"Last 30 minutes\" by default). Please change the " @@ -7650,7 +7710,7 @@ msgstr "" "défaut). Modifie le délai pour qu'il corresponde à la période pendant " "laquelle la simulation s'est déroulée." -#: ../../source/how-to-monitor-simulation.rst:218 +#: ../../source/how-to-monitor-simulation.rst:243 msgid "" "Q: I see “Grafana server not detected. Please make sure the Grafana " "server is running and refresh this page” after going to the Metrics tab " @@ -7660,7 +7720,7 @@ msgstr "" "serveur Grafana fonctionne et actualise cette page\" après avoir accédé à" " l'onglet Métriques dans Ray Dashboard." -#: ../../source/how-to-monitor-simulation.rst:220 +#: ../../source/how-to-monitor-simulation.rst:246 msgid "" "A: You probably don't have Grafana running. Please check the running " "services" @@ -7668,15 +7728,16 @@ msgstr "" "R : Grafana n'est probablement pas en cours d'exécution. Vérifie les " "services en cours d'exécution" -#: ../../source/how-to-monitor-simulation.rst:226 +#: ../../source/how-to-monitor-simulation.rst:252 +#, fuzzy msgid "" "Q: I see \"This site can't be reached\" when going to " -"``_." +"http://127.0.0.1:8265." msgstr "" "Q : Je vois \"This site can't be reached\" quand je vais sur " "``_." -#: ../../source/how-to-monitor-simulation.rst:228 +#: ../../source/how-to-monitor-simulation.rst:254 msgid "" "A: Either the simulation has already finished, or you still need to start" " Prometheus." @@ -7684,22 +7745,22 @@ msgstr "" "R : Soit la simulation est déjà terminée, soit tu dois encore démarrer " "Prometheus." -#: ../../source/how-to-monitor-simulation.rst:232 +#: ../../source/how-to-monitor-simulation.rst:257 msgid "Resources" msgstr "Ressources" -#: ../../source/how-to-monitor-simulation.rst:234 +#: ../../source/how-to-monitor-simulation.rst:259 #, fuzzy msgid "" -"Ray Dashboard: ``_" +"Ray Dashboard: https://docs.ray.io/en/latest/ray-observability/getting-" +"started.html" msgstr "" "Tableau de bord Ray : ``_" -#: ../../source/how-to-monitor-simulation.rst:236 +#: ../../source/how-to-monitor-simulation.rst:261 #, fuzzy -msgid "Ray Metrics: ``_" +msgid "Ray Metrics: https://docs.ray.io/en/latest/cluster/metrics.html" msgstr "" "Ray Metrics : ``_" @@ -7725,19 +7786,19 @@ msgid "" "VCE." msgstr "" -#: ../../source/how-to-run-simulations.rst:10 +#: ../../source/how-to-run-simulations.rst:19 msgid "" -"The :code:`VirtualClientEngine` schedules, launches and manages `virtual`" -" clients. These clients are identical to `non-virtual` clients (i.e. the " +"The ``VirtualClientEngine`` schedules, launches and manages `virtual` " +"clients. These clients are identical to `non-virtual` clients (i.e. the " "ones you launch via the command `flwr.client.start_client `_) in the sense that they can be configure by " "creating a class inheriting, for example, from `flwr.client.NumPyClient " "`_ and therefore behave in an " "identical way. In addition to that, clients managed by the " -":code:`VirtualClientEngine` are:" +"``VirtualClientEngine`` are:" msgstr "" -#: ../../source/how-to-run-simulations.rst:12 +#: ../../source/how-to-run-simulations.rst:26 msgid "" "resource-aware: this means that each client gets assigned a portion of " "the compute and memory on your system. You as a user can control this at " @@ -7746,14 +7807,14 @@ msgid "" "client, the more clients can run concurrently on the same hardware." msgstr "" -#: ../../source/how-to-run-simulations.rst:13 +#: ../../source/how-to-run-simulations.rst:31 msgid "" "self-managed: this means that you as a user do not need to launch clients" -" manually, instead this gets delegated to :code:`VirtualClientEngine`'s " +" manually, instead this gets delegated to ``VirtualClientEngine``'s " "internals." msgstr "" -#: ../../source/how-to-run-simulations.rst:14 +#: ../../source/how-to-run-simulations.rst:33 msgid "" "ephemeral: this means that a client is only materialized when it is " "required in the FL process (e.g. to do `fit() `_, an open-source framework for scalable Python " -"workloads. In particular, Flower's :code:`VirtualClientEngine` makes use " -"of `Actors `_ to " -"spawn `virtual` clients and run their workload." +"workloads. In particular, Flower's ``VirtualClientEngine`` makes use of " +"`Actors `_ to spawn " +"`virtual` clients and run their workload." msgstr "" -#: ../../source/how-to-run-simulations.rst:20 +#: ../../source/how-to-run-simulations.rst:45 msgid "Launch your Flower simulation" msgstr "" -#: ../../source/how-to-run-simulations.rst:22 +#: ../../source/how-to-run-simulations.rst:47 msgid "" "Running Flower simulations still require you to define your client class," " a strategy, and utility functions to download and load (and potentially " @@ -7785,37 +7846,37 @@ msgid "" " as follows:" msgstr "" -#: ../../source/how-to-run-simulations.rst:44 +#: ../../source/how-to-run-simulations.rst:73 #, fuzzy msgid "VirtualClientEngine resources" msgstr "Moteur de client virtuel" -#: ../../source/how-to-run-simulations.rst:45 +#: ../../source/how-to-run-simulations.rst:75 msgid "" "By default the VCE has access to all system resources (i.e. all CPUs, all" " GPUs, etc) since that is also the default behavior when starting Ray. " "However, in some settings you might want to limit how many of your system" " resources are used for simulation. You can do this via the " -":code:`ray_init_args` input argument to :code:`start_simulation` which " -"the VCE internally passes to Ray's :code:`ray.init` command. For a " -"complete list of settings you can configure check the `ray.init " +"``ray_init_args`` input argument to ``start_simulation`` which the VCE " +"internally passes to Ray's ``ray.init`` command. For a complete list of " +"settings you can configure check the `ray.init " "`_" -" documentation. Do not set :code:`ray_init_args` if you want the VCE to " -"use all your system's CPUs and GPUs." +" documentation. Do not set ``ray_init_args`` if you want the VCE to use " +"all your system's CPUs and GPUs." msgstr "" -#: ../../source/how-to-run-simulations.rst:62 +#: ../../source/how-to-run-simulations.rst:97 msgid "Assigning client resources" msgstr "" -#: ../../source/how-to-run-simulations.rst:63 +#: ../../source/how-to-run-simulations.rst:99 msgid "" -"By default the :code:`VirtualClientEngine` assigns a single CPU core (and" -" nothing else) to each virtual client. This means that if your system has" -" 10 cores, that many virtual clients can be concurrently running." +"By default the ``VirtualClientEngine`` assigns a single CPU core (and " +"nothing else) to each virtual client. This means that if your system has " +"10 cores, that many virtual clients can be concurrently running." msgstr "" -#: ../../source/how-to-run-simulations.rst:65 +#: ../../source/how-to-run-simulations.rst:103 msgid "" "More often than not, you would probably like to adjust the resources your" " clients get assigned based on the complexity (i.e. compute and memory " @@ -7826,34 +7887,32 @@ msgid "" "our case Flower clients):" msgstr "" -#: ../../source/how-to-run-simulations.rst:67 -msgid ":code:`num_cpus` indicates the number of CPU cores a client would get." +#: ../../source/how-to-run-simulations.rst:110 +msgid "``num_cpus`` indicates the number of CPU cores a client would get." msgstr "" -#: ../../source/how-to-run-simulations.rst:68 -msgid "" -":code:`num_gpus` indicates the **ratio** of GPU memory a client gets " -"assigned." +#: ../../source/how-to-run-simulations.rst:111 +msgid "``num_gpus`` indicates the **ratio** of GPU memory a client gets assigned." msgstr "" -#: ../../source/how-to-run-simulations.rst:70 +#: ../../source/how-to-run-simulations.rst:113 msgid "Let's see a few examples:" msgstr "" -#: ../../source/how-to-run-simulations.rst:89 +#: ../../source/how-to-run-simulations.rst:132 msgid "" -"While the :code:`client_resources` can be used to control the degree of " +"While the ``client_resources`` can be used to control the degree of " "concurrency in your FL simulation, this does not stop you from running " "dozens, hundreds or even thousands of clients in the same round and " "having orders of magnitude more `dormant` (i.e. not participating in a " "round) clients. Let's say you want to have 100 clients per round but your" " system can only accommodate 8 clients concurrently. The " -":code:`VirtualClientEngine` will schedule 100 jobs to run (each " -"simulating a client sampled by the strategy) and then will execute them " -"in a resource-aware manner in batches of 8." +"``VirtualClientEngine`` will schedule 100 jobs to run (each simulating a " +"client sampled by the strategy) and then will execute them in a resource-" +"aware manner in batches of 8." msgstr "" -#: ../../source/how-to-run-simulations.rst:91 +#: ../../source/how-to-run-simulations.rst:140 msgid "" "To understand all the intricate details on how resources are used to " "schedule FL clients and how to define custom resources, please take a " @@ -7861,19 +7920,19 @@ msgid "" "core/scheduling/resources.html>`_." msgstr "" -#: ../../source/how-to-run-simulations.rst:94 +#: ../../source/how-to-run-simulations.rst:145 #, fuzzy msgid "Simulation examples" msgstr "Exemples de PyTorch" -#: ../../source/how-to-run-simulations.rst:96 +#: ../../source/how-to-run-simulations.rst:147 msgid "" "A few ready-to-run complete examples for Flower simulation in " "Tensorflow/Keras and PyTorch are provided in the `Flower repository " "`_. You can run them on Google Colab too:" msgstr "" -#: ../../source/how-to-run-simulations.rst:98 +#: ../../source/how-to-run-simulations.rst:151 #, fuzzy msgid "" "`Tensorflow/Keras Simulation " @@ -7884,114 +7943,112 @@ msgstr "" "`_" -#: ../../source/how-to-run-simulations.rst:99 +#: ../../source/how-to-run-simulations.rst:154 msgid "" "`PyTorch Simulation `_: 100 clients collaboratively train a CNN model on " "MNIST." msgstr "" -#: ../../source/how-to-run-simulations.rst:104 +#: ../../source/how-to-run-simulations.rst:159 #, fuzzy msgid "Multi-node Flower simulations" msgstr "Simulation de moniteur" -#: ../../source/how-to-run-simulations.rst:106 +#: ../../source/how-to-run-simulations.rst:161 msgid "" -"Flower's :code:`VirtualClientEngine` allows you to run FL simulations " -"across multiple compute nodes. Before starting your multi-node simulation" -" ensure that you:" +"Flower's ``VirtualClientEngine`` allows you to run FL simulations across " +"multiple compute nodes. Before starting your multi-node simulation ensure" +" that you:" msgstr "" -#: ../../source/how-to-run-simulations.rst:108 +#: ../../source/how-to-run-simulations.rst:164 msgid "Have the same Python environment in all nodes." msgstr "" -#: ../../source/how-to-run-simulations.rst:109 +#: ../../source/how-to-run-simulations.rst:165 msgid "Have a copy of your code (e.g. your entire repo) in all nodes." msgstr "" -#: ../../source/how-to-run-simulations.rst:110 +#: ../../source/how-to-run-simulations.rst:166 msgid "" "Have a copy of your dataset in all nodes (more about this in " ":ref:`simulation considerations `)" msgstr "" -#: ../../source/how-to-run-simulations.rst:111 +#: ../../source/how-to-run-simulations.rst:168 msgid "" -"Pass :code:`ray_init_args={\"address\"=\"auto\"}` to `start_simulation " -"`_ so the " -":code:`VirtualClientEngine` attaches to a running Ray instance." +"Pass ``ray_init_args={\"address\"=\"auto\"}`` to `start_simulation `_ so the " +"``VirtualClientEngine`` attaches to a running Ray instance." msgstr "" -#: ../../source/how-to-run-simulations.rst:112 +#: ../../source/how-to-run-simulations.rst:171 msgid "" -"Start Ray on you head node: on the terminal type :code:`ray start " -"--head`. This command will print a few lines, one of which indicates how " -"to attach other nodes to the head node." +"Start Ray on you head node: on the terminal type ``ray start --head``. " +"This command will print a few lines, one of which indicates how to attach" +" other nodes to the head node." msgstr "" -#: ../../source/how-to-run-simulations.rst:113 +#: ../../source/how-to-run-simulations.rst:174 msgid "" "Attach other nodes to the head node: copy the command shown after " "starting the head and execute it on terminal of a new node: for example " -":code:`ray start --address='192.168.1.132:6379'`" +"``ray start --address='192.168.1.132:6379'``" msgstr "" -#: ../../source/how-to-run-simulations.rst:115 +#: ../../source/how-to-run-simulations.rst:178 msgid "" "With all the above done, you can run your code from the head node as you " "would if the simulation was running on a single node." msgstr "" -#: ../../source/how-to-run-simulations.rst:117 +#: ../../source/how-to-run-simulations.rst:181 msgid "" "Once your simulation is finished, if you'd like to dismantle your cluster" -" you simply need to run the command :code:`ray stop` in each node's " -"terminal (including the head node)." +" you simply need to run the command ``ray stop`` in each node's terminal " +"(including the head node)." msgstr "" -#: ../../source/how-to-run-simulations.rst:120 +#: ../../source/how-to-run-simulations.rst:185 msgid "Multi-node simulation good-to-know" msgstr "" -#: ../../source/how-to-run-simulations.rst:122 +#: ../../source/how-to-run-simulations.rst:187 msgid "" "Here we list a few interesting functionality when running multi-node FL " "simulations:" msgstr "" -#: ../../source/how-to-run-simulations.rst:124 +#: ../../source/how-to-run-simulations.rst:189 msgid "" -"User :code:`ray status` to check all nodes connected to your head node as" -" well as the total resources available to the " -":code:`VirtualClientEngine`." +"User ``ray status`` to check all nodes connected to your head node as " +"well as the total resources available to the ``VirtualClientEngine``." msgstr "" -#: ../../source/how-to-run-simulations.rst:126 +#: ../../source/how-to-run-simulations.rst:192 msgid "" "When attaching a new node to the head, all its resources (i.e. all CPUs, " "all GPUs) will be visible by the head node. This means that the " -":code:`VirtualClientEngine` can schedule as many `virtual` clients as " -"that node can possible run. In some settings you might want to exclude " -"certain resources from the simulation. You can do this by appending " -"`--num-cpus=` and/or `--num-" -"gpus=` in any :code:`ray start` command (including " -"when starting the head)" +"``VirtualClientEngine`` can schedule as many `virtual` clients as that " +"node can possible run. In some settings you might want to exclude certain" +" resources from the simulation. You can do this by appending `--num-" +"cpus=` and/or `--num-gpus=` in " +"any ``ray start`` command (including when starting the head)" msgstr "" -#: ../../source/how-to-run-simulations.rst:132 +#: ../../source/how-to-run-simulations.rst:202 #, fuzzy msgid "Considerations for simulations" msgstr "Simulation de moniteur" -#: ../../source/how-to-run-simulations.rst:135 +#: ../../source/how-to-run-simulations.rst:206 msgid "" "We are actively working on these fronts so to make it trivial to run any " "FL workload with Flower simulation." msgstr "" -#: ../../source/how-to-run-simulations.rst:138 +#: ../../source/how-to-run-simulations.rst:209 msgid "" "The current VCE allows you to run Federated Learning workloads in " "simulation mode whether you are prototyping simple scenarios on your " @@ -8002,61 +8059,60 @@ msgid "" "couple of current limitations in our implementation." msgstr "" -#: ../../source/how-to-run-simulations.rst:141 +#: ../../source/how-to-run-simulations.rst:217 #, fuzzy msgid "GPU resources" msgstr "Ressources" -#: ../../source/how-to-run-simulations.rst:143 +#: ../../source/how-to-run-simulations.rst:219 msgid "" "The VCE assigns a share of GPU memory to a client that specifies the key " -":code:`num_gpus` in :code:`client_resources`. This being said, Ray (used " +"``num_gpus`` in ``client_resources``. This being said, Ray (used " "internally by the VCE) is by default:" msgstr "" -#: ../../source/how-to-run-simulations.rst:146 +#: ../../source/how-to-run-simulations.rst:222 msgid "" "not aware of the total VRAM available on the GPUs. This means that if you" -" set :code:`num_gpus=0.5` and you have two GPUs in your system with " -"different (e.g. 32GB and 8GB) VRAM amounts, they both would run 2 clients" -" concurrently." +" set ``num_gpus=0.5`` and you have two GPUs in your system with different" +" (e.g. 32GB and 8GB) VRAM amounts, they both would run 2 clients " +"concurrently." msgstr "" -#: ../../source/how-to-run-simulations.rst:147 +#: ../../source/how-to-run-simulations.rst:225 msgid "" "not aware of other unrelated (i.e. not created by the VCE) workloads are " "running on the GPU. Two takeaways from this are:" msgstr "" -#: ../../source/how-to-run-simulations.rst:149 +#: ../../source/how-to-run-simulations.rst:228 msgid "" "Your Flower server might need a GPU to evaluate the `global model` after " "aggregation (by instance when making use of the `evaluate method `_)" msgstr "" -#: ../../source/how-to-run-simulations.rst:150 +#: ../../source/how-to-run-simulations.rst:231 msgid "" "If you want to run several independent Flower simulations on the same " "machine you need to mask-out your GPUs with " -":code:`CUDA_VISIBLE_DEVICES=\"\"` when launching your " -"experiment." +"``CUDA_VISIBLE_DEVICES=\"\"`` when launching your experiment." msgstr "" -#: ../../source/how-to-run-simulations.rst:153 +#: ../../source/how-to-run-simulations.rst:235 msgid "" -"In addition, the GPU resource limits passed to :code:`client_resources` " -"are not `enforced` (i.e. they can be exceeded) which can result in the " +"In addition, the GPU resource limits passed to ``client_resources`` are " +"not `enforced` (i.e. they can be exceeded) which can result in the " "situation of client using more VRAM than the ratio specified when " "starting the simulation." msgstr "" -#: ../../source/how-to-run-simulations.rst:156 +#: ../../source/how-to-run-simulations.rst:240 #, fuzzy msgid "TensorFlow with GPUs" msgstr "Exemples de TensorFlow" -#: ../../source/how-to-run-simulations.rst:158 +#: ../../source/how-to-run-simulations.rst:242 msgid "" "When `using a GPU with TensorFlow " "`_ nearly your entire GPU memory of" @@ -8068,17 +8124,17 @@ msgid "" "`_." msgstr "" -#: ../../source/how-to-run-simulations.rst:160 +#: ../../source/how-to-run-simulations.rst:249 msgid "" "This would need to be done in the main process (which is where the server" " would run) and in each Actor created by the VCE. By means of " -":code:`actor_kwargs` we can pass the reserved key `\"on_actor_init_fn\"` " -"in order to specify a function to be executed upon actor initialization. " -"In this case, to enable GPU growth for TF workloads. It would look as " +"``actor_kwargs`` we can pass the reserved key `\"on_actor_init_fn\"` in " +"order to specify a function to be executed upon actor initialization. In " +"this case, to enable GPU growth for TF workloads. It would look as " "follows:" msgstr "" -#: ../../source/how-to-run-simulations.rst:179 +#: ../../source/how-to-run-simulations.rst:272 #, fuzzy msgid "" "This is precisely the mechanism used in `Tensorflow/Keras Simulation " @@ -8089,11 +8145,11 @@ msgstr "" "`_" -#: ../../source/how-to-run-simulations.rst:183 +#: ../../source/how-to-run-simulations.rst:276 msgid "Multi-node setups" msgstr "" -#: ../../source/how-to-run-simulations.rst:185 +#: ../../source/how-to-run-simulations.rst:278 msgid "" "The VCE does not currently offer a way to control on which node a " "particular `virtual` client is executed. In other words, if more than a " @@ -8106,7 +8162,7 @@ msgid "" "circumvent data duplication." msgstr "" -#: ../../source/how-to-run-simulations.rst:187 +#: ../../source/how-to-run-simulations.rst:286 msgid "" "By definition virtual clients are `stateless` due to their ephemeral " "nature. A client state can be implemented as part of the Flower client " @@ -8135,17 +8191,17 @@ msgid "Model checkpointing" msgstr "Point de contrôle du modèle" #: ../../source/how-to-save-and-load-model-checkpoints.rst:10 +#, fuzzy msgid "" "Model updates can be persisted on the server-side by customizing " -":code:`Strategy` methods. Implementing custom strategies is always an " -"option, but for many cases it may be more convenient to simply customize " -"an existing strategy. The following code example defines a new " -":code:`SaveModelStrategy` which customized the existing built-in " -":code:`FedAvg` strategy. In particular, it customizes " -":code:`aggregate_fit` by calling :code:`aggregate_fit` in the base class " -"(:code:`FedAvg`). It then continues to save returned (aggregated) weights" -" before it returns those aggregated weights to the caller (i.e., the " -"server):" +"``Strategy`` methods. Implementing custom strategies is always an option," +" but for many cases it may be more convenient to simply customize an " +"existing strategy. The following code example defines a new " +"``SaveModelStrategy`` which customized the existing built-in ``FedAvg`` " +"strategy. In particular, it customizes ``aggregate_fit`` by calling " +"``aggregate_fit`` in the base class (``FedAvg``). It then continues to " +"save returned (aggregated) weights before it returns those aggregated " +"weights to the caller (i.e., the server):" msgstr "" "Les mises à jour du modèle peuvent être conservées côté serveur en " "personnalisant les méthodes :code:`Strategy`. L'implémentation de " @@ -8159,12 +8215,12 @@ msgstr "" "retournés (agrégés) avant de renvoyer ces poids agrégés à l'appelant " "(c'est-à-dire le serveur) :" -#: ../../source/how-to-save-and-load-model-checkpoints.rst:47 +#: ../../source/how-to-save-and-load-model-checkpoints.rst:53 #, fuzzy msgid "Save and load PyTorch checkpoints" msgstr "Sauvegarde et chargement des points de contrôle PyTorch" -#: ../../source/how-to-save-and-load-model-checkpoints.rst:49 +#: ../../source/how-to-save-and-load-model-checkpoints.rst:55 #, fuzzy msgid "" "Similar to the previous example but with a few extra steps, we'll show " @@ -8182,7 +8238,7 @@ msgstr "" "transformés en ``state_dict`` PyTorch en suivant la structure de la " "classe ``OrderedDict``." -#: ../../source/how-to-save-and-load-model-checkpoints.rst:85 +#: ../../source/how-to-save-and-load-model-checkpoints.rst:98 msgid "" "To load your progress, you simply append the following lines to your " "code. Note that this will iterate over all saved checkpoints and load the" @@ -8192,7 +8248,7 @@ msgstr "" "à ton code. Note que cela va itérer sur tous les points de contrôle " "sauvegardés et charger le plus récent :" -#: ../../source/how-to-save-and-load-model-checkpoints.rst:97 +#: ../../source/how-to-save-and-load-model-checkpoints.rst:111 msgid "" "Return/use this object of type ``Parameters`` wherever necessary, such as" " in the ``initial_parameters`` when defining a ``Strategy``." @@ -8215,12 +8271,12 @@ msgstr "" "changements qui nécessitent de modifier le code des projets de la série " "0.x existants." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:8 -#: ../../source/how-to-upgrade-to-flower-next.rst:43 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:10 +#: ../../source/how-to-upgrade-to-flower-next.rst:63 msgid "Install update" msgstr "Installer la mise à jour" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:10 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:12 msgid "" "Here's how to update an existing installation to Flower 1.0 using either " "pip or Poetry:" @@ -8228,11 +8284,11 @@ msgstr "" "Voici comment mettre à jour une installation existante vers Flower 1.0 en" " utilisant soit pip soit Poetry :" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:12 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:14 msgid "pip: add ``-U`` when installing." msgstr "pip : ajoute ``-U`` lors de l'installation." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:14 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:16 msgid "" "``python -m pip install -U flwr`` (when using ``start_server`` and " "``start_client``)" @@ -8240,7 +8296,7 @@ msgstr "" "``python -m pip install -U flwr`` (lors de l'utilisation de " "``start_server`` et ``start_client``)" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:15 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:17 msgid "" "``python -m pip install -U 'flwr[simulation]'`` (when using " "``start_simulation``)" @@ -8248,7 +8304,7 @@ msgstr "" "``python -m pip install -U 'flwr[simulation]'`` (lors de l'utilisation de" " ``start_simulation``)" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:17 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:19 msgid "" "Poetry: update the ``flwr`` dependency in ``pyproject.toml`` and then " "reinstall (don't forget to delete ``poetry.lock`` via ``rm poetry.lock`` " @@ -8258,13 +8314,13 @@ msgstr "" "puis réinstallez (n'oubliez pas de supprimer ``poetry.lock`` via ``rm " "poetry.lock`` avant d'exécuter ``poetry install``)." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:19 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:23 msgid "``flwr = \"^1.0.0\"`` (when using ``start_server`` and ``start_client``)" msgstr "" "``flwr = \"^1.0.0\"`` (lors de l'utilisation de ``start_server`` et " "``start_client``)" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:20 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:24 msgid "" "``flwr = { version = \"^1.0.0\", extras = [\"simulation\"] }`` (when " "using ``start_simulation``)" @@ -8272,22 +8328,22 @@ msgstr "" "``flwr = { version = \"^1.0.0\", extras = [\"simulation\"] }`` (lors de " "l'utilisation de ``start_simulation``)" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:24 -#: ../../source/how-to-upgrade-to-flower-next.rst:100 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:28 +#: ../../source/how-to-upgrade-to-flower-next.rst:120 msgid "Required changes" msgstr "Changements nécessaires" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:26 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:30 msgid "The following breaking changes require manual updates." msgstr "" "Les changements de rupture suivants nécessitent des mises à jour " "manuelles." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:29 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:33 msgid "General" msgstr "Généralités" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:31 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:35 msgid "" "Pass all arguments as keyword arguments (not as positional arguments). " "Here's an example:" @@ -8295,7 +8351,7 @@ msgstr "" "Passe tous les arguments comme des arguments de mots-clés (et non comme " "des arguments de position). Voici un exemple :" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:33 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:38 msgid "" "Flower 0.19 (positional arguments): ``start_client(\"127.0.0.1:8080\", " "FlowerClient())``" @@ -8303,7 +8359,7 @@ msgstr "" "Flower 0.19 (arguments positionnels) : ``start_client(\"127.0.0.1:8080\"," " FlowerClient())``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:34 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:39 msgid "" "Flower 1.0 (keyword arguments): " "``start_client(server_address=\"127.0.0.1:8080\", " @@ -8313,12 +8369,12 @@ msgstr "" "``start_client(server_address=\"127.0.0.1:8080\", " "client=FlowerClient())``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:37 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:43 #: ../../source/ref-api/flwr.client.Client.rst:2 msgid "Client" msgstr "Client" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:39 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:45 msgid "" "Subclasses of ``NumPyClient``: change ``def get_parameters(self):``` to " "``def get_parameters(self, config):``" @@ -8326,7 +8382,7 @@ msgstr "" "Sous-classes de ``NumPyClient`` : changez ``def get_parameters(self):`` " "en ``def get_parameters(self, config):``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:40 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:47 msgid "" "Subclasses of ``Client``: change ``def get_parameters(self):``` to ``def " "get_parameters(self, ins: GetParametersIns):``" @@ -8334,11 +8390,11 @@ msgstr "" "Sous-classes de ``Client`` : changez ``def get_parameters(self):`` en " "``def get_parameters(self, ins : GetParametersIns):``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:43 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:51 msgid "Strategies / ``start_server`` / ``start_simulation``" msgstr "Stratégies / ``démarrer_serveur`` / ``démarrer_simulation``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:45 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:53 msgid "" "Pass ``ServerConfig`` (instead of a dictionary) to ``start_server`` and " "``start_simulation``. Here's an example:" @@ -8346,7 +8402,7 @@ msgstr "" "Passez ``ServerConfig`` (au lieu d'un dictionnaire) à ``start_server`` et" " ``start_simulation``. Voici un exemple :" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:47 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:56 msgid "" "Flower 0.19: ``start_server(..., config={\"num_rounds\": 3, " "\"round_timeout\": 600.0}, ...)``" @@ -8354,7 +8410,7 @@ msgstr "" "Flower 0.19 : ``start_server(..., config={\"num_rounds\" : 3, " "\"round_timeout\" : 600.0}, ...)``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:48 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:58 msgid "" "Flower 1.0: ``start_server(..., " "config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " @@ -8364,7 +8420,7 @@ msgstr "" "config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " "...)``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:50 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:61 msgid "" "Replace ``num_rounds=1`` in ``start_simulation`` with the new " "``config=ServerConfig(...)`` (see previous item)" @@ -8372,7 +8428,7 @@ msgstr "" "Remplacer ``num_rounds=1`` dans ``start_simulation`` par le nouveau " "``config=ServerConfig(...)`` (voir point précédent)" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:51 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:63 msgid "" "Remove ``force_final_distributed_eval`` parameter from calls to " "``start_server``. Distributed evaluation on all clients can be enabled by" @@ -8384,19 +8440,19 @@ msgstr "" "activée en configurant la stratégie pour échantillonner tous les clients " "pour l'évaluation après le dernier tour de formation." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:52 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:66 msgid "Rename parameter/ndarray conversion functions:" msgstr "Renomme les fonctions de conversion des paramètres et des tableaux :" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:54 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:68 msgid "``parameters_to_weights`` --> ``parameters_to_ndarrays``" msgstr "``parameters_to_weights`` --> ``parameters_to_ndarrays``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:55 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:69 msgid "``weights_to_parameters`` --> ``ndarrays_to_parameters``" msgstr "``Poids_à_paramètres`` --> ``Réseaux_à_paramètres``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:57 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:71 msgid "" "Strategy initialization: if the strategy relies on the default values for" " ``fraction_fit`` and ``fraction_evaluate``, set ``fraction_fit`` and " @@ -8413,23 +8469,23 @@ msgstr "" "stratégie) doivent maintenant initialiser manuellement FedAvg avec " "``fraction_fit`` et ``fraction_evaluate`` fixés à ``0.1``." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:58 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:77 msgid "Rename built-in strategy parameters (e.g., ``FedAvg``):" msgstr "Renommer les paramètres de stratégie intégrés (par exemple, ``FedAvg``) :" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:60 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:79 msgid "``fraction_eval`` --> ``fraction_evaluate``" msgstr "``fraction_eval`` --> ``fraction_evaluate``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:61 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:80 msgid "``min_eval_clients`` --> ``min_evaluate_clients``" msgstr "``min_eval_clients` --> ``min_evaluate_clients``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:62 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:81 msgid "``eval_fn`` --> ``evaluate_fn``" msgstr "``eval_fn`` --> ``evaluate_fn``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:64 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:83 msgid "" "Rename ``rnd`` to ``server_round``. This impacts multiple methods and " "functions, for example, ``configure_fit``, ``aggregate_fit``, " @@ -8439,11 +8495,11 @@ msgstr "" "méthodes et fonctions, par exemple, ``configure_fit``, ``aggregate_fit``," " ``configure_evaluate``, ``aggregate_evaluate``, et ``evaluate_fn``." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:65 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:86 msgid "Add ``server_round`` and ``config`` to ``evaluate_fn``:" msgstr "Ajoute ``server_round`` et ``config`` à `evaluate_fn`` :" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:67 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:88 msgid "" "Flower 0.19: ``def evaluate(parameters: NDArrays) -> " "Optional[Tuple[float, Dict[str, Scalar]]]:``" @@ -8451,7 +8507,7 @@ msgstr "" "Flower 0.19 : ``def evaluate(parameters : NDArrays) -> " "Optional[Tuple[float, Dict[str, Scalar]]]]:``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:68 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:90 msgid "" "Flower 1.0: ``def evaluate(server_round: int, parameters: NDArrays, " "config: Dict[str, Scalar]) -> Optional[Tuple[float, Dict[str, " @@ -8461,11 +8517,11 @@ msgstr "" "config : Dict[str, Scalar]) -> Optional[Tuple[float, Dict[str, " "Scalar]]]:``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:71 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:94 msgid "Custom strategies" msgstr "Stratégies personnalisées" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:73 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:96 msgid "" "The type of parameter ``failures`` has changed from " "``List[BaseException]`` to ``List[Union[Tuple[ClientProxy, FitRes], " @@ -8478,7 +8534,7 @@ msgstr "" "``aggregate_fit``) et ``List[Union[Tuple[ClientProxy, EvaluateRes], " "BaseException]]`` (dans ``aggregate_evaluate``)" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:74 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:100 msgid "" "The ``Strategy`` method ``evaluate`` now receives the current round of " "federated learning/evaluation as the first parameter:" @@ -8486,7 +8542,7 @@ msgstr "" "La méthode ``Stratégie`` `évaluer`` reçoit maintenant le cycle actuel " "d'apprentissage/évaluation fédéré comme premier paramètre :" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:76 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:103 msgid "" "Flower 0.19: ``def evaluate(self, parameters: Parameters) -> " "Optional[Tuple[float, Dict[str, Scalar]]]:``" @@ -8494,7 +8550,7 @@ msgstr "" "Flower 0.19 : ``def evaluate(self, parameters : Parameters) -> " "Optional[Tuple[float, Dict[str, Scalar]]]]:``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:77 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:105 msgid "" "Flower 1.0: ``def evaluate(self, server_round: int, parameters: " "Parameters) -> Optional[Tuple[float, Dict[str, Scalar]]]:``" @@ -8502,11 +8558,11 @@ msgstr "" "Flower 1.0 : ``def evaluate(self, server_round : int, parameters : " "Parameters) -> Optional[Tuple[float, Dict[str, Scalar]]]]:``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:80 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:109 msgid "Optional improvements" msgstr "Améliorations facultatives" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:82 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:111 msgid "" "Along with the necessary changes above, there are a number of potential " "improvements that just became possible:" @@ -8515,7 +8571,7 @@ msgstr "" "certain nombre d'améliorations potentielles qui viennent d'être rendues " "possibles :" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:84 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:114 msgid "" "Remove \"placeholder\" methods from subclasses of ``Client`` or " "``NumPyClient``. If you, for example, use server-side evaluation, then " @@ -8527,7 +8583,7 @@ msgstr "" "serveur, alors les implémentations \"placeholder\" de ``evaluate`` ne " "sont plus nécessaires." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:85 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:117 msgid "" "Configure the round timeout via ``start_simulation``: " "``start_simulation(..., config=flwr.server.ServerConfig(num_rounds=3, " @@ -8537,12 +8593,12 @@ msgstr "" "``start_simulation(..., config=flwr.server.ServerConfig(num_rounds=3, " "round_timeout=600.0), ...)``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:89 -#: ../../source/how-to-upgrade-to-flower-next.rst:317 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:121 +#: ../../source/how-to-upgrade-to-flower-next.rst:348 msgid "Further help" msgstr "Aide supplémentaire" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:91 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:123 msgid "" "Most official `Flower code examples " "`_ are already updated" @@ -8571,7 +8627,7 @@ msgid "" "1.8." msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:9 +#: ../../source/how-to-upgrade-to-flower-next.rst:11 msgid "" "This guide shows how to reuse pre-``1.8`` Flower code with minimum code " "changes by using the *compatibility layer* in Flower Next. In another " @@ -8579,11 +8635,11 @@ msgid "" "Next APIs." msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:13 +#: ../../source/how-to-upgrade-to-flower-next.rst:15 msgid "Let's dive in!" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:48 +#: ../../source/how-to-upgrade-to-flower-next.rst:68 #, fuzzy msgid "" "Here's how to update an existing installation of Flower to Flower Next " @@ -8592,26 +8648,26 @@ msgstr "" "Voici comment mettre à jour une installation existante vers Flower 1.0 en" " utilisant soit pip soit Poetry :" -#: ../../source/how-to-upgrade-to-flower-next.rst:54 +#: ../../source/how-to-upgrade-to-flower-next.rst:74 msgid "or if you need Flower Next with simulation:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:61 +#: ../../source/how-to-upgrade-to-flower-next.rst:80 msgid "" "Ensure you set the following version constraint in your " "``requirements.txt``" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:71 +#: ../../source/how-to-upgrade-to-flower-next.rst:90 msgid "or ``pyproject.toml``:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:82 +#: ../../source/how-to-upgrade-to-flower-next.rst:101 #, fuzzy msgid "Using Poetry" msgstr "Utiliser la poésie (recommandé)" -#: ../../source/how-to-upgrade-to-flower-next.rst:84 +#: ../../source/how-to-upgrade-to-flower-next.rst:103 #, fuzzy msgid "" "Update the ``flwr`` dependency in ``pyproject.toml`` and then reinstall " @@ -8622,14 +8678,14 @@ msgstr "" "puis réinstallez (n'oubliez pas de supprimer ``poetry.lock`` via ``rm " "poetry.lock`` avant d'exécuter ``poetry install``)." -#: ../../source/how-to-upgrade-to-flower-next.rst:86 +#: ../../source/how-to-upgrade-to-flower-next.rst:106 #, fuzzy msgid "" "Ensure you set the following version constraint in your " "``pyproject.toml``:" msgstr "Augmente la version mineure de ``pyproject.toml`` d'une unité." -#: ../../source/how-to-upgrade-to-flower-next.rst:102 +#: ../../source/how-to-upgrade-to-flower-next.rst:122 msgid "" "In Flower Next, the *infrastructure* and *application layers* have been " "decoupled. Instead of starting a client in code via ``start_client()``, " @@ -8642,33 +8698,33 @@ msgid "" "way:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:109 +#: ../../source/how-to-upgrade-to-flower-next.rst:131 #, fuzzy msgid "|clientapp_link|_" msgstr "client" -#: ../../source/how-to-upgrade-to-flower-next.rst:110 +#: ../../source/how-to-upgrade-to-flower-next.rst:133 msgid "" "Wrap your existing client with |clientapp_link|_ instead of launching it " "via |startclient_link|_. Here's an example:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:132 +#: ../../source/how-to-upgrade-to-flower-next.rst:156 #, fuzzy msgid "|serverapp_link|_" msgstr "serveur" -#: ../../source/how-to-upgrade-to-flower-next.rst:133 +#: ../../source/how-to-upgrade-to-flower-next.rst:158 msgid "" "Wrap your existing strategy with |serverapp_link|_ instead of starting " "the server via |startserver_link|_. Here's an example:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:154 +#: ../../source/how-to-upgrade-to-flower-next.rst:179 msgid "Deployment" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:155 +#: ../../source/how-to-upgrade-to-flower-next.rst:181 msgid "" "Run the ``SuperLink`` using |flowernext_superlink_link|_ before running, " "in sequence, |flowernext_clientapp_link|_ (2x) and " @@ -8676,13 +8732,13 @@ msgid "" " `server.py` as Python scripts." msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:158 +#: ../../source/how-to-upgrade-to-flower-next.rst:184 msgid "" "Here's an example to start the server without HTTPS (only for " "prototyping):" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:174 +#: ../../source/how-to-upgrade-to-flower-next.rst:200 msgid "" "Here's another example to start with HTTPS. Use the ``--ssl-ca-" "certfile``, ``--ssl-certfile``, and ``--ssl-keyfile`` command line " @@ -8690,19 +8746,19 @@ msgid "" "private key)." msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:201 +#: ../../source/how-to-upgrade-to-flower-next.rst:228 #, fuzzy msgid "Simulation in CLI" msgstr "Simulation de moniteur" -#: ../../source/how-to-upgrade-to-flower-next.rst:202 +#: ../../source/how-to-upgrade-to-flower-next.rst:230 msgid "" "Wrap your existing client and strategy with |clientapp_link|_ and " "|serverapp_link|_, respectively. There is no need to use |startsim_link|_" " anymore. Here's an example:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:232 +#: ../../source/how-to-upgrade-to-flower-next.rst:263 msgid "" "Run |flower_simulation_link|_ in CLI and point to the ``server_app`` / " "``client_app`` object in the code instead of executing the Python script." @@ -8710,24 +8766,24 @@ msgid "" "objects are in a ``sim.py`` module):" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:249 +#: ../../source/how-to-upgrade-to-flower-next.rst:280 msgid "" "Set default resources for each |clientapp_link|_ using the ``--backend-" "config`` command line argument instead of setting the " "``client_resources`` argument in |startsim_link|_. Here's an example:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:275 +#: ../../source/how-to-upgrade-to-flower-next.rst:304 msgid "Simulation in a Notebook" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:276 +#: ../../source/how-to-upgrade-to-flower-next.rst:306 msgid "" "Run |runsim_link|_ in your notebook instead of |startsim_link|_. Here's " "an example:" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:319 +#: ../../source/how-to-upgrade-to-flower-next.rst:350 #, fuzzy msgid "" "Some official `Flower code examples `_ " @@ -8745,19 +8801,19 @@ msgstr "" "Flower `_ et utilise le canal " "``#questions``." -#: ../../source/how-to-upgrade-to-flower-next.rst:325 +#: ../../source/how-to-upgrade-to-flower-next.rst:357 #, fuzzy msgid "Important" msgstr "Changements importants :" -#: ../../source/how-to-upgrade-to-flower-next.rst:328 +#: ../../source/how-to-upgrade-to-flower-next.rst:359 msgid "" "As we continuously enhance Flower Next at a rapid pace, we'll be " "periodically updating this guide. Please feel free to share any feedback " "with us!" msgstr "" -#: ../../source/how-to-upgrade-to-flower-next.rst:334 +#: ../../source/how-to-upgrade-to-flower-next.rst:365 msgid "Happy migrating! 🚀" msgstr "" @@ -8771,7 +8827,7 @@ msgid "" " interfaces may change in future versions.**" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:6 +#: ../../source/how-to-use-built-in-mods.rst:7 msgid "" "In this tutorial, we will learn how to utilize built-in mods to augment " "the behavior of a ``ClientApp``. Mods (sometimes also called Modifiers) " @@ -8779,105 +8835,105 @@ msgid "" "the ``ClientApp``." msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:9 +#: ../../source/how-to-use-built-in-mods.rst:12 msgid "What are Mods?" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:11 +#: ../../source/how-to-use-built-in-mods.rst:14 msgid "" "A Mod is a callable that wraps around a ``ClientApp``. It can manipulate " "or inspect the incoming ``Message`` and the resulting outgoing " "``Message``. The signature for a ``Mod`` is as follows:" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:18 +#: ../../source/how-to-use-built-in-mods.rst:23 msgid "A typical mod function might look something like this:" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:31 +#: ../../source/how-to-use-built-in-mods.rst:36 msgid "Using Mods" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:33 +#: ../../source/how-to-use-built-in-mods.rst:38 msgid "To use mods in your ``ClientApp``, you can follow these steps:" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:36 +#: ../../source/how-to-use-built-in-mods.rst:41 msgid "1. Import the required mods" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:38 +#: ../../source/how-to-use-built-in-mods.rst:43 msgid "First, import the built-in mod you intend to use:" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:46 +#: ../../source/how-to-use-built-in-mods.rst:51 msgid "2. Define your client function" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:48 +#: ../../source/how-to-use-built-in-mods.rst:53 msgid "" "Define your client function (``client_fn``) that will be wrapped by the " "mod(s):" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:57 +#: ../../source/how-to-use-built-in-mods.rst:62 msgid "3. Create the ``ClientApp`` with mods" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:59 +#: ../../source/how-to-use-built-in-mods.rst:64 msgid "" "Create your ``ClientApp`` and pass the mods as a list to the ``mods`` " "argument. The order in which you provide the mods matters:" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:72 +#: ../../source/how-to-use-built-in-mods.rst:78 #, fuzzy msgid "Order of execution" msgstr "Dépréciations" -#: ../../source/how-to-use-built-in-mods.rst:74 +#: ../../source/how-to-use-built-in-mods.rst:80 msgid "" "When the ``ClientApp`` runs, the mods are executed in the order they are " "provided in the list:" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:76 +#: ../../source/how-to-use-built-in-mods.rst:83 msgid "``example_mod_1`` (outermost mod)" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:77 +#: ../../source/how-to-use-built-in-mods.rst:84 msgid "``example_mod_2`` (next mod)" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:78 +#: ../../source/how-to-use-built-in-mods.rst:85 msgid "" "Message handler (core function that handles the incoming ``Message`` and " "returns the outgoing ``Message``)" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:79 +#: ../../source/how-to-use-built-in-mods.rst:87 msgid "``example_mod_2`` (on the way back)" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:80 +#: ../../source/how-to-use-built-in-mods.rst:88 msgid "``example_mod_1`` (outermost mod on the way back)" msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:82 +#: ../../source/how-to-use-built-in-mods.rst:90 msgid "" "Each mod has a chance to inspect and modify the incoming ``Message`` " "before passing it to the next mod, and likewise with the outgoing " "``Message`` before returning it up the stack." msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:87 +#: ../../source/how-to-use-built-in-mods.rst:97 msgid "" "By following this guide, you have learned how to effectively use mods to " "enhance your ``ClientApp``'s functionality. Remember that the order of " "mods is crucial and affects how the input and output are processed." msgstr "" -#: ../../source/how-to-use-built-in-mods.rst:89 +#: ../../source/how-to-use-built-in-mods.rst:101 msgid "Enjoy building a more robust and flexible ``ClientApp`` with mods!" msgstr "" @@ -8886,14 +8942,14 @@ msgstr "" msgid "Use Differential Privacy" msgstr "Confidentialité différentielle" -#: ../../source/how-to-use-differential-privacy.rst:3 +#: ../../source/how-to-use-differential-privacy.rst:4 msgid "" "This guide explains how you can utilize differential privacy in the " "Flower framework. If you are not yet familiar with differential privacy, " "you can refer to :doc:`explanation-differential-privacy`." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:7 +#: ../../source/how-to-use-differential-privacy.rst:10 msgid "" "Differential Privacy in Flower is in a preview phase. If you plan to use " "these features in a production environment with sensitive data, feel free" @@ -8901,7 +8957,7 @@ msgid "" "to best use these features." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:12 +#: ../../source/how-to-use-differential-privacy.rst:17 msgid "" "This approach consists of two separate phases: clipping of the updates " "and adding noise to the aggregated model. For the clipping phase, Flower " @@ -8909,7 +8965,7 @@ msgid "" "the server side or the client side." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:15 +#: ../../source/how-to-use-differential-privacy.rst:21 msgid "" "**Server-side Clipping**: This approach has the advantage of the server " "enforcing uniform clipping across all clients' updates and reducing the " @@ -8918,7 +8974,7 @@ msgid "" "the need to perform the clipping operation for all clients." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:16 +#: ../../source/how-to-use-differential-privacy.rst:26 msgid "" "**Client-side Clipping**: This approach has the advantage of reducing the" " computational overhead on the server. However, it also has the " @@ -8926,73 +8982,72 @@ msgid "" "control over the clipping process." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:21 +#: ../../source/how-to-use-differential-privacy.rst:31 #, fuzzy msgid "Server-side Clipping" msgstr "Logique côté serveur" -#: ../../source/how-to-use-differential-privacy.rst:22 +#: ../../source/how-to-use-differential-privacy.rst:33 msgid "" -"For central DP with server-side clipping, there are two :code:`Strategy` " -"classes that act as wrappers around the actual :code:`Strategy` instance " -"(for example, :code:`FedAvg`). The two wrapper classes are " -":code:`DifferentialPrivacyServerSideFixedClipping` and " -":code:`DifferentialPrivacyServerSideAdaptiveClipping` for fixed and " -"adaptive clipping." +"For central DP with server-side clipping, there are two ``Strategy`` " +"classes that act as wrappers around the actual ``Strategy`` instance (for" +" example, ``FedAvg``). The two wrapper classes are " +"``DifferentialPrivacyServerSideFixedClipping`` and " +"``DifferentialPrivacyServerSideAdaptiveClipping`` for fixed and adaptive " +"clipping." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:25 +#: ../../source/how-to-use-differential-privacy.rst:-1 #, fuzzy msgid "server side clipping" msgstr "Logique côté serveur" -#: ../../source/how-to-use-differential-privacy.rst:31 +#: ../../source/how-to-use-differential-privacy.rst:43 msgid "" -"The code sample below enables the :code:`FedAvg` strategy to use server-" -"side fixed clipping using the " -":code:`DifferentialPrivacyServerSideFixedClipping` wrapper class. The " -"same approach can be used with " -":code:`DifferentialPrivacyServerSideAdaptiveClipping` by adjusting the " +"The code sample below enables the ``FedAvg`` strategy to use server-side " +"fixed clipping using the ``DifferentialPrivacyServerSideFixedClipping`` " +"wrapper class. The same approach can be used with " +"``DifferentialPrivacyServerSideAdaptiveClipping`` by adjusting the " "corresponding input parameters." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:52 +#: ../../source/how-to-use-differential-privacy.rst:64 #, fuzzy msgid "Client-side Clipping" msgstr "Logique côté client" -#: ../../source/how-to-use-differential-privacy.rst:53 +#: ../../source/how-to-use-differential-privacy.rst:66 msgid "" "For central DP with client-side clipping, the server sends the clipping " "value to selected clients on each round. Clients can use existing Flower " -":code:`Mods` to perform the clipping. Two mods are available for fixed " -"and adaptive client-side clipping: :code:`fixedclipping_mod` and " -":code:`adaptiveclipping_mod` with corresponding server-side wrappers " -":code:`DifferentialPrivacyClientSideFixedClipping` and " -":code:`DifferentialPrivacyClientSideAdaptiveClipping`." +"``Mods`` to perform the clipping. Two mods are available for fixed and " +"adaptive client-side clipping: ``fixedclipping_mod`` and " +"``adaptiveclipping_mod`` with corresponding server-side wrappers " +"``DifferentialPrivacyClientSideFixedClipping`` and " +"``DifferentialPrivacyClientSideAdaptiveClipping``." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:57 +#: ../../source/how-to-use-differential-privacy.rst:-1 #, fuzzy msgid "client side clipping" msgstr "Logique côté client" -#: ../../source/how-to-use-differential-privacy.rst:63 +#: ../../source/how-to-use-differential-privacy.rst:78 msgid "" -"The code sample below enables the :code:`FedAvg` strategy to use " -"differential privacy with client-side fixed clipping using both the " -":code:`DifferentialPrivacyClientSideFixedClipping` wrapper class and, on " -"the client, :code:`fixedclipping_mod`:" +"The code sample below enables the ``FedAvg`` strategy to use differential" +" privacy with client-side fixed clipping using both the " +"``DifferentialPrivacyClientSideFixedClipping`` wrapper class and, on the " +"client, ``fixedclipping_mod``:" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:80 +#: ../../source/how-to-use-differential-privacy.rst:97 msgid "" -"In addition to the server-side strategy wrapper, the :code:`ClientApp` " -"needs to configure the matching :code:`fixedclipping_mod` to perform the " -"client-side clipping:" +"In addition to the server-side strategy wrapper, the ``ClientApp`` needs " +"to configure the matching ``fixedclipping_mod`` to perform the client-" +"side clipping:" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:97 +#: ../../source/how-to-use-differential-privacy.rst:115 msgid "" "To utilize local differential privacy (DP) and add noise to the client " "model parameters before transmitting them to the server in Flower, you " @@ -9000,15 +9055,15 @@ msgid "" "clipping norm value, sensitivity, epsilon, and delta." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:99 +#: ../../source/how-to-use-differential-privacy.rst:-1 msgid "local DP mod" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:104 -msgid "Below is a code example that shows how to use :code:`LocalDpMod`:" +#: ../../source/how-to-use-differential-privacy.rst:125 +msgid "Below is a code example that shows how to use ``LocalDpMod``:" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:122 +#: ../../source/how-to-use-differential-privacy.rst:140 msgid "" "Please note that the order of mods, especially those that modify " "parameters, is important when using multiple modifiers. Typically, " @@ -9016,11 +9071,11 @@ msgid "" "parameters." msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:125 +#: ../../source/how-to-use-differential-privacy.rst:145 msgid "Local Training using Privacy Engines" msgstr "" -#: ../../source/how-to-use-differential-privacy.rst:126 +#: ../../source/how-to-use-differential-privacy.rst:147 msgid "" "For ensuring data instance-level privacy during local model training on " "the client side, consider leveraging privacy engines such as Opacus and " @@ -9037,16 +9092,17 @@ msgid "Use strategies" msgstr "Stratégies personnalisées" #: ../../source/how-to-use-strategies.rst:4 +#, fuzzy msgid "" "Flower allows full customization of the learning process through the " -":code:`Strategy` abstraction. A number of built-in strategies are " -"provided in the core framework." +"``Strategy`` abstraction. A number of built-in strategies are provided in" +" the core framework." msgstr "" "Flower permet une personnalisation complète du processus d'apprentissage " "grâce à l'abstraction :code:`Stratégie`. Un certain nombre de stratégies " "intégrées sont fournies dans le cadre principal." -#: ../../source/how-to-use-strategies.rst:6 +#: ../../source/how-to-use-strategies.rst:7 msgid "" "There are three ways to customize the way Flower orchestrates the " "learning process on the server side:" @@ -9054,25 +9110,26 @@ msgstr "" "Il y a trois façons de personnaliser la manière dont Flower orchestre le " "processus d'apprentissage du côté du serveur :" -#: ../../source/how-to-use-strategies.rst:8 -msgid "Use an existing strategy, for example, :code:`FedAvg`" +#: ../../source/how-to-use-strategies.rst:10 +#, fuzzy +msgid "Use an existing strategy, for example, ``FedAvg``" msgstr "Utilise une stratégie existante, par exemple :code:`FedAvg`" -#: ../../source/how-to-use-strategies.rst:9 -#: ../../source/how-to-use-strategies.rst:40 +#: ../../source/how-to-use-strategies.rst:11 +#: ../../source/how-to-use-strategies.rst:43 msgid "Customize an existing strategy with callback functions" msgstr "Personnalise une stratégie existante avec des fonctions de rappel" -#: ../../source/how-to-use-strategies.rst:10 -#: ../../source/how-to-use-strategies.rst:87 +#: ../../source/how-to-use-strategies.rst:12 +#: ../../source/how-to-use-strategies.rst:99 msgid "Implement a novel strategy" msgstr "Mets en place une nouvelle stratégie" -#: ../../source/how-to-use-strategies.rst:14 +#: ../../source/how-to-use-strategies.rst:15 msgid "Use an existing strategy" msgstr "Utilise une stratégie existante" -#: ../../source/how-to-use-strategies.rst:16 +#: ../../source/how-to-use-strategies.rst:17 msgid "" "Flower comes with a number of popular federated learning strategies " "built-in. A built-in strategy can be instantiated as follows:" @@ -9080,18 +9137,19 @@ msgstr "" "Flower intègre un certain nombre de stratégies d'apprentissage fédéré " "populaires. Une stratégie intégrée peut être instanciée comme suit :" -#: ../../source/how-to-use-strategies.rst:25 +#: ../../source/how-to-use-strategies.rst:27 +#, fuzzy msgid "" "This creates a strategy with all parameters left at their default values " -"and passes it to the :code:`start_server` function. It is usually " -"recommended to adjust a few parameters during instantiation:" +"and passes it to the ``start_server`` function. It is usually recommended" +" to adjust a few parameters during instantiation:" msgstr "" "Cela crée une stratégie dont tous les paramètres sont laissés à leur " "valeur par défaut et la transmet à la fonction :code:`start_server`. Il " "est généralement recommandé d'ajuster quelques paramètres lors de " "l'instanciation :" -#: ../../source/how-to-use-strategies.rst:42 +#: ../../source/how-to-use-strategies.rst:45 msgid "" "Existing strategies provide several ways to customize their behaviour. " "Callback functions allow strategies to call user-provided code during " @@ -9101,19 +9159,19 @@ msgstr "" "comportement. Les fonctions de rappel permettent aux stratégies d'appeler" " le code fourni par l'utilisateur pendant l'exécution." -#: ../../source/how-to-use-strategies.rst:45 +#: ../../source/how-to-use-strategies.rst:49 msgid "Configuring client fit and client evaluate" msgstr "Configurer l'adaptation et l'évaluation du client" -#: ../../source/how-to-use-strategies.rst:47 +#: ../../source/how-to-use-strategies.rst:51 +#, fuzzy msgid "" "The server can pass new configuration values to the client each round by " -"providing a function to :code:`on_fit_config_fn`. The provided function " -"will be called by the strategy and must return a dictionary of " -"configuration key values pairs that will be sent to the client. It must " -"return a dictionary of arbitrary configuration values :code:`client.fit`" -" and :code:`client.evaluate` functions during each round of federated " -"learning." +"providing a function to ``on_fit_config_fn``. The provided function will " +"be called by the strategy and must return a dictionary of configuration " +"key values pairs that will be sent to the client. It must return a " +"dictionary of arbitrary configuration values ``client.fit`` and " +"``client.evaluate`` functions during each round of federated learning." msgstr "" "Le serveur peut transmettre de nouvelles valeurs de configuration au " "client à chaque tour en fournissant une fonction à " @@ -9124,14 +9182,14 @@ msgstr "" "et :code:`client.evaluate` au cours de chaque tour d'apprentissage " "fédéré." -#: ../../source/how-to-use-strategies.rst:75 +#: ../../source/how-to-use-strategies.rst:84 #, fuzzy msgid "" -"The :code:`on_fit_config_fn` can be used to pass arbitrary configuration " +"The ``on_fit_config_fn`` can be used to pass arbitrary configuration " "values from server to client, and potentially change these values each " "round, for example, to adjust the learning rate. The client will receive " -"the dictionary returned by the :code:`on_fit_config_fn` in its own " -":code:`client.fit()` function." +"the dictionary returned by the ``on_fit_config_fn`` in its own " +"``client.fit()`` function." msgstr "" "Le :code:`on_fit_config_fn` peut être utilisé pour passer des valeurs de " "configuration arbitraires du serveur au client, et changer poétiquement " @@ -9139,29 +9197,30 @@ msgstr "" "d'apprentissage. Le client recevra le dictionnaire renvoyé par le " ":code:`on_fit_config_fn` dans sa propre fonction :code:`client.fit()`." -#: ../../source/how-to-use-strategies.rst:78 +#: ../../source/how-to-use-strategies.rst:89 +#, fuzzy msgid "" -"Similar to :code:`on_fit_config_fn`, there is also " -":code:`on_evaluate_config_fn` to customize the configuration sent to " -":code:`client.evaluate()`" +"Similar to ``on_fit_config_fn``, there is also ``on_evaluate_config_fn`` " +"to customize the configuration sent to ``client.evaluate()``" msgstr "" "Comme pour :code:`on_fit_config_fn`, il existe aussi " ":code:`on_evaluate_config_fn` pour personnaliser la configuration envoyée" " à :code:`client.evaluate()`" -#: ../../source/how-to-use-strategies.rst:81 +#: ../../source/how-to-use-strategies.rst:93 msgid "Configuring server-side evaluation" msgstr "Configuration de l'évaluation côté serveur" -#: ../../source/how-to-use-strategies.rst:83 +#: ../../source/how-to-use-strategies.rst:95 +#, fuzzy msgid "" "Server-side evaluation can be enabled by passing an evaluation function " -"to :code:`evaluate_fn`." +"to ``evaluate_fn``." msgstr "" "L'évaluation côté serveur peut être activée en passant une fonction " "d'évaluation à :code:`evaluate_fn`." -#: ../../source/how-to-use-strategies.rst:89 +#: ../../source/how-to-use-strategies.rst:101 #, fuzzy msgid "" "Writing a fully custom strategy is a bit more involved, but it provides " @@ -9181,15 +9240,15 @@ msgstr "Tutoriel" msgid "Quickstart tutorials" msgstr "Quickstart tutorials" -#: ../../source/index.rst:75 ../../source/index.rst:79 +#: ../../source/index.rst:81 ../../source/index.rst:85 msgid "How-to guides" msgstr "Guides" -#: ../../source/index.rst:100 +#: ../../source/index.rst:106 msgid "Legacy example guides" msgstr "" -#: ../../source/index.rst:108 ../../source/index.rst:112 +#: ../../source/index.rst:114 ../../source/index.rst:119 msgid "Explanations" msgstr "Explications" @@ -9197,26 +9256,26 @@ msgstr "Explications" msgid "API reference" msgstr "Référence pour l'API" -#: ../../source/index.rst:138 +#: ../../source/index.rst:145 msgid "Reference docs" msgstr "Référence pour la documentation" -#: ../../source/index.rst:154 +#: ../../source/index.rst:160 #, fuzzy msgid "Contributor tutorials" msgstr "Configuration du contributeur" -#: ../../source/index.rst:161 +#: ../../source/index.rst:167 #, fuzzy msgid "Contributor how-to guides" msgstr "Guide pour les contributeurs" -#: ../../source/index.rst:173 +#: ../../source/index.rst:179 #, fuzzy msgid "Contributor explanations" msgstr "Explications" -#: ../../source/index.rst:179 +#: ../../source/index.rst:185 #, fuzzy msgid "Contributor references" msgstr "Configuration du contributeur" @@ -9254,7 +9313,7 @@ msgstr "" " chercheurs, ingénieurs, étudiants, professionnels, académiques, et " "autres hobbyistes." -#: ../../source/index.rst:15 +#: ../../source/index.rst:16 msgid "Join us on Slack" msgstr "Join us on Slack" @@ -9308,16 +9367,16 @@ msgstr "" "` | :ref:`Android ` | :ref:`iOS " "`" -#: ../../source/index.rst:64 +#: ../../source/index.rst:70 msgid "We also made video tutorials for PyTorch:" msgstr "" -#: ../../source/index.rst:69 +#: ../../source/index.rst:75 #, fuzzy msgid "And TensorFlow:" msgstr "Exemples de TensorFlow" -#: ../../source/index.rst:77 +#: ../../source/index.rst:83 msgid "" "Problem-oriented how-to guides show step-by-step how to achieve a " "specific goal." @@ -9325,7 +9384,7 @@ msgstr "" "Guides orientés sur la résolutions étapes par étapes de problèmes ou " "objectifs specifiques." -#: ../../source/index.rst:110 +#: ../../source/index.rst:116 msgid "" "Understanding-oriented concept guides explain and discuss key topics and " "underlying ideas behind Flower and collaborative AI." @@ -9333,29 +9392,29 @@ msgstr "" "Guides orientés sur la compréhension et l'explication des sujets et idées" " de fonds sur lesquels sont construits Flower et l'IA collaborative." -#: ../../source/index.rst:121 +#: ../../source/index.rst:128 #, fuzzy msgid "References" msgstr "Référence" -#: ../../source/index.rst:123 +#: ../../source/index.rst:130 msgid "Information-oriented API reference and other reference material." msgstr "Référence de l'API orientée sur l'information pure." -#: ../../source/index.rst:132::1 +#: ../../source/index.rst:139::1 msgid ":py:obj:`flwr `\\" msgstr "" -#: ../../source/index.rst:132::1 flwr:1 of +#: ../../source/index.rst:139::1 flwr:1 of msgid "Flower main package." msgstr "" -#: ../../source/index.rst:149 +#: ../../source/index.rst:155 #, fuzzy msgid "Contributor docs" msgstr "Configuration du contributeur" -#: ../../source/index.rst:151 +#: ../../source/index.rst:157 #, fuzzy msgid "" "The Flower community welcomes contributions. The following docs are " @@ -9584,17 +9643,16 @@ msgstr "Serveur de Flower" msgid "flower-server-app" msgstr "flower-driver-api" -#: ../../source/ref-api-cli.rst:49 +#: ../../source/ref-api-cli.rst:50 msgid "" -"Note that since version :code:`1.11.0`, :code:`flower-server-app` no " -"longer supports passing a reference to a `ServerApp` attribute. Instead, " -"you need to pass the path to Flower app via the argument :code:`--app`. " -"This is the path to a directory containing a `pyproject.toml`. You can " -"create a valid Flower app by executing :code:`flwr new` and following the" -" prompt." +"Note that since version ``1.11.0``, ``flower-server-app`` no longer " +"supports passing a reference to a `ServerApp` attribute. Instead, you " +"need to pass the path to Flower app via the argument ``--app``. This is " +"the path to a directory containing a `pyproject.toml`. You can create a " +"valid Flower app by executing ``flwr new`` and following the prompt." msgstr "" -#: ../../source/ref-api-cli.rst:62 +#: ../../source/ref-api-cli.rst:64 #, fuzzy msgid "flower-superexec" msgstr "flower-superlink" @@ -12132,7 +12190,7 @@ msgstr "" msgid "The encoding in which to encode the string." msgstr "" -#: flwr.common.EventType.encode:5 of +#: flwr.common.EventType.encode:9 of msgid "errors" msgstr "" @@ -12308,7 +12366,7 @@ msgid "" "string." msgstr "" -#: flwr.common.EventType.replace:3 of +#: flwr.common.EventType.replace:5 of msgid "count" msgstr "" @@ -12344,7 +12402,7 @@ msgid "" "strings and the original string." msgstr "" -#: flwr.common.EventType.rsplit:3 flwr.common.EventType.split:3 of +#: flwr.common.EventType.rsplit:7 flwr.common.EventType.split:7 of msgid "sep" msgstr "" @@ -12359,7 +12417,7 @@ msgid "" " empty strings from the result." msgstr "" -#: flwr.common.EventType.rsplit:9 flwr.common.EventType.split:9 of +#: flwr.common.EventType.rsplit:11 flwr.common.EventType.split:11 of msgid "maxsplit" msgstr "" @@ -12400,7 +12458,7 @@ msgid "" "remaining cased characters have lower case." msgstr "" -#: flwr.common.EventType.translate:3 of +#: flwr.common.EventType.translate:5 of #, fuzzy msgid "table" msgstr "Database" @@ -23884,13 +23942,16 @@ msgstr "" "tels que `PyTorch `_ ou `TensorFlow " "`_." -#: ../../source/ref-example-projects.rst:10 +#: ../../source/ref-example-projects.rst:9 #, fuzzy -msgid "" -"The following examples are available as standalone projects. Quickstart " -"TensorFlow/Keras ---------------------------" +msgid "The following examples are available as standalone projects." msgstr "Les exemples suivants sont disponibles sous forme de projets autonomes." +#: ../../source/ref-example-projects.rst:12 +#, fuzzy +msgid "Quickstart TensorFlow/Keras" +msgstr "Démarrage rapide de TensorFlow" + #: ../../source/ref-example-projects.rst:14 msgid "" "The TensorFlow/Keras quickstart example shows CIFAR-10 image " @@ -23910,14 +23971,14 @@ msgstr "" "`_" -#: ../../source/ref-example-projects.rst:18 +#: ../../source/ref-example-projects.rst:19 #, fuzzy msgid ":doc:`Quickstart TensorFlow (Tutorial) `" msgstr "" "`Quickstart TensorFlow (Tutorial) `_" -#: ../../source/ref-example-projects.rst:19 +#: ../../source/ref-example-projects.rst:20 msgid "" "`Quickstart TensorFlow (Blog Post) `_" @@ -23925,12 +23986,12 @@ msgstr "" "`Quickstart TensorFlow (Blog Post) `_" -#: ../../source/ref-example-projects.rst:23 -#: ../../source/tutorial-quickstart-pytorch.rst:5 +#: ../../source/ref-example-projects.rst:24 +#: ../../source/tutorial-quickstart-pytorch.rst:4 msgid "Quickstart PyTorch" msgstr "Démarrage rapide de PyTorch" -#: ../../source/ref-example-projects.rst:25 +#: ../../source/ref-example-projects.rst:26 msgid "" "The PyTorch quickstart example shows CIFAR-10 image classification with a" " simple Convolutional Neural Network:" @@ -23938,7 +23999,7 @@ msgstr "" "L'exemple de démarrage rapide PyTorch montre la classification d'images " "CIFAR-10 avec un simple réseau neuronal convolutif :" -#: ../../source/ref-example-projects.rst:28 +#: ../../source/ref-example-projects.rst:29 #, fuzzy msgid "" "`Quickstart PyTorch (Code) " @@ -23947,18 +24008,18 @@ msgstr "" "`Quickstart PyTorch (Code) " "`_" -#: ../../source/ref-example-projects.rst:29 +#: ../../source/ref-example-projects.rst:31 #, fuzzy msgid ":doc:`Quickstart PyTorch (Tutorial) `" msgstr "" "`Quickstart PyTorch (Tutorial) `_" -#: ../../source/ref-example-projects.rst:33 +#: ../../source/ref-example-projects.rst:34 msgid "PyTorch: From Centralized To Federated" msgstr "PyTorch : De la centralisation à la fédération" -#: ../../source/ref-example-projects.rst:35 +#: ../../source/ref-example-projects.rst:36 msgid "" "This example shows how a regular PyTorch project can be federated using " "Flower:" @@ -23966,7 +24027,7 @@ msgstr "" "Cet exemple montre comment un projet PyTorch ordinaire peut être fédéré à" " l'aide de Flower :" -#: ../../source/ref-example-projects.rst:37 +#: ../../source/ref-example-projects.rst:38 #, fuzzy msgid "" "`PyTorch: From Centralized To Federated (Code) " @@ -23977,7 +24038,7 @@ msgstr "" "`_" -#: ../../source/ref-example-projects.rst:38 +#: ../../source/ref-example-projects.rst:40 #, fuzzy msgid "" ":doc:`PyTorch: From Centralized To Federated (Tutorial) `_" -#: ../../source/ref-example-projects.rst:42 +#: ../../source/ref-example-projects.rst:44 msgid "Federated Learning on Raspberry Pi and Nvidia Jetson" msgstr "Apprentissage fédéré sur Raspberry Pi et Nvidia Jetson" -#: ../../source/ref-example-projects.rst:44 +#: ../../source/ref-example-projects.rst:46 msgid "" "This example shows how Flower can be used to build a federated learning " "system that run across Raspberry Pi and Nvidia Jetson:" @@ -24000,7 +24061,7 @@ msgstr "" "système d'apprentissage fédéré qui fonctionne sur Raspberry Pi et Nvidia " "Jetson :" -#: ../../source/ref-example-projects.rst:46 +#: ../../source/ref-example-projects.rst:49 #, fuzzy msgid "" "`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) " @@ -24009,7 +24070,7 @@ msgstr "" "`L'apprentissage fédéré sur Raspberry Pi et Nvidia Jetson (Code) " "`_" -#: ../../source/ref-example-projects.rst:47 +#: ../../source/ref-example-projects.rst:51 msgid "" "`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) " "`_" @@ -24032,7 +24093,7 @@ msgstr "" ":fa:`eye,mr-1` Flower peut-il fonctionner sur les ordinateurs portables " "Juptyter / Google Colab ?" -#: ../../source/ref-faq.rst:8 +#: ../../source/ref-faq.rst:9 msgid "" "Yes, it can! Flower even comes with a few under-the-hood optimizations to" " make it work even better on Colab. Here's a quickstart example:" @@ -24041,7 +24102,7 @@ msgstr "" "pour qu'il fonctionne encore mieux sur Colab. Voici un exemple de " "démarrage rapide :" -#: ../../source/ref-faq.rst:10 +#: ../../source/ref-faq.rst:11 #, fuzzy msgid "" "`Flower simulation PyTorch " @@ -24051,7 +24112,7 @@ msgstr "" "`Flower Quickstart (TensorFlow/Keras) " "`_" -#: ../../source/ref-faq.rst:11 +#: ../../source/ref-faq.rst:12 #, fuzzy msgid "" "`Flower simulation TensorFlow/Keras " @@ -24067,7 +24128,7 @@ msgstr "" ":fa:`eye,mr-1` Comment puis-je faire fonctionner l'apprentissage fédéré " "sur un Raspberry Pi ?" -#: ../../source/ref-faq.rst:15 +#: ../../source/ref-faq.rst:16 #, fuzzy msgid "" "Find the `blog post about federated learning on embedded device here " @@ -24086,7 +24147,7 @@ msgstr "" ":fa:`eye,mr-1` Est-ce que Flower prend en charge l'apprentissage fédéré " "sur les appareils Android ?" -#: ../../source/ref-faq.rst:19 +#: ../../source/ref-faq.rst:20 #, fuzzy msgid "" "Yes, it does. Please take a look at our `blog post " @@ -24098,13 +24159,13 @@ msgstr "" "with-flower>`_ ou consultez l'`exemple de code Android sur GitHub " "`_." -#: ../../source/ref-faq.rst:21 +#: ../../source/ref-faq.rst:22 msgid "" "`Android Kotlin example `_" msgstr "" -#: ../../source/ref-faq.rst:22 +#: ../../source/ref-faq.rst:23 msgid "`Android Java example `_" msgstr "" @@ -24114,7 +24175,7 @@ msgstr "" ":fa:`eye,mr-1` Puis-je combiner l'apprentissage fédéré avec la blockchain" " ?" -#: ../../source/ref-faq.rst:26 +#: ../../source/ref-faq.rst:27 msgid "" "Yes, of course. A list of available examples using Flower within a " "blockchain environment is available here:" @@ -24122,20 +24183,20 @@ msgstr "" "Oui, bien sûr, une liste d'exemples disponibles utilisant Flower dans un " "environnement blockchain est disponible ici :" -#: ../../source/ref-faq.rst:28 +#: ../../source/ref-faq.rst:30 msgid "`FLock: A Decentralised AI Training Platform `_." msgstr "" -#: ../../source/ref-faq.rst:29 +#: ../../source/ref-faq.rst:30 msgid "Contribute to on-chain training the model and earn rewards." msgstr "" -#: ../../source/ref-faq.rst:30 +#: ../../source/ref-faq.rst:31 #, fuzzy msgid "Local blockchain with federated learning simulation." msgstr "Mise à l'échelle de l'apprentissage fédéré" -#: ../../source/ref-faq.rst:31 +#: ../../source/ref-faq.rst:32 msgid "" "`Flower meets Nevermined GitHub Repository `_." @@ -24143,7 +24204,7 @@ msgstr "" "`Flower meets Nevermined GitHub Repository `_." -#: ../../source/ref-faq.rst:32 +#: ../../source/ref-faq.rst:33 msgid "" "`Flower meets Nevermined YouTube video " "`_." @@ -24151,7 +24212,7 @@ msgstr "" "`Flower rencontre Nevermined vidéo YouTube " "`_." -#: ../../source/ref-faq.rst:33 +#: ../../source/ref-faq.rst:34 #, fuzzy msgid "" "`Flower meets KOSMoS `_." -#: ../../source/ref-faq.rst:34 +#: ../../source/ref-faq.rst:35 msgid "" "`Flower meets Talan blog post `_ ." -#: ../../source/ref-faq.rst:35 +#: ../../source/ref-faq.rst:36 msgid "" "`Flower meets Talan GitHub Repository " "`_ ." @@ -24471,12 +24532,12 @@ msgid "" "app using Flower." msgstr "" -#: ../../source/tutorial-quickstart-android.rst:5 +#: ../../source/tutorial-quickstart-android.rst:4 #, fuzzy msgid "Quickstart Android" msgstr "Démarrage rapide des Pandas" -#: ../../source/tutorial-quickstart-android.rst:10 +#: ../../source/tutorial-quickstart-android.rst:9 #, fuzzy msgid "" "Let's build a federated learning system using TFLite and Flower on " @@ -24485,7 +24546,7 @@ msgstr "" "Construisons un système d'apprentissage fédéré en utilisant fastai et " "Flower !" -#: ../../source/tutorial-quickstart-android.rst:12 +#: ../../source/tutorial-quickstart-android.rst:11 #, fuzzy msgid "" "Please refer to the `full code example " @@ -24496,11 +24557,11 @@ msgstr "" "`_ " "pour en savoir plus." -#: ../../source/tutorial-quickstart-fastai.rst:5 +#: ../../source/tutorial-quickstart-fastai.rst:4 msgid "Quickstart fastai" msgstr "Démarrage rapide fastai" -#: ../../source/tutorial-quickstart-fastai.rst:7 +#: ../../source/tutorial-quickstart-fastai.rst:6 #, fuzzy msgid "" "In this federated learning tutorial we will learn how to train a " @@ -24512,24 +24573,24 @@ msgstr "" "tout exécuter au sein d'un `virtualenv `_." -#: ../../source/tutorial-quickstart-fastai.rst:12 -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 +#: ../../source/tutorial-quickstart-fastai.rst:10 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:11 msgid "Then, clone the code example directly from GitHub:" msgstr "" -#: ../../source/tutorial-quickstart-fastai.rst:20 +#: ../../source/tutorial-quickstart-fastai.rst:18 msgid "" "This will create a new directory called `quickstart-fastai` containing " "the following files:" msgstr "" -#: ../../source/tutorial-quickstart-fastai.rst:33 -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:33 +#: ../../source/tutorial-quickstart-fastai.rst:31 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:32 #, fuzzy msgid "Next, activate your environment, then run:" msgstr "et active l'environnement virtuel avec :" -#: ../../source/tutorial-quickstart-fastai.rst:43 +#: ../../source/tutorial-quickstart-fastai.rst:41 msgid "" "This example by default runs the Flower Simulation Engine, creating a " "federation of 10 nodes using `FedAvg `_." -#: ../../source/tutorial-quickstart-huggingface.rst:14 +#: ../../source/tutorial-quickstart-huggingface.rst:12 msgid "" "Let's use ``flwr new`` to create a complete Flower+🤗 Hugging Face " "project. It will generate all the files needed to run, by default with " @@ -24605,10 +24666,10 @@ msgid "" "|iidpartitioner|_." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:20 -#: ../../source/tutorial-quickstart-mlx.rst:19 -#: ../../source/tutorial-quickstart-pytorch.rst:19 -#: ../../source/tutorial-quickstart-tensorflow.rst:20 +#: ../../source/tutorial-quickstart-huggingface.rst:17 +#: ../../source/tutorial-quickstart-mlx.rst:17 +#: ../../source/tutorial-quickstart-pytorch.rst:18 +#: ../../source/tutorial-quickstart-tensorflow.rst:18 #, fuzzy msgid "" "Now that we have a rough idea of what this example is about, let's get " @@ -24618,65 +24679,65 @@ msgstr "" "commençons. Nous devons d'abord installer Flower. Tu peux le faire en " "lançant :" -#: ../../source/tutorial-quickstart-huggingface.rst:28 +#: ../../source/tutorial-quickstart-huggingface.rst:25 msgid "" "Then, run the command below. You will be prompted to select one of the " "available templates (choose ``HuggingFace``), give a name to your " "project, and type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:36 -#: ../../source/tutorial-quickstart-mlx.rst:35 -#: ../../source/tutorial-quickstart-pytorch.rst:35 -#: ../../source/tutorial-quickstart-tensorflow.rst:36 +#: ../../source/tutorial-quickstart-huggingface.rst:33 +#: ../../source/tutorial-quickstart-mlx.rst:32 +#: ../../source/tutorial-quickstart-pytorch.rst:34 +#: ../../source/tutorial-quickstart-tensorflow.rst:34 msgid "" "After running it you'll notice a new directory with your project name has" " been created. It should have the following structure:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:50 -#: ../../source/tutorial-quickstart-mlx.rst:49 -#: ../../source/tutorial-quickstart-pytorch.rst:49 -#: ../../source/tutorial-quickstart-tensorflow.rst:50 +#: ../../source/tutorial-quickstart-huggingface.rst:47 +#: ../../source/tutorial-quickstart-mlx.rst:46 +#: ../../source/tutorial-quickstart-pytorch.rst:48 +#: ../../source/tutorial-quickstart-tensorflow.rst:48 msgid "" "If you haven't yet installed the project and its dependencies, you can do" " so by:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:58 -#: ../../source/tutorial-quickstart-pytorch.rst:57 -#: ../../source/tutorial-quickstart-tensorflow.rst:58 +#: ../../source/tutorial-quickstart-huggingface.rst:54 +#: ../../source/tutorial-quickstart-pytorch.rst:55 +#: ../../source/tutorial-quickstart-tensorflow.rst:55 msgid "To run the project, do:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:106 +#: ../../source/tutorial-quickstart-huggingface.rst:102 msgid "You can also run the project with GPU as follows:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:113 +#: ../../source/tutorial-quickstart-huggingface.rst:109 msgid "" "This will use the default arguments where each ``ClientApp`` will use 2 " "CPUs and at most 4 ``ClientApp``\\s will run in a given GPU." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:124 -#: ../../source/tutorial-quickstart-mlx.rst:114 -#: ../../source/tutorial-quickstart-pytorch.rst:113 +#: ../../source/tutorial-quickstart-huggingface.rst:120 +#: ../../source/tutorial-quickstart-mlx.rst:110 +#: ../../source/tutorial-quickstart-pytorch.rst:111 msgid "" "What follows is an explanation of each component in the project you just " "created: dataset partition, the model, defining the ``ClientApp`` and " "defining the ``ServerApp``." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:130 -#: ../../source/tutorial-quickstart-mlx.rst:120 -#: ../../source/tutorial-quickstart-pytorch.rst:119 -#: ../../source/tutorial-quickstart-tensorflow.rst:116 +#: ../../source/tutorial-quickstart-huggingface.rst:124 +#: ../../source/tutorial-quickstart-mlx.rst:114 +#: ../../source/tutorial-quickstart-pytorch.rst:115 +#: ../../source/tutorial-quickstart-tensorflow.rst:112 #, fuzzy msgid "The Data" msgstr "Chargement des données" -#: ../../source/tutorial-quickstart-huggingface.rst:132 +#: ../../source/tutorial-quickstart-huggingface.rst:126 msgid "" "This tutorial uses |flowerdatasets|_ to easily download and partition the" " `IMDB `_ dataset. In " @@ -24689,15 +24750,15 @@ msgid "" "their data partition." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:178 -#: ../../source/tutorial-quickstart-mlx.rst:164 -#: ../../source/tutorial-quickstart-pytorch.rst:157 -#: ../../source/tutorial-quickstart-tensorflow.rst:145 +#: ../../source/tutorial-quickstart-huggingface.rst:171 +#: ../../source/tutorial-quickstart-mlx.rst:155 +#: ../../source/tutorial-quickstart-pytorch.rst:150 +#: ../../source/tutorial-quickstart-tensorflow.rst:139 #, fuzzy msgid "The Model" msgstr "Entraîne le modèle" -#: ../../source/tutorial-quickstart-huggingface.rst:180 +#: ../../source/tutorial-quickstart-huggingface.rst:173 #, fuzzy msgid "" "We will leverage 🤗 Hugging Face to federate the training of language " @@ -24714,13 +24775,13 @@ msgstr "" "données d'évaluations IMDB. L'objectif final est de détecter si " "l'évaluation d'un film est positive ou négative." -#: ../../source/tutorial-quickstart-huggingface.rst:193 +#: ../../source/tutorial-quickstart-huggingface.rst:185 msgid "" "Note that here, ``model_name`` is a string that will be loaded from the " "``Context`` in the ClientApp and ServerApp." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:196 +#: ../../source/tutorial-quickstart-huggingface.rst:188 msgid "" "In addition to loading the pretrained model weights and architecture, we " "also include two utility functions to perform both training (i.e. " @@ -24733,15 +24794,15 @@ msgid "" "perform local training or evaluation:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:239 -#: ../../source/tutorial-quickstart-mlx.rst:210 -#: ../../source/tutorial-quickstart-pytorch.rst:234 -#: ../../source/tutorial-quickstart-tensorflow.rst:176 +#: ../../source/tutorial-quickstart-huggingface.rst:228 +#: ../../source/tutorial-quickstart-mlx.rst:199 +#: ../../source/tutorial-quickstart-pytorch.rst:224 +#: ../../source/tutorial-quickstart-tensorflow.rst:168 #, fuzzy msgid "The ClientApp" msgstr "client" -#: ../../source/tutorial-quickstart-huggingface.rst:241 +#: ../../source/tutorial-quickstart-huggingface.rst:230 msgid "" "The main changes we have to make to use 🤗 Hugging Face with Flower will " "be found in the ``get_weights()`` and ``set_weights()`` functions. Under " @@ -24754,8 +24815,8 @@ msgid "" "them to an existing PyTorch model. Doing this in fairly easy in PyTorch." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:254 -#: ../../source/tutorial-quickstart-pytorch.rst:245 +#: ../../source/tutorial-quickstart-huggingface.rst:241 +#: ../../source/tutorial-quickstart-pytorch.rst:234 msgid "" "The specific implementation of ``get_weights()`` and ``set_weights()`` " "depends on the type of models you use. The ones shown below work for a " @@ -24763,8 +24824,8 @@ msgid "" "have more exotic model architectures." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:269 -#: ../../source/tutorial-quickstart-pytorch.rst:261 +#: ../../source/tutorial-quickstart-huggingface.rst:257 +#: ../../source/tutorial-quickstart-pytorch.rst:250 msgid "" "The rest of the functionality is directly inspired by the centralized " "case. The ``fit()`` method in the client trains the model using the local" @@ -24772,7 +24833,7 @@ msgid "" "model received on a held-out validation set that the client might have:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:296 +#: ../../source/tutorial-quickstart-huggingface.rst:283 msgid "" "Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " "defined above by means of a ``client_fn()`` callback. Note that the " @@ -24783,15 +24844,15 @@ msgid "" "additional hyperparameters in ``pyproject.toml`` and access them here." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:330 -#: ../../source/tutorial-quickstart-mlx.rst:376 -#: ../../source/tutorial-quickstart-pytorch.rst:321 -#: ../../source/tutorial-quickstart-tensorflow.rst:245 +#: ../../source/tutorial-quickstart-huggingface.rst:316 +#: ../../source/tutorial-quickstart-mlx.rst:361 +#: ../../source/tutorial-quickstart-pytorch.rst:307 +#: ../../source/tutorial-quickstart-tensorflow.rst:232 #, fuzzy msgid "The ServerApp" msgstr "serveur" -#: ../../source/tutorial-quickstart-huggingface.rst:332 +#: ../../source/tutorial-quickstart-huggingface.rst:318 msgid "" "To construct a ``ServerApp`` we define a ``server_fn()`` callback with an" " identical signature to that of ``client_fn()`` but the return type is " @@ -24802,13 +24863,13 @@ msgid "" "value defined in the ``pyproject.toml``." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:371 +#: ../../source/tutorial-quickstart-huggingface.rst:356 msgid "" "Congratulations! You've successfully built and run your first federated " "learning system for an LLM." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:376 +#: ../../source/tutorial-quickstart-huggingface.rst:361 msgid "" "Check the source code of the extended version of this tutorial in " "|quickstart_hf_link|_ in the Flower GitHub repository. For a " @@ -24822,12 +24883,12 @@ msgid "" "using Flower to train a neural network on MNIST." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:5 +#: ../../source/tutorial-quickstart-ios.rst:4 #, fuzzy msgid "Quickstart iOS" msgstr "Démarrage rapide XGBoost" -#: ../../source/tutorial-quickstart-ios.rst:10 +#: ../../source/tutorial-quickstart-ios.rst:9 #, fuzzy msgid "" "In this tutorial we will learn how to train a Neural Network on MNIST " @@ -24848,7 +24909,7 @@ msgstr "" "tout exécuter au sein d'un `virtualenv `_." -#: ../../source/tutorial-quickstart-ios.rst:15 +#: ../../source/tutorial-quickstart-ios.rst:17 #, fuzzy msgid "" "Our example consists of one Python *server* and two iPhone *clients* that" @@ -24857,7 +24918,7 @@ msgstr "" "Notre exemple consiste en un *serveur* et deux *clients* ayant tous le " "même modèle." -#: ../../source/tutorial-quickstart-ios.rst:17 +#: ../../source/tutorial-quickstart-ios.rst:20 #, fuzzy msgid "" "*Clients* are responsible for generating individual weight updates for " @@ -24873,7 +24934,7 @@ msgstr "" "cette version améliorée du modèle à chaque *client*. Un cycle complet de " "mises à jour de poids s'appelle un *round*." -#: ../../source/tutorial-quickstart-ios.rst:21 +#: ../../source/tutorial-quickstart-ios.rst:26 #, fuzzy msgid "" "Now that we have a rough idea of what is going on, let's get started to " @@ -24884,17 +24945,17 @@ msgstr "" "commençons. Nous devons d'abord installer Flower. Tu peux le faire en " "exécutant :" -#: ../../source/tutorial-quickstart-ios.rst:27 +#: ../../source/tutorial-quickstart-ios.rst:33 msgid "Or Poetry:" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:34 -#: ../../source/tutorial-quickstart-scikitlearn.rst:40 -#: ../../source/tutorial-quickstart-xgboost.rst:55 +#: ../../source/tutorial-quickstart-ios.rst:40 +#: ../../source/tutorial-quickstart-scikitlearn.rst:43 +#: ../../source/tutorial-quickstart-xgboost.rst:65 msgid "Flower Client" msgstr "Client de la fleur" -#: ../../source/tutorial-quickstart-ios.rst:36 +#: ../../source/tutorial-quickstart-ios.rst:42 msgid "" "Now that we have all our dependencies installed, let's run a simple " "distributed training using CoreML as our local training pipeline and " @@ -24903,88 +24964,88 @@ msgid "" "the Swift SDK. The client implementation can be seen below:" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:72 +#: ../../source/tutorial-quickstart-ios.rst:80 msgid "" -"Let's create a new application project in Xcode and add :code:`flwr` as a" -" dependency in your project. For our application, we will store the logic" -" of our app in :code:`FLiOSModel.swift` and the UI elements in " -":code:`ContentView.swift`. We will focus more on :code:`FLiOSModel.swift`" -" in this quickstart. Please refer to the `full code example " +"Let's create a new application project in Xcode and add ``flwr`` as a " +"dependency in your project. For our application, we will store the logic " +"of our app in ``FLiOSModel.swift`` and the UI elements in " +"``ContentView.swift``. We will focus more on ``FLiOSModel.swift`` in this" +" quickstart. Please refer to the `full code example " "`_ to learn more " "about the app." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:75 -msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:" +#: ../../source/tutorial-quickstart-ios.rst:86 +msgid "Import Flower and CoreML related packages in ``FLiOSModel.swift``:" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:83 +#: ../../source/tutorial-quickstart-ios.rst:94 msgid "" "Then add the mlmodel to the project simply by drag-and-drop, the mlmodel " "will be bundled inside the application during deployment to your iOS " "device. We need to pass the url to access mlmodel and run CoreML machine " "learning processes, it can be retrieved by calling the function " -":code:`Bundle.main.url`. For the MNIST dataset, we need to preprocess it " -"into :code:`MLBatchProvider` object. The preprocessing is done inside " -":code:`DataLoader.swift`." +"``Bundle.main.url``. For the MNIST dataset, we need to preprocess it into" +" ``MLBatchProvider`` object. The preprocessing is done inside " +"``DataLoader.swift``." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:99 +#: ../../source/tutorial-quickstart-ios.rst:112 msgid "" "Since CoreML does not allow the model parameters to be seen before " "training, and accessing the model parameters during or after the training" " can only be done by specifying the layer name, we need to know this " "information beforehand, through looking at the model specification, which" " are written as proto files. The implementation can be seen in " -":code:`MLModelInspect`." +"``MLModelInspect``." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:102 +#: ../../source/tutorial-quickstart-ios.rst:118 msgid "" "After we have all of the necessary information, let's create our Flower " "client." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:117 +#: ../../source/tutorial-quickstart-ios.rst:133 msgid "" "Then start the Flower gRPC client and start communicating to the server " -"by passing our Flower client to the function :code:`startFlwrGRPC`." +"by passing our Flower client to the function ``startFlwrGRPC``." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:124 +#: ../../source/tutorial-quickstart-ios.rst:141 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -"call the provided :code:`MLFlwrClient` and call :code:`startFlwrGRPC()`. " -"The attribute :code:`hostname` and :code:`port` tells the client which " -"server to connect to. This can be done by entering the hostname and port " -"in the application before clicking the start button to start the " -"federated learning process." +"That's it for the client. We only have to implement ``Client`` or call " +"the provided ``MLFlwrClient`` and call ``startFlwrGRPC()``. The attribute" +" ``hostname`` and ``port`` tells the client which server to connect to. " +"This can be done by entering the hostname and port in the application " +"before clicking the start button to start the federated learning process." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:129 -#: ../../source/tutorial-quickstart-scikitlearn.rst:167 -#: ../../source/tutorial-quickstart-xgboost.rst:341 +#: ../../source/tutorial-quickstart-ios.rst:148 +#: ../../source/tutorial-quickstart-scikitlearn.rst:179 +#: ../../source/tutorial-quickstart-xgboost.rst:358 msgid "Flower Server" msgstr "Serveur de Flower" -#: ../../source/tutorial-quickstart-ios.rst:131 +#: ../../source/tutorial-quickstart-ios.rst:150 +#, fuzzy msgid "" "For simple workloads we can start a Flower server and leave all the " "configuration possibilities at their default values. In a file named " -":code:`server.py`, import Flower and start the server:" +"``server.py``, import Flower and start the server:" msgstr "" "Pour les charges de travail simples, nous pouvons démarrer un serveur " "Flower et laisser toutes les possibilités de configuration à leurs " "valeurs par défaut. Dans un fichier nommé :code:`server.py`, importe " "Flower et démarre le serveur :" -#: ../../source/tutorial-quickstart-ios.rst:142 -#: ../../source/tutorial-quickstart-scikitlearn.rst:230 +#: ../../source/tutorial-quickstart-ios.rst:161 +#: ../../source/tutorial-quickstart-scikitlearn.rst:254 msgid "Train the model, federated!" msgstr "Entraîne le modèle, fédéré !" -#: ../../source/tutorial-quickstart-ios.rst:144 -#: ../../source/tutorial-quickstart-xgboost.rst:567 +#: ../../source/tutorial-quickstart-ios.rst:163 +#: ../../source/tutorial-quickstart-xgboost.rst:590 msgid "" "With both client and server ready, we can now run everything and see " "federated learning in action. FL systems usually have a server and " @@ -24995,7 +25056,7 @@ msgstr "" "généralement un serveur et plusieurs clients. Nous devons donc commencer " "par démarrer le serveur :" -#: ../../source/tutorial-quickstart-ios.rst:152 +#: ../../source/tutorial-quickstart-ios.rst:171 msgid "" "Once the server is running we can start the clients in different " "terminals. Build and run the client through your Xcode, one through Xcode" @@ -25005,13 +25066,13 @@ msgid "" "simulator-or-on-a-device>`_." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:156 +#: ../../source/tutorial-quickstart-ios.rst:177 #, fuzzy msgid "" "Congratulations! You've successfully built and run your first federated " "learning system in your ios device. The full `source code " "`_ for this " -"example can be found in :code:`examples/ios`." +"example can be found in ``examples/ios``." msgstr "" "Félicitations ! Tu as réussi à construire et à faire fonctionner ton " "premier système d'apprentissage fédéré. Le code source complet " @@ -25025,11 +25086,11 @@ msgid "" "with Jax to train a linear regression model on a scikit-learn dataset." msgstr "" -#: ../../source/tutorial-quickstart-jax.rst:5 +#: ../../source/tutorial-quickstart-jax.rst:4 msgid "Quickstart JAX" msgstr "Démarrage rapide de JAX" -#: ../../source/tutorial-quickstart-jax.rst:10 +#: ../../source/tutorial-quickstart-jax.rst:9 #, fuzzy msgid "" "This tutorial will show you how to use Flower to build a federated " @@ -25056,41 +25117,44 @@ msgstr "" " Ensuite, nous nous appuyons sur le code d'entraînement centralisé pour " "exécuter l'entraînement de manière fédérée." -#: ../../source/tutorial-quickstart-jax.rst:16 +#: ../../source/tutorial-quickstart-jax.rst:20 +#, fuzzy msgid "" "Before we start building our JAX example, we need install the packages " -":code:`jax`, :code:`jaxlib`, :code:`scikit-learn`, and :code:`flwr`:" +"``jax``, ``jaxlib``, ``scikit-learn``, and ``flwr``:" msgstr "" "Avant de commencer à construire notre exemple JAX, nous devons installer " "les paquets :code:`jax`, :code:`jaxlib`, :code:`scikit-learn`, et " ":code:`flwr` :" -#: ../../source/tutorial-quickstart-jax.rst:24 +#: ../../source/tutorial-quickstart-jax.rst:28 msgid "Linear Regression with JAX" msgstr "Régression linéaire avec JAX" -#: ../../source/tutorial-quickstart-jax.rst:26 +#: ../../source/tutorial-quickstart-jax.rst:30 +#, fuzzy msgid "" "We begin with a brief description of the centralized training code based " -"on a :code:`Linear Regression` model. If you want a more in-depth " -"explanation of what's going on then have a look at the official `JAX " -"documentation `_." +"on a ``Linear Regression`` model. If you want a more in-depth explanation" +" of what's going on then have a look at the official `JAX documentation " +"`_." msgstr "" "Nous commençons par une brève description du code d'entraînement " "centralisé basé sur un modèle :code:`Régression linéaire`. Si tu veux une" " explication plus approfondie de ce qui se passe, jette un coup d'œil à " "la documentation officielle `JAX `_." -#: ../../source/tutorial-quickstart-jax.rst:29 +#: ../../source/tutorial-quickstart-jax.rst:34 +#, fuzzy msgid "" -"Let's create a new file called :code:`jax_training.py` with all the " +"Let's create a new file called ``jax_training.py`` with all the " "components required for a traditional (centralized) linear regression " -"training. First, the JAX packages :code:`jax` and :code:`jaxlib` need to " -"be imported. In addition, we need to import :code:`sklearn` since we use " -":code:`make_regression` for the dataset and :code:`train_test_split` to " -"split the dataset into a training and test set. You can see that we do " -"not yet import the :code:`flwr` package for federated learning. This will" -" be done later." +"training. First, the JAX packages ``jax`` and ``jaxlib`` need to be " +"imported. In addition, we need to import ``sklearn`` since we use " +"``make_regression`` for the dataset and ``train_test_split`` to split the" +" dataset into a training and test set. You can see that we do not yet " +"import the ``flwr`` package for federated learning. This will be done " +"later." msgstr "" "Créons un nouveau fichier appelé :code:`jax_training.py` avec tous les " "composants nécessaires pour un apprentissage traditionnel (centralisé) de" @@ -25102,29 +25166,30 @@ msgstr "" "n'avons pas encore importé le paquet :code:`flwr` pour l'apprentissage " "fédéré, ce qui sera fait plus tard." -#: ../../source/tutorial-quickstart-jax.rst:43 -msgid "" -"The :code:`load_data()` function loads the mentioned training and test " -"sets." +#: ../../source/tutorial-quickstart-jax.rst:51 +#, fuzzy +msgid "The ``load_data()`` function loads the mentioned training and test sets." msgstr "" "La fonction :code:`load_data()` charge les ensembles d'entraînement et de" " test mentionnés." -#: ../../source/tutorial-quickstart-jax.rst:53 +#: ../../source/tutorial-quickstart-jax.rst:63 +#, fuzzy msgid "" -"The model architecture (a very simple :code:`Linear Regression` model) is" -" defined in :code:`load_model()`." +"The model architecture (a very simple ``Linear Regression`` model) is " +"defined in ``load_model()``." msgstr "" "L'architecture du modèle (un modèle :code:`Régression linéaire` très " "simple) est définie dans :code:`load_model()`." -#: ../../source/tutorial-quickstart-jax.rst:65 +#: ../../source/tutorial-quickstart-jax.rst:73 +#, fuzzy msgid "" -"We now need to define the training (function :code:`train()`), which " -"loops over the training set and measures the loss (function " -":code:`loss_fn()`) for each batch of training examples. The loss function" -" is separate since JAX takes derivatives with a :code:`grad()` function " -"(defined in the :code:`main()` function and called in :code:`train()`)." +"We now need to define the training (function ``train()``), which loops " +"over the training set and measures the loss (function ``loss_fn()``) for " +"each batch of training examples. The loss function is separate since JAX " +"takes derivatives with a ``grad()`` function (defined in the ``main()`` " +"function and called in ``train()``)." msgstr "" "Nous devons maintenant définir l'entraînement (fonction :code:`train()`)," " qui boucle sur l'ensemble d'entraînement et mesure la perte (fonction " @@ -25133,22 +25198,24 @@ msgstr "" ":code:`grad()` (définie dans la fonction :code:`main()` et appelée dans " ":code:`train()`)." -#: ../../source/tutorial-quickstart-jax.rst:83 +#: ../../source/tutorial-quickstart-jax.rst:95 +#, fuzzy msgid "" -"The evaluation of the model is defined in the function " -":code:`evaluation()`. The function takes all test examples and measures " -"the loss of the linear regression model." +"The evaluation of the model is defined in the function ``evaluation()``. " +"The function takes all test examples and measures the loss of the linear " +"regression model." msgstr "" "L'évaluation du modèle est définie dans la fonction :code:`evaluation()`." " La fonction prend tous les exemples de test et mesure la perte du modèle" " de régression linéaire." -#: ../../source/tutorial-quickstart-jax.rst:94 +#: ../../source/tutorial-quickstart-jax.rst:107 +#, fuzzy msgid "" "Having defined the data loading, model architecture, training, and " "evaluation we can put everything together and train our model using JAX. " -"As already mentioned, the :code:`jax.grad()` function is defined in " -":code:`main()` and passed to :code:`train()`." +"As already mentioned, the ``jax.grad()`` function is defined in " +"``main()`` and passed to ``train()``." msgstr "" "Après avoir défini le chargement des données, l'architecture du modèle, " "l'entraînement et l'évaluation, nous pouvons tout assembler et entraîner " @@ -25156,13 +25223,13 @@ msgstr "" "fonction :code:`jax.grad()` est définie dans :code:`main()` et transmise " "à :code:`train()`." -#: ../../source/tutorial-quickstart-jax.rst:111 +#: ../../source/tutorial-quickstart-jax.rst:126 msgid "You can now run your (centralized) JAX linear regression workload:" msgstr "" "Tu peux maintenant exécuter ta charge de travail (centralisée) de " "régression linéaire JAX :" -#: ../../source/tutorial-quickstart-jax.rst:117 +#: ../../source/tutorial-quickstart-jax.rst:132 msgid "" "So far this should all look fairly familiar if you've used JAX before. " "Let's take the next step and use what we've built to create a simple " @@ -25173,20 +25240,21 @@ msgstr "" "avons construit pour créer un simple système d'apprentissage fédéré " "composé d'un serveur et de deux clients." -#: ../../source/tutorial-quickstart-jax.rst:121 +#: ../../source/tutorial-quickstart-jax.rst:137 msgid "JAX meets Flower" msgstr "JAX rencontre Flower" -#: ../../source/tutorial-quickstart-jax.rst:123 +#: ../../source/tutorial-quickstart-jax.rst:139 +#, fuzzy msgid "" "The concept of federating an existing workload is always the same and " "easy to understand. We have to start a *server* and then use the code in " -":code:`jax_training.py` for the *clients* that are connected to the " -"*server*. The *server* sends model parameters to the clients. The " -"*clients* run the training and update the parameters. The updated " -"parameters are sent back to the *server*, which averages all received " -"parameter updates. This describes one round of the federated learning " -"process, and we repeat this for multiple rounds." +"``jax_training.py`` for the *clients* that are connected to the *server*." +" The *server* sends model parameters to the clients. The *clients* run " +"the training and update the parameters. The updated parameters are sent " +"back to the *server*, which averages all received parameter updates. This" +" describes one round of the federated learning process, and we repeat " +"this for multiple rounds." msgstr "" "Le concept de fédération d'une charge de travail existante est toujours " "le même et facile à comprendre. Nous devons démarrer un *serveur*, puis " @@ -25198,12 +25266,13 @@ msgstr "" "un tour du processus d'apprentissage fédéré, et nous répétons cette " "opération pour plusieurs tours." -#: ../../source/tutorial-quickstart-jax.rst:145 +#: ../../source/tutorial-quickstart-jax.rst:167 +#, fuzzy msgid "" -"Finally, we will define our *client* logic in :code:`client.py` and build" -" upon the previously defined JAX training in :code:`jax_training.py`. Our" -" *client* needs to import :code:`flwr`, but also :code:`jax` and " -":code:`jaxlib` to update the parameters on our JAX model:" +"Finally, we will define our *client* logic in ``client.py`` and build " +"upon the previously defined JAX training in ``jax_training.py``. Our " +"*client* needs to import ``flwr``, but also ``jax`` and ``jaxlib`` to " +"update the parameters on our JAX model:" msgstr "" "Enfin, nous allons définir la logique de notre *client* dans " ":code:`client.py` et nous appuyer sur la formation JAX définie " @@ -25211,18 +25280,18 @@ msgstr "" ":code:`flwr`, mais aussi :code:`jax` et :code:`jaxlib` pour mettre à jour" " les paramètres de notre modèle JAX :" -#: ../../source/tutorial-quickstart-jax.rst:160 +#: ../../source/tutorial-quickstart-jax.rst:182 +#, fuzzy msgid "" "Implementing a Flower *client* basically means implementing a subclass of" -" either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. " -"Our implementation will be based on :code:`flwr.client.NumPyClient` and " -"we'll call it :code:`FlowerClient`. :code:`NumPyClient` is slightly " -"easier to implement than :code:`Client` if you use a framework with good " -"NumPy interoperability (like JAX) because it avoids some of the " -"boilerplate that would otherwise be necessary. :code:`FlowerClient` needs" -" to implement four methods, two methods for getting/setting model " -"parameters, one method for training the model, and one method for testing" -" the model:" +" either ``flwr.client.Client`` or ``flwr.client.NumPyClient``. Our " +"implementation will be based on ``flwr.client.NumPyClient`` and we'll " +"call it ``FlowerClient``. ``NumPyClient`` is slightly easier to implement" +" than ``Client`` if you use a framework with good NumPy interoperability " +"(like JAX) because it avoids some of the boilerplate that would otherwise" +" be necessary. ``FlowerClient`` needs to implement four methods, two " +"methods for getting/setting model parameters, one method for training the" +" model, and one method for testing the model:" msgstr "" "L'implémentation d'un *client* Flower signifie essentiellement " "l'implémentation d'une sous-classe de :code:`flwr.client.Client` ou " @@ -25236,43 +25305,46 @@ msgstr "" "paramètres du modèle, une méthode pour former le modèle, et une méthode " "pour tester le modèle :" -#: ../../source/tutorial-quickstart-jax.rst:165 -msgid ":code:`set_parameters (optional)`" +#: ../../source/tutorial-quickstart-jax.rst:194 +#, fuzzy +msgid "``set_parameters (optional)``" msgstr ":code:`set_parameters (optional)`" -#: ../../source/tutorial-quickstart-jax.rst:167 -msgid "transform parameters to NumPy :code:`ndarray`'s" +#: ../../source/tutorial-quickstart-jax.rst:193 +#, fuzzy +msgid "transform parameters to NumPy ``ndarray``'s" msgstr "transforme les paramètres en NumPy :code:`ndarray`'s" -#: ../../source/tutorial-quickstart-jax.rst:174 +#: ../../source/tutorial-quickstart-jax.rst:203 msgid "get the updated local model parameters and return them to the server" msgstr "" "récupère les paramètres du modèle local mis à jour et les renvoie au " "serveur" -#: ../../source/tutorial-quickstart-jax.rst:178 +#: ../../source/tutorial-quickstart-jax.rst:208 msgid "return the local loss to the server" msgstr "renvoie la perte locale au serveur" -#: ../../source/tutorial-quickstart-jax.rst:180 +#: ../../source/tutorial-quickstart-jax.rst:210 +#, fuzzy msgid "" "The challenging part is to transform the JAX model parameters from " -":code:`DeviceArray` to :code:`NumPy ndarray` to make them compatible with" -" `NumPyClient`." +"``DeviceArray`` to ``NumPy ndarray`` to make them compatible with " +"`NumPyClient`." msgstr "" "La partie la plus difficile consiste à transformer les paramètres du " "modèle JAX de :code:`DeviceArray` en :code:`NumPy ndarray` pour les " "rendre compatibles avec `NumPyClient`." -#: ../../source/tutorial-quickstart-jax.rst:182 +#: ../../source/tutorial-quickstart-jax.rst:213 +#, fuzzy msgid "" -"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make" -" use of the functions :code:`train()` and :code:`evaluate()` previously " -"defined in :code:`jax_training.py`. So what we really do here is we tell " -"Flower through our :code:`NumPyClient` subclass which of our already " -"defined functions to call for training and evaluation. We included type " -"annotations to give you a better understanding of the data types that get" -" passed around." +"The two ``NumPyClient`` methods ``fit`` and ``evaluate`` make use of the " +"functions ``train()`` and ``evaluate()`` previously defined in " +"``jax_training.py``. So what we really do here is we tell Flower through " +"our ``NumPyClient`` subclass which of our already defined functions to " +"call for training and evaluation. We included type annotations to give " +"you a better understanding of the data types that get passed around." msgstr "" "Les deux méthodes :code:`NumPyClient` :code:`fit` et :code:`evaluate` " "utilisent les fonctions :code:`train()` et :code:`evaluate()` définies " @@ -25283,11 +25355,11 @@ msgstr "" "annotations de type pour te donner une meilleure compréhension des types " "de données qui sont transmis." -#: ../../source/tutorial-quickstart-jax.rst:251 +#: ../../source/tutorial-quickstart-jax.rst:286 msgid "Having defined the federation process, we can run it." msgstr "Après avoir défini le processus de fédération, nous pouvons l'exécuter." -#: ../../source/tutorial-quickstart-jax.rst:280 +#: ../../source/tutorial-quickstart-jax.rst:315 msgid "" "in each window (make sure that the server is still running before you do " "so) and see your JAX project run federated learning across two clients. " @@ -25297,7 +25369,7 @@ msgstr "" "d'exécution avant de le faire) et tu verras que ton projet JAX exécute " "l'apprentissage fédéré sur deux clients. Félicitations !" -#: ../../source/tutorial-quickstart-jax.rst:285 +#: ../../source/tutorial-quickstart-jax.rst:321 #, fuzzy msgid "" "The source code of this example was improved over time and can be found " @@ -25311,7 +25383,7 @@ msgstr "" "Notre exemple est quelque peu simplifié à l'extrême car les deux clients " "chargent le même jeu de données." -#: ../../source/tutorial-quickstart-jax.rst:288 +#: ../../source/tutorial-quickstart-jax.rst:325 msgid "" "You're now prepared to explore this topic further. How about using a more" " sophisticated model or using a different dataset? How about adding more " @@ -25321,12 +25393,12 @@ msgstr "" " modèle plus sophistiqué ou un ensemble de données différent ? Pourquoi " "ne pas ajouter d'autres clients ?" -#: ../../source/tutorial-quickstart-mlx.rst:5 +#: ../../source/tutorial-quickstart-mlx.rst:4 #, fuzzy msgid "Quickstart MLX" msgstr "Démarrage rapide de JAX" -#: ../../source/tutorial-quickstart-mlx.rst:7 +#: ../../source/tutorial-quickstart-mlx.rst:6 #, fuzzy msgid "" "In this federated learning tutorial we will learn how to train simple MLP" @@ -25338,7 +25410,7 @@ msgstr "" "tout exécuter au sein d'un `virtualenv `_." -#: ../../source/tutorial-quickstart-mlx.rst:12 +#: ../../source/tutorial-quickstart-mlx.rst:10 msgid "" "Let's use `flwr new` to create a complete Flower+MLX project. It will " "generate all the files needed to run, by default with the Simulation " @@ -25350,24 +25422,24 @@ msgid "" "api/flwr_datasets.partitioner.IidPartitioner.html#flwr_datasets.partitioner.IidPartitioner>`_." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:27 +#: ../../source/tutorial-quickstart-mlx.rst:25 msgid "" "Then, run the command below. You will be prompted to select of the " "available templates (choose ``MLX``), give a name to your project, and " "type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:57 +#: ../../source/tutorial-quickstart-mlx.rst:53 msgid "To run the project do:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:106 +#: ../../source/tutorial-quickstart-mlx.rst:102 msgid "" "You can also override the parameters defined in " "``[tool.flwr.app.config]`` section in the ``pyproject.toml`` like this:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:122 +#: ../../source/tutorial-quickstart-mlx.rst:116 msgid "" "We will use `Flower Datasets `_ to " "easily download and partition the `MNIST` dataset. In this example you'll" @@ -25378,20 +25450,20 @@ msgid "" "api/flwr_datasets.partitioner.html>`_ available in Flower Datasets:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:166 +#: ../../source/tutorial-quickstart-mlx.rst:157 msgid "" "We define the model as in the `centralized MLX example " "`_, it's a " "simple MLP:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:190 +#: ../../source/tutorial-quickstart-mlx.rst:180 msgid "" "We also define some utility functions to test our model and to iterate " "over batches." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:212 +#: ../../source/tutorial-quickstart-mlx.rst:201 msgid "" "The main changes we have to make to use `MLX` with `Flower` will be found" " in the ``get_params()`` and ``set_params()`` functions. Indeed, MLX " @@ -25400,17 +25472,17 @@ msgid "" "messages to work)." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:218 +#: ../../source/tutorial-quickstart-mlx.rst:206 msgid "The way MLX stores its parameters is as follows:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:231 +#: ../../source/tutorial-quickstart-mlx.rst:219 msgid "" "Therefore, to get our list of ``np.array`` objects, we need to extract " "each array and convert them into a NumPy array:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:240 +#: ../../source/tutorial-quickstart-mlx.rst:228 msgid "" "For the ``set_params()`` function, we perform the reverse operation. We " "receive a list of NumPy arrays and want to convert them into MLX " @@ -25418,24 +25490,24 @@ msgid "" "them to the `weight` and `bias` keys of each layer dict:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:255 +#: ../../source/tutorial-quickstart-mlx.rst:243 msgid "" "The rest of the functionality is directly inspired by the centralized " "case. The ``fit()`` method in the client trains the model using the local" " dataset:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:272 +#: ../../source/tutorial-quickstart-mlx.rst:259 msgid "" "Here, after updating the parameters, we perform the training as in the " "centralized case, and return the new parameters." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:275 +#: ../../source/tutorial-quickstart-mlx.rst:262 msgid "And for the ``evaluate()`` method of the client:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:285 +#: ../../source/tutorial-quickstart-mlx.rst:272 msgid "" "We also begin by updating the parameters with the ones sent by the " "server, and then we compute the loss and accuracy using the functions " @@ -25443,12 +25515,12 @@ msgid "" "the `MLP` model as well as other components such as the optimizer." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:290 +#: ../../source/tutorial-quickstart-mlx.rst:277 #, fuzzy msgid "Putting everything together we have:" msgstr "Tout assembler" -#: ../../source/tutorial-quickstart-mlx.rst:344 +#: ../../source/tutorial-quickstart-mlx.rst:331 msgid "" "Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " "defined above by means of a ``client_fn()`` callback. Note that " @@ -25459,7 +25531,7 @@ msgid "" "method." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:378 +#: ../../source/tutorial-quickstart-mlx.rst:363 msgid "" "To construct a ``ServerApp``, we define a ``server_fn()`` callback with " "an identical signature to that of ``client_fn()``, but the return type is" @@ -25470,15 +25542,15 @@ msgid "" "``FedAvg`` strategy." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:402 -#: ../../source/tutorial-quickstart-pytorch.rst:360 -#: ../../source/tutorial-quickstart-tensorflow.rst:279 +#: ../../source/tutorial-quickstart-mlx.rst:386 +#: ../../source/tutorial-quickstart-pytorch.rst:344 +#: ../../source/tutorial-quickstart-tensorflow.rst:266 msgid "" "Congratulations! You've successfully built and run your first federated " "learning system." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:407 +#: ../../source/tutorial-quickstart-mlx.rst:390 #, fuzzy msgid "" "Check the `source code `_." -#: ../../source/tutorial-quickstart-pytorch.rst:12 +#: ../../source/tutorial-quickstart-pytorch.rst:11 msgid "" "Let's use `flwr new` to create a complete Flower+PyTorch project. It will" " generate all the files needed to run, by default with the Flower " @@ -25546,14 +25618,14 @@ msgid "" "api/flwr_datasets.partitioner.IidPartitioner.html#flwr_datasets.partitioner.IidPartitioner>`_." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:27 +#: ../../source/tutorial-quickstart-pytorch.rst:26 msgid "" "Then, run the command below. You will be prompted to select one of the " "available templates (choose ``PyTorch``), give a name to your project, " "and type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:121 +#: ../../source/tutorial-quickstart-pytorch.rst:117 msgid "" "This tutorial uses `Flower Datasets `_ " "to easily download and partition the `CIFAR-10` dataset. In this example " @@ -25567,13 +25639,13 @@ msgid "" " that correspond to their data partition." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:159 +#: ../../source/tutorial-quickstart-pytorch.rst:152 msgid "" "We defined a simple Convolutional Neural Network (CNN), but feel free to " "replace it with a more sophisticated model if you'd like:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:184 +#: ../../source/tutorial-quickstart-pytorch.rst:177 msgid "" "In addition to defining the model architecture, we also include two " "utility functions to perform both training (i.e. ``train()``) and " @@ -25586,7 +25658,7 @@ msgid "" "training or evaluation:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:236 +#: ../../source/tutorial-quickstart-pytorch.rst:226 msgid "" "The main changes we have to make to use `PyTorch` with `Flower` will be " "found in the ``get_weights()`` and ``set_weights()`` functions. In " @@ -25596,7 +25668,7 @@ msgid "" "PyTorch model. Doing this in fairly easy in PyTorch." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:294 +#: ../../source/tutorial-quickstart-pytorch.rst:282 msgid "" "Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " "defined above by means of a ``client_fn()`` callback. Note that the " @@ -25607,7 +25679,7 @@ msgid "" "additioinal hyperparameters in ``pyproject.toml`` and access them here." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:323 +#: ../../source/tutorial-quickstart-pytorch.rst:309 msgid "" "To construct a ``ServerApp`` we define a ``server_fn()`` callback with an" " identical signature to that of ``client_fn()`` but the return type is " @@ -25621,7 +25693,7 @@ msgid "" "``pyproject.toml``." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:365 +#: ../../source/tutorial-quickstart-pytorch.rst:348 #, fuzzy msgid "" "Check the `source code `_ de cet exemple se trouve dans :code:`examples" "/quickstart-mxnet`." -#: ../../source/tutorial-quickstart-pytorch.rst:372 -#: ../../source/tutorial-quickstart-tensorflow.rst:295 +#: ../../source/tutorial-quickstart-pytorch.rst:354 +#: ../../source/tutorial-quickstart-tensorflow.rst:278 #, fuzzy msgid "Video tutorial" msgstr "Tutoriel" -#: ../../source/tutorial-quickstart-pytorch.rst:376 +#: ../../source/tutorial-quickstart-pytorch.rst:358 msgid "" "The video shown below shows how to setup a PyTorch + Flower project using" " our previously recommended APIs. A new video tutorial will be released " "that shows the new APIs (as the content above does)" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:4 msgid "Quickstart PyTorch Lightning" msgstr "Démarrage rapide de PyTorch Lightning" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:7 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:6 #, fuzzy msgid "" "In this federated learning tutorial we will learn how to train an " @@ -25663,13 +25735,13 @@ msgstr "" "tout exécuter au sein d'un `virtualenv `_." -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:20 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:19 msgid "" "This will create a new directory called `quickstart-pytorch-lightning` " "containing the following files:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:43 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:42 msgid "" "By default, Flower Simulation Engine will be started and it will create a" " federation of 4 nodes using `FedAvg `_." -#: ../../source/tutorial-quickstart-scikitlearn.rst:14 +#: ../../source/tutorial-quickstart-scikitlearn.rst:15 msgid "" "Our example consists of one *server* and two *clients* all having the " "same model." @@ -25735,7 +25808,7 @@ msgstr "" "Notre exemple consiste en un *serveur* et deux *clients* ayant tous le " "même modèle." -#: ../../source/tutorial-quickstart-scikitlearn.rst:16 +#: ../../source/tutorial-quickstart-scikitlearn.rst:17 msgid "" "*Clients* are responsible for generating individual model parameter " "updates for the model based on their local datasets. These updates are " @@ -25751,7 +25824,7 @@ msgstr "" "version améliorée du modèle à chaque *client*. Un cycle complet de mises " "à jour des paramètres s'appelle un *round*." -#: ../../source/tutorial-quickstart-scikitlearn.rst:20 +#: ../../source/tutorial-quickstart-scikitlearn.rst:23 msgid "" "Now that we have a rough idea of what is going on, let's get started. We " "first need to install Flower. You can do this by running:" @@ -25760,22 +25833,23 @@ msgstr "" "commençons. Nous devons d'abord installer Flower. Tu peux le faire en " "lançant :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:26 +#: ../../source/tutorial-quickstart-scikitlearn.rst:30 #, fuzzy msgid "Since we want to use scikit-learn, let's go ahead and install it:" msgstr "Puisque nous voulons utiliser scikt-learn, allons-y et installons-le :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:32 +#: ../../source/tutorial-quickstart-scikitlearn.rst:36 msgid "Or simply install all dependencies using Poetry:" msgstr "Ou installe simplement toutes les dépendances à l'aide de Poetry :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:42 +#: ../../source/tutorial-quickstart-scikitlearn.rst:45 +#, fuzzy msgid "" "Now that we have all our dependencies installed, let's run a simple " "distributed training with two clients and one server. However, before " "setting up the client and server, we will define all functionalities that" -" we need for our federated learning setup within :code:`utils.py`. The " -":code:`utils.py` contains different functions defining all the machine " +" we need for our federated learning setup within ``utils.py``. The " +"``utils.py`` contains different functions defining all the machine " "learning basics:" msgstr "" "Maintenant que toutes nos dépendances sont installées, exécutons une " @@ -25786,40 +25860,45 @@ msgstr "" "contient différentes fonctions définissant toutes les bases de " "l'apprentissage automatique :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:45 -msgid ":code:`get_model_parameters()`" +#: ../../source/tutorial-quickstart-scikitlearn.rst:51 +#, fuzzy +msgid "``get_model_parameters()``" msgstr ":code:`get_model_parameters()`" -#: ../../source/tutorial-quickstart-scikitlearn.rst:46 -msgid "Returns the parameters of a :code:`sklearn` LogisticRegression model" +#: ../../source/tutorial-quickstart-scikitlearn.rst:52 +#, fuzzy +msgid "Returns the parameters of a ``sklearn`` LogisticRegression model" msgstr "" "Renvoie les paramètres d'un modèle de régression logistique " ":code:`sklearn`" -#: ../../source/tutorial-quickstart-scikitlearn.rst:47 -msgid ":code:`set_model_params()`" +#: ../../source/tutorial-quickstart-scikitlearn.rst:53 +#, fuzzy +msgid "``set_model_params()``" msgstr ":code:`set_model_params()`" -#: ../../source/tutorial-quickstart-scikitlearn.rst:48 +#: ../../source/tutorial-quickstart-scikitlearn.rst:54 #, fuzzy -msgid "Sets the parameters of a :code:`sklearn` LogisticRegression model" +msgid "Sets the parameters of a ``sklearn`` LogisticRegression model" msgstr "Définit les paramètres d'un modèle de régression logistique :code:`sklean`" -#: ../../source/tutorial-quickstart-scikitlearn.rst:49 -msgid ":code:`set_initial_params()`" +#: ../../source/tutorial-quickstart-scikitlearn.rst:56 +#, fuzzy +msgid "``set_initial_params()``" msgstr ":code:`set_initial_params()`" -#: ../../source/tutorial-quickstart-scikitlearn.rst:50 +#: ../../source/tutorial-quickstart-scikitlearn.rst:56 msgid "Initializes the model parameters that the Flower server will ask for" msgstr "Initialise les paramètres du modèle que le serveur de Flower demandera" -#: ../../source/tutorial-quickstart-scikitlearn.rst:52 +#: ../../source/tutorial-quickstart-scikitlearn.rst:58 +#, fuzzy msgid "" -"Please check out :code:`utils.py` `here " +"Please check out ``utils.py`` `here " "`_ for more details. The pre-defined functions are used in" -" the :code:`client.py` and imported. The :code:`client.py` also requires " -"to import several packages such as Flower and scikit-learn:" +" the ``client.py`` and imported. The ``client.py`` also requires to " +"import several packages such as Flower and scikit-learn:" msgstr "" "Tu peux consulter :code:`utils.py` `ici " "`_. The " -":code:`FederatedDataset.load_partition()` method loads the partitioned " -"training set for each partition ID defined in the :code:`--partition-id` " +"``FederatedDataset.load_partition()`` method loads the partitioned " +"training set for each partition ID defined in the ``--partition-id`` " "argument." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:95 +#: ../../source/tutorial-quickstart-scikitlearn.rst:106 +#, fuzzy msgid "" "Next, the logistic regression model is defined and initialized with " -":code:`utils.set_initial_params()`." +"``utils.set_initial_params()``." msgstr "" "Ensuite, le modèle de régression logistique est défini et initialisé avec" " :code:`utils.set_initial_params()`." -#: ../../source/tutorial-quickstart-scikitlearn.rst:107 +#: ../../source/tutorial-quickstart-scikitlearn.rst:119 +#, fuzzy msgid "" "The Flower server interacts with clients through an interface called " -":code:`Client`. When the server selects a particular client for training," -" it sends training instructions over the network. The client receives " -"those instructions and calls one of the :code:`Client` methods to run " -"your code (i.e., to fit the logistic regression we defined earlier)." +"``Client``. When the server selects a particular client for training, it " +"sends training instructions over the network. The client receives those " +"instructions and calls one of the ``Client`` methods to run your code " +"(i.e., to fit the logistic regression we defined earlier)." msgstr "" "Le serveur Flower interagit avec les clients par le biais d'une interface" " appelée :code:`Client`. Lorsque le serveur sélectionne un client " @@ -25862,13 +25943,13 @@ msgstr "" "méthodes :code:`Client` pour exécuter ton code (c'est-à-dire pour ajuster" " la régression logistique que nous avons définie plus tôt)." -#: ../../source/tutorial-quickstart-scikitlearn.rst:113 +#: ../../source/tutorial-quickstart-scikitlearn.rst:124 +#, fuzzy msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses scikit-learn. Implementing :code:`NumPyClient` usually " -"means defining the following methods (:code:`set_parameters` is optional " -"though):" +"Flower provides a convenience class called ``NumPyClient`` which makes it" +" easier to implement the ``Client`` interface when your workload uses " +"scikit-learn. Implementing ``NumPyClient`` usually means defining the " +"following methods (``set_parameters`` is optional though):" msgstr "" "Flower fournit une classe de commodité appelée :code:`NumPyClient` qui " "facilite la mise en œuvre de l'interface :code:`Client` lorsque ta charge" @@ -25876,15 +25957,16 @@ msgstr "" "signifie généralement définir les méthodes suivantes " "(:code:`set_parameters` est cependant facultatif) :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:119 +#: ../../source/tutorial-quickstart-scikitlearn.rst:130 msgid "return the model weight as a list of NumPy ndarrays" msgstr "renvoie le poids du modèle sous la forme d'une liste de ndarrays NumPy" -#: ../../source/tutorial-quickstart-scikitlearn.rst:120 -msgid ":code:`set_parameters` (optional)" +#: ../../source/tutorial-quickstart-scikitlearn.rst:132 +#, fuzzy +msgid "``set_parameters`` (optional)" msgstr ":code:`set_parameters` (optionnel)" -#: ../../source/tutorial-quickstart-scikitlearn.rst:121 +#: ../../source/tutorial-quickstart-scikitlearn.rst:132 msgid "" "update the local model weights with the parameters received from the " "server" @@ -25892,51 +25974,53 @@ msgstr "" "mettre à jour les poids du modèle local avec les paramètres reçus du " "serveur" -#: ../../source/tutorial-quickstart-scikitlearn.rst:122 -msgid "is directly imported with :code:`utils.set_model_params()`" +#: ../../source/tutorial-quickstart-scikitlearn.rst:133 +#, fuzzy +msgid "is directly imported with ``utils.set_model_params()``" msgstr "est directement importé avec :code:`utils.set_model_params()`" -#: ../../source/tutorial-quickstart-scikitlearn.rst:124 +#: ../../source/tutorial-quickstart-scikitlearn.rst:135 msgid "set the local model weights" msgstr "fixe les poids du modèle local" -#: ../../source/tutorial-quickstart-scikitlearn.rst:125 +#: ../../source/tutorial-quickstart-scikitlearn.rst:136 msgid "train the local model" msgstr "entraîne le modèle local" -#: ../../source/tutorial-quickstart-scikitlearn.rst:126 +#: ../../source/tutorial-quickstart-scikitlearn.rst:137 #, fuzzy msgid "return the updated local model weights" msgstr "recevoir les poids du modèle local mis à jour" -#: ../../source/tutorial-quickstart-scikitlearn.rst:128 +#: ../../source/tutorial-quickstart-scikitlearn.rst:139 msgid "test the local model" msgstr "teste le modèle local" -#: ../../source/tutorial-quickstart-scikitlearn.rst:130 +#: ../../source/tutorial-quickstart-scikitlearn.rst:141 msgid "The methods can be implemented in the following way:" msgstr "Les méthodes peuvent être mises en œuvre de la manière suivante :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:153 +#: ../../source/tutorial-quickstart-scikitlearn.rst:163 +#, fuzzy msgid "" -"We can now create an instance of our class :code:`MnistClient` and add " -"one line to actually run this client:" +"We can now create an instance of our class ``MnistClient`` and add one " +"line to actually run this client:" msgstr "" "Nous pouvons maintenant créer une instance de notre classe " ":code:`MnistClient` et ajouter une ligne pour exécuter ce client :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:160 +#: ../../source/tutorial-quickstart-scikitlearn.rst:170 #, fuzzy msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " -"implement a client of type :code:`NumPyClient` you'll need to first call " -"its :code:`to_client()` method. The string :code:`\"0.0.0.0:8080\"` tells" -" the client which server to connect to. In our case we can run the server" -" and the client on the same machine, therefore we use " -":code:`\"0.0.0.0:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we pass to the client." +"That's it for the client. We only have to implement ``Client`` or " +"``NumPyClient`` and call ``fl.client.start_client()``. If you implement a" +" client of type ``NumPyClient`` you'll need to first call its " +"``to_client()`` method. The string ``\"0.0.0.0:8080\"`` tells the client " +"which server to connect to. In our case we can run the server and the " +"client on the same machine, therefore we use ``\"0.0.0.0:8080\"``. If we " +"run a truly federated workload with the server and clients running on " +"different machines, all that needs to change is the ``server_address`` we" +" pass to the client." msgstr "" "C'est tout pour le client. Il nous suffit d'implémenter :code:`Client` ou" " :code:`NumPyClient` et d'appeler :code:`fl.client.start_client()`. La " @@ -25948,7 +26032,7 @@ msgstr "" "machines différentes, tout ce qui doit changer est :code:`server_address`" " que nous transmettons au client." -#: ../../source/tutorial-quickstart-scikitlearn.rst:169 +#: ../../source/tutorial-quickstart-scikitlearn.rst:181 msgid "" "The following Flower server is a little bit more advanced and returns an " "evaluation function for the server-side evaluation. First, we import " @@ -25959,19 +26043,20 @@ msgstr "" " à nouveau toutes les bibliothèques requises telles que Flower et scikit-" "learn." -#: ../../source/tutorial-quickstart-scikitlearn.rst:172 -msgid ":code:`server.py`, import Flower and start the server:" +#: ../../source/tutorial-quickstart-scikitlearn.rst:185 +#, fuzzy +msgid "``server.py``, import Flower and start the server:" msgstr ":code:`server.py`, importe Flower et démarre le serveur :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:185 +#: ../../source/tutorial-quickstart-scikitlearn.rst:198 #, fuzzy msgid "" -"The number of federated learning rounds is set in :code:`fit_round()` and" -" the evaluation is defined in :code:`get_evaluate_fn()`. The evaluation " -"function is called after each federated learning round and gives you " -"information about loss and accuracy. Note that we also make use of Flower" -" Datasets here to load the test split of the MNIST dataset for server-" -"side evaluation." +"The number of federated learning rounds is set in ``fit_round()`` and the" +" evaluation is defined in ``get_evaluate_fn()``. The evaluation function " +"is called after each federated learning round and gives you information " +"about loss and accuracy. Note that we also make use of Flower Datasets " +"here to load the test split of the MNIST dataset for server-side " +"evaluation." msgstr "" "Le nombre de tours d'apprentissage fédéré est défini dans " ":code:`fit_round()` et l'évaluation est définie dans " @@ -25979,15 +26064,16 @@ msgstr "" "chaque tour d'apprentissage fédéré et te donne des informations sur la " "perte et la précision." -#: ../../source/tutorial-quickstart-scikitlearn.rst:213 +#: ../../source/tutorial-quickstart-scikitlearn.rst:228 +#, fuzzy msgid "" -"The :code:`main` contains the server-side parameter initialization " -":code:`utils.set_initial_params()` as well as the aggregation strategy " -":code:`fl.server.strategy:FedAvg()`. The strategy is the default one, " +"The ``main`` contains the server-side parameter initialization " +"``utils.set_initial_params()`` as well as the aggregation strategy " +"``fl.server.strategy:FedAvg()``. The strategy is the default one, " "federated averaging (or FedAvg), with two clients and evaluation after " "each federated learning round. The server can be started with the command" -" :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " -"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`." +" ``fl.server.start_server(server_address=\"0.0.0.0:8080\", " +"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))``." msgstr "" "Le :code:`main` contient l'initialisation des paramètres côté serveur " ":code:`utils.set_initial_params()` ainsi que la stratégie d'agrégation " @@ -25997,7 +26083,7 @@ msgstr "" " commande :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " "strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`." -#: ../../source/tutorial-quickstart-scikitlearn.rst:232 +#: ../../source/tutorial-quickstart-scikitlearn.rst:256 msgid "" "With both client and server ready, we can now run everything and see " "federated learning in action. Federated learning systems usually have a " @@ -26009,8 +26095,8 @@ msgstr "" "fédéré ont généralement un serveur et plusieurs clients. Nous devons donc" " commencer par lancer le serveur :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:239 -#: ../../source/tutorial-quickstart-xgboost.rst:575 +#: ../../source/tutorial-quickstart-scikitlearn.rst:264 +#: ../../source/tutorial-quickstart-xgboost.rst:598 msgid "" "Once the server is running we can start the clients in different " "terminals. Open a new terminal and start the first client:" @@ -26019,13 +26105,13 @@ msgstr "" "dans différents terminaux. Ouvre un nouveau terminal et démarre le " "premier client :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:246 -#: ../../source/tutorial-quickstart-xgboost.rst:582 +#: ../../source/tutorial-quickstart-scikitlearn.rst:271 +#: ../../source/tutorial-quickstart-xgboost.rst:605 msgid "Open another terminal and start the second client:" msgstr "Ouvre un autre terminal et démarre le deuxième client :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:252 -#: ../../source/tutorial-quickstart-xgboost.rst:588 +#: ../../source/tutorial-quickstart-scikitlearn.rst:277 +#: ../../source/tutorial-quickstart-xgboost.rst:611 msgid "" "Each client will have its own dataset. You should now see how the " "training does in the very first terminal (the one that started the " @@ -26035,13 +26121,14 @@ msgstr "" "voir comment la formation se déroule dans le tout premier terminal (celui" " qui a démarré le serveur) :" -#: ../../source/tutorial-quickstart-scikitlearn.rst:286 +#: ../../source/tutorial-quickstart-scikitlearn.rst:311 +#, fuzzy msgid "" "Congratulations! You've successfully built and run your first federated " "learning system. The full `source code " "`_ for this example can be found in :code:`examples/sklearn-logreg-" -"mnist`." +"mnist>`_ for this example can be found in ``examples/sklearn-logreg-" +"mnist``." msgstr "" "Félicitations ! Tu as réussi à construire et à faire fonctionner ton " "premier système d'apprentissage fédéré. Le code source complet " @@ -26055,11 +26142,11 @@ msgid "" "with TensorFlow to train a CNN model on CIFAR-10." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:5 +#: ../../source/tutorial-quickstart-tensorflow.rst:4 msgid "Quickstart TensorFlow" msgstr "Démarrage rapide de TensorFlow" -#: ../../source/tutorial-quickstart-tensorflow.rst:7 +#: ../../source/tutorial-quickstart-tensorflow.rst:6 #, fuzzy msgid "" "In this tutorial we will learn how to train a Convolutional Neural " @@ -26071,7 +26158,7 @@ msgstr "" "tout exécuter au sein d'un `virtualenv `_." -#: ../../source/tutorial-quickstart-tensorflow.rst:13 +#: ../../source/tutorial-quickstart-tensorflow.rst:11 msgid "" "Let's use `flwr new` to create a complete Flower+TensorFlow project. It " "will generate all the files needed to run, by default with the Flower " @@ -26083,14 +26170,14 @@ msgid "" "api/flwr_datasets.partitioner.IidPartitioner.html#flwr_datasets.partitioner.IidPartitioner>`_." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:28 +#: ../../source/tutorial-quickstart-tensorflow.rst:26 msgid "" "Then, run the command below. You will be prompted to select one of the " "available templates (choose ``TensorFlow``), give a name to your project," " and type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:118 +#: ../../source/tutorial-quickstart-tensorflow.rst:114 msgid "" "This tutorial uses `Flower Datasets `_ " "to easily download and partition the `CIFAR-10` dataset. In this example " @@ -26104,14 +26191,14 @@ msgid "" " correspond to their data partition." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:147 +#: ../../source/tutorial-quickstart-tensorflow.rst:141 msgid "" "Next, we need a model. We defined a simple Convolutional Neural Network " "(CNN), but feel free to replace it with a more sophisticated model if " "you'd like:" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:178 +#: ../../source/tutorial-quickstart-tensorflow.rst:170 msgid "" "With `TensorFlow`, we can use the built-in ``get_weights()`` and " "``set_weights()`` functions, which simplifies the implementation with " @@ -26122,7 +26209,7 @@ msgid "" "set that the client might have:" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:212 +#: ../../source/tutorial-quickstart-tensorflow.rst:203 msgid "" "Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " "defined above by means of a ``client_fn()`` callback. Note that the " @@ -26134,7 +26221,7 @@ msgid "" "``pyproject.toml`` and access them here." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:247 +#: ../../source/tutorial-quickstart-tensorflow.rst:234 msgid "" "To construct a ``ServerApp`` we define a ``server_fn()`` callback with an" " identical signature to that of ``client_fn()`` but the return type is " @@ -26146,7 +26233,7 @@ msgid "" "the global model to federate." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:284 +#: ../../source/tutorial-quickstart-tensorflow.rst:270 #, fuzzy msgid "" "Check the source code of the extended version of this tutorial in " @@ -26158,7 +26245,7 @@ msgstr "" "mxnet/client.py>`_ de cet exemple se trouve dans :code:`examples" "/quickstart-mxnet`." -#: ../../source/tutorial-quickstart-tensorflow.rst:299 +#: ../../source/tutorial-quickstart-tensorflow.rst:282 msgid "" "The video shown below shows how to setup a TensorFlow + Flower project " "using our previously recommended APIs. A new video tutorial will be " @@ -26171,16 +26258,16 @@ msgid "" "with XGBoost to train classification models on trees." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:5 +#: ../../source/tutorial-quickstart-xgboost.rst:4 msgid "Quickstart XGBoost" msgstr "Démarrage rapide XGBoost" -#: ../../source/tutorial-quickstart-xgboost.rst:14 +#: ../../source/tutorial-quickstart-xgboost.rst:13 #, fuzzy msgid "Federated XGBoost" msgstr "Formation fédérée" -#: ../../source/tutorial-quickstart-xgboost.rst:16 +#: ../../source/tutorial-quickstart-xgboost.rst:15 msgid "" "EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient " "implementation of gradient-boosted decision tree (**GBDT**), that " @@ -26190,19 +26277,19 @@ msgid "" "concurrently, unlike the sequential approach taken by GBDT." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:20 +#: ../../source/tutorial-quickstart-xgboost.rst:21 msgid "" "Often, for tabular data on medium-sized datasets with fewer than 10k " "training examples, XGBoost surpasses the results of deep learning " "techniques." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:23 +#: ../../source/tutorial-quickstart-xgboost.rst:25 #, fuzzy msgid "Why federated XGBoost?" msgstr "Qu'est-ce que l'apprentissage fédéré ?" -#: ../../source/tutorial-quickstart-xgboost.rst:25 +#: ../../source/tutorial-quickstart-xgboost.rst:27 msgid "" "Indeed, as the demand for data privacy and decentralized learning grows, " "there's an increasing requirement to implement federated XGBoost systems " @@ -26210,7 +26297,7 @@ msgid "" "detection." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:27 +#: ../../source/tutorial-quickstart-xgboost.rst:31 msgid "" "Federated learning ensures that raw data remains on the local device, " "making it an attractive approach for sensitive domains where data " @@ -26219,10 +26306,10 @@ msgid "" "solution for these specific challenges." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:30 +#: ../../source/tutorial-quickstart-xgboost.rst:36 msgid "" "In this tutorial we will learn how to train a federated XGBoost model on " -"HIGGS dataset using Flower and :code:`xgboost` package. We use a simple " +"HIGGS dataset using Flower and ``xgboost`` package. We use a simple " "example (`full code xgboost-quickstart " "`_)" " with two *clients* and one *server* to demonstrate how federated XGBoost" @@ -26231,11 +26318,11 @@ msgid "" "comprehensive>`_) to run various experiments." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:37 +#: ../../source/tutorial-quickstart-xgboost.rst:46 msgid "Environment Setup" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:39 +#: ../../source/tutorial-quickstart-xgboost.rst:48 #, fuzzy msgid "" "First of all, it is recommended to create a virtual environment and run " @@ -26246,20 +26333,20 @@ msgstr "" "tout exécuter au sein d'un `virtualenv `_." -#: ../../source/tutorial-quickstart-xgboost.rst:41 +#: ../../source/tutorial-quickstart-xgboost.rst:51 msgid "" "We first need to install Flower and Flower Datasets. You can do this by " "running :" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:47 +#: ../../source/tutorial-quickstart-xgboost.rst:57 #, fuzzy msgid "" -"Since we want to use :code:`xgboost` package to build up XGBoost trees, " -"let's go ahead and install :code:`xgboost`:" +"Since we want to use ``xgboost`` package to build up XGBoost trees, let's" +" go ahead and install ``xgboost``:" msgstr "Puisque nous voulons utiliser scikt-learn, allons-y et installons-le :" -#: ../../source/tutorial-quickstart-xgboost.rst:57 +#: ../../source/tutorial-quickstart-xgboost.rst:67 msgid "" "*Clients* are responsible for generating individual weight-updates for " "the model based on their local datasets. Now that we have all our " @@ -26267,131 +26354,129 @@ msgid "" "clients and one server." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:60 +#: ../../source/tutorial-quickstart-xgboost.rst:71 #, fuzzy msgid "" -"In a file called :code:`client.py`, import xgboost, Flower, Flower " -"Datasets and other related functions:" +"In a file called ``client.py``, import xgboost, Flower, Flower Datasets " +"and other related functions:" msgstr "" "Dans un fichier appelé :code:`client.py`, importe Flower et les paquets " "liés à PyTorch :" -#: ../../source/tutorial-quickstart-xgboost.rst:87 +#: ../../source/tutorial-quickstart-xgboost.rst:99 msgid "Dataset partition and hyper-parameter selection" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:89 +#: ../../source/tutorial-quickstart-xgboost.rst:101 msgid "" "Prior to local training, we require loading the HIGGS dataset from Flower" " Datasets and conduct data partitioning for FL:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:102 +#: ../../source/tutorial-quickstart-xgboost.rst:115 msgid "" "In this example, we split the dataset into 30 partitions with uniform " -"distribution (:code:`IidPartitioner(num_partitions=30)`). Then, we load " -"the partition for the given client based on :code:`partition_id`:" +"distribution (``IidPartitioner(num_partitions=30)``). Then, we load the " +"partition for the given client based on ``partition_id``:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:121 +#: ../../source/tutorial-quickstart-xgboost.rst:135 msgid "" "After that, we do train/test splitting on the given partition (client's " -"local data), and transform data format for :code:`xgboost` package." +"local data), and transform data format for ``xgboost`` package." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:134 +#: ../../source/tutorial-quickstart-xgboost.rst:149 msgid "" -"The functions of :code:`train_test_split` and " -":code:`transform_dataset_to_dmatrix` are defined as below:" +"The functions of ``train_test_split`` and " +"``transform_dataset_to_dmatrix`` are defined as below:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:158 +#: ../../source/tutorial-quickstart-xgboost.rst:174 msgid "Finally, we define the hyper-parameters used for XGBoost training." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:174 +#: ../../source/tutorial-quickstart-xgboost.rst:190 msgid "" -"The :code:`num_local_round` represents the number of iterations for local" -" tree boost. We use CPU for the training in default. One can shift it to " -"GPU by setting :code:`tree_method` to :code:`gpu_hist`. We use AUC as " -"evaluation metric." +"The ``num_local_round`` represents the number of iterations for local " +"tree boost. We use CPU for the training in default. One can shift it to " +"GPU by setting ``tree_method`` to ``gpu_hist``. We use AUC as evaluation " +"metric." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:181 +#: ../../source/tutorial-quickstart-xgboost.rst:195 msgid "Flower client definition for XGBoost" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:183 +#: ../../source/tutorial-quickstart-xgboost.rst:197 msgid "" "After loading the dataset we define the Flower client. We follow the " -"general rule to define :code:`XgbClient` class inherited from " -":code:`fl.client.Client`." +"general rule to define ``XgbClient`` class inherited from " +"``fl.client.Client``." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:205 +#: ../../source/tutorial-quickstart-xgboost.rst:219 msgid "" -"All required parameters defined above are passed to :code:`XgbClient`'s " +"All required parameters defined above are passed to ``XgbClient``'s " "constructor." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:207 +#: ../../source/tutorial-quickstart-xgboost.rst:221 msgid "" -"Then, we override :code:`get_parameters`, :code:`fit` and " -":code:`evaluate` methods insides :code:`XgbClient` class as follows." +"Then, we override ``get_parameters``, ``fit`` and ``evaluate`` methods " +"insides ``XgbClient`` class as follows." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:221 +#: ../../source/tutorial-quickstart-xgboost.rst:236 msgid "" "Unlike neural network training, XGBoost trees are not started from a " -"specified random weights. In this case, we do not use " -":code:`get_parameters` and :code:`set_parameters` to initialise model " -"parameters for XGBoost. As a result, let's return an empty tensor in " -":code:`get_parameters` when it is called by the server at the first " -"round." +"specified random weights. In this case, we do not use ``get_parameters`` " +"and ``set_parameters`` to initialise model parameters for XGBoost. As a " +"result, let's return an empty tensor in ``get_parameters`` when it is " +"called by the server at the first round." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:262 +#: ../../source/tutorial-quickstart-xgboost.rst:278 msgid "" -"In :code:`fit`, at the first round, we call :code:`xgb.train()` to build " -"up the first set of trees. From the second round, we load the global " -"model sent from server to new build Booster object, and then update model" -" weights on local training data with function :code:`local_boost` as " -"follows:" +"In ``fit``, at the first round, we call ``xgb.train()`` to build up the " +"first set of trees. From the second round, we load the global model sent " +"from server to new build Booster object, and then update model weights on" +" local training data with function ``local_boost`` as follows:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:281 +#: ../../source/tutorial-quickstart-xgboost.rst:298 msgid "" -"Given :code:`num_local_round`, we update trees by calling " -":code:`bst_input.update` method. After training, the last " -":code:`N=num_local_round` trees will be extracted to send to the server." +"Given ``num_local_round``, we update trees by calling " +"``bst_input.update`` method. After training, the last " +"``N=num_local_round`` trees will be extracted to send to the server." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:313 +#: ../../source/tutorial-quickstart-xgboost.rst:330 msgid "" -"In :code:`evaluate`, after loading the global model, we call " -":code:`bst.eval_set` function to conduct evaluation on valid set. The AUC" -" value will be returned." +"In ``evaluate``, after loading the global model, we call ``bst.eval_set``" +" function to conduct evaluation on valid set. The AUC value will be " +"returned." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:316 +#: ../../source/tutorial-quickstart-xgboost.rst:333 #, fuzzy msgid "" -"Now, we can create an instance of our class :code:`XgbClient` and add one" -" line to actually run this client:" +"Now, we can create an instance of our class ``XgbClient`` and add one " +"line to actually run this client:" msgstr "" "Nous pouvons maintenant créer une instance de notre classe " ":code:`MnistClient` et ajouter une ligne pour exécuter ce client :" -#: ../../source/tutorial-quickstart-xgboost.rst:332 +#: ../../source/tutorial-quickstart-xgboost.rst:350 #, fuzzy msgid "" -"That's it for the client. We only have to implement :code:`Client` and " -"call :code:`fl.client.start_client()`. The string :code:`\"[::]:8080\"` " -"tells the client which server to connect to. In our case we can run the " -"server and the client on the same machine, therefore we use " -":code:`\"[::]:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we point the client at." +"That's it for the client. We only have to implement ``Client`` and call " +"``fl.client.start_client()``. The string ``\"[::]:8080\"`` tells the " +"client which server to connect to. In our case we can run the server and " +"the client on the same machine, therefore we use ``\"[::]:8080\"``. If we" +" run a truly federated workload with the server and clients running on " +"different machines, all that needs to change is the ``server_address`` we" +" point the client at." msgstr "" "C'est tout pour le client. Il nous suffit d'implémenter :code:`Client` ou" " :code:`NumPyClient` et d'appeler :code:`fl.client.start_client()`. La " @@ -26403,7 +26488,7 @@ msgstr "" "différentes, tout ce qui doit changer est l'adresse " ":code:`server_address` vers laquelle nous dirigeons le client." -#: ../../source/tutorial-quickstart-xgboost.rst:343 +#: ../../source/tutorial-quickstart-xgboost.rst:360 #, fuzzy msgid "" "These updates are then sent to the *server* which will aggregate them to " @@ -26417,90 +26502,88 @@ msgstr "" "cette version améliorée du modèle à chaque *client*. Un cycle complet de " "mises à jour de poids s'appelle un *round*." -#: ../../source/tutorial-quickstart-xgboost.rst:346 +#: ../../source/tutorial-quickstart-xgboost.rst:364 #, fuzzy msgid "" -"In a file named :code:`server.py`, import Flower and FedXgbBagging from " -":code:`flwr.server.strategy`." +"In a file named ``server.py``, import Flower and FedXgbBagging from " +"``flwr.server.strategy``." msgstr "" "Dans un fichier appelé :code:`client.py`, importe Flower et les paquets " "liés au MXNet :" -#: ../../source/tutorial-quickstart-xgboost.rst:348 +#: ../../source/tutorial-quickstart-xgboost.rst:367 msgid "We first define a strategy for XGBoost bagging aggregation." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:380 +#: ../../source/tutorial-quickstart-xgboost.rst:401 msgid "" -"We use two clients for this example. An " -":code:`evaluate_metrics_aggregation` function is defined to collect and " -"wighted average the AUC values from clients. The :code:`config_func` " -"function is to return the current FL round number to client's " -":code:`fit()` and :code:`evaluate()` methods." +"We use two clients for this example. An ``evaluate_metrics_aggregation`` " +"function is defined to collect and wighted average the AUC values from " +"clients. The ``config_func`` function is to return the current FL round " +"number to client's ``fit()`` and ``evaluate()`` methods." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:384 +#: ../../source/tutorial-quickstart-xgboost.rst:406 #, fuzzy msgid "Then, we start the server:" msgstr "Démarrer le serveur" -#: ../../source/tutorial-quickstart-xgboost.rst:396 +#: ../../source/tutorial-quickstart-xgboost.rst:418 msgid "Tree-based bagging aggregation" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:398 +#: ../../source/tutorial-quickstart-xgboost.rst:420 msgid "" "You must be curious about how bagging aggregation works. Let's look into " "the details." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:400 +#: ../../source/tutorial-quickstart-xgboost.rst:422 msgid "" -"In file :code:`flwr.server.strategy.fedxgb_bagging.py`, we define " -":code:`FedXgbBagging` inherited from :code:`flwr.server.strategy.FedAvg`." -" Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` " -"and :code:`evaluate` methods as follows:" +"In file ``flwr.server.strategy.fedxgb_bagging.py``, we define " +"``FedXgbBagging`` inherited from ``flwr.server.strategy.FedAvg``. Then, " +"we override the ``aggregate_fit``, ``aggregate_evaluate`` and " +"``evaluate`` methods as follows:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:496 +#: ../../source/tutorial-quickstart-xgboost.rst:519 msgid "" -"In :code:`aggregate_fit`, we sequentially aggregate the clients' XGBoost " -"trees by calling :code:`aggregate()` function:" +"In ``aggregate_fit``, we sequentially aggregate the clients' XGBoost " +"trees by calling ``aggregate()`` function:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:555 +#: ../../source/tutorial-quickstart-xgboost.rst:579 msgid "" "In this function, we first fetch the number of trees and the number of " "parallel trees for the current and previous model by calling " -":code:`_get_tree_nums`. Then, the fetched information will be aggregated." -" After that, the trees (containing model weights) are aggregated to " +"``_get_tree_nums``. Then, the fetched information will be aggregated. " +"After that, the trees (containing model weights) are aggregated to " "generate a new tree model." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:560 +#: ../../source/tutorial-quickstart-xgboost.rst:584 msgid "" "After traversal of all clients' models, a new global model is generated, " "followed by the serialisation, and sending back to each client." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:565 +#: ../../source/tutorial-quickstart-xgboost.rst:588 msgid "Launch Federated XGBoost!" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:641 +#: ../../source/tutorial-quickstart-xgboost.rst:664 msgid "" "Congratulations! You've successfully built and run your first federated " -"XGBoost system. The AUC values can be checked in " -":code:`metrics_distributed`. One can see that the average AUC increases " -"over FL rounds." +"XGBoost system. The AUC values can be checked in ``metrics_distributed``." +" One can see that the average AUC increases over FL rounds." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:646 +#: ../../source/tutorial-quickstart-xgboost.rst:668 #, fuzzy msgid "" "The full `source code `_ for this example can be found in :code:`examples" -"/xgboost-quickstart`." +"/xgboost-quickstart/>`_ for this example can be found in ``examples" +"/xgboost-quickstart``." msgstr "" "Félicitations ! Tu as réussi à construire et à faire fonctionner ton " "premier système d'apprentissage fédéré. Le code source complet " @@ -26508,11 +26591,11 @@ msgstr "" "mxnet/client.py>`_ de cet exemple se trouve dans :code:`examples" "/quickstart-mxnet`." -#: ../../source/tutorial-quickstart-xgboost.rst:650 +#: ../../source/tutorial-quickstart-xgboost.rst:673 msgid "Comprehensive Federated XGBoost" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:652 +#: ../../source/tutorial-quickstart-xgboost.rst:675 msgid "" "Now that you have known how federated XGBoost work with Flower, it's time" " to run some more comprehensive experiments by customising the " @@ -26525,12 +26608,12 @@ msgid "" "client cohorts in a resource-aware manner. Let's take a look!" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:659 +#: ../../source/tutorial-quickstart-xgboost.rst:685 #, fuzzy msgid "Cyclic training" msgstr "Formation centralisée" -#: ../../source/tutorial-quickstart-xgboost.rst:661 +#: ../../source/tutorial-quickstart-xgboost.rst:687 msgid "" "In addition to bagging aggregation, we offer a cyclic training scheme, " "which performs FL in a client-by-client fashion. Instead of aggregating " @@ -26540,183 +26623,181 @@ msgid "" "for next round's boosting." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:665 -msgid "" -"To do this, we first customise a :code:`ClientManager` in " -":code:`server_utils.py`:" +#: ../../source/tutorial-quickstart-xgboost.rst:693 +msgid "To do this, we first customise a ``ClientManager`` in ``server_utils.py``:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:705 +#: ../../source/tutorial-quickstart-xgboost.rst:733 msgid "" -"The customised :code:`ClientManager` samples all available clients in " -"each FL round based on the order of connection to the server. Then, we " -"define a new strategy :code:`FedXgbCyclic` in " -":code:`flwr.server.strategy.fedxgb_cyclic.py`, in order to sequentially " +"The customised ``ClientManager`` samples all available clients in each FL" +" round based on the order of connection to the server. Then, we define a " +"new strategy ``FedXgbCyclic`` in " +"``flwr.server.strategy.fedxgb_cyclic.py``, in order to sequentially " "select only one client in given round and pass the received model to next" " client." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:746 +#: ../../source/tutorial-quickstart-xgboost.rst:775 msgid "" -"Unlike the original :code:`FedAvg`, we don't perform aggregation here. " +"Unlike the original ``FedAvg``, we don't perform aggregation here. " "Instead, we just make a copy of the received client model as global model" -" by overriding :code:`aggregate_fit`." +" by overriding ``aggregate_fit``." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:749 +#: ../../source/tutorial-quickstart-xgboost.rst:778 msgid "" -"Also, the customised :code:`configure_fit` and :code:`configure_evaluate`" -" methods ensure the clients to be sequentially selected given FL round:" +"Also, the customised ``configure_fit`` and ``configure_evaluate`` methods" +" ensure the clients to be sequentially selected given FL round:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:813 +#: ../../source/tutorial-quickstart-xgboost.rst:840 msgid "Customised data partitioning" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:815 +#: ../../source/tutorial-quickstart-xgboost.rst:842 msgid "" -"In :code:`dataset.py`, we have a function :code:`instantiate_partitioner`" -" to instantiate the data partitioner based on the given " -":code:`num_partitions` and :code:`partitioner_type`. Currently, we " -"provide four supported partitioner type to simulate the uniformity/non-" -"uniformity in data quantity (uniform, linear, square, exponential)." +"In ``dataset.py``, we have a function ``instantiate_partitioner`` to " +"instantiate the data partitioner based on the given ``num_partitions`` " +"and ``partitioner_type``. Currently, we provide four supported " +"partitioner type to simulate the uniformity/non-uniformity in data " +"quantity (uniform, linear, square, exponential)." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:846 +#: ../../source/tutorial-quickstart-xgboost.rst:873 #, fuzzy msgid "Customised centralised/distributed evaluation" msgstr "Évaluation centralisée" -#: ../../source/tutorial-quickstart-xgboost.rst:848 +#: ../../source/tutorial-quickstart-xgboost.rst:875 msgid "" "To facilitate centralised evaluation, we define a function in " -":code:`server_utils.py`:" +"``server_utils.py``:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:880 +#: ../../source/tutorial-quickstart-xgboost.rst:907 msgid "" "This function returns a evaluation function which instantiates a " -":code:`Booster` object and loads the global model weights to it. The " -"evaluation is conducted by calling :code:`eval_set()` method, and the " -"tested AUC value is reported." +"``Booster`` object and loads the global model weights to it. The " +"evaluation is conducted by calling ``eval_set()`` method, and the tested " +"AUC value is reported." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:883 +#: ../../source/tutorial-quickstart-xgboost.rst:911 msgid "" "As for distributed evaluation on the clients, it's same as the quick-" -"start example by overriding the :code:`evaluate()` method insides the " -":code:`XgbClient` class in :code:`client_utils.py`." +"start example by overriding the ``evaluate()`` method insides the " +"``XgbClient`` class in ``client_utils.py``." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:887 +#: ../../source/tutorial-quickstart-xgboost.rst:916 #, fuzzy msgid "Flower simulation" msgstr "Simulation de moniteur" -#: ../../source/tutorial-quickstart-xgboost.rst:888 +#: ../../source/tutorial-quickstart-xgboost.rst:918 msgid "" -"We also provide an example code (:code:`sim.py`) to use the simulation " +"We also provide an example code (``sim.py``) to use the simulation " "capabilities of Flower to simulate federated XGBoost training on either a" " single machine or a cluster of machines." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:922 +#: ../../source/tutorial-quickstart-xgboost.rst:954 msgid "" -"After importing all required packages, we define a :code:`main()` " -"function to perform the simulation process:" +"After importing all required packages, we define a ``main()`` function to" +" perform the simulation process:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:977 +#: ../../source/tutorial-quickstart-xgboost.rst:1010 msgid "" "We first load the dataset and perform data partitioning, and the pre-" -"processed data is stored in a :code:`list`. After the simulation begins, " -"the clients won't need to pre-process their partitions again." +"processed data is stored in a ``list``. After the simulation begins, the " +"clients won't need to pre-process their partitions again." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:980 +#: ../../source/tutorial-quickstart-xgboost.rst:1014 msgid "Then, we define the strategies and other hyper-parameters:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1031 +#: ../../source/tutorial-quickstart-xgboost.rst:1065 msgid "" "After that, we start the simulation by calling " -":code:`fl.simulation.start_simulation`:" +"``fl.simulation.start_simulation``:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1051 +#: ../../source/tutorial-quickstart-xgboost.rst:1085 msgid "" -"One of key parameters for :code:`start_simulation` is :code:`client_fn` " -"which returns a function to construct a client. We define it as follows:" +"One of key parameters for ``start_simulation`` is ``client_fn`` which " +"returns a function to construct a client. We define it as follows:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1094 +#: ../../source/tutorial-quickstart-xgboost.rst:1126 msgid "Arguments parser" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1096 +#: ../../source/tutorial-quickstart-xgboost.rst:1128 msgid "" -"In :code:`utils.py`, we define the arguments parsers for clients, server " -"and simulation, allowing users to specify different experimental " -"settings. Let's first see the sever side:" +"In ``utils.py``, we define the arguments parsers for clients, server and " +"simulation, allowing users to specify different experimental settings. " +"Let's first see the sever side:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1142 +#: ../../source/tutorial-quickstart-xgboost.rst:1175 msgid "" "This allows user to specify training strategies / the number of total " "clients / FL rounds / participating clients / clients for evaluation, and" -" evaluation fashion. Note that with :code:`--centralised-eval`, the sever" -" will do centralised evaluation and all functionalities for client " +" evaluation fashion. Note that with ``--centralised-eval``, the sever " +"will do centralised evaluation and all functionalities for client " "evaluation will be disabled." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1146 +#: ../../source/tutorial-quickstart-xgboost.rst:1180 msgid "Then, the argument parser on client side:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1200 +#: ../../source/tutorial-quickstart-xgboost.rst:1234 msgid "" "This defines various options for client data partitioning. Besides, " "clients also have an option to conduct evaluation on centralised test set" -" by setting :code:`--centralised-eval`, as well as an option to perform " -"scaled learning rate based on the number of clients by setting :code" -":`--scaled-lr`." +" by setting ``--centralised-eval``, as well as an option to perform " +"scaled learning rate based on the number of clients by setting " +"``--scaled-lr``." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1204 +#: ../../source/tutorial-quickstart-xgboost.rst:1239 msgid "We also have an argument parser for simulation:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1282 +#: ../../source/tutorial-quickstart-xgboost.rst:1317 msgid "This integrates all arguments for both client and server sides." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1285 +#: ../../source/tutorial-quickstart-xgboost.rst:1320 #, fuzzy msgid "Example commands" msgstr "Exemples de PyTorch" -#: ../../source/tutorial-quickstart-xgboost.rst:1287 +#: ../../source/tutorial-quickstart-xgboost.rst:1322 msgid "" "To run a centralised evaluated experiment with bagging strategy on 5 " "clients with exponential distribution for 50 rounds, we first start the " "server as below:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1294 +#: ../../source/tutorial-quickstart-xgboost.rst:1329 #, fuzzy msgid "Then, on each client terminal, we start the clients:" msgstr "Ouvre un autre terminal et démarre le deuxième client :" -#: ../../source/tutorial-quickstart-xgboost.rst:1300 +#: ../../source/tutorial-quickstart-xgboost.rst:1335 msgid "To run the same experiment with Flower simulation:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1306 +#: ../../source/tutorial-quickstart-xgboost.rst:1341 #, fuzzy msgid "" "The full `code `_ for this comprehensive example can be found in" -" :code:`examples/xgboost-comprehensive`." +" ``examples/xgboost-comprehensive``." msgstr "" "Félicitations ! Tu as réussi à construire et à faire fonctionner ton " "premier système d'apprentissage fédéré. Le code source complet " @@ -28715,7 +28796,7 @@ msgstr "" "chose d'autre, comme la régression linéaire classique." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 -msgid "|b8714c45b74b4d8fb008e2ebb3bc1d44|" +msgid "|4627c2bb6cc443ae9e079f81f33c9dd9|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 @@ -28734,7 +28815,7 @@ msgstr "" " Go." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 -msgid "|75f1561efcfd422ea67d28d1513120dc|" +msgid "|131af8322dc5466b827afd24be98f8c0|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 @@ -28765,7 +28846,7 @@ msgstr "" "chanson." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 -msgid "|6a1f51b235304558a9bdaaabfc93b8d2|" +msgid "|f92920b87f3a40179bf7ddd0b6144c53|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 @@ -28786,7 +28867,7 @@ msgstr "" " données pour la même tâche." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 -msgid "|35e70dab1fb544af9aa3a9c09c4f9797|" +msgid "|d62da263071d45a496f543e41fce3a19|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 @@ -28807,7 +28888,7 @@ msgstr "" "cloud." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 -msgid "|d7efb5705dd3467f991ed23746824a07|" +msgid "|ad851971645b4e1fbf8d15bcc0b2ee11|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 @@ -28828,7 +28909,7 @@ msgstr "" "appuyés." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 -msgid "|94e7b021c7b540bfbedf7f082a41ff87|" +msgid "|929e9a6de6b34edb8488e644e2bb5221|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 @@ -28853,7 +28934,7 @@ msgstr "" " sur un serveur centralisé." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 -msgid "|a80714782dde439ab73936518f91fc3c|" +msgid "|404cf9c9e8d64784a55646c0f9479cbc|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 @@ -28872,7 +28953,7 @@ msgstr "" "suffisantes pour former un bon modèle." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 -msgid "|c62080ca6197473da57d191c8225a9d9|" +msgid "|b021ff9d25814458b1e631f8985a648b|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 @@ -29094,7 +29175,7 @@ msgstr "" "partir d'un point de contrôle précédemment sauvegardé." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 -msgid "|21a8f1e6a5b14a7bbb8559979d0e8a2b|" +msgid "|e6ca84e1df244f238288a768352678e5|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 @@ -29129,7 +29210,7 @@ msgstr "" "rendements décroissants." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 -msgid "|c310f2a22f7b4917bf42775aae7a1c09|" +msgid "|39c2422082554a21963baffb33a0d057|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 @@ -29162,7 +29243,7 @@ msgstr "" "données locales, ou même de quelques étapes (mini-batchs)." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 -msgid "|a0c5b43401194535a8460bcf02e65f9a|" +msgid "|07ecf5fcd6814e88906accec6fa0fbfb|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 @@ -29193,7 +29274,7 @@ msgstr "" " l'entraînement local." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 -msgid "|aabfdbd5564e41a790f8ea93cc21a444|" +msgid "|57e78c0ca8a94ba5a64a04b1f2280e55|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 @@ -29252,7 +29333,7 @@ msgstr "" "times as much as each of the 100 examples." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 -msgid "|c9cc8f160fa647b09e742fe4dc8edb54|" +msgid "|9819b40e59ee40a4921e1244e8c99bac|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 @@ -29395,7 +29476,7 @@ msgstr "" "quel cadre de ML et n'importe quel langage de programmation." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 -msgid "|7e83aad011cd4907b2f02f907c6922e9|" +msgid "|797bf279c4894b5ead31dc9b0534ed62|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 @@ -30606,16 +30687,6 @@ msgstr "" #~ "typically display information on your " #~ "terminal as follows:" #~ msgstr "" -#~ "contenant des informations pertinentes, " -#~ "notamment : le niveau du message " -#~ "de journal (par exemple :code:`INFO`, " -#~ ":code:`DEBUG`), un horodatage, la ligne " -#~ "à partir de laquelle l'enregistrement a" -#~ " eu lieu, ainsi que le message " -#~ "de journal lui-même. De cette " -#~ "façon, le logger afficherait typiquement " -#~ "des informations sur ton terminal comme" -#~ " suit :" #~ msgid "Saving log to file" #~ msgstr "Enregistrement du journal dans un fichier" @@ -30636,22 +30707,6 @@ msgstr "" #~ "`_" #~ " function. For example:" #~ msgstr "" -#~ "Par défaut, le journal de Flower " -#~ "est affiché dans le terminal à " -#~ "partir duquel tu as lancé ta " -#~ "charge de travail d'apprentissage fédéré. " -#~ "Cela s'applique à la fois à la " -#~ "fédération basée sur gRPC (c'est-à-dire " -#~ "lorsque tu fais :code:`fl.server.start_server`) " -#~ "et à l'utilisation du " -#~ ":code:`VirtualClientEngine` (c'est-à-dire lorsque tu" -#~ " fais :code:`fl.simulation.start_simulation`). Dans " -#~ "certaines situations, tu peux vouloir " -#~ "sauvegarder ce journal sur le disque." -#~ " Tu peux le faire en appelant " -#~ "la fonction `fl.common.logger.configure() " -#~ "`_." -#~ " Par exemple :" #~ msgid "" #~ "With the above, Flower will record " @@ -30722,24 +30777,6 @@ msgstr "" #~ "should you wish to backup or " #~ "analyze the logs somewhere else." #~ msgstr "" -#~ "La fonction :code:`fl.common.logger.configure` " -#~ "permet également de spécifier un hôte" -#~ " vers lequel les journaux peuvent " -#~ "être envoyés (via :code:`POST`) par " -#~ "l'intermédiaire d'un :code:`logging.handler.HTTPHandler`" -#~ " natif de Python. Il s'agit d'une " -#~ "fonction particulièrement utile dans les " -#~ "charges de travail d'apprentissage fédéré " -#~ "basées sur :code:`gRPC` où la collecte" -#~ " des journaux de toutes les entités" -#~ " (c'est-à-dire le serveur et les " -#~ "clients) pourrait s'avérer fastidieuse. Notez" -#~ " que dans la simulation Flower, le" -#~ " serveur affiche automatiquement tous les" -#~ " journaux. Vous pouvez toujours spécifier" -#~ " un :code:`HTTPHandler` si vous souhaitez" -#~ " sauvegarder ou analyser les journaux " -#~ "à un autre endroit." #~ msgid "Enable SSL connections" #~ msgstr "Collecte centralisée des données" @@ -31638,24 +31675,9 @@ msgstr "" #~ "`_ to " #~ "learn more about the app." #~ msgstr "" -#~ "Créons un nouveau projet d'application " -#~ "dans Xcode et ajoutons :code:`flwr` " -#~ "comme dépendance dans ton projet. Pour" -#~ " notre application, nous stockerons la " -#~ "logique de notre application dans " -#~ ":code:`FLiOSModel.swift` et les éléments de" -#~ " l'interface utilisateur dans " -#~ ":code:`ContentView.swift`.Nous nous concentrerons " -#~ "davantage sur :code:`FLiOSModel.swift` dans ce" -#~ " quickstart. N'hésite pas à te " -#~ "référer à l'`exemple de code complet " -#~ "`_ pour" -#~ " en savoir plus sur l'application." #~ msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:" #~ msgstr "" -#~ "Importe les paquets liés à Flower " -#~ "et CoreML dans :code:`FLiOSModel.swift` :" #~ msgid "" #~ "Then add the mlmodel to the " @@ -31672,20 +31694,6 @@ msgstr "" #~ " preprocessing is done inside " #~ ":code:`DataLoader.swift`." #~ msgstr "" -#~ "Ensuite, ajoute le mlmodel au projet " -#~ "simplement par glisser-déposer, le " -#~ "mlmodel sera regroupé à l'intérieur de" -#~ " l'application lors du déploiement sur " -#~ "ton appareil iOS. Nous devons passer " -#~ "l'url pour accéder au mlmodel et " -#~ "exécuter les processus d'apprentissage " -#~ "automatique CoreML, elle peut être " -#~ "récupérée en appelant la fonction " -#~ ":code:`Bundle.main.url`. Pour l'ensemble de " -#~ "données MNIST, nous devons le prétraiter" -#~ " dans l'objet :code:`MLBatchProvider`. Le " -#~ "prétraitement est effectué à l'intérieur " -#~ "de :code:`DataLoader.swift`." #~ msgid "" #~ "Since CoreML does not allow the " @@ -31713,10 +31721,6 @@ msgstr "" #~ " by passing our Flower client to " #~ "the function :code:`startFlwrGRPC`." #~ msgstr "" -#~ "Lance ensuite le client Flower gRPC " -#~ "et commence à communiquer avec le " -#~ "serveur en passant notre client Flower" -#~ " à la fonction :code:`startFlwrGRPC`." #~ msgid "" #~ "That's it for the client. We only" @@ -31731,17 +31735,6 @@ msgstr "" #~ "button to start the federated learning" #~ " process." #~ msgstr "" -#~ "C'est tout pour le client. Il nous" -#~ " suffit d'implémenter :code:`Client` ou " -#~ "d'appeler le :code:`MLFlwrClient` fourni et" -#~ " d'appeler :code:`startFlwrGRPC()`. L'attribut " -#~ ":code:`hostname` et :code:`port` indique au" -#~ " client à quel serveur se connecter." -#~ " Pour ce faire, il suffit d'entrer" -#~ " le nom d'hôte et le port dans" -#~ " l'application avant de cliquer sur " -#~ "le bouton de démarrage pour lancer " -#~ "le processus d'apprentissage fédéré." #~ msgid "" #~ "Once the server is running we can" @@ -37800,3 +37793,586 @@ msgstr "" #~ msgid "|fbf2da0da3cc4f8ab3b3eff852d80c41|" #~ msgstr "" +#~ msgid "" +#~ "Install `xz` (to install different " +#~ "Python versions) and `pandoc` to build" +#~ " the docs::" +#~ msgstr "" + +#~ msgid "" +#~ "Ensure you system (Ubuntu 22.04+) is " +#~ "up-to-date, and you have all " +#~ "necessary packages::" +#~ msgstr "" + +#~ msgid "" +#~ "Let's create the Python environment for" +#~ " all-things Flower. If you wish " +#~ "to use :code:`pyenv`, we provide two " +#~ "convenience scripts that you can use." +#~ " If you prefer using something else" +#~ " than :code:`pyenv`, create a new " +#~ "environment, activate and skip to the" +#~ " last point where all packages are" +#~ " installed." +#~ msgstr "" + +#~ msgid "" +#~ "If in a hurry, bypass the hook " +#~ "using ``--no-verify`` with the ``git " +#~ "commit`` command. ::" +#~ msgstr "" + +#~ msgid "" +#~ "Flower's documentation uses `Sphinx " +#~ "`_. There's no " +#~ "convenience script to re-build the " +#~ "documentation yet, but it's pretty " +#~ "easy::" +#~ msgstr "" + +#~ msgid "" +#~ "Some quickstart examples may have " +#~ "limitations or requirements that prevent " +#~ "them from running on every environment." +#~ " For more information, please see " +#~ "`Limitations`_." +#~ msgstr "" + +#~ msgid "" +#~ "Change the application code. For " +#~ "example, change the ``seed`` in " +#~ "``quickstart_docker/task.py`` to ``43`` and " +#~ "save it:" +#~ msgstr "" + +#~ msgid ":code:`fit`" +#~ msgstr ":code:`fit`" + +#~ msgid "" +#~ "\\small\n" +#~ "\\frac{∆ \\times \\sqrt{2 \\times " +#~ "\\log\\left(\\frac{1.25}{\\delta}\\right)}}{\\epsilon}\n" +#~ "\n" +#~ msgstr "" + +#~ msgid "Enable node authentication in :code:`SuperLink`" +#~ msgstr "" + +#~ msgid "" +#~ "To enable node authentication, first you" +#~ " need to configure SSL/TLS connections " +#~ "to secure the SuperLink<>SuperNode " +#~ "communication. You can find the complete" +#~ " guide `here `_. After " +#~ "configuring secure connections, you can " +#~ "enable client authentication in a " +#~ "long-running Flower :code:`SuperLink`. Use " +#~ "the following terminal command to start" +#~ " a Flower :code:`SuperNode` that has " +#~ "both secure connections and node " +#~ "authentication enabled:" +#~ msgstr "" + +#~ msgid "" +#~ "The first flag :code:`--auth-list-" +#~ "public-keys` expects a path to a " +#~ "CSV file storing all known node " +#~ "public keys. You need to store all" +#~ " known node public keys that are " +#~ "allowed to participate in a federation" +#~ " in one CSV file (:code:`.csv`)." +#~ msgstr "" + +#~ msgid "" +#~ "The second and third flags :code" +#~ ":`--auth-superlink-private-key` and :code" +#~ ":`--auth-superlink-public-key` expect paths" +#~ " to the server's private and public" +#~ " keys. For development purposes, you " +#~ "can generate a private and public " +#~ "key pair using :code:`ssh-keygen -t " +#~ "ecdsa -b 384`." +#~ msgstr "" + +#~ msgid "Enable node authentication in :code:`SuperNode`" +#~ msgstr "" + +#~ msgid "" +#~ "Similar to the long-running Flower " +#~ "server (:code:`SuperLink`), you can easily " +#~ "enable node authentication in the " +#~ "long-running Flower client (:code:`SuperNode`)." +#~ " Use the following terminal command " +#~ "to start an authenticated :code:`SuperNode`:" +#~ msgstr "" + +#~ msgid "" +#~ "The :code:`--auth-supernode-private-key` " +#~ "flag expects a path to the node's" +#~ " private key file and the :code" +#~ ":`--auth-supernode-public-key` flag expects" +#~ " a path to the node's public " +#~ "key file. For development purposes, you" +#~ " can generate a private and public" +#~ " key pair using :code:`ssh-keygen -t" +#~ " ecdsa -b 384`." +#~ msgstr "" + +#~ msgid "" +#~ "You should now have learned how to" +#~ " start a long-running Flower server" +#~ " (:code:`SuperLink`) and client " +#~ "(:code:`SuperNode`) with node authentication " +#~ "enabled. You should also know the " +#~ "significance of the private key and " +#~ "store it safely to minimize security " +#~ "risks." +#~ msgstr "" + +#~ msgid "" +#~ "If you have not added ``conda-" +#~ "forge`` to your channels, you will " +#~ "first need to run the following::" +#~ msgstr "" + +#~ msgid "" +#~ "Once the ``conda-forge`` channel has " +#~ "been enabled, ``flwr`` can be installed" +#~ " with ``conda``::" +#~ msgstr "" + +#~ msgid "or with ``mamba``::" +#~ msgstr "" + +#~ msgid "" +#~ "For central DP with server-side " +#~ "clipping, there are two :code:`Strategy` " +#~ "classes that act as wrappers around " +#~ "the actual :code:`Strategy` instance (for " +#~ "example, :code:`FedAvg`). The two wrapper " +#~ "classes are " +#~ ":code:`DifferentialPrivacyServerSideFixedClipping` and " +#~ ":code:`DifferentialPrivacyServerSideAdaptiveClipping` for " +#~ "fixed and adaptive clipping." +#~ msgstr "" + +#~ msgid "" +#~ "The code sample below enables the " +#~ ":code:`FedAvg` strategy to use server-" +#~ "side fixed clipping using the " +#~ ":code:`DifferentialPrivacyServerSideFixedClipping` wrapper " +#~ "class. The same approach can be " +#~ "used with " +#~ ":code:`DifferentialPrivacyServerSideAdaptiveClipping` by " +#~ "adjusting the corresponding input parameters." +#~ msgstr "" + +#~ msgid "" +#~ "For central DP with client-side " +#~ "clipping, the server sends the clipping" +#~ " value to selected clients on each" +#~ " round. Clients can use existing " +#~ "Flower :code:`Mods` to perform the " +#~ "clipping. Two mods are available for " +#~ "fixed and adaptive client-side clipping:" +#~ " :code:`fixedclipping_mod` and " +#~ ":code:`adaptiveclipping_mod` with corresponding " +#~ "server-side wrappers " +#~ ":code:`DifferentialPrivacyClientSideFixedClipping` and " +#~ ":code:`DifferentialPrivacyClientSideAdaptiveClipping`." +#~ msgstr "" + +#~ msgid "" +#~ "The code sample below enables the " +#~ ":code:`FedAvg` strategy to use differential" +#~ " privacy with client-side fixed " +#~ "clipping using both the " +#~ ":code:`DifferentialPrivacyClientSideFixedClipping` wrapper " +#~ "class and, on the client, " +#~ ":code:`fixedclipping_mod`:" +#~ msgstr "" + +#~ msgid "" +#~ "In addition to the server-side " +#~ "strategy wrapper, the :code:`ClientApp` needs" +#~ " to configure the matching " +#~ ":code:`fixedclipping_mod` to perform the " +#~ "client-side clipping:" +#~ msgstr "" + +#~ msgid "Below is a code example that shows how to use :code:`LocalDpMod`:" +#~ msgstr "" + +#~ msgid "" +#~ "Note that since version :code:`1.11.0`, " +#~ ":code:`flower-server-app` no longer " +#~ "supports passing a reference to a " +#~ "`ServerApp` attribute. Instead, you need " +#~ "to pass the path to Flower app " +#~ "via the argument :code:`--app`. This is" +#~ " the path to a directory containing" +#~ " a `pyproject.toml`. You can create a" +#~ " valid Flower app by executing " +#~ ":code:`flwr new` and following the " +#~ "prompt." +#~ msgstr "" + +#~ msgid "" +#~ "Since CoreML does not allow the " +#~ "model parameters to be seen before " +#~ "training, and accessing the model " +#~ "parameters during or after the training" +#~ " can only be done by specifying " +#~ "the layer name, we need to know" +#~ " this information beforehand, through " +#~ "looking at the model specification, " +#~ "which are written as proto files. " +#~ "The implementation can be seen in " +#~ ":code:`MLModelInspect`." +#~ msgstr "" + +#~ msgid "" +#~ "Prior to local training, we need " +#~ "to load the MNIST dataset, a " +#~ "popular image classification dataset of " +#~ "handwritten digits for machine learning, " +#~ "and partition the dataset for FL. " +#~ "This can be conveniently achieved using" +#~ " `Flower Datasets `_." +#~ " The :code:`FederatedDataset.load_partition()` method" +#~ " loads the partitioned training set " +#~ "for each partition ID defined in " +#~ "the :code:`--partition-id` argument." +#~ msgstr "" + +#~ msgid "" +#~ "In this tutorial we will learn how" +#~ " to train a federated XGBoost model" +#~ " on HIGGS dataset using Flower and" +#~ " :code:`xgboost` package. We use a " +#~ "simple example (`full code xgboost-" +#~ "quickstart `_) with two *clients* " +#~ "and one *server* to demonstrate how " +#~ "federated XGBoost works, and then we " +#~ "dive into a more complex example " +#~ "(`full code xgboost-comprehensive " +#~ "`_) to run various experiments." +#~ msgstr "" + +#~ msgid "" +#~ "In this example, we split the " +#~ "dataset into 30 partitions with uniform" +#~ " distribution (:code:`IidPartitioner(num_partitions=30)`)." +#~ " Then, we load the partition for " +#~ "the given client based on " +#~ ":code:`partition_id`:" +#~ msgstr "" + +#~ msgid "" +#~ "After that, we do train/test splitting" +#~ " on the given partition (client's " +#~ "local data), and transform data format" +#~ " for :code:`xgboost` package." +#~ msgstr "" + +#~ msgid "" +#~ "The functions of :code:`train_test_split` and" +#~ " :code:`transform_dataset_to_dmatrix` are defined " +#~ "as below:" +#~ msgstr "" + +#~ msgid "" +#~ "The :code:`num_local_round` represents the " +#~ "number of iterations for local tree " +#~ "boost. We use CPU for the training" +#~ " in default. One can shift it " +#~ "to GPU by setting :code:`tree_method` to" +#~ " :code:`gpu_hist`. We use AUC as " +#~ "evaluation metric." +#~ msgstr "" + +#~ msgid "" +#~ "After loading the dataset we define " +#~ "the Flower client. We follow the " +#~ "general rule to define :code:`XgbClient` " +#~ "class inherited from :code:`fl.client.Client`." +#~ msgstr "" + +#~ msgid "" +#~ "All required parameters defined above " +#~ "are passed to :code:`XgbClient`'s constructor." +#~ msgstr "" + +#~ msgid "" +#~ "Then, we override :code:`get_parameters`, " +#~ ":code:`fit` and :code:`evaluate` methods " +#~ "insides :code:`XgbClient` class as follows." +#~ msgstr "" + +#~ msgid "" +#~ "Unlike neural network training, XGBoost " +#~ "trees are not started from a " +#~ "specified random weights. In this case," +#~ " we do not use :code:`get_parameters` " +#~ "and :code:`set_parameters` to initialise model" +#~ " parameters for XGBoost. As a result," +#~ " let's return an empty tensor in " +#~ ":code:`get_parameters` when it is called " +#~ "by the server at the first round." +#~ msgstr "" + +#~ msgid "" +#~ "In :code:`fit`, at the first round, " +#~ "we call :code:`xgb.train()` to build up" +#~ " the first set of trees. From " +#~ "the second round, we load the " +#~ "global model sent from server to " +#~ "new build Booster object, and then " +#~ "update model weights on local training" +#~ " data with function :code:`local_boost` as" +#~ " follows:" +#~ msgstr "" + +#~ msgid "" +#~ "Given :code:`num_local_round`, we update trees" +#~ " by calling :code:`bst_input.update` method. " +#~ "After training, the last " +#~ ":code:`N=num_local_round` trees will be " +#~ "extracted to send to the server." +#~ msgstr "" + +#~ msgid "" +#~ "In :code:`evaluate`, after loading the " +#~ "global model, we call :code:`bst.eval_set` " +#~ "function to conduct evaluation on valid" +#~ " set. The AUC value will be " +#~ "returned." +#~ msgstr "" + +#~ msgid "" +#~ "We use two clients for this " +#~ "example. An :code:`evaluate_metrics_aggregation` " +#~ "function is defined to collect and " +#~ "wighted average the AUC values from " +#~ "clients. The :code:`config_func` function is" +#~ " to return the current FL round " +#~ "number to client's :code:`fit()` and " +#~ ":code:`evaluate()` methods." +#~ msgstr "" + +#~ msgid "" +#~ "In file :code:`flwr.server.strategy.fedxgb_bagging.py`," +#~ " we define :code:`FedXgbBagging` inherited " +#~ "from :code:`flwr.server.strategy.FedAvg`. Then, we" +#~ " override the :code:`aggregate_fit`, " +#~ ":code:`aggregate_evaluate` and :code:`evaluate` " +#~ "methods as follows:" +#~ msgstr "" + +#~ msgid "" +#~ "In :code:`aggregate_fit`, we sequentially " +#~ "aggregate the clients' XGBoost trees by" +#~ " calling :code:`aggregate()` function:" +#~ msgstr "" + +#~ msgid "" +#~ "In this function, we first fetch " +#~ "the number of trees and the number" +#~ " of parallel trees for the current" +#~ " and previous model by calling " +#~ ":code:`_get_tree_nums`. Then, the fetched " +#~ "information will be aggregated. After " +#~ "that, the trees (containing model " +#~ "weights) are aggregated to generate a" +#~ " new tree model." +#~ msgstr "" + +#~ msgid "" +#~ "Congratulations! You've successfully built and" +#~ " run your first federated XGBoost " +#~ "system. The AUC values can be " +#~ "checked in :code:`metrics_distributed`. One " +#~ "can see that the average AUC " +#~ "increases over FL rounds." +#~ msgstr "" + +#~ msgid "" +#~ "To do this, we first customise a" +#~ " :code:`ClientManager` in :code:`server_utils.py`:" +#~ msgstr "" + +#~ msgid "" +#~ "The customised :code:`ClientManager` samples " +#~ "all available clients in each FL " +#~ "round based on the order of " +#~ "connection to the server. Then, we " +#~ "define a new strategy :code:`FedXgbCyclic` " +#~ "in :code:`flwr.server.strategy.fedxgb_cyclic.py`, in " +#~ "order to sequentially select only one" +#~ " client in given round and pass " +#~ "the received model to next client." +#~ msgstr "" + +#~ msgid "" +#~ "Unlike the original :code:`FedAvg`, we " +#~ "don't perform aggregation here. Instead, " +#~ "we just make a copy of the " +#~ "received client model as global model" +#~ " by overriding :code:`aggregate_fit`." +#~ msgstr "" + +#~ msgid "" +#~ "Also, the customised :code:`configure_fit` and" +#~ " :code:`configure_evaluate` methods ensure the" +#~ " clients to be sequentially selected " +#~ "given FL round:" +#~ msgstr "" + +#~ msgid "" +#~ "In :code:`dataset.py`, we have a " +#~ "function :code:`instantiate_partitioner` to " +#~ "instantiate the data partitioner based " +#~ "on the given :code:`num_partitions` and " +#~ ":code:`partitioner_type`. Currently, we provide " +#~ "four supported partitioner type to " +#~ "simulate the uniformity/non-uniformity in " +#~ "data quantity (uniform, linear, square, " +#~ "exponential)." +#~ msgstr "" + +#~ msgid "" +#~ "To facilitate centralised evaluation, we " +#~ "define a function in :code:`server_utils.py`:" +#~ msgstr "" + +#~ msgid "" +#~ "This function returns a evaluation " +#~ "function which instantiates a :code:`Booster`" +#~ " object and loads the global model" +#~ " weights to it. The evaluation is " +#~ "conducted by calling :code:`eval_set()` " +#~ "method, and the tested AUC value " +#~ "is reported." +#~ msgstr "" + +#~ msgid "" +#~ "As for distributed evaluation on the " +#~ "clients, it's same as the quick-" +#~ "start example by overriding the " +#~ ":code:`evaluate()` method insides the " +#~ ":code:`XgbClient` class in :code:`client_utils.py`." +#~ msgstr "" + +#~ msgid "" +#~ "We also provide an example code " +#~ "(:code:`sim.py`) to use the simulation " +#~ "capabilities of Flower to simulate " +#~ "federated XGBoost training on either a" +#~ " single machine or a cluster of " +#~ "machines." +#~ msgstr "" + +#~ msgid "" +#~ "After importing all required packages, " +#~ "we define a :code:`main()` function to" +#~ " perform the simulation process:" +#~ msgstr "" + +#~ msgid "" +#~ "We first load the dataset and " +#~ "perform data partitioning, and the " +#~ "pre-processed data is stored in a " +#~ ":code:`list`. After the simulation begins, " +#~ "the clients won't need to pre-" +#~ "process their partitions again." +#~ msgstr "" + +#~ msgid "" +#~ "After that, we start the simulation " +#~ "by calling :code:`fl.simulation.start_simulation`:" +#~ msgstr "" + +#~ msgid "" +#~ "One of key parameters for " +#~ ":code:`start_simulation` is :code:`client_fn` which" +#~ " returns a function to construct a" +#~ " client. We define it as follows:" +#~ msgstr "" + +#~ msgid "" +#~ "In :code:`utils.py`, we define the " +#~ "arguments parsers for clients, server " +#~ "and simulation, allowing users to " +#~ "specify different experimental settings. Let's" +#~ " first see the sever side:" +#~ msgstr "" + +#~ msgid "" +#~ "This allows user to specify training " +#~ "strategies / the number of total " +#~ "clients / FL rounds / participating " +#~ "clients / clients for evaluation, and" +#~ " evaluation fashion. Note that with " +#~ ":code:`--centralised-eval`, the sever will " +#~ "do centralised evaluation and all " +#~ "functionalities for client evaluation will " +#~ "be disabled." +#~ msgstr "" + +#~ msgid "" +#~ "This defines various options for client" +#~ " data partitioning. Besides, clients also" +#~ " have an option to conduct evaluation" +#~ " on centralised test set by setting" +#~ " :code:`--centralised-eval`, as well as " +#~ "an option to perform scaled learning " +#~ "rate based on the number of " +#~ "clients by setting :code:`--scaled-lr`." +#~ msgstr "" + +#~ msgid "|b8714c45b74b4d8fb008e2ebb3bc1d44|" +#~ msgstr "" + +#~ msgid "|75f1561efcfd422ea67d28d1513120dc|" +#~ msgstr "" + +#~ msgid "|6a1f51b235304558a9bdaaabfc93b8d2|" +#~ msgstr "" + +#~ msgid "|35e70dab1fb544af9aa3a9c09c4f9797|" +#~ msgstr "" + +#~ msgid "|d7efb5705dd3467f991ed23746824a07|" +#~ msgstr "" + +#~ msgid "|94e7b021c7b540bfbedf7f082a41ff87|" +#~ msgstr "" + +#~ msgid "|a80714782dde439ab73936518f91fc3c|" +#~ msgstr "" + +#~ msgid "|c62080ca6197473da57d191c8225a9d9|" +#~ msgstr "" + +#~ msgid "|21a8f1e6a5b14a7bbb8559979d0e8a2b|" +#~ msgstr "" + +#~ msgid "|c310f2a22f7b4917bf42775aae7a1c09|" +#~ msgstr "" + +#~ msgid "|a0c5b43401194535a8460bcf02e65f9a|" +#~ msgstr "" + +#~ msgid "|aabfdbd5564e41a790f8ea93cc21a444|" +#~ msgstr "" + +#~ msgid "|c9cc8f160fa647b09e742fe4dc8edb54|" +#~ msgstr "" + +#~ msgid "|7e83aad011cd4907b2f02f907c6922e9|" +#~ msgstr "" + diff --git a/doc/locales/ko/LC_MESSAGES/framework-docs.po b/doc/locales/ko/LC_MESSAGES/framework-docs.po index 78c7bdd427a7..daae837e3bfc 100644 --- a/doc/locales/ko/LC_MESSAGES/framework-docs.po +++ b/doc/locales/ko/LC_MESSAGES/framework-docs.po @@ -7,7 +7,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower main\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-09-25 00:30+0000\n" +"POT-Creation-Date: 2024-09-26 00:29+0000\n" "PO-Revision-Date: 2024-08-23 13:09+0000\n" "Last-Translator: Seulki Yun \n" "Language: ko\n" @@ -62,23 +62,23 @@ msgid "" "or not by reading the Flower source code." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:22 +#: ../../source/contributor-explanation-public-and-private-apis.rst:23 #, fuzzy msgid "Flower public API" msgstr "Flower ClientApp." -#: ../../source/contributor-explanation-public-and-private-apis.rst:24 +#: ../../source/contributor-explanation-public-and-private-apis.rst:25 msgid "Flower has a well-defined public API. Let's look at this in more detail." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:28 +#: ../../source/contributor-explanation-public-and-private-apis.rst:29 msgid "" "Every component that is reachable by recursively following " "``__init__.__all__`` starting from the root package (``flwr``) is part of" " the public API." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:30 +#: ../../source/contributor-explanation-public-and-private-apis.rst:32 msgid "" "If you want to determine whether a component " "(class/function/generator/...) is part of the public API or not, you need" @@ -86,13 +86,13 @@ msgid "" "src/py/flwr`` to look at the Python sub-packages contained ``flwr``:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:43 +#: ../../source/contributor-explanation-public-and-private-apis.rst:46 msgid "" "Contrast this with the definition of ``__all__`` in the root " "``src/py/flwr/__init__.py``:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:55 +#: ../../source/contributor-explanation-public-and-private-apis.rst:59 msgid "" "You can see that ``flwr`` has six subpackages (``cli``, ``client``, " "``common``, ``proto``, ``server``, ``simulation``), but only four of them" @@ -100,7 +100,7 @@ msgid "" "``simulation``)." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:57 +#: ../../source/contributor-explanation-public-and-private-apis.rst:63 msgid "" "What does this mean? It means that ``client``, ``common``, ``server`` and" " ``simulation`` are part of the public API, but ``cli`` and ``proto`` are" @@ -111,21 +111,21 @@ msgid "" "even be removed completely." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:62 +#: ../../source/contributor-explanation-public-and-private-apis.rst:70 msgid "Therefore, as a Flower user:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:64 +#: ../../source/contributor-explanation-public-and-private-apis.rst:72 msgid "``from flwr import client`` ✅ Ok, you're importing a public API." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:65 +#: ../../source/contributor-explanation-public-and-private-apis.rst:73 msgid "" "``from flwr import proto`` ❌ Not recommended, you're importing a private " "API." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:67 +#: ../../source/contributor-explanation-public-and-private-apis.rst:75 msgid "" "What about components that are nested deeper in the hierarchy? Let's look" " at Flower strategies to see another typical pattern. Flower strategies " @@ -134,7 +134,7 @@ msgid "" "``src/py/flwr/server/strategy/__init__.py``:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:81 +#: ../../source/contributor-explanation-public-and-private-apis.rst:91 msgid "" "What's notable here is that all strategies are implemented in dedicated " "modules (e.g., ``fedavg.py``). In ``__init__.py``, we *import* the " @@ -146,33 +146,33 @@ msgid "" "the public API (as long as we update the import path in ``__init__.py``)." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:86 +#: ../../source/contributor-explanation-public-and-private-apis.rst:99 msgid "Therefore:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:88 +#: ../../source/contributor-explanation-public-and-private-apis.rst:101 msgid "" "``from flwr.server.strategy import FedAvg`` ✅ Ok, you're importing a " "class that is part of the public API." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:89 +#: ../../source/contributor-explanation-public-and-private-apis.rst:103 msgid "" "``from flwr.server.strategy import fedavg`` ❌ Not recommended, you're " "importing a private module." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:91 +#: ../../source/contributor-explanation-public-and-private-apis.rst:106 msgid "" "This approach is also implemented in the tooling that automatically " "builds API reference docs." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:94 +#: ../../source/contributor-explanation-public-and-private-apis.rst:110 msgid "Flower public API of private packages" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:96 +#: ../../source/contributor-explanation-public-and-private-apis.rst:112 msgid "" "We also use this to define the public API of private subpackages. Public," " in this context, means the API that other ``flwr`` subpackages should " @@ -180,14 +180,14 @@ msgid "" "not exported via ``src/py/flwr/server/__init__.py``'s ``__all__``)." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:100 +#: ../../source/contributor-explanation-public-and-private-apis.rst:117 msgid "" "Still, the private sub-package ``flwr.server.driver`` defines a " "\"public\" API using ``__all__`` in " "``src/py/flwr/server/driver/__init__.py``:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:114 +#: ../../source/contributor-explanation-public-and-private-apis.rst:132 msgid "" "The interesting part is that both ``GrpcDriver`` and ``InMemoryDriver`` " "are never used by Flower framework users, only by other parts of the " @@ -199,7 +199,7 @@ msgid "" "``InMemoryDriver`` class definition)." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:117 +#: ../../source/contributor-explanation-public-and-private-apis.rst:140 msgid "" "This is because ``flwr.server.driver`` defines a public interface for " "other ``flwr`` subpackages. This allows codeowners of " @@ -234,16 +234,16 @@ msgid "" "development environment." msgstr "시작하기 전에, 로컬 개발 환경에서 몇 가지 전제 조건을 충족해야 합니다." -#: ../../source/contributor-how-to-build-docker-images.rst:12 +#: ../../source/contributor-how-to-build-docker-images.rst:13 #, fuzzy msgid "Clone the ``flower`` repository." msgstr "플라워 레포지토리를 클론합니다." -#: ../../source/contributor-how-to-build-docker-images.rst:18 +#: ../../source/contributor-how-to-build-docker-images.rst:19 msgid "Verify the Docker daemon is running." msgstr "Docker 데몬이 실행 중인지 확인하십시오." -#: ../../source/contributor-how-to-build-docker-images.rst:20 +#: ../../source/contributor-how-to-build-docker-images.rst:21 msgid "" "The build instructions that assemble the images are located in the " "respective Dockerfiles. You can find them in the subdirectories of " @@ -252,7 +252,7 @@ msgstr "" "이미지들을 조합하는 빌드 명령어들은 해당 Dockerfile에 있습니다. \"src/docker\" 의 하위 디렉토리에서 찾을 수 " "있습니다." -#: ../../source/contributor-how-to-build-docker-images.rst:23 +#: ../../source/contributor-how-to-build-docker-images.rst:24 msgid "" "Flower Docker images are configured via build arguments. Through build " "arguments, we can make the creation of images more flexible. For example," @@ -267,141 +267,141 @@ msgstr "" " 있습니다. 일부 빌드 전달인자들은 기본값이며, 이미지를 빌드할 때 지정해야 합니다. 각 이미지에 사용할 수 있는 모든 빌드 " "전달인자는 아래 표 중에 있습니다." -#: ../../source/contributor-how-to-build-docker-images.rst:30 +#: ../../source/contributor-how-to-build-docker-images.rst:32 #, fuzzy msgid "Building the Base Image" msgstr "기본 이미지 빌드" -#: ../../source/contributor-how-to-build-docker-images.rst:36 -#: ../../source/contributor-how-to-build-docker-images.rst:98 +#: ../../source/contributor-how-to-build-docker-images.rst:38 +#: ../../source/contributor-how-to-build-docker-images.rst:104 msgid "Build argument" msgstr "빌드 전달인자" -#: ../../source/contributor-how-to-build-docker-images.rst:37 -#: ../../source/contributor-how-to-build-docker-images.rst:99 +#: ../../source/contributor-how-to-build-docker-images.rst:39 +#: ../../source/contributor-how-to-build-docker-images.rst:105 msgid "Description" msgstr "설명" -#: ../../source/contributor-how-to-build-docker-images.rst:38 -#: ../../source/contributor-how-to-build-docker-images.rst:100 +#: ../../source/contributor-how-to-build-docker-images.rst:40 +#: ../../source/contributor-how-to-build-docker-images.rst:106 msgid "Required" msgstr "필수" -#: ../../source/contributor-how-to-build-docker-images.rst:39 -#: ../../source/contributor-how-to-build-docker-images.rst:101 -#: ../../source/docker/persist-superlink-state.rst:18 -#: ../../source/docker/pin-version.rst:11 +#: ../../source/contributor-how-to-build-docker-images.rst:41 +#: ../../source/contributor-how-to-build-docker-images.rst:107 +#: ../../source/docker/persist-superlink-state.rst:19 +#: ../../source/docker/pin-version.rst:12 #: ../../source/docker/set-environment-variables.rst:8 msgid "Example" msgstr "예시" -#: ../../source/contributor-how-to-build-docker-images.rst:40 +#: ../../source/contributor-how-to-build-docker-images.rst:42 msgid "``DISTRO``" msgstr "``DISTRO``" -#: ../../source/contributor-how-to-build-docker-images.rst:41 +#: ../../source/contributor-how-to-build-docker-images.rst:43 msgid "The Linux distribution to use as the base image." msgstr "기본 이미지 사용을 위한 Linux 배포판." -#: ../../source/contributor-how-to-build-docker-images.rst:42 -#: ../../source/contributor-how-to-build-docker-images.rst:46 -#: ../../source/contributor-how-to-build-docker-images.rst:50 -#: ../../source/contributor-how-to-build-docker-images.rst:66 -#: ../../source/contributor-how-to-build-docker-images.rst:70 -#: ../../source/contributor-how-to-build-docker-images.rst:104 +#: ../../source/contributor-how-to-build-docker-images.rst:44 +#: ../../source/contributor-how-to-build-docker-images.rst:48 +#: ../../source/contributor-how-to-build-docker-images.rst:52 +#: ../../source/contributor-how-to-build-docker-images.rst:68 +#: ../../source/contributor-how-to-build-docker-images.rst:75 +#: ../../source/contributor-how-to-build-docker-images.rst:110 msgid "No" msgstr "아니오" -#: ../../source/contributor-how-to-build-docker-images.rst:43 +#: ../../source/contributor-how-to-build-docker-images.rst:45 msgid "``ubuntu``" msgstr "``ubuntu``" -#: ../../source/contributor-how-to-build-docker-images.rst:44 +#: ../../source/contributor-how-to-build-docker-images.rst:46 msgid "``DISTRO_VERSION``" msgstr "``DISTRO_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:45 +#: ../../source/contributor-how-to-build-docker-images.rst:47 msgid "Version of the Linux distribution." msgstr "Linux 배포판 버전." -#: ../../source/contributor-how-to-build-docker-images.rst:47 +#: ../../source/contributor-how-to-build-docker-images.rst:49 msgid ":substitution-code:`|ubuntu_version|`" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:48 +#: ../../source/contributor-how-to-build-docker-images.rst:50 msgid "``PYTHON_VERSION``" msgstr "``PYTHON_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:49 +#: ../../source/contributor-how-to-build-docker-images.rst:51 msgid "Version of ``python`` to be installed." msgstr "설치 된 ``python`` 버전." -#: ../../source/contributor-how-to-build-docker-images.rst:51 +#: ../../source/contributor-how-to-build-docker-images.rst:53 msgid "``3.11`` or ``3.11.1``" msgstr "``3.11`` 또는 ``3.11.1``" -#: ../../source/contributor-how-to-build-docker-images.rst:52 +#: ../../source/contributor-how-to-build-docker-images.rst:54 msgid "``PIP_VERSION``" msgstr "``PIP_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:53 +#: ../../source/contributor-how-to-build-docker-images.rst:55 msgid "Version of ``pip`` to be installed." msgstr "설치 된 ``pip`` 버전." -#: ../../source/contributor-how-to-build-docker-images.rst:54 -#: ../../source/contributor-how-to-build-docker-images.rst:58 -#: ../../source/contributor-how-to-build-docker-images.rst:62 -#: ../../source/contributor-how-to-build-docker-images.rst:108 +#: ../../source/contributor-how-to-build-docker-images.rst:56 +#: ../../source/contributor-how-to-build-docker-images.rst:60 +#: ../../source/contributor-how-to-build-docker-images.rst:64 +#: ../../source/contributor-how-to-build-docker-images.rst:114 msgid "Yes" msgstr "예" -#: ../../source/contributor-how-to-build-docker-images.rst:55 +#: ../../source/contributor-how-to-build-docker-images.rst:57 msgid ":substitution-code:`|pip_version|`" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:56 +#: ../../source/contributor-how-to-build-docker-images.rst:58 msgid "``SETUPTOOLS_VERSION``" msgstr "``SETUPTOOLS_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:57 +#: ../../source/contributor-how-to-build-docker-images.rst:59 msgid "Version of ``setuptools`` to be installed." msgstr "설치 된 ``setuptools`` 버전." -#: ../../source/contributor-how-to-build-docker-images.rst:59 +#: ../../source/contributor-how-to-build-docker-images.rst:61 #, fuzzy msgid ":substitution-code:`|setuptools_version|`" msgstr "``SETUPTOOLS_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:60 +#: ../../source/contributor-how-to-build-docker-images.rst:62 msgid "``FLWR_VERSION``" msgstr "``FLWR_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:61 +#: ../../source/contributor-how-to-build-docker-images.rst:63 msgid "Version of Flower to be installed." msgstr "설치 된 Flower 버전." -#: ../../source/contributor-how-to-build-docker-images.rst:63 +#: ../../source/contributor-how-to-build-docker-images.rst:65 msgid ":substitution-code:`|stable_flwr_version|`" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:64 +#: ../../source/contributor-how-to-build-docker-images.rst:66 msgid "``FLWR_PACKAGE``" msgstr "``FLWR_PACKAGE``" -#: ../../source/contributor-how-to-build-docker-images.rst:65 +#: ../../source/contributor-how-to-build-docker-images.rst:67 msgid "The Flower package to be installed." msgstr "설치 할 Flower 패키지." -#: ../../source/contributor-how-to-build-docker-images.rst:67 +#: ../../source/contributor-how-to-build-docker-images.rst:69 msgid "``flwr`` or ``flwr-nightly``" msgstr "``flwr`` 또는 ``flwr-nightly``" -#: ../../source/contributor-how-to-build-docker-images.rst:68 +#: ../../source/contributor-how-to-build-docker-images.rst:70 #, fuzzy msgid "``FLWR_VERSION_REF``" msgstr "``FLWR_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:69 +#: ../../source/contributor-how-to-build-docker-images.rst:71 msgid "" "A `direct reference " "`_를 참조하세요." -#: ../../source/contributor-how-to-contribute-translations.rst:29 +#: ../../source/contributor-how-to-contribute-translations.rst:28 msgid "" "Once you are signed in to Weblate, you can navigate to the `Flower " "Framework project `_로 이동할 수 " "있습니다. 여기에서 웹사이트에 있는 다양한 기존 언어들을 확인할 수 있습니다." -#: ../../source/contributor-how-to-contribute-translations.rst:34 +#: ../../source/contributor-how-to-contribute-translations.rst:32 msgid "" "Once you have selected the language you want to contribute to, you should" " see a similar interface to this:" msgstr "기여하고자 하는 언어를 선택하면, 다음과 같은 인터페이스가 나타납니다:" -#: ../../source/contributor-how-to-contribute-translations.rst:39 +#: ../../source/contributor-how-to-contribute-translations.rst:37 msgid "" "The most straight forward option here is to click on the ``Translate`` " "button on the top right (in the ``Translation status`` section). This " @@ -564,11 +564,11 @@ msgstr "" "여기서 가장 간단한 옵션은 오른쪽 상단(``Translation status`` 부분)에 있는 ``Translate`` 버튼을 " "클릭하는 것 입니다. 번역되지 않은 문장에 대한 번역 인터페이스로 자동으로 이동합니다." -#: ../../source/contributor-how-to-contribute-translations.rst:43 +#: ../../source/contributor-how-to-contribute-translations.rst:41 msgid "This is what the interface looks like:" msgstr "인터페이스는 다음과 같습니다:" -#: ../../source/contributor-how-to-contribute-translations.rst:47 +#: ../../source/contributor-how-to-contribute-translations.rst:45 msgid "" "You input your translation in the text box at the top and then, once you " "are happy with it, you either press ``Save and continue`` (to save the " @@ -582,7 +582,7 @@ msgstr "" "볼 수 있도록 번역을 제안 항목에 추가), ``Skip``(아무것도 저장하지 않고 다음 미번역 문장으로 이동) 중 하나를 선택하면 " "됩니다." -#: ../../source/contributor-how-to-contribute-translations.rst:54 +#: ../../source/contributor-how-to-contribute-translations.rst:51 msgid "" "In order to help with the translations, you can see on the bottom the " "``Nearby strings``, the ``Comments`` (from other contributors), the " @@ -593,14 +593,14 @@ msgstr "" "번역에 도움을 주기위해 하단에서 `주변 문자열``, ``의견``(다른 기여자의), ``자동 제안``(기계 번역의), ``다른 " "언어``의 번역 및 해당 문장의 번역``히스토리``를 볼 수 있습니다." -#: ../../source/contributor-how-to-contribute-translations.rst:59 +#: ../../source/contributor-how-to-contribute-translations.rst:56 msgid "" "On the right, under the ``String information`` section, you can also " "click the link under ``Source string location`` in order to view the " "source of the doc file containing the string." msgstr "오른쪽의 ``문자열 정보``에서 ``원본 문자열 위치``를 클릭하여 해당 문장이 포함된 문서의 파일 소스를 볼 수도 있습니다." -#: ../../source/contributor-how-to-contribute-translations.rst:63 +#: ../../source/contributor-how-to-contribute-translations.rst:60 msgid "" "For more information about translating using Weblate, you can check out " "this `in-depth guide " @@ -609,11 +609,11 @@ msgstr "" "Weblate를 통한 번역에 대한 자세한 정보는 `in-depth guide " "`_를 확인하세요." -#: ../../source/contributor-how-to-contribute-translations.rst:67 +#: ../../source/contributor-how-to-contribute-translations.rst:64 msgid "Add new languages" msgstr "새 언어 추가" -#: ../../source/contributor-how-to-contribute-translations.rst:69 +#: ../../source/contributor-how-to-contribute-translations.rst:66 msgid "" "If you want to add a new language, you will first have to contact us, " "either on `Slack `_, or by opening an issue" @@ -637,17 +637,17 @@ msgstr "" "위해 VSCode Remote Containers 확장을 사용하고 있습니다. 그것이 무엇인지 알아보기 위해 다음 인용문을 " "읽어보세요:" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:7 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:8 +#, fuzzy msgid "" "The Visual Studio Code Remote - Containers extension lets you use a " "Docker container as a fully-featured development environment. It allows " "you to open any folder inside (or mounted into) a container and take " "advantage of Visual Studio Code's full feature set. A " -":code:`devcontainer.json` file in your project tells VS Code how to " -"access (or create) a development container with a well-defined tool and " -"runtime stack. This container can be used to run an application or to " -"separate tools, libraries, or runtimes needed for working with a " -"codebase." +"``devcontainer.json`` file in your project tells VS Code how to access " +"(or create) a development container with a well-defined tool and runtime " +"stack. This container can be used to run an application or to separate " +"tools, libraries, or runtimes needed for working with a codebase." msgstr "" "Visual Studio Code Remote - 컨테이너 확장을 사용하면 Docker 컨테이너를 모든 기능을 갖춘 개발 환경으로 " "사용할 수 있습니다. 이 확장 기능을 사용하면 컨테이너 내부(또는 컨테이너에 마운트된)의 모든 폴더를 열고 Visual Studio" @@ -655,7 +655,7 @@ msgstr "" "도구와 런타임 스택을 사용하여 개발 컨테이너에 액세스(또는 생성)하는 방법을 VS Code에 알려줍니다. 이 컨테이너는 " "애플리케이션을 실행하거나 코드베이스 작업에 필요한 도구, 라이브러리 또는 런타임을 분리하는 데 사용할 수 있습니다." -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:9 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:16 msgid "" "Workspace files are mounted from the local file system or copied or " "cloned into the container. Extensions are installed and run inside the " @@ -667,24 +667,25 @@ msgstr "" "실행되며, 도구, 플랫폼 및 파일 시스템에 완전한 접근 권한을 갖습니다. 이는 다른 컨테이너에 연결하는 것만으로 전체 개발 환경을 " "원활하게 전환할 수 있음을 의미합니다." -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:11 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:22 msgid "" "Source: `Official VSCode documentation " "`_" msgstr "출처 : 공식 VSCode 문서" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:15 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:26 msgid "Getting started" msgstr "시작하기" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:17 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:28 +#, fuzzy msgid "" -"Configuring and setting up the :code:`Dockerfile` as well the " -"configuration for the devcontainer can be a bit more involved. The good " -"thing is you don't have to do it. Usually it should be enough to install " -"`Docker `_ on your system and " -"ensure its available on your command line. Additionally, install the " -"`VSCode Containers Extension `_ on your system and ensure its" +" available on your command line. Additionally, install the `VSCode " +"Containers Extension `_." msgstr "" "`Dockerfile`을 설정하고 구성하는 것과 개발 컨테이너 구성은 약간 복잡할 수 있습니다. 다행히도, 이를 직접 할 필요는 " @@ -692,7 +693,7 @@ msgstr "" "설치하고 커맨드 라인에서 사용할 수 있는지 확인하는 것으로 충분합니다. 추가로 `VSCode Containers Extension " "`_을 설치하세요." -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:19 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:35 msgid "" "Now you should be good to go. When starting VSCode, it will ask you to " "run in the container environment and - if you confirm - automatically " @@ -705,13 +706,13 @@ msgstr "" " 것입니다. VSCode에 수동으로 개발 컨테이너를 사용하도록 지시하려면, 확장을 설치한 후, VSCode 창의 왼쪽 하단에 있는 " "초록색 부을 클릭하고 *(Re)Open Folder in Container* 옵션을 선택하세요." -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:21 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:41 msgid "" "In some cases your setup might be more involved. For those cases consult " "the following sources:" msgstr "경우에 따라 설정이 더 복잡할 수도 있습니다. 이러한 경우에는 다음 소스를 참조하세요:" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:23 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:44 msgid "" "`Developing inside a Container " "`_" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:24 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:46 msgid "" "`Remote development in Containers " "`_" @@ -748,13 +749,13 @@ msgstr "" "다음, 재설치하세요(``poetry 설치``이전에 ``poetry.lock`` (``rm poetry.lock``)를 제거하는 것을" " 잊지 마세요)." -#: ../../source/contributor-how-to-install-development-versions.rst:12 +#: ../../source/contributor-how-to-install-development-versions.rst:14 msgid "" "``flwr = { version = \"1.0.0a0\", allow-prereleases = true }`` (without " "extras)" msgstr "``flwr = { version = \"1.0.0a0\", allow-prereleases = true }`` (extras 제외)" -#: ../../source/contributor-how-to-install-development-versions.rst:13 +#: ../../source/contributor-how-to-install-development-versions.rst:15 msgid "" "``flwr = { version = \"1.0.0a0\", allow-prereleases = true, extras = " "[\"simulation\"] }`` (with extras)" @@ -762,17 +763,17 @@ msgstr "" "``flwr = { version = \"1.0.0a0\", allow-prereleases = true, extras = " "[\"simulation\"] }`` (extras 포함)" -#: ../../source/contributor-how-to-install-development-versions.rst:15 +#: ../../source/contributor-how-to-install-development-versions.rst:18 msgid "" "Install ``flwr`` from a local copy of the Flower source code via " "``pyproject.toml``:" msgstr "``pyproject.toml``을 통해 Flower 소스 코드의 로컬 복사본에서 ``flwr``을 설치하세요:" -#: ../../source/contributor-how-to-install-development-versions.rst:17 +#: ../../source/contributor-how-to-install-development-versions.rst:20 msgid "``flwr = { path = \"../../\", develop = true }`` (without extras)" msgstr "``flwr = { path = \"../../\", develop = true }`` (extras 제외)" -#: ../../source/contributor-how-to-install-development-versions.rst:18 +#: ../../source/contributor-how-to-install-development-versions.rst:21 msgid "" "``flwr = { path = \"../../\", develop = true, extras = [\"simulation\"] " "}`` (with extras)" @@ -780,11 +781,11 @@ msgstr "" "``flwr = { path = \"../../\", develop = true, extras = [\"simulation\"] " "}`` (extras 포함)" -#: ../../source/contributor-how-to-install-development-versions.rst:20 +#: ../../source/contributor-how-to-install-development-versions.rst:23 msgid "Install ``flwr`` from a local wheel file via ``pyproject.toml``:" msgstr "``pyproject.toml``을 통해 로컬 wheel file에서 ``flwr``을 설치하세요:" -#: ../../source/contributor-how-to-install-development-versions.rst:22 +#: ../../source/contributor-how-to-install-development-versions.rst:25 msgid "" "``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\" }`` (without" " extras)" @@ -792,7 +793,7 @@ msgstr "" "``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\" }`` (extras " "제외)" -#: ../../source/contributor-how-to-install-development-versions.rst:23 +#: ../../source/contributor-how-to-install-development-versions.rst:26 msgid "" "``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\", extras = " "[\"simulation\"] }`` (with extras)" @@ -800,7 +801,7 @@ msgstr "" "``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\", extras = " "[\"simulation\"] }`` (extras 포함)" -#: ../../source/contributor-how-to-install-development-versions.rst:25 +#: ../../source/contributor-how-to-install-development-versions.rst:29 msgid "" "Please refer to the Poetry documentation for further details: `Poetry " "Dependency Specification `_" -#: ../../source/contributor-how-to-install-development-versions.rst:28 +#: ../../source/contributor-how-to-install-development-versions.rst:33 msgid "Using pip (recommended on Colab)" msgstr "pip 사용하기(Colab에서 권장)" -#: ../../source/contributor-how-to-install-development-versions.rst:30 +#: ../../source/contributor-how-to-install-development-versions.rst:35 msgid "Install a ``flwr`` pre-release from PyPI:" msgstr "PyPI에서 ``flwr`` 사전 릴리즈를 설치하기:" -#: ../../source/contributor-how-to-install-development-versions.rst:32 +#: ../../source/contributor-how-to-install-development-versions.rst:37 msgid "``pip install -U --pre flwr`` (without extras)" msgstr "``pip install -U --pre flwr`` (extras 제외)" -#: ../../source/contributor-how-to-install-development-versions.rst:33 +#: ../../source/contributor-how-to-install-development-versions.rst:38 msgid "``pip install -U --pre 'flwr[simulation]'`` (with extras)" msgstr "``pip install -U --pre 'flwr[simulation]'`` (extras 포함)" -#: ../../source/contributor-how-to-install-development-versions.rst:35 +#: ../../source/contributor-how-to-install-development-versions.rst:40 msgid "" "Python packages can be installed from git repositories. Use one of the " "following commands to install the Flower directly from GitHub." @@ -833,17 +834,17 @@ msgstr "" "Python 패키지는 git 저장소에서 설치할 수 있습니다. 다음 명령어 중 하나를 사용하여 GitHub에서 직접 Flower를 " "설치하세요." -#: ../../source/contributor-how-to-install-development-versions.rst:37 +#: ../../source/contributor-how-to-install-development-versions.rst:43 msgid "Install ``flwr`` from the default GitHub branch (``main``):" msgstr "기본 GitHub branch (``main``)에서 ``flwr`` 를 설치하기:" -#: ../../source/contributor-how-to-install-development-versions.rst:39 +#: ../../source/contributor-how-to-install-development-versions.rst:45 msgid "" "``pip install flwr@git+https://github.com/adap/flower.git`` (without " "extras)" msgstr "``pip install flwr@git+https://github.com/adap/flower.git`` (extras 제외)" -#: ../../source/contributor-how-to-install-development-versions.rst:40 +#: ../../source/contributor-how-to-install-development-versions.rst:46 msgid "" "``pip install 'flwr[simulation]@git+https://github.com/adap/flower.git'``" " (with extras)" @@ -851,11 +852,11 @@ msgstr "" "``pip install 'flwr[simulation]@git+https://github.com/adap/flower.git'``" " (extras 포함)" -#: ../../source/contributor-how-to-install-development-versions.rst:42 +#: ../../source/contributor-how-to-install-development-versions.rst:49 msgid "Install ``flwr`` from a specific GitHub branch (``branch-name``):" msgstr "특정 GitHub branch (``branch-name``)에서 ``flwr`` 설치하기:" -#: ../../source/contributor-how-to-install-development-versions.rst:44 +#: ../../source/contributor-how-to-install-development-versions.rst:51 msgid "" "``pip install flwr@git+https://github.com/adap/flower.git@branch-name`` " "(without extras)" @@ -863,7 +864,7 @@ msgstr "" "``pip install flwr@git+https://github.com/adap/flower.git@branch-name`` " "(extras 제외)" -#: ../../source/contributor-how-to-install-development-versions.rst:45 +#: ../../source/contributor-how-to-install-development-versions.rst:53 msgid "" "``pip install 'flwr[simulation]@git+https://github.com/adap/flower.git" "@branch-name'`` (with extras)" @@ -871,11 +872,11 @@ msgstr "" "``pip install 'flwr[simulation]@git+https://github.com/adap/flower.git" "@branch-name'`` (extras 포함)" -#: ../../source/contributor-how-to-install-development-versions.rst:49 +#: ../../source/contributor-how-to-install-development-versions.rst:57 msgid "Open Jupyter Notebooks on Google Colab" msgstr "Google Colab에서 Jupyter Notebooks 열기" -#: ../../source/contributor-how-to-install-development-versions.rst:51 +#: ../../source/contributor-how-to-install-development-versions.rst:59 msgid "" "Open the notebook ``doc/source/tutorial-series-get-started-with-flower-" "pytorch.ipynb``:" @@ -883,7 +884,7 @@ msgstr "" "``doc/source/tutorial-series-get-started-with-flower-" "pytorch.ipynb``notebook을 엽니다:" -#: ../../source/contributor-how-to-install-development-versions.rst:53 +#: ../../source/contributor-how-to-install-development-versions.rst:61 msgid "" "https://colab.research.google.com/github/adap/flower/blob/main/doc/source" "/tutorial-series-get-started-with-flower-pytorch.ipynb" @@ -891,7 +892,7 @@ msgstr "" "https://colab.research.google.com/github/adap/flower/blob/main/doc/source" "/tutorial-series-get-started-with-flower-pytorch.ipynb" -#: ../../source/contributor-how-to-install-development-versions.rst:55 +#: ../../source/contributor-how-to-install-development-versions.rst:63 msgid "" "Open a development version of the same notebook from branch `branch-name`" " by changing ``main`` to ``branch-name`` (right after ``blob``):" @@ -899,7 +900,7 @@ msgstr "" "``main``을 ``branch-name``(``blob`` 바로 뒤)으로 변경하여 동일한 notebook의 개발 버전을 브랜치 " "`branch-name`에서 엽니다 :" -#: ../../source/contributor-how-to-install-development-versions.rst:57 +#: ../../source/contributor-how-to-install-development-versions.rst:66 msgid "" "https://colab.research.google.com/github/adap/flower/blob/branch-" "name/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb" @@ -907,21 +908,21 @@ msgstr "" "https://colab.research.google.com/github/adap/flower/blob/branch-" "name/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb" -#: ../../source/contributor-how-to-install-development-versions.rst:59 +#: ../../source/contributor-how-to-install-development-versions.rst:68 msgid "Install a `whl` on Google Colab:" msgstr "Google Colab에서 `whl` 설치하기:" -#: ../../source/contributor-how-to-install-development-versions.rst:61 +#: ../../source/contributor-how-to-install-development-versions.rst:70 msgid "" "In the vertical icon grid on the left hand side, select ``Files`` > " "``Upload to session storage``" msgstr "왼쪽의 수직 아이콘 그리드에서 ``Files`` > ``Upload to session storage``를 선택하세요" -#: ../../source/contributor-how-to-install-development-versions.rst:62 +#: ../../source/contributor-how-to-install-development-versions.rst:72 msgid "Upload the whl (e.g., ``flwr-1.8.0-py3-none-any.whl``)" msgstr "whl (예:``flwr-1.8.0-py3-none-any.whl``)을 업로드하세요" -#: ../../source/contributor-how-to-install-development-versions.rst:63 +#: ../../source/contributor-how-to-install-development-versions.rst:73 msgid "" "Change ``!pip install -q 'flwr[simulation]' torch torchvision " "matplotlib`` to ``!pip install -q 'flwr-1.8.0-py3-none-" @@ -941,11 +942,11 @@ msgid "" "change in the future." msgstr "이 문서는 현재 릴리즈 과정을 설명합니다. 이는 앞으로 변경될 수도 있습니다." -#: ../../source/contributor-how-to-release-flower.rst:7 +#: ../../source/contributor-how-to-release-flower.rst:8 msgid "During the release" msgstr "릴리즈 동안에" -#: ../../source/contributor-how-to-release-flower.rst:9 +#: ../../source/contributor-how-to-release-flower.rst:10 msgid "" "The version number of a release is stated in ``pyproject.toml``. To " "release a new version of Flower, the following things need to happen (in " @@ -954,7 +955,7 @@ msgstr "" "릴리즈의 버전 번호는 ``pyproject.toml``에 명시되어 있습니다. Flower의 새 버전을 릴리즈하려면 다음 작업이 " "순서대로 수행되어야 합니다:" -#: ../../source/contributor-how-to-release-flower.rst:11 +#: ../../source/contributor-how-to-release-flower.rst:13 msgid "" "Run ``python3 src/py/flwr_tool/update_changelog.py `` in " "order to add every new change to the changelog (feel free to make manual " @@ -964,7 +965,7 @@ msgstr "" "src/py/flwr_tool/update_changelog.py ``을 실행합니다 (변경 로그가 " "만족스러워질 때까지 수동으로 변경해도 됩니다)." -#: ../../source/contributor-how-to-release-flower.rst:12 +#: ../../source/contributor-how-to-release-flower.rst:16 msgid "" "Once the changelog has been updated with all the changes, run ``./dev" "/prepare-release-changelog.sh v``, where ```` " @@ -978,7 +979,7 @@ msgstr "" "버전 번호입니다 (앞에 ``v``가 추가된 것을 주의하세요). 이 명령어는 변경 로그의 ``Unreleased``헤더를 해당 버전과" " 현재 날짜로 교체하고, 기여자들에게 감사 메시지가 추가됩니다. 이러한 변경 사항으로 pull request합니다." -#: ../../source/contributor-how-to-release-flower.rst:13 +#: ../../source/contributor-how-to-release-flower.rst:22 msgid "" "Once the pull request is merged, tag the release commit with the version " "number as soon as the PR is merged: ``git tag v`` (notice " @@ -990,93 +991,93 @@ msgstr "" "v`` (버전 번호 앞에 ``v``가 추가된 것을 확인), 그 다음 ``git push --tags``. " "이렇게 하면 올바른 아티팩트와 변경 로그의 관련 부분이 포함된 초안 릴리즈가 GitHub에 생성됩니다." -#: ../../source/contributor-how-to-release-flower.rst:14 +#: ../../source/contributor-how-to-release-flower.rst:26 msgid "Check the draft release on GitHub, and if everything is good, publish it." msgstr "GitHub에서 릴리즈 초안을 확인하고, 모든 것이 양호하면 게시하세요." -#: ../../source/contributor-how-to-release-flower.rst:17 +#: ../../source/contributor-how-to-release-flower.rst:29 msgid "After the release" msgstr "릴리즈 후에" -#: ../../source/contributor-how-to-release-flower.rst:19 +#: ../../source/contributor-how-to-release-flower.rst:31 msgid "Create a pull request which contains the following changes:" msgstr "다음 변경 사항이 포함된 pull request를 만듭니다:" -#: ../../source/contributor-how-to-release-flower.rst:21 +#: ../../source/contributor-how-to-release-flower.rst:33 msgid "Increase the minor version in ``pyproject.toml`` by one." msgstr "``pyproject.toml``의 마이너 버전을 하나씩 늘립니다." -#: ../../source/contributor-how-to-release-flower.rst:22 +#: ../../source/contributor-how-to-release-flower.rst:34 msgid "Update all files which contain the current version number if necessary." msgstr "필요한 경우 현재 버전 번호가 포함된 모든 파일을 업데이트합니다." -#: ../../source/contributor-how-to-release-flower.rst:23 +#: ../../source/contributor-how-to-release-flower.rst:35 msgid "Add a new ``Unreleased`` section in ``changelog.md``." msgstr "``changelog.md``에 ``Unreleased`` 섹션을 새로 추가합니다." -#: ../../source/contributor-how-to-release-flower.rst:25 +#: ../../source/contributor-how-to-release-flower.rst:37 msgid "" "Merge the pull request on the same day (i.e., before a new nightly " "release gets published to PyPI)." msgstr "pull request를 같은 날(즉, 새로운 nightly 릴리즈가 PyPI에 게시되기 전에) 병합하세요." -#: ../../source/contributor-how-to-release-flower.rst:28 +#: ../../source/contributor-how-to-release-flower.rst:41 msgid "Publishing a pre-release" msgstr "사전 릴리즈 게시" -#: ../../source/contributor-how-to-release-flower.rst:31 +#: ../../source/contributor-how-to-release-flower.rst:44 msgid "Pre-release naming" msgstr "사전 릴리즈 이름" -#: ../../source/contributor-how-to-release-flower.rst:33 +#: ../../source/contributor-how-to-release-flower.rst:46 msgid "" "PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases" " MUST use one of the following naming patterns:" msgstr "PyPI는 사전 릴리즈(알파, 베타, 릴리스 후보)를 지원합니다. 사전 릴리즈는 반드시 다음 명명 패턴 중 하나를 사용해야 합니다:" -#: ../../source/contributor-how-to-release-flower.rst:35 +#: ../../source/contributor-how-to-release-flower.rst:49 msgid "Alpha: ``MAJOR.MINOR.PATCHaN``" msgstr "Alpha: ``MAJOR.MINOR.PATCHaN``" -#: ../../source/contributor-how-to-release-flower.rst:36 +#: ../../source/contributor-how-to-release-flower.rst:50 msgid "Beta: ``MAJOR.MINOR.PATCHbN``" msgstr "Beta: ``MAJOR.MINOR.PATCHbN``" -#: ../../source/contributor-how-to-release-flower.rst:37 +#: ../../source/contributor-how-to-release-flower.rst:51 msgid "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``" msgstr "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``" -#: ../../source/contributor-how-to-release-flower.rst:39 +#: ../../source/contributor-how-to-release-flower.rst:53 msgid "Examples include:" msgstr "예시:" -#: ../../source/contributor-how-to-release-flower.rst:41 +#: ../../source/contributor-how-to-release-flower.rst:55 msgid "``1.0.0a0``" msgstr "``1.0.0a0``" -#: ../../source/contributor-how-to-release-flower.rst:42 +#: ../../source/contributor-how-to-release-flower.rst:56 msgid "``1.0.0b0``" msgstr "``1.0.0b0``" -#: ../../source/contributor-how-to-release-flower.rst:43 +#: ../../source/contributor-how-to-release-flower.rst:57 msgid "``1.0.0rc0``" msgstr "``1.0.0rc0``" -#: ../../source/contributor-how-to-release-flower.rst:44 +#: ../../source/contributor-how-to-release-flower.rst:58 msgid "``1.0.0rc1``" msgstr "``1.0.0rc1``" -#: ../../source/contributor-how-to-release-flower.rst:46 +#: ../../source/contributor-how-to-release-flower.rst:60 msgid "" "This is in line with PEP-440 and the recommendations from the Python " "Packaging Authority (PyPA):" msgstr "이는 PEP-440 및 Python Packaging Authority (PyPA)의 권장 사항과 일치합니다:" -#: ../../source/contributor-how-to-release-flower.rst:49 +#: ../../source/contributor-how-to-release-flower.rst:63 msgid "`PEP-440 `_" msgstr "`PEP-440 `_" -#: ../../source/contributor-how-to-release-flower.rst:50 +#: ../../source/contributor-how-to-release-flower.rst:64 msgid "" "`PyPA Choosing a versioning scheme " "`_" -#: ../../source/contributor-how-to-release-flower.rst:52 +#: ../../source/contributor-how-to-release-flower.rst:67 msgid "" "Note that the approach defined by PyPA is not compatible with SemVer " "2.0.0 spec, for details consult the `Semantic Versioning Specification " @@ -1096,15 +1097,15 @@ msgstr "" "관리 사양 `_ (특히 항목 11이 " "우선순위)을 참조하세요." -#: ../../source/contributor-how-to-release-flower.rst:55 +#: ../../source/contributor-how-to-release-flower.rst:73 msgid "Pre-release classification" msgstr "사전 릴리즈 분류" -#: ../../source/contributor-how-to-release-flower.rst:57 +#: ../../source/contributor-how-to-release-flower.rst:75 msgid "Should the next pre-release be called alpha, beta, or release candidate?" msgstr "다음 사전 릴리즈를 알파, 베타 또는 릴리스 후보라고 불러야 하나요?" -#: ../../source/contributor-how-to-release-flower.rst:59 +#: ../../source/contributor-how-to-release-flower.rst:77 msgid "" "RC: feature complete, no known issues (apart from issues that are " "classified as \"won't fix\" for the next stable release) - if no issues " @@ -1113,11 +1114,11 @@ msgstr "" "RC: 기능 완료, 알려진 문제 없음(다음 stable 릴리즈에서 \"수정되지 않음\"으로 분류된 문제 제외) - 문제가 나타나지 " "않으면 다음 stable 릴리즈가 됩니다" -#: ../../source/contributor-how-to-release-flower.rst:60 +#: ../../source/contributor-how-to-release-flower.rst:80 msgid "Beta: feature complete, allowed to have known issues" msgstr "베타: 기능 완료, 알려진 문제 발생 가능" -#: ../../source/contributor-how-to-release-flower.rst:61 +#: ../../source/contributor-how-to-release-flower.rst:81 msgid "Alpha: not feature complete, allowed to have known issues" msgstr "알파: 기능 미완성, 알려진 문제가 있을 수 있음" @@ -1135,12 +1136,12 @@ msgstr "" "가상 환경 내에서 파이썬 설정을 실행하는 것이 좋습니다. 이 가이드에서는 pyenv virtualenv, poetry 또는 " "Anaconda를 사용하여 가상 환경을 만드는 세 가지 예제를 보여줍니다. 안내를 따르거나 원하는 설정을 선택할 수 있습니다." -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:9 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:10 msgid "Python Version" msgstr "Python 버전" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:11 -#: ../../source/how-to-install-flower.rst:8 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:12 +#: ../../source/how-to-install-flower.rst:7 msgid "" "Flower requires at least `Python 3.9 `_, " "but `Python 3.10 `_ or above is " @@ -1149,7 +1150,7 @@ msgstr "" "Flower는 `Python 3.9 `_이상이 필요하지만, `Python " "3.10 `_이상을 권장합니다." -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:14 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:17 msgid "" "Due to a known incompatibility with `ray " "`_, we currently recommend utilizing at " @@ -1160,11 +1161,11 @@ msgstr "" " 시뮬레이션을 실행할 때는 최대 `Python 3.11 `_을 사용하는 것이" " 좋습니다." -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:19 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:22 msgid "Virtualenv with Pyenv/Virtualenv" msgstr "Pyenv/Virtualenv를 사용한 가상 환경" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:21 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:24 msgid "" "One of the recommended virtual environment is `pyenv " "`_/`virtualenv `_입니다. 자세한 내용은 `Flower " "examples `_를 참조하세요." -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:23 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:29 msgid "" "Once Pyenv is set up, you can use it to install `Python Version 3.10 " "`_ or above:" @@ -1183,19 +1184,19 @@ msgstr "" "Pyenv가 설정되면 이를 사용하여 'Python 버전 3.10 `_ " "이상'을 설치할 수 있습니다:" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:29 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:36 msgid "Create the virtualenv with:" msgstr "가상 환경을 만듭니다:" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:36 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:42 msgid "Activate the virtualenv by running the following command:" msgstr "다음 명령을 실행하여 가상 환경을 활성화합니다:" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:44 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:49 msgid "Virtualenv with Poetry" msgstr "Poetry를 사용한 가상 환경" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:46 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:51 msgid "" "The Flower examples are based on `Poetry `_ to manage dependencies. After installing Poetry you " @@ -1204,21 +1205,22 @@ msgstr "" "Flower examples은 의존성을 관리하기 위해 `Poetry `_를 기반으로 합니다. Poetry를 설치한 후 가상 환경을 생성하기만 하면 됩니다:" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:52 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:58 msgid "" "If you open a new terminal you can activate the previously created " "virtual environment with the following command:" msgstr "새 터미널을 열면 다음 명령을 사용하여 이전에 생성한 가상 환경을 활성화할 수 있습니다:" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:60 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:66 msgid "Virtualenv with Anaconda" msgstr "Anaconda를 사용한 가상 환경" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:62 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:68 +#, fuzzy msgid "" "If you prefer to use Anaconda for your virtual environment then install " "and setup the `conda `_ package. After setting it up you can " +"/user-guide/install/index.html>`_ package. After setting it up you can " "create a virtual environment with:" msgstr "" "가상 환경에서 Anaconda를 사용하려면 `conda " @@ -1226,15 +1228,15 @@ msgstr "" "guide/install/index.html>`_ 패키지를 설치 및 설정하세요. 설정 후 다음을 사용하여 가상 환경을 만들 수 " "있습니다:" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:68 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:76 msgid "and activate the virtual environment with:" msgstr "그 후 가상 환경을 활성화합니다:" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:76 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:83 msgid "And then?" msgstr "그다음은?" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:78 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:85 msgid "" "As soon as you created your virtual environment you clone one of the " "`Flower examples `_." @@ -1246,11 +1248,11 @@ msgstr "" msgid "Write documentation" msgstr "문서 작성" -#: ../../source/contributor-how-to-write-documentation.rst:6 +#: ../../source/contributor-how-to-write-documentation.rst:5 msgid "Project layout" msgstr "프로젝트 레이아웃" -#: ../../source/contributor-how-to-write-documentation.rst:8 +#: ../../source/contributor-how-to-write-documentation.rst:7 msgid "" "The Flower documentation lives in the ``doc`` directory. The Sphinx-based" " documentation system supports both reStructuredText (``.rst`` files) and" @@ -1260,7 +1262,7 @@ msgstr "" "텍스트(``.rst`` 파일)와 Markdown(``.md`` 파일)을 모두 지원합니다." #: ../../source/contributor-how-to-write-documentation.rst:10 -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:169 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:193 msgid "" "Note that, in order to build the documentation locally (with ``poetry run" " make html``, like described below), `Pandoc " @@ -1270,20 +1272,20 @@ msgstr "" "로컬에서 문서를 작성하려면(아래 설명과 같이 ``poetry run make html``로) `Pandoc " "`_이 시스템에 설치되어 있어야 합니다." -#: ../../source/contributor-how-to-write-documentation.rst:14 +#: ../../source/contributor-how-to-write-documentation.rst:15 msgid "Edit an existing page" msgstr "기존 페이지 편집" -#: ../../source/contributor-how-to-write-documentation.rst:16 +#: ../../source/contributor-how-to-write-documentation.rst:17 msgid "Edit an existing ``.rst`` (or ``.md``) file under ``doc/source/``" msgstr "doc/source/``에서 기존 ``.rst``(또는 ``.md``) 파일을 편집합니다" -#: ../../source/contributor-how-to-write-documentation.rst:17 +#: ../../source/contributor-how-to-write-documentation.rst:18 #: ../../source/contributor-how-to-write-documentation.rst:27 msgid "Compile the docs: ``cd doc``, then ``poetry run make html``" msgstr "문서를 컴파일합니다: ``cd doc``, ``poetry run make html`` 순으로 컴파일합니다" -#: ../../source/contributor-how-to-write-documentation.rst:18 +#: ../../source/contributor-how-to-write-documentation.rst:19 #: ../../source/contributor-how-to-write-documentation.rst:28 msgid "Open ``doc/build/html/index.html`` in the browser to check the result" msgstr "브라우저에서 ``doc/build/html/index.html``을 열어 결과를 확인합니다" @@ -1318,11 +1320,11 @@ msgstr "" "Flower에 대한 기여를 환영합니다! 하지만 어디서부터 시작해야 할지 알기란 쉽지 않습니다. 그래서 저희는 여러분의 PR이 " "Flower 코드베이스에 채택될 가능성을 높이기 위해 어디서부터 시작해야 하는지 몇 가지 권장 사항을 정리해 보았습니다." -#: ../../source/contributor-ref-good-first-contributions.rst:11 +#: ../../source/contributor-ref-good-first-contributions.rst:9 msgid "Where to start" msgstr "시작 위치" -#: ../../source/contributor-ref-good-first-contributions.rst:13 +#: ../../source/contributor-ref-good-first-contributions.rst:11 msgid "" "Until the Flower core library matures it will be easier to get PR's " "accepted if they only touch non-core areas of the codebase. Good " @@ -1331,23 +1333,23 @@ msgstr "" "Flower 코어 라이브러리가 완성될 때까지는 코드베이스의 비핵심 영역만 건드리는 것이 PR을 승인받기가 더 쉬울 것입니다. " "시작하기에 좋은 후보자는 다음과 같습니다:" -#: ../../source/contributor-ref-good-first-contributions.rst:17 +#: ../../source/contributor-ref-good-first-contributions.rst:14 msgid "Documentation: What's missing? What could be expressed more clearly?" msgstr "문서: 무엇이 누락되었나요? 무엇을 더 명확하게 표현할 수 있을까요?" -#: ../../source/contributor-ref-good-first-contributions.rst:18 +#: ../../source/contributor-ref-good-first-contributions.rst:15 msgid "Baselines: See below." msgstr "Baselines: 아래를 참조하세요." -#: ../../source/contributor-ref-good-first-contributions.rst:19 +#: ../../source/contributor-ref-good-first-contributions.rst:16 msgid "Examples: See below." msgstr "예시: 아래를 참조하세요." -#: ../../source/contributor-ref-good-first-contributions.rst:23 +#: ../../source/contributor-ref-good-first-contributions.rst:19 msgid "Request for Flower Baselines" msgstr "Flower Baselines 요청" -#: ../../source/contributor-ref-good-first-contributions.rst:25 +#: ../../source/contributor-ref-good-first-contributions.rst:21 msgid "" "If you are not familiar with Flower Baselines, you should probably check-" "out our `contributing guide for baselines " @@ -1357,7 +1359,7 @@ msgstr "" "`_를 " "확인해보세요." -#: ../../source/contributor-ref-good-first-contributions.rst:27 +#: ../../source/contributor-ref-good-first-contributions.rst:25 msgid "" "You should then check out the open `issues " "`_" @@ -1370,7 +1372,7 @@ msgstr "" " baseline 요청을 확인해야 합니다. 작업하고 싶은 기준선을 찾았지만 담당자가 없는 경우, 자유롭게 자신에게 할당하고 작업을 " "시작하세요!" -#: ../../source/contributor-ref-good-first-contributions.rst:31 +#: ../../source/contributor-ref-good-first-contributions.rst:30 msgid "" "Otherwise, if you don't find a baseline you'd like to work on, be sure to" " open a new issue with the baseline request template!" @@ -1416,12 +1418,13 @@ msgstr "" "때문에 다이어그램과 추상화가 실제로는 정확하지 않을 수 있습니다. SecAgg 프로토콜은 SecAgg+ 프로토콜의 특수한 경우로 " "간주할 수 있습니다." -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:8 -msgid "The :code:`SecAgg+` abstraction" +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:9 +#, fuzzy +msgid "The ``SecAgg+`` abstraction" msgstr "The :code:`SecAgg+` 추상화" -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:10 -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:161 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:11 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:163 msgid "" "In this implementation, each client will be assigned with a unique index " "(int) for secure aggregation, and thus many python dictionaries used have" @@ -1430,18 +1433,19 @@ msgstr "" "구현에서는 각 클라이언트에 secure aggregation를 위한 고유 인덱스(int)가 할당되므로 사용되는 많은 파이썬 " "dictionaries에는 ClientProxy 타입이 아닌 int 타입의 키가 있습니다." -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:65 -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:198 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:67 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:204 msgid "" "The Flower server will execute and process received results in the " "following order:" msgstr "Flower 서버는 수신된 결과를 다음 순서로 실행하고 처리합니다:" -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:159 -msgid "The :code:`LightSecAgg` abstraction" +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:161 +#, fuzzy +msgid "The ``LightSecAgg`` abstraction" msgstr "The :code:`LightSecAgg` 추상" -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:271 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:277 msgid "Types" msgstr "타입" @@ -1455,7 +1459,7 @@ msgid "" "are not used to contributing to GitHub projects." msgstr "이 가이드는 Flower에 참여하고 싶지만 GitHub 프로젝트에 기여하는 데 익숙하지 않은 분들을 위한 것입니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:6 +#: ../../source/contributor-tutorial-contribute-on-github.rst:7 msgid "" "If you're familiar with how contributing on GitHub works, you can " "directly checkout our :doc:`getting started guide for contributors " @@ -1464,15 +1468,15 @@ msgstr "" "깃허브에서 기여하는 방식에 익숙하다면 :doc:`기여자를 위한 시작 가이드`를 직접 확인하세요." -#: ../../source/contributor-tutorial-contribute-on-github.rst:10 +#: ../../source/contributor-tutorial-contribute-on-github.rst:12 msgid "Setting up the repository" msgstr "레포지토리 설정하기" -#: ../../source/contributor-tutorial-contribute-on-github.rst:12 +#: ../../source/contributor-tutorial-contribute-on-github.rst:29 msgid "**Create a GitHub account and setup Git**" msgstr "**GitHub 계정을 만들고 Git을 설정합니다**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:13 +#: ../../source/contributor-tutorial-contribute-on-github.rst:15 msgid "" "Git is a distributed version control tool. This allows for an entire " "codebase's history to be stored and every developer's machine. It is a " @@ -1484,7 +1488,7 @@ msgstr "" "컴퓨터에 설치해야 하는 소프트웨어로, 이 `가이드 `_를 따라 설정할 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:16 +#: ../../source/contributor-tutorial-contribute-on-github.rst:21 msgid "" "GitHub, itself, is a code hosting platform for version control and " "collaboration. It allows for everyone to collaborate and work from " @@ -1493,13 +1497,13 @@ msgstr "" "GitHub는 그 자체로 버전 관리 및 협업을 위한 코드 호스팅 플랫폼입니다. 누구나 원격 레포지토리에서 어디서든 협업하고 작업할 " "수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:18 +#: ../../source/contributor-tutorial-contribute-on-github.rst:25 msgid "" "If you haven't already, you will need to create an account on `GitHub " "`_." msgstr "아직 계정을 만들지 않았다면 `GitHub `_에서 계정을 만들어야 합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:20 +#: ../../source/contributor-tutorial-contribute-on-github.rst:28 msgid "" "The idea behind the generic Git and GitHub workflow boils down to this: " "you download code from a remote repository on GitHub, make changes " @@ -1509,14 +1513,15 @@ msgstr "" "일반적인 Git 및 GitHub 워크플로우의 기본 개념은 다음과 같이 요약됩니다. GitHub의 원격 레포지토리에서 코드를 " "다운로드하고 로컬에서 변경한 후 Git을 사용하여 추적한 다음 새 기록을 다시 GitHub에 업로드하는 것입니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:23 +#: ../../source/contributor-tutorial-contribute-on-github.rst:42 msgid "**Forking the Flower repository**" msgstr "**Flower 레포지토리 포크하기**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:24 +#: ../../source/contributor-tutorial-contribute-on-github.rst:32 +#, fuzzy msgid "" "A fork is a personal copy of a GitHub repository. To create one for " -"Flower, you must navigate to ``_ (while " +"Flower, you must navigate to https://github.com/adap/flower (while " "connected to your GitHub account) and click the ``Fork`` button situated " "on the top right of the page." msgstr "" @@ -1524,7 +1529,7 @@ msgstr "" "``_로 이동하여(GitHub 계정에 연결된 상태에서) 페이지 오른쪽 " "상단에 있는 ``포크`` 버튼을 클릭해야 합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:29 +#: ../../source/contributor-tutorial-contribute-on-github.rst:38 msgid "" "You can change the name if you want, but this is not necessary as this " "version of Flower will be yours and will sit inside your own account " @@ -1534,11 +1539,11 @@ msgstr "" "원하는 경우 이름을 변경할 수 있지만, 이 버전의 Flower는 자신의 계정(즉, 자신의 리포지토리 목록)에 위치하게 되므로 변경할" " 필요는 없습니다. 만들기가 완료되면 왼쪽 상단에Flower 버전이 표시되는 것을 볼 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:34 +#: ../../source/contributor-tutorial-contribute-on-github.rst:59 msgid "**Cloning your forked repository**" msgstr "**포크된 레포지토리 클론하기**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:35 +#: ../../source/contributor-tutorial-contribute-on-github.rst:45 msgid "" "The next step is to download the forked repository on your machine to be " "able to make changes to it. On your forked repository page, you should " @@ -1548,27 +1553,27 @@ msgstr "" "다음 단계는 컴퓨터에서 포크된 레포지토리를 변경할 수 있도록 다운로드하는 것입니다. 포크된 포지토리 페이지에서 먼저 오른쪽의 " "``Code`` 버튼을 클릭하면 레포지토리의 HTTPS 링크를 복사할 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:41 +#: ../../source/contributor-tutorial-contribute-on-github.rst:52 msgid "" "Once you copied the \\, you can open a terminal on your machine, " "navigate to the place you want to download the repository to and type:" msgstr "\\를 복사한 후에는 컴퓨터에서 터미널을 열고 레포지토리를 다운로드할 위치로 이동하여 입력하면 됩니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:47 +#: ../../source/contributor-tutorial-contribute-on-github.rst:59 msgid "" "This will create a ``flower/`` (or the name of your fork if you renamed " "it) folder in the current working directory." msgstr "현재 작업 디렉터리에``flower/``(또는 포크 이름을 변경한 경우 포크 이름) 폴더가 생성됩니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:49 +#: ../../source/contributor-tutorial-contribute-on-github.rst:78 msgid "**Add origin**" msgstr "**origin 추가**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:50 +#: ../../source/contributor-tutorial-contribute-on-github.rst:62 msgid "You can then go into the repository folder:" msgstr "그런 다음 레포지토리 폴더로 이동할 수 있습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:56 +#: ../../source/contributor-tutorial-contribute-on-github.rst:68 msgid "" "And here we will need to add an origin to our repository. The origin is " "the \\ of the remote fork repository. To obtain it, we can do as " @@ -1578,27 +1583,27 @@ msgstr "" "여기에 레포지토리에 origin을 추가해야 합니다. origin은 원격 포크 레포지토리의 \\입니다. origin을 " "얻으려면 앞서 설명한 대로 GitHub 계정의 포크 레포지토리로 이동하여 링크를 복사하면 됩니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:61 +#: ../../source/contributor-tutorial-contribute-on-github.rst:75 msgid "" "Once the \\ is copied, we can type the following command in our " "terminal:" msgstr "\\ 이 복사되면 터미널에 다음 명령을 입력하면 됩니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:68 +#: ../../source/contributor-tutorial-contribute-on-github.rst:102 msgid "**Add upstream**" msgstr "**Upstream 추가하기**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:69 +#: ../../source/contributor-tutorial-contribute-on-github.rst:81 msgid "" "Now we will add an upstream address to our repository. Still in the same " "directory, we must run the following command:" msgstr "이제 레포지토리에 upstream 주소를 추가하겠습니다. 여전히 같은 디렉터리에서 다음 명령을 실행해야 합니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:76 +#: ../../source/contributor-tutorial-contribute-on-github.rst:88 msgid "The following diagram visually explains what we did in the previous steps:" msgstr "다음 다이어그램은 이전 단계에서 수행한 작업을 시각적으로 설명합니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:80 +#: ../../source/contributor-tutorial-contribute-on-github.rst:92 msgid "" "The upstream is the GitHub remote address of the parent repository (in " "this case Flower), i.e. the one we eventually want to contribute to and " @@ -1610,17 +1615,17 @@ msgstr "" " 기록이 필요한 레포지토리입니다. origin은 우리가 만든 포크된 레포지토리의 GitHub 원격 주소, 즉 우리 계정에 있는 " "사본(포크)입니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:84 +#: ../../source/contributor-tutorial-contribute-on-github.rst:97 msgid "" "To make sure our local version of the fork is up-to-date with the latest " "changes from the Flower repository, we can execute the following command:" msgstr "로컬 버전의 포크가 Flower 레포지토리의 최신 변경 사항으로 최신 상태인지 확인하려면 다음 명령을 실행하면 됩니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:93 +#: ../../source/contributor-tutorial-contribute-on-github.rst:105 msgid "Setting up the coding environment" msgstr "코딩 환경 설정" -#: ../../source/contributor-tutorial-contribute-on-github.rst:95 +#: ../../source/contributor-tutorial-contribute-on-github.rst:107 msgid "" "This can be achieved by following this :doc:`getting started guide for " "contributors ` (note " @@ -1631,50 +1636,50 @@ msgstr "" "as-a-contributor>'를 참조하세요(레포지토리를 복제할 필요는 없습니다). 코드를 작성하고 테스트할 수 있게 되면 드디어" " 변경을 시작할 수 있습니다!" -#: ../../source/contributor-tutorial-contribute-on-github.rst:100 +#: ../../source/contributor-tutorial-contribute-on-github.rst:113 msgid "Making changes" msgstr "변경하기" -#: ../../source/contributor-tutorial-contribute-on-github.rst:102 +#: ../../source/contributor-tutorial-contribute-on-github.rst:115 msgid "" "Before making any changes make sure you are up-to-date with your " "repository:" msgstr "변경하기 전에 레포지토리를 최신 상태로 유지하세요:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:108 +#: ../../source/contributor-tutorial-contribute-on-github.rst:121 msgid "And with Flower's repository:" msgstr "Flower의 레포지토리도 있습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:114 +#: ../../source/contributor-tutorial-contribute-on-github.rst:134 msgid "**Create a new branch**" msgstr "**새 브랜치 만들기**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:115 +#: ../../source/contributor-tutorial-contribute-on-github.rst:128 msgid "" "To make the history cleaner and easier to work with, it is good practice " "to create a new branch for each feature/project that needs to be " "implemented." msgstr "히스토리를 더 깔끔하고 작업하기 쉽게 만들려면 구현해야 하는 각 기능/프로젝트에 대해 새 브랜치를 만드는 것이 좋습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:118 +#: ../../source/contributor-tutorial-contribute-on-github.rst:131 msgid "" "To do so, just run the following command inside the repository's " "directory:" msgstr "이렇게 하려면 레포지토리 디렉토리에서 다음 명령을 실행하면 됩니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:124 +#: ../../source/contributor-tutorial-contribute-on-github.rst:136 msgid "**Make changes**" msgstr "**변경하기**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:125 +#: ../../source/contributor-tutorial-contribute-on-github.rst:137 msgid "Write great code and create wonderful changes using your favorite editor!" msgstr "선호하는 편집기를 사용하여 멋진 코드를 작성하고 훌륭한 변화를 만들어 보세요!" -#: ../../source/contributor-tutorial-contribute-on-github.rst:127 +#: ../../source/contributor-tutorial-contribute-on-github.rst:149 msgid "**Test and format your code**" msgstr "**코드 테스트 및 서식 지정**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:128 +#: ../../source/contributor-tutorial-contribute-on-github.rst:139 msgid "" "Don't forget to test and format your code! Otherwise your code won't be " "able to be merged into the Flower repository. This is done so the " @@ -1683,57 +1688,60 @@ msgstr "" "코드를 테스트하고 서식을 지정하는 것을 잊지 마세요! 그렇지 않으면 코드를 Flower 레포지토리에 병합할 수 없습니다. 이는 " "코드베이스가 일관성을 유지하고 이해하기 쉽도록 하기 위한 것입니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:131 +#: ../../source/contributor-tutorial-contribute-on-github.rst:143 msgid "To do so, we have written a few scripts that you can execute:" msgstr "이를 위해 실행할 수 있는 몇 가지 스크립트를 작성했습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:140 +#: ../../source/contributor-tutorial-contribute-on-github.rst:162 msgid "**Stage changes**" msgstr "**변경사항 스테이징**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:141 +#: ../../source/contributor-tutorial-contribute-on-github.rst:152 msgid "" "Before creating a commit that will update your history, you must specify " "to Git which files it needs to take into account." msgstr "기록을 업데이트할 커밋을 만들기 전에 어떤 파일을 고려해야 하는지 Git에 지정해야 합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:143 +#: ../../source/contributor-tutorial-contribute-on-github.rst:155 msgid "This can be done with:" msgstr "이 작업을 수행할 수 있습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:149 +#: ../../source/contributor-tutorial-contribute-on-github.rst:161 +#, fuzzy msgid "" "To check which files have been modified compared to the last version " "(last commit) and to see which files are staged for commit, you can use " -"the :code:`git status` command." +"the ``git status`` command." msgstr "" "마지막 버전(마지막 커밋)과 비교하여 수정된 파일을 확인하고 커밋을 위해 스테이징된 파일을 확인하려면 :code:`git " "status` 명령을 사용하면 됩니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:152 +#: ../../source/contributor-tutorial-contribute-on-github.rst:173 msgid "**Commit changes**" msgstr "**변경사항 커밋**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:153 +#: ../../source/contributor-tutorial-contribute-on-github.rst:165 +#, fuzzy msgid "" -"Once you have added all the files you wanted to commit using :code:`git " -"add`, you can finally create your commit using this command:" +"Once you have added all the files you wanted to commit using ``git add``," +" you can finally create your commit using this command:" msgstr ":code:`git add`를 사용하여 커밋하려는 모든 파일을 추가한 후, 마지막으로 이 명령을 사용하여 커밋을 생성할 수 있습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:159 +#: ../../source/contributor-tutorial-contribute-on-github.rst:172 +#, fuzzy msgid "" "The \\ is there to explain to others what the commit " "does. It should be written in an imperative style and be concise. An " -"example would be :code:`git commit -m \"Add images to README\"`." +"example would be ``git commit -m \"Add images to README\"``." msgstr "" "커밋의 내용을 다른 사람에게 설명하기 위해 \\가 있습니다. 명령형 스타일로 작성해야 하며 간결해야" " 합니다. 예를 들면 :code:`git commit -m \"Add images to README\"`." -#: ../../source/contributor-tutorial-contribute-on-github.rst:162 +#: ../../source/contributor-tutorial-contribute-on-github.rst:185 msgid "**Push the changes to the fork**" msgstr "**변경 사항을 포크에 푸시**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:163 +#: ../../source/contributor-tutorial-contribute-on-github.rst:176 msgid "" "Once we have committed our changes, we have effectively updated our local" " history, but GitHub has no way of knowing this unless we push our " @@ -1742,41 +1750,41 @@ msgstr "" "변경 사항을 커밋하면 로컬 히스토리를 효과적으로 업데이트한 것이지만, 변경 사항을 원본의 원격 주소로 푸시하지 않는 한 " "GitHub는 이를 알 방법이 없습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:170 +#: ../../source/contributor-tutorial-contribute-on-github.rst:184 msgid "" "Once this is done, you will see on the GitHub that your forked repo was " "updated with the changes you have made." msgstr "이 작업이 완료되면 변경한 내용으로 포크된 레포지토리가 업데이트된 것을 GitHub에서 확인할 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:174 +#: ../../source/contributor-tutorial-contribute-on-github.rst:188 msgid "Creating and merging a pull request (PR)" msgstr "pull request(PR) 만들기 및 병합하기" -#: ../../source/contributor-tutorial-contribute-on-github.rst:176 +#: ../../source/contributor-tutorial-contribute-on-github.rst:226 msgid "**Create the PR**" msgstr "**PR 만들기**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:177 +#: ../../source/contributor-tutorial-contribute-on-github.rst:191 msgid "" "Once you have pushed changes, on the GitHub webpage of your repository " "you should see the following message:" msgstr "변경 사항을 푸시하고 나면 레포지토리의 GitHub 웹페이지에 다음 메시지가 표시됩니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:181 +#: ../../source/contributor-tutorial-contribute-on-github.rst:196 msgid "Otherwise you can always find this option in the ``Branches`` page." msgstr "그렇지 않으면 언제든지 ``Branches`` 페이지에서 이 옵션을 찾을 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:183 +#: ../../source/contributor-tutorial-contribute-on-github.rst:198 msgid "" "Once you click the ``Compare & pull request`` button, you should see " "something similar to this:" msgstr "``Compare & pull request`` 버튼을 클릭하면 이와 비슷한 화면이 표시됩니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:187 +#: ../../source/contributor-tutorial-contribute-on-github.rst:203 msgid "At the top you have an explanation of which branch will be merged where:" msgstr "상단에는 어느 지점이 어디에 병합될 것인지에 대한 설명이 있습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:191 +#: ../../source/contributor-tutorial-contribute-on-github.rst:207 msgid "" "In this example you can see that the request is to merge the branch " "``doc-fixes`` from my forked repository to branch ``main`` from the " @@ -1785,7 +1793,7 @@ msgstr "" "이 예제에서는 내 포크된 레포지토리의 ``doc-fixes`` 브랜치를 Flower 레포지토리의 ``main`` 브랜치에 병합하라는" " 요청을 볼 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:193 +#: ../../source/contributor-tutorial-contribute-on-github.rst:210 msgid "" "The title should be changed to adhere to the :ref:`pr_title_format` " "guidelines, otherwise it won't be possible to merge the PR. So in this " @@ -1794,7 +1802,7 @@ msgstr "" "제목은 :ref:`pr_title_format` 가이드라인을 준수하도록 변경해야 하며, 그렇지 않으면 PR을 병합할 수 없습니다. " "따라서 이 경우 올바른 제목은 ``docs(framework:skip) Fix typos``이 될 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:196 +#: ../../source/contributor-tutorial-contribute-on-github.rst:214 msgid "" "The input box in the middle is there for you to describe what your PR " "does and to link it to existing issues. We have placed comments (that " @@ -1804,11 +1812,11 @@ msgstr "" "가운데에 있는 입력 상자는 PR의 기능을 설명하고 기존 이슈에 연결할 수 있는 곳입니다. 프로세스를 안내하기 위해 코멘트(PR이 " "열리면 렌더링되지 않음)를 배치했습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:199 +#: ../../source/contributor-tutorial-contribute-on-github.rst:218 msgid "It is important to follow the instructions described in comments." msgstr "코멘트에 설명된 지침을 따르는 것이 중요합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:201 +#: ../../source/contributor-tutorial-contribute-on-github.rst:220 msgid "" "At the bottom you will find the button to open the PR. This will notify " "reviewers that a new PR has been opened and that they should look over it" @@ -1817,94 +1825,94 @@ msgstr "" "하단에는 PR을 여는 버튼이 있습니다. 이렇게 하면 검토자에게 새 PR이 열렸으며 병합하거나 변경을 요청하기 위해 검토해야 함을 " "알립니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:204 +#: ../../source/contributor-tutorial-contribute-on-github.rst:224 msgid "" "If your PR is not yet ready for review, and you don't want to notify " "anyone, you have the option to create a draft pull request:" msgstr "PR이 아직 검토할 준비가 되지 않았고 다른 사람에게 알리고 싶지 않은 경우 pull request 초안을 만드는 옵션이 있습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:230 msgid "**Making new changes**" msgstr "**new changes 만들기**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:209 +#: ../../source/contributor-tutorial-contribute-on-github.rst:229 msgid "" "Once the PR has been opened (as draft or not), you can still push new " "commits to it the same way we did before, by making changes to the branch" " associated with the PR." msgstr "PR이 초안으로 열렸든 아니든, PR과 연결된 브랜치를 변경하여 이전과 같은 방식으로 새 커밋을 푸시할 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:211 +#: ../../source/contributor-tutorial-contribute-on-github.rst:253 msgid "**Review the PR**" msgstr "**PR 검토하기**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:212 +#: ../../source/contributor-tutorial-contribute-on-github.rst:233 msgid "" "Once the PR has been opened or once the draft PR has been marked as " "ready, a review from code owners will be automatically requested:" msgstr "PR이 열리거나 초안 PR이 준비됨으로 표시되면 코드 소유자의 검토가 자동으로 요청됩니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:216 +#: ../../source/contributor-tutorial-contribute-on-github.rst:238 msgid "" "Code owners will then look into the code, ask questions, request changes " "or validate the PR." msgstr "그러면 코드 소유자는 코드를 살펴보고, 질문하고, 변경을 요청하거나 PR의 유효성을 검사합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:218 +#: ../../source/contributor-tutorial-contribute-on-github.rst:241 msgid "Merging will be blocked if there are ongoing requested changes." msgstr "진행 중인 변경 요청이 있는 경우 병합이 차단됩니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:222 +#: ../../source/contributor-tutorial-contribute-on-github.rst:245 msgid "" "To resolve them, just push the necessary changes to the branch associated" " with the PR:" msgstr "이를 해결하려면 PR과 연결된 브랜치에 필요한 변경 사항을 푸시하면 됩니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:226 +#: ../../source/contributor-tutorial-contribute-on-github.rst:250 msgid "And resolve the conversation:" msgstr "그리고 소통을 통해 해결하세요:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:230 +#: ../../source/contributor-tutorial-contribute-on-github.rst:254 msgid "" "Once all the conversations have been resolved, you can re-request a " "review." msgstr "모든 대화가 해결되면 검토를 다시 요청할 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:233 +#: ../../source/contributor-tutorial-contribute-on-github.rst:274 msgid "**Once the PR is merged**" msgstr "**PR이 병합되면**" -#: ../../source/contributor-tutorial-contribute-on-github.rst:234 +#: ../../source/contributor-tutorial-contribute-on-github.rst:256 msgid "" "If all the automatic tests have passed and reviewers have no more changes" " to request, they can approve the PR and merge it." msgstr "모든 자동 테스트가 통과되고 검토자가 더 이상 요청할 변경 사항이 없는 경우 PR을 승인하고 병합할 수 있습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:238 +#: ../../source/contributor-tutorial-contribute-on-github.rst:261 msgid "" "Once it is merged, you can delete the branch on GitHub (a button should " "appear to do so) and also delete it locally by doing:" msgstr "병합이 완료되면 GitHub에서 브랜치를 삭제할 수 있으며(삭제 버튼이 표시되어야 함), 로컬에서도 삭제할 수 있습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:245 +#: ../../source/contributor-tutorial-contribute-on-github.rst:269 msgid "Then you should update your forked repository by doing:" msgstr "그런 다음 다음을 수행하여 포크된 레포지토리를 업데이트해야 합니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:254 +#: ../../source/contributor-tutorial-contribute-on-github.rst:277 msgid "Example of first contribution" msgstr "첫 번째 기여의 예" -#: ../../source/contributor-tutorial-contribute-on-github.rst:257 +#: ../../source/contributor-tutorial-contribute-on-github.rst:280 msgid "Problem" msgstr "문제" -#: ../../source/contributor-tutorial-contribute-on-github.rst:259 +#: ../../source/contributor-tutorial-contribute-on-github.rst:282 msgid "" "For our documentation, we've started to use the `Diàtaxis framework " "`_." msgstr "저희 문서에는 'Diàtaxis 프레임워크 `_'를 사용하기 시작했습니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:261 +#: ../../source/contributor-tutorial-contribute-on-github.rst:285 msgid "" "Our \"How to\" guides should have titles that continue the sentence \"How" " to …\", for example, \"How to upgrade to Flower 1.0\"." @@ -1912,19 +1920,19 @@ msgstr "" "'How to' 가이드의 제목은 \"How to …\"라는 문장을 이어가는 제목이어야 합니다(예: \"How to upgrade " "to Flower 1.0\")." -#: ../../source/contributor-tutorial-contribute-on-github.rst:263 +#: ../../source/contributor-tutorial-contribute-on-github.rst:288 msgid "" "Most of our guides do not follow this new format yet, and changing their " "title is (unfortunately) more involved than one might think." msgstr "대부분의 가이드는 아직 이 새로운 형식을 따르지 않으며, 안타깝게도 제목을 변경하는 작업은 생각보다 복잡합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:265 +#: ../../source/contributor-tutorial-contribute-on-github.rst:291 msgid "" "This issue is about changing the title of a doc from present continuous " "to present simple." msgstr "이번 이슈는 문서 제목을 현재 연속형에서 현재 단순형으로 변경하는 것에 관한 것입니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:267 +#: ../../source/contributor-tutorial-contribute-on-github.rst:294 msgid "" "Let's take the example of \"Saving Progress\" which we changed to \"Save " "Progress\". Does this pass our check?" @@ -1932,19 +1940,19 @@ msgstr "" "\"How to saving progress\"을 \"How to save progress\"으로 변경한 예를 들어 보겠습니다. " "이것이 우리의 점검을 통과했나요?" -#: ../../source/contributor-tutorial-contribute-on-github.rst:269 +#: ../../source/contributor-tutorial-contribute-on-github.rst:297 msgid "Before: \"How to saving progress\" ❌" msgstr "Before: \"How to saving progress\" ❌" -#: ../../source/contributor-tutorial-contribute-on-github.rst:271 +#: ../../source/contributor-tutorial-contribute-on-github.rst:299 msgid "After: \"How to save progress\" ✅" msgstr "After: \"How to save progress\" ✅" -#: ../../source/contributor-tutorial-contribute-on-github.rst:274 +#: ../../source/contributor-tutorial-contribute-on-github.rst:302 msgid "Solution" msgstr "해결법" -#: ../../source/contributor-tutorial-contribute-on-github.rst:276 +#: ../../source/contributor-tutorial-contribute-on-github.rst:304 msgid "" "This is a tiny change, but it'll allow us to test your end-to-end setup. " "After cloning and setting up the Flower repo, here's what you should do:" @@ -1952,17 +1960,17 @@ msgstr "" "이것은 사소한 변경이지만 end-to-end 설정을 테스트할 수 있습니다. Flower 레포지토리를 복제하고 설정한 후에는 다음과 " "같이 하세요:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:278 +#: ../../source/contributor-tutorial-contribute-on-github.rst:307 msgid "Find the source file in ``doc/source``" msgstr "``doc/source``에서 소스 파일을 찾습니다" -#: ../../source/contributor-tutorial-contribute-on-github.rst:279 +#: ../../source/contributor-tutorial-contribute-on-github.rst:308 msgid "" "Make the change in the ``.rst`` file (beware, the dashes under the title " "should be the same length as the title itself)" msgstr "``.rst`` 파일에서 변경합니다(제목 아래의 대시는 제목 자체의 길이와 같아야 합니다)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:280 +#: ../../source/contributor-tutorial-contribute-on-github.rst:310 msgid "" "Build the docs and `check the result `_" @@ -1970,11 +1978,11 @@ msgstr "" "문서를 빌드하고 '결과 확인 `_'합니다" -#: ../../source/contributor-tutorial-contribute-on-github.rst:283 +#: ../../source/contributor-tutorial-contribute-on-github.rst:314 msgid "Rename file" msgstr "파일 이름 바꾸기" -#: ../../source/contributor-tutorial-contribute-on-github.rst:285 +#: ../../source/contributor-tutorial-contribute-on-github.rst:316 msgid "" "You might have noticed that the file name still reflects the old wording." " If we just change the file, then we break all existing links to it - it " @@ -1984,19 +1992,19 @@ msgstr "" "파일 이름에 여전히 이전 문구가 반영되어 있는 것을 보셨을 것입니다. 파일만 변경하면 파일에 대한 기존 링크가 모두 끊어지는데, " "링크를 끊으면 검색 엔진 순위에 영향을 줄 수 있으므로 이를 방지하는 것이 **매우 중요**합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:288 +#: ../../source/contributor-tutorial-contribute-on-github.rst:320 msgid "Here's how to change the file name:" msgstr "파일 이름을 변경하는 방법은 다음과 같습니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:290 +#: ../../source/contributor-tutorial-contribute-on-github.rst:322 msgid "Change the file name to ``save-progress.rst``" msgstr "파일 이름을 ``save-progress.rst``로 변경합니다" -#: ../../source/contributor-tutorial-contribute-on-github.rst:291 +#: ../../source/contributor-tutorial-contribute-on-github.rst:323 msgid "Add a redirect rule to ``doc/source/conf.py``" msgstr "'doc/source/conf.py'에 리디렉션 규칙을 추가합니다" -#: ../../source/contributor-tutorial-contribute-on-github.rst:293 +#: ../../source/contributor-tutorial-contribute-on-github.rst:325 msgid "" "This will cause a redirect from ``saving-progress.html`` to ``save-" "progress.html``, old links will continue to work." @@ -2004,11 +2012,11 @@ msgstr "" "이렇게 하면 ``saving-progress.html``에서 ``save-progress.html``로 리디렉션되며, 이전 링크는 " "계속 작동합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:296 +#: ../../source/contributor-tutorial-contribute-on-github.rst:329 msgid "Apply changes in the index file" msgstr "인덱스 파일에 변경 사항 적용" -#: ../../source/contributor-tutorial-contribute-on-github.rst:298 +#: ../../source/contributor-tutorial-contribute-on-github.rst:331 msgid "" "For the lateral navigation bar to work properly, it is very important to " "update the ``index.rst`` file as well. This is where we define the whole " @@ -2017,39 +2025,39 @@ msgstr "" "횡방향 내비게이션 바가 제대로 작동하려면 ``index.rst`` 파일도 업데이트하는 것이 매우 중요합니다. 이 파일은 탐색 모음의" " 전체 배열을 정의하는 곳입니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:301 +#: ../../source/contributor-tutorial-contribute-on-github.rst:335 msgid "Find and modify the file name in ``index.rst``" msgstr "``index.rst``에서 파일 이름을 찾아 수정합니다" -#: ../../source/contributor-tutorial-contribute-on-github.rst:304 +#: ../../source/contributor-tutorial-contribute-on-github.rst:338 msgid "Open PR" msgstr "PR 열기" -#: ../../source/contributor-tutorial-contribute-on-github.rst:306 +#: ../../source/contributor-tutorial-contribute-on-github.rst:340 msgid "" "Commit the changes (commit messages are always imperative: \"Do " "something\", in this case \"Change …\")" msgstr "변경 사항을 커밋합니다(커밋 메시지는 항상 필수 메시지입니다:\"Do something\"(이 경우 는 \"Change …\" )" -#: ../../source/contributor-tutorial-contribute-on-github.rst:307 +#: ../../source/contributor-tutorial-contribute-on-github.rst:342 msgid "Push the changes to your fork" msgstr "변경 사항을 포크에 푸시합니다" -#: ../../source/contributor-tutorial-contribute-on-github.rst:308 +#: ../../source/contributor-tutorial-contribute-on-github.rst:343 msgid "" "Open a PR (as shown above) with title ``docs(framework) Update how-to " "guide title``" msgstr "``docs(framework) Update how-to guide title`` 제목으로 PR(위와 같이)을 엽니다" -#: ../../source/contributor-tutorial-contribute-on-github.rst:309 +#: ../../source/contributor-tutorial-contribute-on-github.rst:344 msgid "Wait for it to be approved!" msgstr "승인될 때까지 기다리세요!" -#: ../../source/contributor-tutorial-contribute-on-github.rst:310 +#: ../../source/contributor-tutorial-contribute-on-github.rst:345 msgid "Congrats! 🥳 You're now officially a Flower contributor!" msgstr "축하합니다! 이제 공식적으로 Flower 기여자가 되셨습니다!" -#: ../../source/contributor-tutorial-contribute-on-github.rst:314 +#: ../../source/contributor-tutorial-contribute-on-github.rst:348 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:573 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:1012 #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:811 @@ -2058,41 +2066,42 @@ msgstr "축하합니다! 이제 공식적으로 Flower 기여자가 되셨습니 msgid "Next steps" msgstr "다음 단계" -#: ../../source/contributor-tutorial-contribute-on-github.rst:316 +#: ../../source/contributor-tutorial-contribute-on-github.rst:350 msgid "" "Once you have made your first PR, and want to contribute more, be sure to" " check out the following :" msgstr "첫 번째 PR을 작성하고 더 많은 기여를 하고 싶다면 다음을 확인하세요:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:318 +#: ../../source/contributor-tutorial-contribute-on-github.rst:353 +#, fuzzy msgid "" ":doc:`Good first contributions `, where you should particularly look into the " -":code:`baselines` contributions." +"``baselines`` contributions." msgstr "" ":doc:`훌륭한 첫 번째 기여 `, 특히 " ":code:`baselines` 기여를 살펴봐야 합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:322 +#: ../../source/contributor-tutorial-contribute-on-github.rst:357 #: ../../source/fed/0000-20200102-fed-template.md:60 msgid "Appendix" msgstr "부록" -#: ../../source/contributor-tutorial-contribute-on-github.rst:327 +#: ../../source/contributor-tutorial-contribute-on-github.rst:362 msgid "PR title format" msgstr "PR 제목 형식" -#: ../../source/contributor-tutorial-contribute-on-github.rst:329 +#: ../../source/contributor-tutorial-contribute-on-github.rst:364 msgid "We enforce the following PR title format:" msgstr "다음과 같은 PR 제목 형식을 적용합니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:335 +#: ../../source/contributor-tutorial-contribute-on-github.rst:370 msgid "" "(or ``(:skip) `` to ignore the PR in the " "changelog)" msgstr "(또는 ``(:skip) ``를 사용하면 변경 로그에서 PR을 무시합니다.)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:337 +#: ../../source/contributor-tutorial-contribute-on-github.rst:372 msgid "" "Where ```` needs to be in ``{ci, fix, feat, docs, refactor, " "break}``, ```` should be in ``{framework, baselines, datasets, " @@ -2105,49 +2114,49 @@ msgstr "" "':skip' 플래그를 사용해야 하는 여러 프로젝트를 수정하는 경우}``로 입력해야 하며, ````는 대문자로 " "시작해야 합니다." -#: ../../source/contributor-tutorial-contribute-on-github.rst:341 +#: ../../source/contributor-tutorial-contribute-on-github.rst:377 msgid "Valid examples:" msgstr "유효한 예시입니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:343 +#: ../../source/contributor-tutorial-contribute-on-github.rst:379 msgid "``feat(framework) Add flwr build CLI command``" msgstr "``feat(framework) Add flwr build CLI command``" -#: ../../source/contributor-tutorial-contribute-on-github.rst:344 +#: ../../source/contributor-tutorial-contribute-on-github.rst:380 msgid "``refactor(examples:skip) Improve quickstart-pytorch logging``" msgstr "``refactor(examples:skip) Improve quickstart-pytorch logging``" -#: ../../source/contributor-tutorial-contribute-on-github.rst:345 +#: ../../source/contributor-tutorial-contribute-on-github.rst:381 msgid "``ci(*:skip) Enforce PR title format``" msgstr "``ci(*:skip) Enforce PR title format``" -#: ../../source/contributor-tutorial-contribute-on-github.rst:347 +#: ../../source/contributor-tutorial-contribute-on-github.rst:383 msgid "Invalid examples:" msgstr "잘못된 예시입니다:" -#: ../../source/contributor-tutorial-contribute-on-github.rst:349 +#: ../../source/contributor-tutorial-contribute-on-github.rst:385 msgid "``feat(framework): Add flwr build CLI command`` (extra ``:``)" msgstr "``feat(framework): Add flwr build CLI command`` ( ``:``제외)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:350 +#: ../../source/contributor-tutorial-contribute-on-github.rst:386 msgid "" "``feat(*) Add flwr build CLI command`` (missing ``skip`` flag along with " "``*``)" msgstr "``feat(*) Add flwr build CLI command`` (``skip`` flag와 함께 ``*``누락)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:351 +#: ../../source/contributor-tutorial-contribute-on-github.rst:387 msgid "``feat(skip) Add flwr build CLI command`` (missing ````)" msgstr "``feat(skip) Add flwr build CLI command`` (````누락)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:352 +#: ../../source/contributor-tutorial-contribute-on-github.rst:388 msgid "``feat(framework) add flwr build CLI command`` (non capitalised verb)" msgstr "``feat(framework) add flwr build CLI command`` (대문자로 표기되지 않은 동사)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:353 +#: ../../source/contributor-tutorial-contribute-on-github.rst:389 msgid "``feat(framework) Add flwr build CLI command.`` (dot at the end)" msgstr "``feat(framework) Add flwr build CLI command.`` (끝에 마침표)" -#: ../../source/contributor-tutorial-contribute-on-github.rst:354 +#: ../../source/contributor-tutorial-contribute-on-github.rst:390 msgid "``Add flwr build CLI command.`` (missing ``()``)" msgstr "``Add flwr build CLI command.`` ( ``()``누락)" @@ -2157,9 +2166,9 @@ msgstr "기여자로 시작하기" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 #: ../../source/docker/run-as-subprocess.rst:11 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:15 -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:22 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:12 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:16 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:18 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:13 #: ../../source/docker/tutorial-quickstart-docker.rst:11 msgid "Prerequisites" msgstr "전제 조건" @@ -2181,8 +2190,9 @@ msgid "(Optional) `pyenv-virtualenv ` msgstr "(선택 사항) `pyenv-virtualenv `_" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:12 +#, fuzzy msgid "" -"Flower uses :code:`pyproject.toml` to manage dependencies and configure " +"Flower uses ``pyproject.toml`` to manage dependencies and configure " "development tools (the ones which support it). Poetry is a build tool " "which supports `PEP 517 `_." msgstr "" @@ -2190,11 +2200,11 @@ msgstr "" ":code:`pyproject.toml`을 사용합니다. Poetry는 `PEP 517 " "`_을 지원하는 빌드 도구입니다." -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:18 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:17 msgid "Developer Machine Setup" msgstr "개발자 머신 설정" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:21 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:20 #, fuzzy msgid "Preliminaries" msgstr "사전 준비" @@ -2213,105 +2223,112 @@ msgid "" "installation actions to add `brew` to your PATH." msgstr "`homebrew `_를 설치합니다. 설치 후 `brew`를 PATH에 추가하는 작업을 잊지 마세요." -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:28 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:29 +#, fuzzy msgid "" "Install `xz` (to install different Python versions) and `pandoc` to build" -" the docs::" +" the docs:" msgstr "xz`(다른 Python 버전을 설치하려면)와 `pandoc`을 설치하여 문서를 빌드합니다::" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:34 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:36 msgid "For Ubuntu" msgstr "Ubuntu의 경우" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:35 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:38 +#, fuzzy msgid "" "Ensure you system (Ubuntu 22.04+) is up-to-date, and you have all " -"necessary packages::" +"necessary packages:" msgstr "시스템(우분투 22.04 이상)이 최신 상태이고 필요한 패키지가 모두 설치되어 있는지 확인하세요:" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:44 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:47 msgid "Create Flower Dev Environment" msgstr "Flower 개발 환경 만들기" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:46 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:49 +#, fuzzy msgid "" -"1. Clone the `Flower repository `_ from " -"GitHub::" +"Clone the `Flower repository `_ from " +"GitHub:" msgstr "1. GitHub: 에서 ``Flower 레포지토리 `_를 복제합니다::" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:52 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:56 +#, fuzzy msgid "" "Let's create the Python environment for all-things Flower. If you wish to" -" use :code:`pyenv`, we provide two convenience scripts that you can use. " -"If you prefer using something else than :code:`pyenv`, create a new " -"environment, activate and skip to the last point where all packages are " -"installed." +" use ``pyenv``, we provide two convenience scripts that you can use. If " +"you prefer using something else than ``pyenv``, create a new environment," +" activate and skip to the last point where all packages are installed." msgstr "" "Flower의 모든 것을 위한 파이썬 환경을 만들어 보겠습니다.:code:`pyenv`를 사용하고자 하는 경우 사용할 수 있는 두 " "가지 편의 스크립트를 제공합니다.:code:`pyenv`가 아닌 다른 것을 사용하려면 새 환경을 생성하고 활성화한 후 모든 패키지가" " 설치된 마지막 지점으로 건너뛰세요." -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:54 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:61 +#, fuzzy msgid "" -"If you don't have :code:`pyenv` installed, the following script that will" -" install it, set it up, and create the virtual environment (with " -":code:`Python 3.9.20` by default)::" +"If you don't have ``pyenv`` installed, the following script that will " +"install it, set it up, and create the virtual environment (with ``Python " +"3.9.20`` by default):" msgstr "" ":code:`pyenv`가 설치되어 있지 않은 경우 다음 스크립트를 사용하여 설치, 설정 및 가상 환경을 생성합니다(기본적으로 " ":code:`Python 3.9.20` 사용):" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:58 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:68 +#, fuzzy msgid "" -"If you already have :code:`pyenv` installed (along with the :code:`pyenv-" -"virtualenv` plugin), you can use the following convenience script (with " -":code:`Python 3.9.20` by default)::" +"If you already have ``pyenv`` installed (along with the ``pyenv-" +"virtualenv`` plugin), you can use the following convenience script (with " +"``Python 3.9.20`` by default):" msgstr "" ":code:`pyenv`가 이미 설치되어 있는 경우( :code:`pyenv-virtualenv` 플러그인과 함께) 다음과 같은 " "편의 스크립트를 사용할 수 있습니다(기본적으로 코드:`Python 3.9.20` 사용):" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:62 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:75 +#, fuzzy msgid "" -"3. Install the Flower package in development mode (think :code:`pip " -"install -e`) along with all necessary dependencies::" +"3. Install the Flower package in development mode (think ``pip install " +"-e``) along with all necessary dependencies:" msgstr "" "3. 필요한 모든 dependencies와 함께 개발 모드에서 Flower 패키지를 설치합니다(예:code:`pip install " "-e`)::" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:69 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:83 msgid "Convenience Scripts" msgstr "편의 스크립트" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:71 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:85 +#, fuzzy msgid "" "The Flower repository contains a number of convenience scripts to make " -"recurring development tasks easier and less error-prone. See the " -":code:`/dev` subdirectory for a full list. The following scripts are " -"amongst the most important ones:" +"recurring development tasks easier and less error-prone. See the ``/dev``" +" subdirectory for a full list. The following scripts are amongst the most" +" important ones:" msgstr "" "Flower 레포지토리에는 반복적인 개발 작업을 더 쉽고 오류를 줄이기 위한 여러 가지 편의 스크립트가 포함되어 있습니다. 전체 " "목록은 :code:`/dev` 하위 디렉터리를 참조하세요. 다음 스크립트는 가장 중요한 스크립트 중 하나입니다:" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:77 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:90 msgid "Create/Delete Virtual Environment" msgstr "가상 환경 생성/삭제" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:85 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:98 msgid "Compile ProtoBuf Definitions" msgstr "ProtoBuf 정의 컴파일" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:92 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:105 msgid "Auto-Format Code" msgstr "자동 포맷 코드" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:99 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:112 msgid "Run Linters and Tests" msgstr "린터 및 테스트 실행" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:106 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:119 msgid "Add a pre-commit hook" msgstr "사전 커밋 훅 추가" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:108 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:121 msgid "" "Developers may integrate a pre-commit hook into their workflow utilizing " "the `pre-commit `_ library. The pre-" @@ -2322,90 +2339,95 @@ msgstr "" " 워크플로에 통합할 수 있습니다. 사전 커밋 훅은 두 가지 기본 작업을 실행하도록 구성됩니다:``./dev/format.sh`` 및" " ``./dev/test.sh`` 스크립트." -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:110 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:125 msgid "There are multiple ways developers can use this:" msgstr "개발자가 이것을 사용할 수 있는 여러가지 방법이 있습니다:" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:112 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:127 msgid "Install the pre-commit hook to your local git directory by simply running:" msgstr "간단하게 실행하여 로컬 git 디렉터리에 사전 커밋 훅을 설치하세요:" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:118 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:133 msgid "" "Each ``git commit`` will trigger the execution of formatting and " "linting/test scripts." msgstr "각 ``git 커밋``은 포맷 및 린팅/테스트 스크립트의 실행을 트리거합니다." -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:119 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:135 +#, fuzzy msgid "" "If in a hurry, bypass the hook using ``--no-verify`` with the ``git " -"commit`` command. ::" +"commit`` command." msgstr "급한 경우 ``git commit`` 명령과 함께 `--no-verify``를 사용하여 훅을 넘기세요:" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:124 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142 msgid "" "For developers who prefer not to install the hook permanently, it is " "possible to execute a one-time check prior to committing changes by using" " the following command:" msgstr "훅을 영구적으로 설치하지 않으려는 개발자의 경우 다음 명령을 사용하여 변경 사항을 커밋하기 전에 일회성 검사를 실행할 수 있습니다:" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:130 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:149 msgid "" "This executes the formatting and linting checks/tests on all the files " "without modifying the default behavior of ``git commit``." msgstr "이렇게 하면 ``git commit``의 기본 동작을 수정하지 않고 모든 파일에 대해 포맷 및 린팅 검사/테스트를 실행합니다." -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:133 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:153 msgid "Run Github Actions (CI) locally" msgstr "로컬에서 Github Action(CI) 실행하기" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:135 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:155 +#, fuzzy msgid "" "Developers could run the full set of Github Actions workflows under their" " local environment by using `Act `_. " "Please refer to the installation instructions under the linked repository" -" and run the next command under Flower main cloned repository folder::" +" and run the next command under Flower main cloned repository folder:" msgstr "" "개발자는 `Act `_를 사용하여 로컬 환경에서 전체 Github " "Actions 워크플로우 세트를 실행할 수 있습니다. 링크된 레포지토리 아래의 설치 지침을 참조하여 Flower 메인 클론 " "레포지토리 폴더 아래에서 다음 명령을 실행하세요::" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:164 msgid "" "The Flower default workflow would run by setting up the required Docker " "machines underneath." msgstr "Flower 기본 워크플로우는 아래에 필요한 Docker 머신을 설정하여 실행합니다." -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:147 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:168 msgid "Build Release" msgstr "릴리즈 빌드" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:149 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:170 +#, fuzzy msgid "" "Flower uses Poetry to build releases. The necessary command is wrapped in" -" a simple script::" +" a simple script:" msgstr "Flower는 Poetry를 사용하여 릴리즈를 빌드합니다. 필요한 명령은 간단한 스크립트로 래핑됩니다::" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:154 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:177 +#, fuzzy msgid "" -"The resulting :code:`.whl` and :code:`.tar.gz` releases will be stored in" -" the :code:`/dist` subdirectory." +"The resulting ``.whl`` and ``.tar.gz`` releases will be stored in the " +"``/dist`` subdirectory." msgstr "결과물인 :code:`.whl` 및 :code:`.tar.gz` 릴리즈는 :code:`/dist` 하위 디렉터리에 저장됩니다." -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:159 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:181 msgid "Build Documentation" msgstr "문서 빌드" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:161 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:183 +#, fuzzy msgid "" "Flower's documentation uses `Sphinx `_. " "There's no convenience script to re-build the documentation yet, but it's" -" pretty easy::" +" pretty easy:" msgstr "" "Flower의 문서는 `Sphinx `_를 사용합니다. 아직 문서를 다시 작성할" " 수 있는 편리한 스크립트는 없지만 다음과 같이 쉽게 작성할 수 있습니다:" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:167 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:191 msgid "This will generate HTML documentation in ``doc/build/html``." msgstr "그러면 ``doc/build/html``에 HTML 문서가 생성됩니다." @@ -2461,7 +2483,7 @@ msgstr "" "사용자 ID를 변경하려면 ``sudo chown -R 49999:49999 certificates/*``를 실행하면 됩니다." #: ../../source/docker/enable-tls.rst:23 -#: ../../source/docker/persist-superlink-state.rst:14 +#: ../../source/docker/persist-superlink-state.rst:15 msgid "" "If you later want to delete the directory, you can change the user ID " "back to the current user ID by running ``sudo chown -R $USER:$(id -gn) " @@ -2484,22 +2506,22 @@ msgstr "" msgid "Understanding the command" msgstr "" -#: ../../source/docker/enable-tls.rst:44 ../../source/docker/enable-tls.rst:91 +#: ../../source/docker/enable-tls.rst:45 ../../source/docker/enable-tls.rst:92 #: ../../source/docker/enable-tls.rst:125 #: ../../source/docker/tutorial-quickstart-docker.rst:66 #: ../../source/docker/tutorial-quickstart-docker.rst:103 -#: ../../source/docker/tutorial-quickstart-docker.rst:213 -#: ../../source/docker/tutorial-quickstart-docker.rst:300 +#: ../../source/docker/tutorial-quickstart-docker.rst:217 +#: ../../source/docker/tutorial-quickstart-docker.rst:305 #, fuzzy msgid "``docker run``: This tells Docker to run a container from an image." msgstr "``docker run``: 새 Docker 컨테이너를 실행하는 명령입니다." -#: ../../source/docker/enable-tls.rst:45 ../../source/docker/enable-tls.rst:92 +#: ../../source/docker/enable-tls.rst:46 ../../source/docker/enable-tls.rst:93 #: ../../source/docker/enable-tls.rst:126 #: ../../source/docker/tutorial-quickstart-docker.rst:67 #: ../../source/docker/tutorial-quickstart-docker.rst:104 -#: ../../source/docker/tutorial-quickstart-docker.rst:214 -#: ../../source/docker/tutorial-quickstart-docker.rst:301 +#: ../../source/docker/tutorial-quickstart-docker.rst:218 +#: ../../source/docker/tutorial-quickstart-docker.rst:306 msgid "``--rm``: Remove the container once it is stopped or the command exits." msgstr "" @@ -2604,12 +2626,12 @@ msgstr "" msgid "the network." msgstr "" -#: ../../source/docker/enable-tls.rst:71 +#: ../../source/docker/enable-tls.rst:72 #, fuzzy msgid "SuperNode" msgstr "run\\_supernode" -#: ../../source/docker/enable-tls.rst:73 +#: ../../source/docker/enable-tls.rst:74 #, fuzzy msgid "" "Assuming that the ``ca.crt`` certificate already exists locally, we can " @@ -2620,7 +2642,7 @@ msgstr "" "디렉터리에 마운트할 수 있습니다. 이렇게 하면 SuperNode가 컨테이너 내의 인증서에 액세스할 수 있습니다. 컨테이너를 시작할 " "때 ``--root-certificates`` 플래그를 사용하세요." -#: ../../source/docker/enable-tls.rst:78 +#: ../../source/docker/enable-tls.rst:79 msgid "" "If you're generating self-signed certificates and the ``ca.crt`` " "certificate doesn't exist on the SuperNode, you can copy it over after " @@ -2735,16 +2757,16 @@ msgstr "" msgid "Getting Started" msgstr "시작하기" -#: ../../source/docker/index.rst:20 +#: ../../source/docker/index.rst:19 msgid "Running in Production" msgstr "" -#: ../../source/docker/index.rst:29 +#: ../../source/docker/index.rst:28 #, fuzzy msgid "Advanced Options" msgstr "고급 Docker 옵션" -#: ../../source/docker/index.rst:41 +#: ../../source/docker/index.rst:40 #, fuzzy msgid "Run Flower using Docker Compose" msgstr "Docker를 사용하여 Flower 실행" @@ -2770,7 +2792,7 @@ msgid "" " on your host system and a name for the database file." msgstr "" -#: ../../source/docker/persist-superlink-state.rst:10 +#: ../../source/docker/persist-superlink-state.rst:11 msgid "" "By default, the SuperLink container runs with a non-root user called " "``app`` with the user ID ``49999``. It is recommended to create a new " @@ -2778,7 +2800,7 @@ msgid "" "the mounted directory has the proper permissions." msgstr "" -#: ../../source/docker/persist-superlink-state.rst:20 +#: ../../source/docker/persist-superlink-state.rst:21 #, fuzzy msgid "" "In the example below, we create a new directory called ``state``, change " @@ -2791,7 +2813,7 @@ msgstr "" "``state`` 디렉터리를 컨테이너의 ``/app/state`` 디렉터리에 마운트하도록 지시합니다. 또한 " "``--database`` 플래그를 사용하여 데이터베이스 파일의 이름을 지정합니다." -#: ../../source/docker/persist-superlink-state.rst:35 +#: ../../source/docker/persist-superlink-state.rst:36 #, fuzzy msgid "" "As soon as the SuperLink starts, the file ``state.db`` is created in the " @@ -2824,18 +2846,18 @@ msgstr "" "태그 뒤에 있는 이미지가 업데이트될 수 있습니다. 이러한 업데이트에는 일반적으로 Flower의 기능을 변경해서는 안 되는 시스템 " "의존성에 대한 보안 업데이트가 포함됩니다. 그러나 항상 동일한 이미지를 사용하려면 태그 대신 이미지의 해시를 지정할 수 있습니다." -#: ../../source/docker/pin-version.rst:13 +#: ../../source/docker/pin-version.rst:14 #, fuzzy msgid "" "The following command returns the current image digest referenced by the " ":substitution-code:`superlink:|stable_flwr_version|` tag:" msgstr "다음 명령은 ``superlink:1.8.0`` 태그가 참조하는 현재 이미지 해시를 반환합니다:" -#: ../../source/docker/pin-version.rst:22 +#: ../../source/docker/pin-version.rst:23 msgid "This will output" msgstr "" -#: ../../source/docker/pin-version.rst:29 +#: ../../source/docker/pin-version.rst:30 #, fuzzy msgid "Next, we can pin the digest when running a new SuperLink container:" msgstr "다음으로, 새 SuperLink 컨테이너를 실행할 때 해시를 고정할 수 있습니다:" @@ -2892,7 +2914,7 @@ msgstr "" "Docker 이미지 빌드 과정에서 루트 사용자로 전환하여 누락된 시스템 의존성을 설치하려면 Dockerfile 내에서 ``USER " "root`` 지시어를 사용할 수 있습니다." -#: ../../source/docker/run-as-root-user.rst:29 +#: ../../source/docker/run-as-root-user.rst:30 #, fuzzy msgid "SuperNode Dockerfile" msgstr "SuperNode Dockerfile 만들기" @@ -2919,12 +2941,12 @@ msgid "" "done by extending the SuperNode image:" msgstr "" -#: ../../source/docker/run-as-subprocess.rst:16 +#: ../../source/docker/run-as-subprocess.rst:17 #, fuzzy msgid "Dockerfile.supernode" msgstr "Flower SuperNode" -#: ../../source/docker/run-as-subprocess.rst:30 +#: ../../source/docker/run-as-subprocess.rst:31 #, fuzzy msgid "" "Next, build the SuperNode Docker image by running the following command " @@ -2958,83 +2980,83 @@ msgid "" " Engine via Docker Compose." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:11 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:12 msgid "" "Some quickstart examples may have limitations or requirements that " "prevent them from running on every environment. For more information, " -"please see `Limitations`_." +"please see Limitations_." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:17 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:14 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:18 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:15 #: ../../source/docker/tutorial-quickstart-docker.rst:13 #, fuzzy msgid "Before you start, make sure that:" msgstr "시작하기 전에 Docker daemon이 실행 중인지 확인하세요:" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:19 -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:26 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:20 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:22 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:17 #: ../../source/docker/tutorial-quickstart-docker.rst:15 msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:20 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:17 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:21 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:18 #: ../../source/docker/tutorial-quickstart-docker.rst:16 #, fuzzy msgid "The Docker daemon is running." msgstr "Docker 데몬이 실행 중인지 확인하십시오." -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:21 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:18 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:22 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:19 msgid "Docker Compose is `installed `_." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:24 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:25 #, fuzzy msgid "Run the Quickstart Example" msgstr "예시 요청" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:26 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:27 msgid "" "Clone the quickstart example you like to run. For example, ``quickstart-" "pytorch``:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:34 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:35 msgid "" "Download the `compose.yml " "`_" " file into the example directory:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:41 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:44 #, fuzzy msgid "Build and start the services using the following command:" msgstr "다음 명령을 실행하여 가상 환경을 활성화합니다:" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:47 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:50 #, fuzzy msgid "" "Append the following lines to the end of the ``pyproject.toml`` file and " "save it:" msgstr "``pyproject.toml``에 다음 버전 제약 조건을 설정했는지 확인하세요:" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:49 -#: ../../source/docker/tutorial-quickstart-docker.rst:319 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:52 +#: ../../source/docker/tutorial-quickstart-docker.rst:324 #, fuzzy msgid "pyproject.toml" msgstr "또는 ``pyproject.toml``:" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:58 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:61 msgid "" "You can customize the string that follows ``tool.flwr.federations.`` to " "fit your needs. However, please note that the string cannot contain a dot" " (``.``)." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:61 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:64 msgid "" "In this example, ``local-deployment`` has been used. Just remember to " "replace ``local-deployment`` with your chosen name in both the " @@ -3042,57 +3064,57 @@ msgid "" "command." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:65 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:68 #, fuzzy msgid "Run the example:" msgstr "전체 코드 예제" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:71 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:74 msgid "Follow the logs of the SuperExec service:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:77 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:80 msgid "" "That is all it takes! You can monitor the progress of the run through the" " logs of the SuperExec." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:80 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:84 msgid "Run a Different Quickstart Example" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:82 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:86 msgid "" "To run a different quickstart example, such as ``quickstart-tensorflow``," " first, shut down the Docker Compose services of the current example:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:89 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:93 msgid "After that, you can repeat the steps above." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:92 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:98 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:96 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:102 #, fuzzy msgid "Limitations" msgstr "동기" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:97 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:101 #, fuzzy msgid "Quickstart Example" msgstr "빠른 시작" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:99 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:103 #, fuzzy msgid "quickstart-fastai" msgstr "빠른 시작 튜토리얼" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:100 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:102 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:110 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:112 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:116 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:118 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:104 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:106 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:115 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:117 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:121 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:123 #: ../../source/ref-changelog.md:33 ../../source/ref-changelog.md:399 #: ../../source/ref-changelog.md:676 ../../source/ref-changelog.md:740 #: ../../source/ref-changelog.md:798 ../../source/ref-changelog.md:867 @@ -3100,80 +3122,80 @@ msgstr "빠른 시작 튜토리얼" msgid "None" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:101 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:105 #, fuzzy msgid "quickstart-huggingface" msgstr "빠른 시작 튜토리얼" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:103 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:107 #, fuzzy msgid "quickstart-jax" msgstr "빠른 시작" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:104 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:106 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:120 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:108 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:110 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:125 msgid "" "The example has not yet been updated to work with the latest ``flwr`` " "version." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:105 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:109 #, fuzzy msgid "quickstart-mlcube" msgstr "빠른 시작" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:107 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:111 #, fuzzy msgid "quickstart-mlx" msgstr "빠른 시작" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:108 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:112 msgid "" "`Requires to run on macOS with Apple Silicon `_." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:109 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:114 #, fuzzy msgid "quickstart-monai" msgstr "빠른 시작" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:111 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:116 #, fuzzy msgid "quickstart-pandas" msgstr "빠른 시작 튜토리얼" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:113 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:118 msgid "quickstart-pytorch-lightning" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:114 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:119 msgid "" "Requires an older pip version that is not supported by the Flower Docker " "images." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:115 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:120 #, fuzzy msgid "quickstart-pytorch" msgstr "빠른 시작 튜토리얼" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:117 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:122 msgid "quickstart-sklearn-tabular" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:119 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:124 #, fuzzy msgid "quickstart-tabnet" msgstr "빠른 시작 튜토리얼" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:121 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:126 #, fuzzy msgid "quickstart-tensorflow" msgstr "빠른 시작 튜토리얼" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:122 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:127 msgid "Only runs on AMD64." msgstr "" @@ -3190,18 +3212,18 @@ msgid "" "environment variables for a container." msgstr "Docker 컨테이너 내에서 변수를 설정하려면 ``-e =`` 플래그를 사용하면 됩니다." -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:3 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:2 #, fuzzy msgid "Deploy Flower on Multiple Machines with Docker Compose" msgstr "빠른 시작 튜토리얼" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:5 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:4 msgid "" "This guide will help you set up a Flower project on multiple machines " "using Docker Compose." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:8 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:7 msgid "" "You will learn how to run the Flower client and server components on two " "separate machines, with Flower configured to use TLS encryption and " @@ -3210,7 +3232,7 @@ msgid "" " to the :doc:`../explanation-flower-architecture` explainer page." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:15 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:13 msgid "" "This guide assumes you have completed the :doc:`tutorial-quickstart-" "docker-compose` tutorial. It is highly recommended that you follow and " @@ -3218,139 +3240,139 @@ msgid "" "guide." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:24 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:20 msgid "Before you begin, make sure you have the following prerequisites:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:28 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:23 msgid "The Docker daemon is running on your local machine and the remote machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:30 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:24 msgid "" "Docker Compose V2 is installed on both your local machine and the remote " "machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:32 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:25 msgid "You can connect to the remote machine from your local machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:33 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:26 msgid "Ports ``9091`` and ``9093`` are accessible on the remote machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:37 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:30 msgid "" "The guide uses the |quickstart_sklearn_tabular|_ example as an example " "project." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:40 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:32 msgid "" "If your project has a different name or location, please remember to " "adjust the commands/paths accordingly." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:45 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:36 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:22 #: ../../source/docker/tutorial-quickstart-docker.rst:19 msgid "Step 1: Set Up" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:47 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:38 msgid "Clone the Flower repository and change to the ``distributed`` directory:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:55 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:45 msgid "Get the IP address from the remote machine and save it for later." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:57 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:46 msgid "" "Use the ``certs.yml`` Compose file to generate your own self-signed " "certificates. If you have certificates, you can continue with Step 2." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:62 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:215 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:51 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:221 msgid "These certificates should be used only for development purposes." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:64 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:53 msgid "" "For production environments, you may have to use dedicated services to " "obtain your certificates." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:67 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:56 msgid "" "First, set the environment variables ``SUPERLINK_IP`` and " "``SUPEREXEC_IP`` with the IP address from the remote machine. For " "example, if the IP is ``192.168.2.33``, execute:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:76 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:65 msgid "Next, generate the self-signed certificates:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:84 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:72 msgid "Step 2: Copy the Server Compose Files" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:86 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:74 msgid "" "Use the method that works best for you to copy the ``server`` directory, " "the certificates, and your Flower project to the remote machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:89 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:77 msgid "For example, you can use ``scp`` to copy the directories:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:100 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:87 msgid "Step 3: Start the Flower Server Components" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:102 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:89 msgid "" "Log into the remote machine using ``ssh`` and run the following command " "to start the SuperLink and SuperExec services:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:115 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:102 msgid "" "The Path of the ``PROJECT_DIR`` should be relative to the location of the" " ``server`` Docker Compose files." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:118 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:105 msgid "Go back to your terminal on your local machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:122 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:108 #, fuzzy msgid "Step 4: Start the Flower Client Components" msgstr "서버(SuperLink)" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:124 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:110 msgid "" "On your local machine, run the following command to start the client " "components:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:135 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:120 msgid "" "The Path of the ``PROJECT_DIR`` should be relative to the location of the" " ``client`` Docker Compose files." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:140 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:124 #, fuzzy msgid "Step 5: Run Your Flower Project" msgstr "Flower SuperNode를 실행합니다." -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:142 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:126 msgid "" "Specify the remote SuperExec IP addresses and the path to the root " "certificate in the ``[tool.flwr.federations.remote-superexec]`` table in " @@ -3358,36 +3380,36 @@ msgid "" "``remote-superexec``:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:147 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:130 msgid "examples/quickstart-sklearn-tabular/pyproject.toml" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:156 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:139 msgid "" "The Path of the ``root-certificates`` should be relative to the location " "of the ``pyproject.toml`` file." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:159 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:142 msgid "To run the project, execute:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:165 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:148 msgid "" "That's it! With these steps, you've set up Flower on two separate " "machines and are ready to start using it." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:170 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:152 msgid "Step 6: Clean Up" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:172 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:154 #, fuzzy msgid "Shut down the Flower client components:" msgstr "Flower 클라이언트 앱을 실행합니다." -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:179 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:161 msgid "Shut down the Flower server components and delete the SuperLink state:" msgstr "" @@ -3409,7 +3431,7 @@ msgid "" " understanding the basic workflow that uses the minimum configurations." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:31 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:32 #: ../../source/docker/tutorial-quickstart-docker.rst:21 msgid "Create a new Flower project (PyTorch):" msgstr "" @@ -3431,7 +3453,7 @@ msgstr "" msgid "Step 2: Start the SuperLink" msgstr "서버(SuperLink)" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:60 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:62 #: ../../source/docker/tutorial-quickstart-docker.rst:52 msgid "Open your terminal and run:" msgstr "" @@ -3457,8 +3479,8 @@ msgstr "" #: ../../source/docker/tutorial-quickstart-docker.rst:71 #: ../../source/docker/tutorial-quickstart-docker.rst:108 -#: ../../source/docker/tutorial-quickstart-docker.rst:215 -#: ../../source/docker/tutorial-quickstart-docker.rst:304 +#: ../../source/docker/tutorial-quickstart-docker.rst:219 +#: ../../source/docker/tutorial-quickstart-docker.rst:309 msgid "" "``--network flwr-network``: Make the container join the network named " "``flwr-network``." @@ -3470,8 +3492,8 @@ msgstr "" #: ../../source/docker/tutorial-quickstart-docker.rst:73 #: ../../source/docker/tutorial-quickstart-docker.rst:110 -#: ../../source/docker/tutorial-quickstart-docker.rst:216 -#: ../../source/docker/tutorial-quickstart-docker.rst:306 +#: ../../source/docker/tutorial-quickstart-docker.rst:220 +#: ../../source/docker/tutorial-quickstart-docker.rst:311 msgid "" "``--detach``: Run the container in the background, freeing up the " "terminal." @@ -3587,13 +3609,13 @@ msgid "" "extends the ClientApp image and installs the required dependencies." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:148 +#: ../../source/docker/tutorial-quickstart-docker.rst:149 msgid "" "Create a ClientApp Dockerfile called ``Dockerfile.clientapp`` and paste " "the following code into it:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:150 +#: ../../source/docker/tutorial-quickstart-docker.rst:152 #, fuzzy msgid "Dockerfile.clientapp" msgstr "flower 클라이언트 앱" @@ -3678,7 +3700,7 @@ msgstr "" msgid "the default command run when the container is started." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:184 +#: ../../source/docker/tutorial-quickstart-docker.rst:186 msgid "" "Note that `flwr `__ is already installed " "in the ``flwr/clientapp`` base image, so only other package dependencies " @@ -3687,7 +3709,7 @@ msgid "" "after it has been copied into the Docker image (see line 5)." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:189 +#: ../../source/docker/tutorial-quickstart-docker.rst:192 #, fuzzy msgid "" "Next, build the ClientApp Docker image by running the following command " @@ -3696,7 +3718,7 @@ msgstr "" "다음으로, Docker파일과 ServerApp 코드가 있는 디렉터리에서 다음 명령을 실행하여 ServerApp Docker 이미지를" " 빌드합니다." -#: ../../source/docker/tutorial-quickstart-docker.rst:198 +#: ../../source/docker/tutorial-quickstart-docker.rst:201 #, fuzzy msgid "" "The image name was set as ``flwr_clientapp`` with the tag ``0.0.1``. " @@ -3706,7 +3728,7 @@ msgstr "" "이미지에``flwr_serverapp``이라는 이름을 붙이고 ``0.0.1``이라는 태그를 붙였습니다. 여기서 선택한 값은 예시일 " "뿐이라는 점을 기억하세요. 필요에 따라 변경할 수 있습니다." -#: ../../source/docker/tutorial-quickstart-docker.rst:201 +#: ../../source/docker/tutorial-quickstart-docker.rst:205 msgid "Start the first ClientApp container:" msgstr "" @@ -3727,34 +3749,34 @@ msgstr "" msgid "``supernode-1:9094``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:222 +#: ../../source/docker/tutorial-quickstart-docker.rst:226 msgid "Start the second ClientApp container:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:233 +#: ../../source/docker/tutorial-quickstart-docker.rst:237 msgid "Step 5: Start the SuperExec" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:235 +#: ../../source/docker/tutorial-quickstart-docker.rst:239 #, fuzzy msgid "" "The procedure for building and running a SuperExec image is almost " "identical to the ClientApp image." msgstr "ServerApp 이미지를 빌드하고 실행하는 절차는 SuperNode 이미지와 거의 동일합니다." -#: ../../source/docker/tutorial-quickstart-docker.rst:237 +#: ../../source/docker/tutorial-quickstart-docker.rst:242 msgid "" "Similar to the ClientApp image, you will need to create a Dockerfile that" " extends the SuperExec image and installs the required FAB dependencies." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:240 +#: ../../source/docker/tutorial-quickstart-docker.rst:245 msgid "" "Create a SuperExec Dockerfile called ``Dockerfile.superexec`` and paste " "the following code in:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:242 +#: ../../source/docker/tutorial-quickstart-docker.rst:248 msgid "Dockerfile.superexec" msgstr "" @@ -3784,13 +3806,13 @@ msgstr "" msgid "``flwr.superexec.deployment:executor`` executor to run the ServerApps." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:277 +#: ../../source/docker/tutorial-quickstart-docker.rst:283 msgid "" "Afterward, in the directory that holds the Dockerfile, execute this " "Docker command to build the SuperExec image:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:285 +#: ../../source/docker/tutorial-quickstart-docker.rst:290 msgid "Start the SuperExec container:" msgstr "" @@ -3804,7 +3826,7 @@ msgid "" "``http://localhost:9093``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:305 +#: ../../source/docker/tutorial-quickstart-docker.rst:310 msgid "``--name superexec``: Assign the name ``superexec`` to the container." msgstr "" @@ -3825,78 +3847,78 @@ msgstr "" msgid "connect to the SuperLink running on port ``9091``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:315 +#: ../../source/docker/tutorial-quickstart-docker.rst:320 msgid "Step 6: Run the Quickstart Project" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:317 +#: ../../source/docker/tutorial-quickstart-docker.rst:322 #, fuzzy msgid "Add the following lines to the ``pyproject.toml``:" msgstr "``pyproject.toml``에 다음 버전 제약 조건을 설정했는지 확인하세요:" -#: ../../source/docker/tutorial-quickstart-docker.rst:326 +#: ../../source/docker/tutorial-quickstart-docker.rst:331 msgid "Run the ``quickstart-docker`` project by executing the command:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:332 +#: ../../source/docker/tutorial-quickstart-docker.rst:337 msgid "Follow the SuperExec logs to track the execution of the run:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:339 +#: ../../source/docker/tutorial-quickstart-docker.rst:344 msgid "Step 7: Update the Application" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:341 +#: ../../source/docker/tutorial-quickstart-docker.rst:346 msgid "" -"Change the application code. For example, change the ``seed`` in " +"Change the application code. For example, change the ``seed`` in " "``quickstart_docker/task.py`` to ``43`` and save it:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:344 +#: ../../source/docker/tutorial-quickstart-docker.rst:349 msgid "quickstart_docker/task.py" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:351 +#: ../../source/docker/tutorial-quickstart-docker.rst:356 #, fuzzy msgid "Stop the current ClientApp containers:" msgstr "현재 클라이언트 속성입니다." -#: ../../source/docker/tutorial-quickstart-docker.rst:357 +#: ../../source/docker/tutorial-quickstart-docker.rst:362 #, fuzzy msgid "Rebuild the FAB and ClientApp image:" msgstr "기본 이미지 빌드" -#: ../../source/docker/tutorial-quickstart-docker.rst:363 +#: ../../source/docker/tutorial-quickstart-docker.rst:368 msgid "Launch two new ClientApp containers based on the newly built image:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:378 +#: ../../source/docker/tutorial-quickstart-docker.rst:383 msgid "Run the updated project:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:385 +#: ../../source/docker/tutorial-quickstart-docker.rst:390 msgid "Step 8: Clean Up" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:387 +#: ../../source/docker/tutorial-quickstart-docker.rst:392 msgid "Remove the containers and the bridge network:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:401 -#: ../../source/docker/tutorial-quickstart-docker.rst:399 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:408 +#: ../../source/docker/tutorial-quickstart-docker.rst:404 #, fuzzy msgid "Where to Go Next" msgstr "시작 위치" -#: ../../source/docker/tutorial-quickstart-docker.rst:401 +#: ../../source/docker/tutorial-quickstart-docker.rst:406 msgid ":doc:`enable-tls`" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:402 +#: ../../source/docker/tutorial-quickstart-docker.rst:407 msgid ":doc:`persist-superlink-state`" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:403 +#: ../../source/docker/tutorial-quickstart-docker.rst:408 msgid ":doc:`tutorial-quickstart-docker-compose`" msgstr "" @@ -3919,179 +3941,179 @@ msgid "" "configuration that best suits your project's needs." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:23 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:24 msgid "Clone the Docker Compose ``complete`` directory:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:37 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:38 msgid "" "Export the path of the newly created project. The path should be relative" " to the location of the Docker Compose files:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:44 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:45 msgid "" "Setting the ``PROJECT_DIR`` helps Docker Compose locate the " "``pyproject.toml`` file, allowing it to install dependencies in the " "SuperExec and SuperNode images correctly." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:48 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:49 #, fuzzy msgid "Step 2: Run Flower in Insecure Mode" msgstr "Flower SuperNode를 실행합니다." -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:50 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:51 msgid "" "To begin, start Flower with the most basic configuration. In this setup, " "Flower will run without TLS and without persisting the state." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:55 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:56 msgid "" "Without TLS, the data sent between the services remains **unencrypted**. " "Use it only for development purposes." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:58 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:59 msgid "" "For production-oriented use cases, :ref:`enable TLS` for secure data" " transmission." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:68 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:179 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:70 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:184 #, fuzzy msgid "``docker compose``: The Docker command to run the Docker Compose tool." msgstr "``docker run``: 새 Docker 컨테이너를 실행하는 명령입니다." -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:69 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:180 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:71 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:185 msgid "" "``-f compose.yml``: Specify the YAML file that contains the basic Flower " "service definitions." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:70 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:185 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:72 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:190 msgid "" "``--build``: Rebuild the images for each service if they don't already " "exist." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:71 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:186 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:73 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:191 msgid "" "``-d``: Detach the containers from the terminal and run them in the " "background." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:74 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:76 msgid "Step 3: Run the Quickstart Project" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:76 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:78 msgid "" "Now that the Flower services have been started via Docker Compose, it is " "time to run the quickstart example." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:79 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:81 msgid "" "To ensure the ``flwr`` CLI connects to the SuperExec, you need to specify" " the SuperExec addresses in the ``pyproject.toml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:82 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:226 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:84 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:232 msgid "Add the following lines to the ``quickstart-compose/pyproject.toml``:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:84 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:228 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:86 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:234 msgid "quickstart-compose/pyproject.toml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:91 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:93 msgid "Execute the command to run the quickstart example:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:97 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:99 msgid "Monitor the SuperExec logs and wait for the summary to appear:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:104 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:106 msgid "Step 4: Update the Application" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:106 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:108 msgid "In the next step, change the application code." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:108 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:110 msgid "" "For example, go to the ``task.py`` file in the ``quickstart-" "compose/quickstart_compose/`` directory and add a ``print`` call in the " "``get_weights`` function:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:111 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:114 msgid "quickstart-compose/quickstart_compose/task.py" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:120 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:125 #, fuzzy msgid "Rebuild and restart the services." msgstr "이미 *서버*를 시작할 수 있습니다:" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:124 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:129 msgid "" "If you have modified the dependencies listed in your ``pyproject.toml`` " "file, it is essential to rebuild images." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:127 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:132 msgid "If you haven't made any changes, you can skip this step." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:129 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:134 msgid "Run the following command to rebuild and restart the services:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:135 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:140 msgid "Run the updated quickstart example:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:142 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:147 msgid "In the SuperExec logs, you should find the ``Get weights`` line:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:159 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:164 msgid "Step 5: Persisting the SuperLink State" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:161 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:166 msgid "" "In this step, Flower services are configured to persist the state of the " "SuperLink service, ensuring that it maintains its state even after a " "restart." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:166 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:171 msgid "" "When working with Docker Compose on Linux, you may need to create the " "``state`` directory first and change its ownership to ensure proper " "access and permissions." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:169 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:174 msgid "" "For more information, consult the following page: :doc:`persist-" "superlink-state`." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:171 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:220 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:176 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:226 msgid "Run the command:" msgstr "" @@ -4112,17 +4134,17 @@ msgid "" "rules>`_." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:188 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:241 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:369 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:193 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:247 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:375 msgid "Rerun the ``quickstart-compose`` project:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:194 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:199 msgid "Check the content of the ``state`` directory:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:201 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:206 msgid "" "You should see a ``state.db`` file in the ``state`` directory. If you " "restart the service, the state file will be used to restore the state " @@ -4130,121 +4152,121 @@ msgid "" "if the containers are stopped and started again." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:208 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:214 msgid "Step 6: Run Flower with TLS" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:210 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:216 msgid "" "To demonstrate how to enable TLS, generate self-signed certificates using" " the ``certs.yml`` Compose file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:217 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:223 msgid "" "For production environments, use a service like `Let's Encrypt " "`_ to obtain your certificates." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:235 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:241 msgid "Restart the services with TLS enabled:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:249 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:255 msgid "Step 7: Add another SuperNode" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:251 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:257 msgid "" "You can add more SuperNodes and ClientApps by duplicating their " "definitions in the ``compose.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:254 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:260 msgid "" "Just give each new SuperNode and ClientApp service a unique service name " "like ``supernode-3``, ``clientapp-3``, etc." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:257 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:263 msgid "In ``compose.yml``, add the following:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:259 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:265 msgid "compose.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:310 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:316 msgid "" "If you also want to enable TLS for the new SuperNodes, duplicate the " "SuperNode definition for each new SuperNode service in the ``with-" "tls.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:313 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:319 msgid "" "Make sure that the names of the services match with the one in the " "``compose.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:315 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:321 msgid "In ``with-tls.yml``, add the following:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:317 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:323 msgid "with-tls.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:339 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:345 msgid "Step 8: Persisting the SuperLink State and Enabling TLS" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:341 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:347 msgid "" "To run Flower with persisted SuperLink state and enabled TLS, a slight " "change in the ``with-state.yml`` file is required:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:344 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:350 msgid "Comment out the lines 2-4 and uncomment the lines 5-9:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:346 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:352 msgid "with-state.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:363 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:369 #, fuzzy msgid "Restart the services:" msgstr "이미 *서버*를 시작할 수 있습니다:" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:377 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:383 msgid "Step 9: Merge Multiple Compose Files" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:379 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:385 msgid "" "You can merge multiple Compose files into a single file. For instance, if" " you wish to combine the basic configuration with the TLS configuration, " "execute the following command:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:387 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:394 msgid "" "This will merge the contents of ``compose.yml`` and ``with-tls.yml`` into" " a new file called ``my_compose.yml``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:391 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:398 msgid "Step 10: Clean Up" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:393 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:400 #, fuzzy msgid "Remove all services and volumes:" msgstr "R에서 모든 항목을 제거합니다." -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:403 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:410 #, fuzzy msgid ":doc:`run-quickstart-examples-docker-compose`" msgstr "빠른 시작 튜토리얼" @@ -4263,7 +4285,7 @@ msgstr "" "다른 버전의 Flower를 사용하려면 태그를 변경하여 사용할 수 있습니다(예: Flower nightly). 사용 가능한 모든 " "버전은 `Docker Hub `__에 있습니다." -#: ../../source/docker/use-a-different-version.rst:9 +#: ../../source/docker/use-a-different-version.rst:10 #, fuzzy msgid "" "When using Flower nightly, the SuperLink nightly image must be paired " @@ -4298,34 +4320,35 @@ msgstr "" "연합식으로 ` 와 비교했을 때 몇 가지 사항만 " "변경 하면 됩니다." -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:9 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:10 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:12 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:12 msgid "Centralized Training" msgstr "중앙 집중식 훈련" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:10 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:14 +#, fuzzy msgid "" "All files are revised based on :doc:`Example: PyTorch - From Centralized " "To Federated `. The only " -"thing to do is modifying the file called :code:`cifar.py`, revised part " -"is shown below:" +"thing to do is modifying the file called ``cifar.py``, revised part is " +"shown below:" msgstr "" "모든 파일은 :doc:`예제: 파이토치 -중앙 집중식에서 연합식으로 `를 기반으로 수정합니다. :code:`cifar.py`라는 파일을 수정하기만 하면 되며, 수정된 부분은 " "아래와 같습니다:" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:13 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:18 msgid "" "The model architecture defined in class Net() is added with Batch " "Normalization layers accordingly." msgstr "Net() 클래스에 정의된 모델 아키텍처는 그에 따라 배치 정규화 레이어가 추가됩니다." -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:41 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:157 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:47 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:171 msgid "You can now run your machine learning workload:" msgstr "이제 머신 러닝 워크로드를 실행할 수 있습니다:" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:47 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:53 msgid "" "So far this should all look fairly familiar if you've used PyTorch " "before. Let's take the next step and use what we've built to create a " @@ -4335,19 +4358,20 @@ msgstr "" "지금까지는 파이토치를 사용해 본 적이 있다면 상당히 익숙하게 보일 것입니다. 다음 단계로 넘어가서 우리가 구축한 것을 사용하여 " "FedBN 내에서 하나의 서버와 두 개의 클라이언트로 구성된 연합학습 시스템을 만들어 보겠습니다." -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:51 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:167 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:58 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:182 msgid "Federated Training" msgstr "연합 훈련" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:53 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:60 +#, fuzzy msgid "" "If you have read :doc:`Example: PyTorch - From Centralized To Federated " "`, the following parts are" -" easy to follow, only :code:`get_parameters` and :code:`set_parameters` " -"function in :code:`client.py` needed to revise. If not, please read the " -":doc:`Example: PyTorch - From Centralized To Federated `. first." +" easy to follow, only ``get_parameters`` and ``set_parameters`` function " +"in ``client.py`` needed to revise. If not, please read the :doc:`Example:" +" PyTorch - From Centralized To Federated `. first." msgstr "" ":doc:`예제: 파이토치 - 중앙 집중식에서 연합식으로 `를 읽었다면, 다음 부분은 쉽게 따라할 수 있으며 :code:`client.py`의 " @@ -4355,30 +4379,32 @@ msgstr "" ":doc:`예제: 파이토치 - 중앙 집중식에서 연합식으로 `를 먼저 읽어보세요." -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:56 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:66 +#, fuzzy msgid "" "Our example consists of one *server* and two *clients*. In FedBN, " -":code:`server.py` keeps unchanged, we can start the server directly." +"``server.py`` keeps unchanged, we can start the server directly." msgstr "" "이 예제는 하나의 *서버*와 두 개의 *클라이언트*로 구성됩니다. FedBN에서 :code:`server.py`는 변경되지 않고 " "그대로 유지되므로 서버를 바로 시작할 수 있습니다." -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:62 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:73 +#, fuzzy msgid "" -"Finally, we will revise our *client* logic by changing " -":code:`get_parameters` and :code:`set_parameters` in :code:`client.py`, " -"we will exclude batch normalization parameters from model parameter list " -"when sending to or receiving from the server." +"Finally, we will revise our *client* logic by changing ``get_parameters``" +" and ``set_parameters`` in ``client.py``, we will exclude batch " +"normalization parameters from model parameter list when sending to or " +"receiving from the server." msgstr "" "마지막으로, :code:`client.py`에서 :code:`get_parameters` 및 " ":code:`set_parameters`를 변경하여 *client* 로직을 수정할 것입니다. 서버로 보내거나 서버에서 받을 때 모델" " 파라미터 목록에서 배치 정규화 파라미터를 제외할 수 있습니다." -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:85 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:102 msgid "Now, you can now open two additional terminal windows and run" msgstr "이제 두 개의 터미널 창을 추가로 열고 다음을 실행할 수 있습니다" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:91 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:108 msgid "" "in each window (make sure that the server is still running before you do " "so) and see your (previously centralized) PyTorch project run federated " @@ -4387,13 +4413,13 @@ msgstr "" "를 입력하고(클릭하기 전에 서버가 계속 실행 중인지 확인하세요), (이전에 중앙 집중된) PyTorch 프로젝트가 두 클라이언트에서" " FedBN으로 연합 학습을 실행하는 것을 확인합니다. 축하합니다!" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:94 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:310 -#: ../../source/tutorial-quickstart-jax.rst:283 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:113 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:349 +#: ../../source/tutorial-quickstart-jax.rst:319 msgid "Next Steps" msgstr "다음 단계" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:96 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:115 msgid "" "The full source code for this example can be found `here " "`_을 " "참조하세요." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:15 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:18 +#, fuzzy msgid "" -"Let's create a new file called :code:`cifar.py` with all the components " +"Let's create a new file called ``cifar.py`` with all the components " "required for a traditional (centralized) training on CIFAR-10. First, all" -" required packages (such as :code:`torch` and :code:`torchvision`) need " -"to be imported. You can see that we do not import any package for " -"federated learning. You can keep all these imports as they are even when " -"we add the federated learning components at a later point." +" required packages (such as ``torch`` and ``torchvision``) need to be " +"imported. You can see that we do not import any package for federated " +"learning. You can keep all these imports as they are even when we add the" +" federated learning components at a later point." msgstr "" "CIFAR-10에 대한 기존 (중앙 집중식) 교육에 필요한 모든 구성 요소가 포함된 :code:`cifar.py`라는 새 파일을 " "생성해 보겠습니다. 먼저, 필요한 모든 패키지(예: :code:`torch` 및 :code:`torchvision`)를 가져와야 " "합니다. 연합 학습을 위한 패키지를 가져오지 않는 것을 확인 할 수 있습니. 나중에 연합 학습 구성 요소를 추가할 때에도 이러한 " "모든 가져오기를 그대로 유지할 수 있습니다." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:32 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:36 +#, fuzzy msgid "" "As already mentioned we will use the CIFAR-10 dataset for this machine " "learning workload. The model architecture (a very simple Convolutional " -"Neural Network) is defined in :code:`class Net()`." +"Neural Network) is defined in ``class Net()``." msgstr "" "이미 언급했듯이 이 머신 러닝 워크로드에는 CIFAR-10 데이터 세트를 사용합니다. 모델 아키텍처(매우 간단한 컨볼루션 신경망)는" " :code:`class Net()`에 정의되어 있습니다." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:56 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:62 +#, fuzzy msgid "" -"The :code:`load_data()` function loads the CIFAR-10 training and test " -"sets. The :code:`transform` normalized the data after loading." +"The ``load_data()`` function loads the CIFAR-10 training and test sets. " +"The ``transform`` normalized the data after loading." msgstr "" ":code:`load_data()` 함수는 CIFAR-10 훈련 및 테스트 세트를 로드합니다. :code:`transform`은 " "로드 후 데이터를 정규화합니다." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:74 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:84 +#, fuzzy msgid "" -"We now need to define the training (function :code:`train()`) which loops" -" over the training set, measures the loss, backpropagates it, and then " +"We now need to define the training (function ``train()``) which loops " +"over the training set, measures the loss, backpropagates it, and then " "takes one optimizer step for each batch of training examples." msgstr "" "이제 학습 집합을 반복하고, 손실을 측정하고, 이를 역전파한 다음 각 학습 예제 배치에 대해 하나의 최적화 단계를 수행하는 " "학습(함수 :code:`train()`)을 정의해야 합니다." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:76 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:88 +#, fuzzy msgid "" -"The evaluation of the model is defined in the function :code:`test()`. " -"The function loops over all test samples and measures the loss of the " -"model based on the test dataset." +"The evaluation of the model is defined in the function ``test()``. The " +"function loops over all test samples and measures the loss of the model " +"based on the test dataset." msgstr "" "모델 평가는 :code:`test()` 함수에 정의되어 있습니다. 이 함수는 모든 테스트 샘플을 반복하고 테스트 데이터 세트에 따라" " 모델의 손실을 측정합니다." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:136 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:149 msgid "" "Having defined the data loading, model architecture, training, and " "evaluation we can put everything together and train our CNN on CIFAR-10." msgstr "데이터 로딩, 모델 아키텍처, 훈련 및 평가를 정의했으면 모든 것을 종합하여 CIFAR-10에서 CNN을 훈련할 수 있습니다." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:163 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:177 msgid "" "So far, this should all look fairly familiar if you've used PyTorch " "before. Let's take the next step and use what we've built to create a " @@ -4507,7 +4538,7 @@ msgstr "" "지금까지는 파이토치를 사용해 본 적이 있다면 상당히 익숙하게 보일 것입니다. 다음 단계로 넘어가서 구축한 것을 사용하여 하나의 " "서버와 두 개의 클라이언트로 구성된 간단한 연합 학습 시스템을 만들어 보겠습니다." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:169 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:184 msgid "" "The simple machine learning project discussed in the previous section " "trains the model on a single dataset (CIFAR-10), we call this centralized" @@ -4522,17 +4553,18 @@ msgstr "" "것입니다. 일반적으로 머신 러닝 워크로드를 연합 방식으로 실행하려면 대부분의 코드를 변경하고 모든 것을 처음부터 다시 설정해야 " "합니다. 이는 상당한 노력이 필요할 수 있습니다." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:173 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:191 msgid "" "However, with Flower you can evolve your pre-existing code into a " "federated learning setup without the need for a major rewrite." msgstr "하지만 Flower를 사용하면 대대적인 재작성 없이도 기존 코드를 연합 학습 설정으로 발전시킬 수 있습니다." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:175 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:194 +#, fuzzy msgid "" "The concept is easy to understand. We have to start a *server* and then " -"use the code in :code:`cifar.py` for the *clients* that are connected to " -"the *server*. The *server* sends model parameters to the clients. The " +"use the code in ``cifar.py`` for the *clients* that are connected to the " +"*server*. The *server* sends model parameters to the clients. The " "*clients* run the training and update the parameters. The updated " "parameters are sent back to the *server* which averages all received " "parameter updates. This describes one round of the federated learning " @@ -4543,46 +4575,48 @@ msgstr "" "업데이트된 파라미터는 *서버*로 다시 전송되며, *서버*는 수신된 모든 파라미터 업데이트의 평균을 구합니다. 이것은 연합 학습 " "프로세스의 한 라운드를 설명하며 여러 라운드에 걸쳐 이 과정을 반복합니다." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:181 -#: ../../source/tutorial-quickstart-jax.rst:129 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:201 +#: ../../source/tutorial-quickstart-jax.rst:147 +#, fuzzy msgid "" "Our example consists of one *server* and two *clients*. Let's set up " -":code:`server.py` first. The *server* needs to import the Flower package " -":code:`flwr`. Next, we use the :code:`start_server` function to start a " -"server and tell it to perform three rounds of federated learning." +"``server.py`` first. The *server* needs to import the Flower package " +"``flwr``. Next, we use the ``start_server`` function to start a server " +"and tell it to perform three rounds of federated learning." msgstr "" "이 예제는 하나의 *서버*와 두 개의 *클라이언트*로 구성됩니다. 먼저 :code:`server.py`를 설정해 보겠습니다. " "*server*는 Flower 패키지 :code:`flwr`를 가져와야 합니다. 다음으로, :code:`start_server` " "함수를 사용하여 서버를 시작하고 세 차례의 연합 학습을 수행하도록 지시합니다." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:191 -#: ../../source/tutorial-quickstart-jax.rst:139 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:215 +#: ../../source/tutorial-quickstart-jax.rst:161 msgid "We can already start the *server*:" msgstr "이미 *서버*를 시작할 수 있습니다:" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:197 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:221 +#, fuzzy msgid "" -"Finally, we will define our *client* logic in :code:`client.py` and build" -" upon the previously defined centralized training in :code:`cifar.py`. " -"Our *client* needs to import :code:`flwr`, but also :code:`torch` to " -"update the parameters on our PyTorch model:" +"Finally, we will define our *client* logic in ``client.py`` and build " +"upon the previously defined centralized training in ``cifar.py``. Our " +"*client* needs to import ``flwr``, but also ``torch`` to update the " +"parameters on our PyTorch model:" msgstr "" "마지막으로, :code:`client.py`에서 *client* 로직을 정의하고 :code:`cifar.py`에서 이전에 정의한 " "중앙 집중식 학습을 기반으로 구축합니다. *클라이언트*는 :code:`flwr`을 가져와야 하며, PyTorch 모델의 파라미터를 " "업데이트하기 위해 :code:`torch`도 가져와야 합니다:" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:213 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:238 +#, fuzzy msgid "" "Implementing a Flower *client* basically means implementing a subclass of" -" either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. " -"Our implementation will be based on :code:`flwr.client.NumPyClient` and " -"we'll call it :code:`CifarClient`. :code:`NumPyClient` is slightly easier" -" to implement than :code:`Client` if you use a framework with good NumPy " -"interoperability (like PyTorch or TensorFlow/Keras) because it avoids " -"some of the boilerplate that would otherwise be necessary. " -":code:`CifarClient` needs to implement four methods, two methods for " -"getting/setting model parameters, one method for training the model, and " -"one method for testing the model:" +" either ``flwr.client.Client`` or ``flwr.client.NumPyClient``. Our " +"implementation will be based on ``flwr.client.NumPyClient`` and we'll " +"call it ``CifarClient``. ``NumPyClient`` is slightly easier to implement " +"than ``Client`` if you use a framework with good NumPy interoperability " +"(like PyTorch or TensorFlow/Keras) because it avoids some of the " +"boilerplate that would otherwise be necessary. ``CifarClient`` needs to " +"implement four methods, two methods for getting/setting model parameters," +" one method for training the model, and one method for testing the model:" msgstr "" "Flower *클라이언트*를 구현한다는 것은 기본적으로 :code:`flwr.client.Client` 또는 " ":code:`flwr.client.NumPyClient`의 서브클래스를 구현하는 것을 의미합니다. 우리의 구현은 " @@ -4592,112 +4626,119 @@ msgstr "" "code:`CifarClient`는 모델 파라미터를 가져오거나 설정하는 메서드 2개, 모델 학습을 위한 메서드 1개, 모델 테스트를" " 위한 메서드 1개 등 네 가지 메서드를 구현해야 합니다:" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:218 -msgid ":code:`set_parameters`" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:249 +#, fuzzy +msgid "``set_parameters``" msgstr ":code:`set_parameters`" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:219 -#: ../../source/tutorial-quickstart-jax.rst:166 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:248 +#: ../../source/tutorial-quickstart-jax.rst:192 msgid "" "set the model parameters on the local model that are received from the " "server" msgstr "서버에서 수신한 로컬 모델의 모델 파라미터를 설정합니다" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:220 -#: ../../source/tutorial-quickstart-jax.rst:168 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:249 +#: ../../source/tutorial-quickstart-jax.rst:194 +#, fuzzy msgid "" -"loop over the list of model parameters received as NumPy " -":code:`ndarray`'s (think list of neural network layers)" +"loop over the list of model parameters received as NumPy ``ndarray``'s " +"(think list of neural network layers)" msgstr "(신경망 레이어 목록으로 생각하면 됩니다) NumPy :code:`ndarray`로 받은 모델 파라미터 목록에 대해 반복합니다" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:221 -#: ../../source/tutorial-quickstart-jax.rst:169 -#: ../../source/tutorial-quickstart-scikitlearn.rst:118 -msgid ":code:`get_parameters`" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:252 +#: ../../source/tutorial-quickstart-jax.rst:197 +#: ../../source/tutorial-quickstart-scikitlearn.rst:129 +#, fuzzy +msgid "``get_parameters``" msgstr ":code:`get_parameters`" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:222 -#: ../../source/tutorial-quickstart-jax.rst:170 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:252 +#: ../../source/tutorial-quickstart-jax.rst:197 +#, fuzzy msgid "" -"get the model parameters and return them as a list of NumPy " -":code:`ndarray`'s (which is what :code:`flwr.client.NumPyClient` expects)" +"get the model parameters and return them as a list of NumPy ``ndarray``'s" +" (which is what ``flwr.client.NumPyClient`` expects)" msgstr "" "모델 매개변수를 가져와서 NumPy :code:`ndarray`의 목록으로 반환합니다(이는 " ":code:`flwr.client.NumPyClient`가 기대하는 바와 같습니다)" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:223 -#: ../../source/tutorial-quickstart-jax.rst:171 -#: ../../source/tutorial-quickstart-scikitlearn.rst:123 -msgid ":code:`fit`" -msgstr ":code:`fit`" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:257 +#: ../../source/tutorial-quickstart-jax.rst:202 +#: ../../source/tutorial-quickstart-scikitlearn.rst:136 +#, fuzzy +msgid "``fit``" +msgstr "``DISTRO``" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:224 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:228 -#: ../../source/tutorial-quickstart-jax.rst:172 -#: ../../source/tutorial-quickstart-jax.rst:176 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:255 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:260 +#: ../../source/tutorial-quickstart-jax.rst:200 +#: ../../source/tutorial-quickstart-jax.rst:205 msgid "" "update the parameters of the local model with the parameters received " "from the server" msgstr "서버에서 받은 파라미터로 로컬 모델의 파라미터를 업데이트합니다" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:225 -#: ../../source/tutorial-quickstart-jax.rst:173 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:257 +#: ../../source/tutorial-quickstart-jax.rst:202 msgid "train the model on the local training set" msgstr "로컬 훈련 세트에서 모델을 훈련합니다" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:226 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:258 msgid "get the updated local model weights and return them to the server" msgstr "업데이트된 로컬 모델 가중치를 가져와 서버로 반환합니다" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:227 -#: ../../source/tutorial-quickstart-jax.rst:175 -#: ../../source/tutorial-quickstart-scikitlearn.rst:127 -msgid ":code:`evaluate`" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:263 +#: ../../source/tutorial-quickstart-jax.rst:208 +#: ../../source/tutorial-quickstart-scikitlearn.rst:139 +#, fuzzy +msgid "``evaluate``" msgstr ":code:`evaluate`" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:229 -#: ../../source/tutorial-quickstart-jax.rst:177 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:262 +#: ../../source/tutorial-quickstart-jax.rst:207 msgid "evaluate the updated model on the local test set" msgstr "로컬 테스트 세트에서 업데이트된 모델을 평가합니다" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:263 msgid "return the local loss and accuracy to the server" msgstr "로컬 손실 및 정확도를 서버에 반환합니다" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:232 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:265 +#, fuzzy msgid "" -"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make" -" use of the functions :code:`train()` and :code:`test()` previously " -"defined in :code:`cifar.py`. So what we really do here is we tell Flower " -"through our :code:`NumPyClient` subclass which of our already defined " -"functions to call for training and evaluation. We included type " -"annotations to give you a better understanding of the data types that get" -" passed around." +"The two ``NumPyClient`` methods ``fit`` and ``evaluate`` make use of the " +"functions ``train()`` and ``test()`` previously defined in ``cifar.py``. " +"So what we really do here is we tell Flower through our ``NumPyClient`` " +"subclass which of our already defined functions to call for training and " +"evaluation. We included type annotations to give you a better " +"understanding of the data types that get passed around." msgstr "" "두 개의 :code:`NumPyClient` 메서드인 :code:`fit`과 :code:`evaluate`는 이전에 " ":code:`cifar.py`에 정의된 함수인 :code:`train()`과 :code:`test()`를 활용합니다. 따라서 여기서" " 실제로 하는 일은 :code:`NumPyClient` 서브클래스를 통해 이미 정의된 함수 중 훈련과 평가를 위해 호출할 함수를 " "Flower에 알려주는 것입니다. 전달되는 데이터 유형을 더 잘 이해할 수 있도록 type annotations을 포함했습니다." -#: ../../source/example-pytorch-from-centralized-to-federated.rst:280 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:315 +#, fuzzy msgid "" "All that's left to do it to define a function that loads both model and " -"data, creates a :code:`CifarClient`, and starts this client. You load " -"your data and model by using :code:`cifar.py`. Start :code:`CifarClient` " -"with the function :code:`fl.client.start_client()` by pointing it at the " -"same IP address we used in :code:`server.py`:" +"data, creates a ``CifarClient``, and starts this client. You load your " +"data and model by using ``cifar.py``. Start ``CifarClient`` with the " +"function ``fl.client.start_client()`` by pointing it at the same IP " +"address we used in ``server.py``:" msgstr "" "이제 모델과 데이터를 모두 로드하는 함수를 정의하고, :code:`CifarClient`를 생성하고, 이 클라이언트를 시작하는 " "작업만 남았습니다. 코드:`cifar.py`를 사용하여 데이터와 모델을 로드합니다. :code:`server.py`에서 사용한 것과" " 동일한 IP 주소를 지정하여 :code:`fl.client.start_client()` 함수로 " ":code:`CifarClient`를 시작합니다:" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:301 -#: ../../source/tutorial-quickstart-jax.rst:274 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:338 +#: ../../source/tutorial-quickstart-jax.rst:309 msgid "And that's it. You can now open two additional terminal windows and run" msgstr "여기까지입니다. 이제 두 개의 터미널 창을 추가로 열고 다음을 실행할 수 있습니다" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:307 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:344 msgid "" "in each window (make sure that the server is running before you do so) " "and see your (previously centralized) PyTorch project run federated " @@ -4706,7 +4747,7 @@ msgstr "" "를 입력하고(그 전에 서버가 실행 중인지 확인하세요) (이전에는 중앙 집중식) PyTorch 프로젝트가 두 클라이언트에서 연합 " "학습을 실행하는 것을 확인합니다. 축하합니다!" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:312 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:351 msgid "" "The full source code for this example: `PyTorch: From Centralized To " "Federated (Code) `_를 확인하는 것이 좋습니다." -#: ../../source/how-to-authenticate-supernodes.rst:15 +#: ../../source/how-to-authenticate-supernodes.rst:20 msgid "" "This guide covers a preview feature that might change in future versions " "of Flower." msgstr "이 가이드에서는 향후 버전의 Flower에서 변경될 수 있는 미리보기 기능에 대해 설명합니다." -#: ../../source/how-to-authenticate-supernodes.rst:18 +#: ../../source/how-to-authenticate-supernodes.rst:24 msgid "" "For increased security, node authentication can only be used when " "encrypted connections (SSL/TLS) are enabled." msgstr "보안을 강화하기 위해 노드 인증은 암호화된 연결(SSL/TLS)을 사용하도록 설정한 경우에만 사용할 수 있습니다." -#: ../../source/how-to-authenticate-supernodes.rst:21 -msgid "Enable node authentication in :code:`SuperLink`" +#: ../../source/how-to-authenticate-supernodes.rst:28 +#, fuzzy +msgid "Enable node authentication in ``SuperLink``" msgstr ":code:`SuperLink`에서 노드 인증 활성화" -#: ../../source/how-to-authenticate-supernodes.rst:23 +#: ../../source/how-to-authenticate-supernodes.rst:30 +#, fuzzy msgid "" "To enable node authentication, first you need to configure SSL/TLS " "connections to secure the SuperLink<>SuperNode communication. You can " "find the complete guide `here `_. After configuring secure connections, you" -" can enable client authentication in a long-running Flower " -":code:`SuperLink`. Use the following terminal command to start a Flower " -":code:`SuperNode` that has both secure connections and node " -"authentication enabled:" +" can enable client authentication in a long-running Flower ``SuperLink``." +" Use the following terminal command to start a Flower ``SuperNode`` that " +"has both secure connections and node authentication enabled:" msgstr "" "노드 인증을 활성화하려면 먼저 SuperLink<>SuperNode 통신을 보호하기 위해 SSL/TLS 연결을 구성해야 합니다. " "전체 가이드는 `여기 `. Here's a nonsensical example that customizes :code:`FedAvg`" -" by adding a custom ``\"hello\": \"world\"`` configuration key/value pair" -" to the config dict of a *single client* (only the first client in the " -"list, the other clients in this round to not receive this \"special\" " -"config value):" +"strategies>`. Here's a nonsensical example that customizes ``FedAvg`` by " +"adding a custom ``\"hello\": \"world\"`` configuration key/value pair to " +"the config dict of a *single client* (only the first client in the list, " +"the other clients in this round to not receive this \"special\" config " +"value):" msgstr "" "이는 기존 전략을 사용자 지정하거나 :doc:`implementing a custom strategy from scratch " "`를 통해 수행할 수 있습니다. 다음은 사용자 지정 ``\"hello\"'를 " @@ -6350,27 +6405,29 @@ msgstr "" " 정보를 표시합니다:" #: ../../source/how-to-configure-logging.rst:13 +#, fuzzy msgid "" "containing relevant information including: log message level (e.g. " -":code:`INFO`, :code:`DEBUG`), a timestamp, the line where the logging " -"took place from, as well as the log message itself. In this way, the " -"logger would typically display information on your terminal as follows:" +"``INFO``, ``DEBUG``), a timestamp, the line where the logging took place " +"from, as well as the log message itself. In this way, the logger would " +"typically display information on your terminal as follows:" msgstr "" "로그 메시지 수준(예: :code:`INFO`, :code:`DEBUG`), 타임스탬프, 로깅이 발생한 줄, 로그 메시지 자체 등 " "관련 정보를 포함합니다. 이러한 방식으로 로거는 일반적으로 다음과 같은 정보를 터미널에 표시합니다:" -#: ../../source/how-to-configure-logging.rst:34 +#: ../../source/how-to-configure-logging.rst:35 msgid "Saving log to file" msgstr "파일에 로그 저장" -#: ../../source/how-to-configure-logging.rst:36 +#: ../../source/how-to-configure-logging.rst:37 +#, fuzzy msgid "" "By default, the Flower log is outputted to the terminal where you launch " "your Federated Learning workload from. This applies for both gRPC-based " -"federation (i.e. when you do :code:`fl.server.start_server`) and when " -"using the :code:`VirtualClientEngine` (i.e. when you do " -":code:`fl.simulation.start_simulation`). In some situations you might " -"want to save this log to disk. You can do so by calling the " +"federation (i.e. when you do ``fl.server.start_server``) and when using " +"the ``VirtualClientEngine`` (i.e. when you do " +"``fl.simulation.start_simulation``). In some situations you might want to" +" save this log to disk. You can do so by calling the " "`fl.common.logger.configure() " "`_" " function. For example:" @@ -6383,22 +6440,23 @@ msgstr "" "`_" " 함수를 호출하여 저장할 수 있습니다. 예를 들어:" -#: ../../source/how-to-configure-logging.rst:53 +#: ../../source/how-to-configure-logging.rst:59 +#, fuzzy msgid "" "With the above, Flower will record the log you see on your terminal to " -":code:`log.txt`. This file will be created in the same directory as were " -"you are running the code from. If we inspect we see the log above is also" -" recorded but prefixing with :code:`identifier` each line:" +"``log.txt``. This file will be created in the same directory as were you " +"are running the code from. If we inspect we see the log above is also " +"recorded but prefixing with ``identifier`` each line:" msgstr "" "위와 같이 하면 Flower는 터미널에 표시되는 로그를 :code:`log.txt`에 기록합니다. 이 파일은 코드를 실행한 " "디렉터리와 동일한 디렉터리에 생성됩니다. 검사해보면 위의 로그도 기록되지만 각 줄 앞에 :code:`identifier` 접두사가 " "붙는 것을 확인할 수 있습니다:" -#: ../../source/how-to-configure-logging.rst:74 +#: ../../source/how-to-configure-logging.rst:81 msgid "Log your own messages" msgstr "나만의 메시지 기록" -#: ../../source/how-to-configure-logging.rst:76 +#: ../../source/how-to-configure-logging.rst:83 msgid "" "You might expand the information shown by default with the Flower logger " "by adding more messages relevant to your application. You can achieve " @@ -6407,27 +6465,27 @@ msgstr "" "애플리케이션과 관련된 메시지를 더 추가하여 Flower 로거에 기본적으로 표시되는 정보를 확장할 수 있습니다. 다음과 같이 쉽게 " "추가할 수 있습니다." -#: ../../source/how-to-configure-logging.rst:102 +#: ../../source/how-to-configure-logging.rst:114 msgid "" "In this way your logger will show, in addition to the default messages, " "the ones introduced by the clients as specified above." msgstr "이렇게 하면 로거에 기본 메시지 외에 위에서 지정한 대로 클라이언트가 소개한 메시지가 표시됩니다." -#: ../../source/how-to-configure-logging.rst:128 +#: ../../source/how-to-configure-logging.rst:140 msgid "Log to a remote service" msgstr "원격 서비스에 로그인" -#: ../../source/how-to-configure-logging.rst:130 +#: ../../source/how-to-configure-logging.rst:142 +#, fuzzy msgid "" -"The :code:`fl.common.logger.configure` function, also allows specifying a" -" host to which logs can be pushed (via :code:`POST`) through a native " -"Python :code:`logging.handler.HTTPHandler`. This is a particularly useful" -" feature in :code:`gRPC`-based Federated Learning workloads where " -"otherwise gathering logs from all entities (i.e. the server and the " -"clients) might be cumbersome. Note that in Flower simulation, the server " -"automatically displays all logs. You can still specify a " -":code:`HTTPHandler` should you wish to backup or analyze the logs " -"somewhere else." +"The ``fl.common.logger.configure`` function, also allows specifying a " +"host to which logs can be pushed (via ``POST``) through a native Python " +"``logging.handler.HTTPHandler``. This is a particularly useful feature in" +" ``gRPC``-based Federated Learning workloads where otherwise gathering " +"logs from all entities (i.e. the server and the clients) might be " +"cumbersome. Note that in Flower simulation, the server automatically " +"displays all logs. You can still specify a ``HTTPHandler`` should you " +"wish to backup or analyze the logs somewhere else." msgstr "" "또한 :code:`fl.common.logger.configure` 함수를 사용하면 네이티브 Python " ":code:`logging.handler.HTTPHandler`를 통해 로그를 푸시할 수 있는 호스트를 지정할 수 " @@ -6440,15 +6498,16 @@ msgid "Enable SSL connections" msgstr "SSL 연결 사용" #: ../../source/how-to-enable-ssl-connections.rst:4 +#, fuzzy msgid "" "This guide describes how to a SSL-enabled secure Flower server " -"(:code:`SuperLink`) can be started and how a Flower client " -"(:code:`SuperNode`) can establish a secure connections to it." +"(``SuperLink``) can be started and how a Flower client (``SuperNode``) " +"can establish a secure connections to it." msgstr "" "이 가이드에서는 SSL을 지원하는 보안 Flower 서버(:코드:`SuperLink`)를 시작하는 방법과 Flower " "클라이언트(:코드:`SuperNode`)가 이 서버에 보안 연결을 설정하는 방법을 설명합니다." -#: ../../source/how-to-enable-ssl-connections.rst:7 +#: ../../source/how-to-enable-ssl-connections.rst:8 msgid "" "A complete code example demonstrating a secure connection can be found " "`here `_'에서 확인할 수 있습니다." -#: ../../source/how-to-enable-ssl-connections.rst:10 +#: ../../source/how-to-enable-ssl-connections.rst:11 +#, fuzzy msgid "" -"The code example comes with a :code:`README.md` file which explains how " -"to start it. Although it is already SSL-enabled, it might be less " +"The code example comes with a ``README.md`` file which explains how to " +"start it. Although it is already SSL-enabled, it might be less " "descriptive on how it does so. Stick to this guide for a deeper " "introduction to the topic." msgstr "" @@ -6473,26 +6533,29 @@ msgid "Certificates" msgstr "인증서" #: ../../source/how-to-enable-ssl-connections.rst:18 +#, fuzzy msgid "" "Using SSL-enabled connections requires certificates to be passed to the " "server and client. For the purpose of this guide we are going to generate" " self-signed certificates. As this can become quite complex we are going " -"to ask you to run the script in :code:`examples/advanced-" -"tensorflow/certificates/generate.sh` with the following command sequence:" +"to ask you to run the script in ``examples/advanced-" +"tensorflow/certificates/generate.sh`` with the following command " +"sequence:" msgstr "" "SSL 사용 연결을 사용하려면 서버와 클라이언트에 인증서를 전달해야 합니다. 이 가이드에서는 자체 서명된 인증서를 생성하겠습니다. " "이 과정은 상당히 복잡할 수 있으므로 다음 명령 시퀀스를 사용하여 :code:`examples/advanced-" "tensorflow/certificates/generate.sh`에서 스크립트를 실행하도록 요청하겠습니다:" #: ../../source/how-to-enable-ssl-connections.rst:29 +#, fuzzy msgid "" -"This will generate the certificates in :code:`examples/advanced-" -"tensorflow/.cache/certificates`." +"This will generate the certificates in ``examples/advanced-" +"tensorflow/.cache/certificates``." msgstr "" "이렇게 하면 :code:`examples/advanced-tensorflow/.cache/certificates`에 인증서가 " "생성됩니다." -#: ../../source/how-to-enable-ssl-connections.rst:31 +#: ../../source/how-to-enable-ssl-connections.rst:32 msgid "" "The approach for generating SSL certificates in the context of this " "example can serve as an inspiration and starting point, but it should not" @@ -6506,40 +6569,41 @@ msgstr "" "됩니다. 프로덕션 환경용 인증서를 올바르게 생성하는 문제에 대해서는 다른 출처를 참조하세요. 중요하지 않은 프로토타이핑 또는 연구 " "프로젝트의 경우, 이 가이드에 언급된 스크립트를 사용하여 생성한 자체 서명 인증서를 사용하는 것으로 충분할 수 있습니다." -#: ../../source/how-to-enable-ssl-connections.rst:39 +#: ../../source/how-to-enable-ssl-connections.rst:40 msgid "Server (SuperLink)" msgstr "서버(SuperLink)" -#: ../../source/how-to-enable-ssl-connections.rst:41 +#: ../../source/how-to-enable-ssl-connections.rst:42 msgid "" "Use the following terminal command to start a sever (SuperLink) that uses" " the previously generated certificates:" msgstr "다음 터미널 명령을 사용하여 이전에 생성한 인증서를 사용하는 서버(SuperLink)를 시작합니다:" -#: ../../source/how-to-enable-ssl-connections.rst:50 +#: ../../source/how-to-enable-ssl-connections.rst:52 msgid "" "When providing certificates, the server expects a tuple of three " "certificates paths: CA certificate, server certificate and server private" " key." msgstr "인증서를 제공할 때 서버는 세 가지 인증서 경로의 튜플을 기대합니다: CA 인증서, 서버 인증서 및 서버 개인 키입니다." -#: ../../source/how-to-enable-ssl-connections.rst:54 +#: ../../source/how-to-enable-ssl-connections.rst:56 msgid "Client (SuperNode)" msgstr "클라이언트(SuperNode)" -#: ../../source/how-to-enable-ssl-connections.rst:56 +#: ../../source/how-to-enable-ssl-connections.rst:58 msgid "" "Use the following terminal command to start a client (SuperNode) that " "uses the previously generated certificates:" msgstr "다음 터미널 명령을 사용하여 이전에 생성한 인증서를 사용하는 클라이언트(SuperNode)를 시작합니다:" -#: ../../source/how-to-enable-ssl-connections.rst:64 +#: ../../source/how-to-enable-ssl-connections.rst:67 +#, fuzzy msgid "" -"When setting :code:`root_certificates`, the client expects a file path to" -" PEM-encoded root certificates." +"When setting ``root_certificates``, the client expects a file path to " +"PEM-encoded root certificates." msgstr "코드:`root_certificates`를 설정하면 클라이언트는 PEM 인코딩된 루트 인증서의 파일 경로를 예상합니다." -#: ../../source/how-to-enable-ssl-connections.rst:70 +#: ../../source/how-to-enable-ssl-connections.rst:73 msgid "" "You should now have learned how to generate self-signed certificates " "using the given script, start an SSL-enabled server and have a client " @@ -6548,21 +6612,21 @@ msgstr "" "이제 주어진 스크립트를 사용하여 자체 서명 인증서를 생성하고, SSL 사용 서버를 시작하고, 클라이언트가 보안 연결을 설정하는 " "방법을 배웠을 것입니다." -#: ../../source/how-to-enable-ssl-connections.rst:75 +#: ../../source/how-to-enable-ssl-connections.rst:78 msgid "Additional resources" msgstr "추가 리소스" -#: ../../source/how-to-enable-ssl-connections.rst:77 +#: ../../source/how-to-enable-ssl-connections.rst:80 msgid "" "These additional sources might be relevant if you would like to dive " "deeper into the topic of certificates:" msgstr "인증서에 대해 더 자세히 알아보고 싶다면 이러한 추가 자료를 참고하세요:" -#: ../../source/how-to-enable-ssl-connections.rst:79 +#: ../../source/how-to-enable-ssl-connections.rst:83 msgid "`Let's Encrypt `_" msgstr "'암호화하세요 `_'" -#: ../../source/how-to-enable-ssl-connections.rst:80 +#: ../../source/how-to-enable-ssl-connections.rst:84 msgid "`certbot `_" msgstr "`인증봇 `_" @@ -6584,13 +6648,15 @@ msgstr "" "결정합니다. Flower는 아래에 설명된 것과 동일한 API를 기반으로 하는 몇 가지 기본 제공 전략을 제공합니다." #: ../../source/how-to-implement-strategies.rst:11 -msgid "The :code:`Strategy` abstraction" +#, fuzzy +msgid "The ``Strategy`` abstraction" msgstr ":code:`Strategy` 추상화" #: ../../source/how-to-implement-strategies.rst:13 +#, fuzzy msgid "" "All strategy implementation are derived from the abstract base class " -":code:`flwr.server.strategy.Strategy`, both built-in implementations and " +"``flwr.server.strategy.Strategy``, both built-in implementations and " "third party implementations. This means that custom strategy " "implementations have the exact same capabilities at their disposal as " "built-in ones." @@ -6605,55 +6671,60 @@ msgid "" "implemented:" msgstr "전략 추상화에서는 구현해야 하는 몇 가지 추상적인 메서드를 정의합니다:" -#: ../../source/how-to-implement-strategies.rst:75 +#: ../../source/how-to-implement-strategies.rst:67 +#, fuzzy msgid "" -"Creating a new strategy means implementing a new :code:`class` (derived " -"from the abstract base class :code:`Strategy`) that implements for the " -"previously shown abstract methods:" +"Creating a new strategy means implementing a new ``class`` (derived from " +"the abstract base class ``Strategy``) that implements for the previously " +"shown abstract methods:" msgstr "" "새 전략을 생성한다는 것은 이전에 표시된 추상 메서드에 대해 구현하는 새로운 :code:`class`(추상 기본 클래스 " ":code:`Strategy`에서 파생됨)를 구현하는 것을 의미합니다:" -#: ../../source/how-to-implement-strategies.rst:100 +#: ../../source/how-to-implement-strategies.rst:97 msgid "The Flower server calls these methods in the following order:" msgstr "Flower 서버는 다음 순서로 이러한 메서드를 호출합니다:" -#: ../../source/how-to-implement-strategies.rst:177 +#: ../../source/how-to-implement-strategies.rst:174 msgid "The following sections describe each of those methods in more detail." msgstr "다음 섹션에서는 이러한 각 방법에 대해 자세히 설명합니다." -#: ../../source/how-to-implement-strategies.rst:180 -msgid "The :code:`initialize_parameters` method" +#: ../../source/how-to-implement-strategies.rst:177 +#, fuzzy +msgid "The ``initialize_parameters`` method" msgstr ":code:`initialize_parameters` 메서드" -#: ../../source/how-to-implement-strategies.rst:182 +#: ../../source/how-to-implement-strategies.rst:179 +#, fuzzy msgid "" -":code:`initialize_parameters` is called only once, at the very beginning " -"of an execution. It is responsible for providing the initial global model" -" parameters in a serialized form (i.e., as a :code:`Parameters` object)." +"``initialize_parameters`` is called only once, at the very beginning of " +"an execution. It is responsible for providing the initial global model " +"parameters in a serialized form (i.e., as a ``Parameters`` object)." msgstr "" "code:`initialize_parameters`는 실행을 처음 시작할 때 한 번만 호출됩니다. 이 함수는 초기 전역 모델 " "파라미터를 직렬화된 형식(즉, :code:`Parameters` 객체)으로 제공하는 역할을 합니다." -#: ../../source/how-to-implement-strategies.rst:184 +#: ../../source/how-to-implement-strategies.rst:183 +#, fuzzy msgid "" "Built-in strategies return user-provided initial parameters. The " "following example shows how initial parameters can be passed to " -":code:`FedAvg`:" +"``FedAvg``:" msgstr "" "기본 제공 전략은 사용자가 제공한 초기 매개 변수를 반환합니다. 다음 예는 초기 매개 변수를 :code:`FedAvg`에 전달하는 " "방법을 보여줍니다:" #: ../../source/how-to-implement-strategies.rst:209 +#, fuzzy msgid "" -"The Flower server will call :code:`initialize_parameters`, which either " -"returns the parameters that were passed to :code:`initial_parameters`, or" -" :code:`None`. If no parameters are returned from " -":code:`initialize_parameters` (i.e., :code:`None`), the server will " -"randomly select one client and ask it to provide its parameters. This is " -"a convenience feature and not recommended in practice, but it can be " -"useful for prototyping. In practice, it is recommended to always use " -"server-side parameter initialization." +"The Flower server will call ``initialize_parameters``, which either " +"returns the parameters that were passed to ``initial_parameters``, or " +"``None``. If no parameters are returned from ``initialize_parameters`` " +"(i.e., ``None``), the server will randomly select one client and ask it " +"to provide its parameters. This is a convenience feature and not " +"recommended in practice, but it can be useful for prototyping. In " +"practice, it is recommended to always use server-side parameter " +"initialization." msgstr "" "Flower 서버는 :code:`initialize_parameters`를 호출하여 " ":code:`initial_parameters`에 전달된 파라미터를 반환하거나 :code:`None`을 반환합니다. " @@ -6661,7 +6732,7 @@ msgstr "" "클라이언트 하나를 선택하여 해당 클라이언트에 매개변수를 제공하도록 요청합니다. 이는 편의 기능이며 실제로는 권장하지 않지만 " "프로토타이핑에는 유용할 수 있습니다. 실제로는 항상 서버 측 매개변수 초기화를 사용하는 것이 좋습니다." -#: ../../source/how-to-implement-strategies.rst:213 +#: ../../source/how-to-implement-strategies.rst:218 msgid "" "Server-side parameter initialization is a powerful mechanism. It can be " "used, for example, to resume training from a previously saved checkpoint." @@ -6672,220 +6743,240 @@ msgstr "" "서버 측 파라미터 초기화는 강력한 메커니즘입니다. 예를 들어 이전에 저장한 체크포인트에서 학습을 재개하는 데 사용할 수 있습니다. " "또한 연합 학습을 사용하여 사전 학습된 모델을 미세 조정하는 등 하이브리드 접근 방식을 구현하는 데 필요한 기본 기능입니다." -#: ../../source/how-to-implement-strategies.rst:216 -msgid "The :code:`configure_fit` method" +#: ../../source/how-to-implement-strategies.rst:224 +#, fuzzy +msgid "The ``configure_fit`` method" msgstr ":code:`configure_fit` 메서드" -#: ../../source/how-to-implement-strategies.rst:218 +#: ../../source/how-to-implement-strategies.rst:226 +#, fuzzy msgid "" -":code:`configure_fit` is responsible for configuring the upcoming round " -"of training. What does *configure* mean in this context? Configuring a " -"round means selecting clients and deciding what instructions to send to " -"these clients. The signature of :code:`configure_fit` makes this clear:" +"``configure_fit`` is responsible for configuring the upcoming round of " +"training. What does *configure* mean in this context? Configuring a round" +" means selecting clients and deciding what instructions to send to these " +"clients. The signature of ``configure_fit`` makes this clear:" msgstr "" ":code:`configure_fit`은 다가오는 학 라운드를 구성하는 역할을 합니다. 이 문맥에서 *구성*은 무엇을 의미하나요? " "라운드를 구성한다는 것은 클라이언트를 선택하고 이 클라이언트에게 어떤 지침을 보낼지 결정하는 것을 의미합니다. " "code:`configure_fit`의 시그니처를 보면 이를 명확히 알 수 있습니다:" -#: ../../source/how-to-implement-strategies.rst:231 +#: ../../source/how-to-implement-strategies.rst:239 +#, fuzzy msgid "" "The return value is a list of tuples, each representing the instructions " "that will be sent to a particular client. Strategy implementations " -"usually perform the following steps in :code:`configure_fit`:" +"usually perform the following steps in ``configure_fit``:" msgstr "" "반환 값은 튜플 목록으로, 각 튜플은 특정 클라이언트로 전송될 명령어를 나타냅니다. 전략 구현은 일반적으로 " ":code:`configure_fit`에서 다음 단계를 수행합니다:" -#: ../../source/how-to-implement-strategies.rst:233 -#: ../../source/how-to-implement-strategies.rst:280 +#: ../../source/how-to-implement-strategies.rst:243 +#: ../../source/how-to-implement-strategies.rst:307 +#, fuzzy msgid "" -"Use the :code:`client_manager` to randomly sample all (or a subset of) " -"available clients (each represented as a :code:`ClientProxy` object)" +"Use the ``client_manager`` to randomly sample all (or a subset of) " +"available clients (each represented as a ``ClientProxy`` object)" msgstr "" ":code:`client_manager`를 사용하여 사용 가능한 모든 클라이언트(또는 그 하위 집합)를 무작위로 샘플링합니다(각각 " ":code:`ClientProxy` 개체로 표시됨)" -#: ../../source/how-to-implement-strategies.rst:234 +#: ../../source/how-to-implement-strategies.rst:245 +#, fuzzy msgid "" -"Pair each :code:`ClientProxy` with the same :code:`FitIns` holding the " -"current global model :code:`parameters` and :code:`config` dict" +"Pair each ``ClientProxy`` with the same ``FitIns`` holding the current " +"global model ``parameters`` and ``config`` dict" msgstr "" "각 :code:`ClientProxy`를 현재 글로벌 모델 :code:`parameters` 및 :code:`config` " "dict를 보유한 동일한 :code:`FitIns`와 쌍을 이룹니다" -#: ../../source/how-to-implement-strategies.rst:236 +#: ../../source/how-to-implement-strategies.rst:248 +#, fuzzy msgid "" -"More sophisticated implementations can use :code:`configure_fit` to " -"implement custom client selection logic. A client will only participate " -"in a round if the corresponding :code:`ClientProxy` is included in the " -"list returned from :code:`configure_fit`." +"More sophisticated implementations can use ``configure_fit`` to implement" +" custom client selection logic. A client will only participate in a round" +" if the corresponding ``ClientProxy`` is included in the list returned " +"from ``configure_fit``." msgstr "" "보다 정교한 구현은 :code:`configure_fit`을 사용하여 사용자 지정 클라이언트 선택 로직을 구현할 수 있습니다. " "클라이언트는 :code:`configure_fit`에서 반환된 목록에 해당 :code:`ClientProxy`가 포함된 경우에만 " "라운드에 참여합니다." -#: ../../source/how-to-implement-strategies.rst:240 +#: ../../source/how-to-implement-strategies.rst:254 +#, fuzzy msgid "" "The structure of this return value provides a lot of flexibility to the " "user. Since instructions are defined on a per-client basis, different " "instructions can be sent to each client. This enables custom strategies " "to train, for example, different models on different clients, or use " -"different hyperparameters on different clients (via the :code:`config` " -"dict)." +"different hyperparameters on different clients (via the ``config`` dict)." msgstr "" "이 반환 값의 구조는 사용자에게 많은 유연성을 제공합니다. instructions은 클라이언트별로 정의되므로 각 클라이언트에 서로 " "다른 명령어를 전송할 수 있습니다. 이를 통해 예를 들어 클라이언트마다 다른 모델을 학습시키거나 클라이언트마다 다른 하이퍼파라미터를" " 사용하는 사용자 지정 전략을 사용할 수 있습니다(:code:`config` dict를 통해)." -#: ../../source/how-to-implement-strategies.rst:243 -msgid "The :code:`aggregate_fit` method" +#: ../../source/how-to-implement-strategies.rst:261 +#, fuzzy +msgid "The ``aggregate_fit`` method" msgstr ":code:`aggregate_fit` 메서드" -#: ../../source/how-to-implement-strategies.rst:245 +#: ../../source/how-to-implement-strategies.rst:263 +#, fuzzy msgid "" -":code:`aggregate_fit` is responsible for aggregating the results returned" -" by the clients that were selected and asked to train in " -":code:`configure_fit`." +"``aggregate_fit`` is responsible for aggregating the results returned by " +"the clients that were selected and asked to train in ``configure_fit``." msgstr "" "code:`aggregate_fit`은 :code:`configure_fit`에서 훈련하도록 선택되고 요청된 클라이언트가 반환한 " "결과를 집계하는 역할을 담당합니다." -#: ../../source/how-to-implement-strategies.rst:258 +#: ../../source/how-to-implement-strategies.rst:277 +#, fuzzy msgid "" "Of course, failures can happen, so there is no guarantee that the server " "will get results from all the clients it sent instructions to (via " -":code:`configure_fit`). :code:`aggregate_fit` therefore receives a list " -"of :code:`results`, but also a list of :code:`failures`." +"``configure_fit``). ``aggregate_fit`` therefore receives a list of " +"``results``, but also a list of ``failures``." msgstr "" "물론 실패가 발생할 수 있으므로 서버가 명령을 보낸 모든 클라이언트로부터 결과를 얻을 수 있다는 보장은 " "없습니다(:code:`configure_fit`을 통해). 따라서 :code:`aggregate_fit`은 " ":code:`results` 목록뿐만 아니라 :code:`failures` 목록도 받습니다." -#: ../../source/how-to-implement-strategies.rst:260 +#: ../../source/how-to-implement-strategies.rst:282 +#, fuzzy msgid "" -":code:`aggregate_fit` returns an optional :code:`Parameters` object and a" -" dictionary of aggregated metrics. The :code:`Parameters` return value is" -" optional because :code:`aggregate_fit` might decide that the results " -"provided are not sufficient for aggregation (e.g., too many failures)." +"``aggregate_fit`` returns an optional ``Parameters`` object and a " +"dictionary of aggregated metrics. The ``Parameters`` return value is " +"optional because ``aggregate_fit`` might decide that the results provided" +" are not sufficient for aggregation (e.g., too many failures)." msgstr "" "code:`aggregate_fit`은 선택적 :code:`Parameters` 개체와 집계된 메트릭의 dictionary를 " "반환합니다. :code:`Parameters` 반환 값은 :code:`aggregate_fit`이 제공된 결과가 집계에 충분하지 " "않다고 판단할 수 있으므로(예: 실패 수가 너무 많음) 선택 사항입니다." -#: ../../source/how-to-implement-strategies.rst:263 -msgid "The :code:`configure_evaluate` method" +#: ../../source/how-to-implement-strategies.rst:288 +#, fuzzy +msgid "The ``configure_evaluate`` method" msgstr ":code:`configure_evaluate` 메서드" -#: ../../source/how-to-implement-strategies.rst:265 +#: ../../source/how-to-implement-strategies.rst:290 +#, fuzzy msgid "" -":code:`configure_evaluate` is responsible for configuring the upcoming " -"round of evaluation. What does *configure* mean in this context? " -"Configuring a round means selecting clients and deciding what " -"instructions to send to these clients. The signature of " -":code:`configure_evaluate` makes this clear:" +"``configure_evaluate`` is responsible for configuring the upcoming round " +"of evaluation. What does *configure* mean in this context? Configuring a " +"round means selecting clients and deciding what instructions to send to " +"these clients. The signature of ``configure_evaluate`` makes this clear:" msgstr "" ":code:`configure_evaluate`는 다가오는 평가 라운드를 구성하는 역할을 합니다. 이 문맥에서 *구성*은 무엇을 " "의미하나요? 라운드를 구성한다는 것은 클라이언트를 선택하고 이러한 클라이언트에 전송할 지침을 결정하는 것을 의미합니다. " ":code:`configure_evaluate`의 시그니처를 보면 이를 명확히 알 수 있습니다:" -#: ../../source/how-to-implement-strategies.rst:278 +#: ../../source/how-to-implement-strategies.rst:303 +#, fuzzy msgid "" "The return value is a list of tuples, each representing the instructions " "that will be sent to a particular client. Strategy implementations " -"usually perform the following steps in :code:`configure_evaluate`:" +"usually perform the following steps in ``configure_evaluate``:" msgstr "" "반환 값은 튜플 목록으로, 각 튜플은 특정 클라이언트로 전송될 명령어를 나타냅니다. 전략 구현은 일반적으로 " ":code:`configure_evaluate`에서 다음 단계를 수행합니다:" -#: ../../source/how-to-implement-strategies.rst:281 +#: ../../source/how-to-implement-strategies.rst:309 +#, fuzzy msgid "" -"Pair each :code:`ClientProxy` with the same :code:`EvaluateIns` holding " -"the current global model :code:`parameters` and :code:`config` dict" +"Pair each ``ClientProxy`` with the same ``EvaluateIns`` holding the " +"current global model ``parameters`` and ``config`` dict" msgstr "" "각 :code:`ClientProxy`를 현재 글로벌 모델 :code:`parameters` 및 :code:`config` " "dict를 보유한 동일한 :code:`EvaluateIns`와 쌍을 이룹니다" -#: ../../source/how-to-implement-strategies.rst:283 +#: ../../source/how-to-implement-strategies.rst:312 +#, fuzzy msgid "" -"More sophisticated implementations can use :code:`configure_evaluate` to " +"More sophisticated implementations can use ``configure_evaluate`` to " "implement custom client selection logic. A client will only participate " -"in a round if the corresponding :code:`ClientProxy` is included in the " -"list returned from :code:`configure_evaluate`." +"in a round if the corresponding ``ClientProxy`` is included in the list " +"returned from ``configure_evaluate``." msgstr "" "보다 정교한 구현은 :code:`configure_evaluate`를 사용하여 사용자 지정 클라이언트 선택 로직을 구현할 수 " "있습니다. 클라이언트는 :code:`configure_evaluate`에서 반환된 목록에 해당 :code:`ClientProxy`가" " 포함된 경우에만 라운드에 참여합니다." -#: ../../source/how-to-implement-strategies.rst:287 +#: ../../source/how-to-implement-strategies.rst:318 +#, fuzzy msgid "" "The structure of this return value provides a lot of flexibility to the " "user. Since instructions are defined on a per-client basis, different " "instructions can be sent to each client. This enables custom strategies " "to evaluate, for example, different models on different clients, or use " -"different hyperparameters on different clients (via the :code:`config` " -"dict)." +"different hyperparameters on different clients (via the ``config`` dict)." msgstr "" "이 반환 값의 구조는 사용자에게 많은 유연성을 제공합니다. 명령어는 클라이언트별로 정의되므로 각 클라이언트에 서로 다른 명령어를 " "전송할 수 있습니다. 이를 통해 사용자 지정 전략을 통해 예를 들어 클라이언트마다 다른 모델을 평가하거나 클라이언트마다 다른 " "하이퍼파라미터를 사용할 수 있습니다(:code:`config` dict를 통해)." -#: ../../source/how-to-implement-strategies.rst:291 -msgid "The :code:`aggregate_evaluate` method" +#: ../../source/how-to-implement-strategies.rst:325 +#, fuzzy +msgid "The ``aggregate_evaluate`` method" msgstr ":code:`aggregate_evaluate` 메서드" -#: ../../source/how-to-implement-strategies.rst:293 +#: ../../source/how-to-implement-strategies.rst:327 +#, fuzzy msgid "" -":code:`aggregate_evaluate` is responsible for aggregating the results " +"``aggregate_evaluate`` is responsible for aggregating the results " "returned by the clients that were selected and asked to evaluate in " -":code:`configure_evaluate`." +"``configure_evaluate``." msgstr "" "code:`aggregate_evaluate`는 :code:`configure_evaluate`에서 선택되어 평가를 요청한 " "클라이언트가 반환한 결과를 집계하는 역할을 담당합니다." -#: ../../source/how-to-implement-strategies.rst:306 +#: ../../source/how-to-implement-strategies.rst:341 +#, fuzzy msgid "" "Of course, failures can happen, so there is no guarantee that the server " "will get results from all the clients it sent instructions to (via " -":code:`configure_evaluate`). :code:`aggregate_evaluate` therefore " -"receives a list of :code:`results`, but also a list of :code:`failures`." +"``configure_evaluate``). ``aggregate_evaluate`` therefore receives a list" +" of ``results``, but also a list of ``failures``." msgstr "" "물론 실패가 발생할 수 있으므로 서버가 명령을 보낸 모든 클라이언트로부터 결과를 얻을 수 있다는 보장은 " "없습니다(:code:`configure_evaluate`를 통해). 따라서 :code:`aggregate_evaluate`는 " ":code:`results` 목록뿐만 아니라 :code:`failures` 목록도 받습니다." -#: ../../source/how-to-implement-strategies.rst:308 +#: ../../source/how-to-implement-strategies.rst:346 +#, fuzzy msgid "" -":code:`aggregate_evaluate` returns an optional :code:`float` (loss) and a" -" dictionary of aggregated metrics. The :code:`float` return value is " -"optional because :code:`aggregate_evaluate` might decide that the results" -" provided are not sufficient for aggregation (e.g., too many failures)." +"``aggregate_evaluate`` returns an optional ``float`` (loss) and a " +"dictionary of aggregated metrics. The ``float`` return value is optional " +"because ``aggregate_evaluate`` might decide that the results provided are" +" not sufficient for aggregation (e.g., too many failures)." msgstr "" "code:`aggregate_evaluate`는 선택적 :code:`float`(손실)와 집계된 메트릭의 dictionary를 " "반환합니다. code:`float` 반환 값은 :code:`aggregate_evaluate`가 제공된 결과가 집계에 충분하지 " "않다고 판단할 수 있으므로(예: 실패 수가 너무 많음) 선택 사항입니다." -#: ../../source/how-to-implement-strategies.rst:311 -msgid "The :code:`evaluate` method" +#: ../../source/how-to-implement-strategies.rst:352 +#, fuzzy +msgid "The ``evaluate`` method" msgstr ":code:`evaluate` 메서드" -#: ../../source/how-to-implement-strategies.rst:313 +#: ../../source/how-to-implement-strategies.rst:354 +#, fuzzy msgid "" -":code:`evaluate` is responsible for evaluating model parameters on the " -"server-side. Having :code:`evaluate` in addition to " -":code:`configure_evaluate`/:code:`aggregate_evaluate` enables strategies " -"to perform both servers-side and client-side (federated) evaluation." +"``evaluate`` is responsible for evaluating model parameters on the " +"server-side. Having ``evaluate`` in addition to " +"``configure_evaluate``/``aggregate_evaluate`` enables strategies to " +"perform both servers-side and client-side (federated) evaluation." msgstr "" ":code:`evaluate`는 서버 측에서 모델 매개변수를 평가하는 역할을 담당합니다. " "code:`configure_evaluate`/:code:`aggregate_evaluate`와 함께 " ":code:`evaluate`를 사용하면 서버 측과 클라이언트 측(federated) 평가를 모두 수행할 수 있는 전략을 사용할 수" " 있습니다." -#: ../../source/how-to-implement-strategies.rst:323 +#: ../../source/how-to-implement-strategies.rst:364 +#, fuzzy msgid "" "The return value is again optional because the strategy might not need to" " implement server-side evaluation or because the user-defined " -":code:`evaluate` method might not complete successfully (e.g., it might " -"fail to load the server-side evaluation data)." +"``evaluate`` method might not complete successfully (e.g., it might fail " +"to load the server-side evaluation data)." msgstr "" "반환 값은 전략에서 서버 측 평가를 구현할 필요가 없거나 사용자 정의 :code:`evaluate` 메서드가 성공적으로 완료되지 " "않을 수 있기 때문에(예: 서버 측 평가 데이터를 로드하지 못할 수 있음) 다시 선택 사항으로 설정할 수 있습니다." @@ -6894,60 +6985,63 @@ msgstr "" msgid "Install Flower" msgstr "Flower 설치" -#: ../../source/how-to-install-flower.rst:6 +#: ../../source/how-to-install-flower.rst:5 msgid "Python version" msgstr "Python 버전" -#: ../../source/how-to-install-flower.rst:12 +#: ../../source/how-to-install-flower.rst:11 msgid "Install stable release" msgstr "안정적인 릴리즈 설치" -#: ../../source/how-to-install-flower.rst:15 -#: ../../source/how-to-upgrade-to-flower-next.rst:46 +#: ../../source/how-to-install-flower.rst:14 +#: ../../source/how-to-upgrade-to-flower-next.rst:66 msgid "Using pip" msgstr "pip 사용" -#: ../../source/how-to-install-flower.rst:17 -msgid "" -"Stable releases are available on `PyPI " -"`_::" +#: ../../source/how-to-install-flower.rst:16 +#, fuzzy +msgid "Stable releases are available on `PyPI `_:" msgstr "안정적인 릴리즈는 `PyPI `_:: 에서 확인할 수 있습니다::" -#: ../../source/how-to-install-flower.rst:21 +#: ../../source/how-to-install-flower.rst:22 +#, fuzzy msgid "" "For simulations that use the Virtual Client Engine, ``flwr`` should be " -"installed with the ``simulation`` extra::" +"installed with the ``simulation`` extra:" msgstr "가상 클라이언트 엔진을 사용하는 시뮬레이션의 경우 ``flwr``을 ``simulation``extra와 함께 설치해야 합니다:" -#: ../../source/how-to-install-flower.rst:27 +#: ../../source/how-to-install-flower.rst:30 msgid "Using conda (or mamba)" msgstr "conda(또는 mamba) 사용" -#: ../../source/how-to-install-flower.rst:29 +#: ../../source/how-to-install-flower.rst:32 msgid "Flower can also be installed from the ``conda-forge`` channel." msgstr "Flower은 'conda-forge' 채널에서도 설치할 수 있습니다." -#: ../../source/how-to-install-flower.rst:31 +#: ../../source/how-to-install-flower.rst:34 +#, fuzzy msgid "" "If you have not added ``conda-forge`` to your channels, you will first " -"need to run the following::" +"need to run the following:" msgstr "채널에 'conda-forge'를 추가하지 않은 경우 먼저 다음을 실행해야 합니다:" -#: ../../source/how-to-install-flower.rst:36 +#: ../../source/how-to-install-flower.rst:42 +#, fuzzy msgid "" "Once the ``conda-forge`` channel has been enabled, ``flwr`` can be " -"installed with ``conda``::" +"installed with ``conda``:" msgstr "conda-forge`` 채널이 활성화되면 ``flwr``을 ``conda``로 설치할 수 있습니다::" -#: ../../source/how-to-install-flower.rst:40 -msgid "or with ``mamba``::" +#: ../../source/how-to-install-flower.rst:49 +#, fuzzy +msgid "or with ``mamba``:" msgstr "또는 ``mamba``::" -#: ../../source/how-to-install-flower.rst:46 +#: ../../source/how-to-install-flower.rst:56 msgid "Verify installation" msgstr "설치 확인" -#: ../../source/how-to-install-flower.rst:48 +#: ../../source/how-to-install-flower.rst:58 #, fuzzy msgid "" "The following command can be used to verify if Flower was successfully " @@ -6957,54 +7051,58 @@ msgstr "" "다음 명령을 사용하여 Flower가 성공적으로 설치되었는지 확인할 수 있습니다. 모든 것이 정상적으로 작동하면 명령줄에 " "Flower의 버전이 출력됩니다:" -#: ../../source/how-to-install-flower.rst:58 +#: ../../source/how-to-install-flower.rst:68 msgid "Advanced installation options" msgstr "고급 설치 옵션" -#: ../../source/how-to-install-flower.rst:61 +#: ../../source/how-to-install-flower.rst:71 msgid "Install via Docker" msgstr "Docker를 통해 설치" -#: ../../source/how-to-install-flower.rst:63 +#: ../../source/how-to-install-flower.rst:73 #, fuzzy msgid ":doc:`Run Flower using Docker `" msgstr ":doc:`Docker를 사용하여 Flower를 실행하는 방법 `" -#: ../../source/how-to-install-flower.rst:66 +#: ../../source/how-to-install-flower.rst:76 msgid "Install pre-release" msgstr "사전 릴리즈 설치" -#: ../../source/how-to-install-flower.rst:68 +#: ../../source/how-to-install-flower.rst:78 +#, fuzzy msgid "" "New (possibly unstable) versions of Flower are sometimes available as " "pre-release versions (alpha, beta, release candidate) before the stable " -"release happens::" +"release happens:" msgstr "" "새(불안정할 수 있는) 버전의 Flower는 안정 버전이 출시되기 전에 사전 릴리즈 버전(알파, 베타, 릴리즈 후보)으로 제공되는 " "경우가 있습니다:" -#: ../../source/how-to-install-flower.rst:72 +#: ../../source/how-to-install-flower.rst:85 +#, fuzzy msgid "" "For simulations that use the Virtual Client Engine, ``flwr`` pre-releases" -" should be installed with the ``simulation`` extra::" +" should be installed with the ``simulation`` extra:" msgstr "" "가상 클라이언트 엔진을 사용하는 시뮬레이션의 경우 ``flwr`` 사전 릴리즈를 ``simulation`` extra와 함께 " "설치해야 합니다:" -#: ../../source/how-to-install-flower.rst:77 +#: ../../source/how-to-install-flower.rst:93 msgid "Install nightly release" msgstr "야간 릴리즈 설치" -#: ../../source/how-to-install-flower.rst:79 +#: ../../source/how-to-install-flower.rst:95 +#, fuzzy msgid "" "The latest (potentially unstable) changes in Flower are available as " -"nightly releases::" +"nightly releases:" msgstr "Flower의 최신 (불안정할 수 있는) 변경 사항은 다음과 같이 야간 릴리즈로 제공됩니다:" -#: ../../source/how-to-install-flower.rst:83 +#: ../../source/how-to-install-flower.rst:101 +#, fuzzy msgid "" "For simulations that use the Virtual Client Engine, ``flwr-nightly`` " -"should be installed with the ``simulation`` extra::" +"should be installed with the ``simulation`` extra:" msgstr "" "가상 클라이언트 엔진을 사용하는 시뮬레이션의 경우, ``flwr-nightly``를 ``simulation`` extr와 함께 " "설치해야 합니다::" @@ -7025,7 +7123,7 @@ msgstr "" "강력하며 클라이언트별 리소스 할당 방법을 결정하고 총 사용량을 제한할 수 있습니다. 리소스 소비에 대한 인사이트를 통해 더 현명한 " "결정을 내리고 실행 시간을 단축할 수 있습니다." -#: ../../source/how-to-monitor-simulation.rst:6 +#: ../../source/how-to-monitor-simulation.rst:9 msgid "" "The specific instructions assume you are using macOS and have the " "`Homebrew `_ package manager installed." @@ -7033,11 +7131,11 @@ msgstr "" "구체적인 지침은 macOS를 사용 중이고 'Homebrew `_ 패키지 관리자가 설치되어 있다고 " "가정합니다." -#: ../../source/how-to-monitor-simulation.rst:10 +#: ../../source/how-to-monitor-simulation.rst:13 msgid "Downloads" msgstr "다운로드" -#: ../../source/how-to-monitor-simulation.rst:16 +#: ../../source/how-to-monitor-simulation.rst:19 msgid "" "`Prometheus `_ is used for data collection, while" " `Grafana `_ will enable you to visualize the " @@ -7048,27 +7146,27 @@ msgstr "" "`_는 수집된 데이터를 시각화할 수 있게 해줍니다. 이 두 도구는 모두 Flower가 " "내부적으로 사용하는 `Ray `_와 잘 통합되어 있습니다." -#: ../../source/how-to-monitor-simulation.rst:18 +#: ../../source/how-to-monitor-simulation.rst:23 msgid "" "Overwrite the configuration files (depending on your device, it might be " "installed on a different path)." msgstr "구성 파일을 덮어씁니다(장치에 따라 다른 경로에 설치되어 있을 수 있음)." -#: ../../source/how-to-monitor-simulation.rst:20 +#: ../../source/how-to-monitor-simulation.rst:26 msgid "If you are on an M1 Mac, it should be:" msgstr "M1 Mac을 사용 중이라면:" -#: ../../source/how-to-monitor-simulation.rst:27 +#: ../../source/how-to-monitor-simulation.rst:33 msgid "On the previous generation Intel Mac devices, it should be:" msgstr "이전 세대 Intel Mac 장치에서는:" -#: ../../source/how-to-monitor-simulation.rst:34 +#: ../../source/how-to-monitor-simulation.rst:40 msgid "" "Open the respective configuration files and change them. Depending on " "your device, use one of the two following commands:" msgstr "각 구성 파일을 열고 변경합니다. 장치에 따라 다음 두 명령 중 하나를 사용합니다:" -#: ../../source/how-to-monitor-simulation.rst:44 +#: ../../source/how-to-monitor-simulation.rst:51 msgid "" "and then delete all the text in the file and paste a new Prometheus " "config you see below. You may adjust the time intervals to your " @@ -7077,7 +7175,7 @@ msgstr "" "를 입력한 다음 파일의 모든 텍스트를 삭제하고 아래에 표시된 새 Prometheus 설정을 붙여넣습니다. 요구 사항에 따라 시간 " "간격을 조정할 수 있습니다:" -#: ../../source/how-to-monitor-simulation.rst:59 +#: ../../source/how-to-monitor-simulation.rst:67 msgid "" "Now after you have edited the Prometheus configuration, do the same with " "the Grafana configuration files. Open those using one of the following " @@ -7086,55 +7184,56 @@ msgstr "" "이제 Prometheus 구성을 편집한 후 Grafana 구성 파일에 대해서도 동일한 작업을 수행합니다. 이전과 마찬가지로 다음 " "명령 중 하나를 사용하여 파일을 엽니다:" -#: ../../source/how-to-monitor-simulation.rst:69 +#: ../../source/how-to-monitor-simulation.rst:78 msgid "" "Your terminal editor should open and allow you to apply the following " "configuration as before." msgstr "터미널 편집기가 열리면 이전과 마찬가지로 다음 구성을 적용할 수 있습니다." -#: ../../source/how-to-monitor-simulation.rst:84 +#: ../../source/how-to-monitor-simulation.rst:94 msgid "" "Congratulations, you just downloaded all the necessary software needed " "for metrics tracking. Now, let’s start it." msgstr "축하합니다. 매트릭 트레킹에 필요한 모든 소프트웨어를 다운로드하셨습니다. 이제 시작해 보겠습니다." -#: ../../source/how-to-monitor-simulation.rst:88 +#: ../../source/how-to-monitor-simulation.rst:98 msgid "Tracking metrics" msgstr "매트릭 트래킹" -#: ../../source/how-to-monitor-simulation.rst:90 +#: ../../source/how-to-monitor-simulation.rst:100 msgid "" "Before running your Flower simulation, you have to start the monitoring " "tools you have just installed and configured." msgstr "Flower 시뮬레이션을 실행하기 전에 방금 설치 및 구성한 모니터링 도구를 시작해야 합니다." -#: ../../source/how-to-monitor-simulation.rst:97 +#: ../../source/how-to-monitor-simulation.rst:108 msgid "" "Please include the following argument in your Python code when starting a" " simulation." msgstr "시뮬레이션을 시작할 때 Python 코드에 다음 전달인자를 포함하세요." -#: ../../source/how-to-monitor-simulation.rst:108 +#: ../../source/how-to-monitor-simulation.rst:119 msgid "Now, you are ready to start your workload." msgstr "이제 워크로드를 시작할 준비가 되었습니다." -#: ../../source/how-to-monitor-simulation.rst:110 +#: ../../source/how-to-monitor-simulation.rst:121 msgid "" "Shortly after the simulation starts, you should see the following logs in" " your terminal:" msgstr "시뮬레이션이 시작되고 얼마 지나지 않아 터미널에 다음 로그가 표시됩니다:" -#: ../../source/how-to-monitor-simulation.rst:117 -msgid "You can look at everything at ``_ ." +#: ../../source/how-to-monitor-simulation.rst:127 +#, fuzzy +msgid "You can look at everything at http://127.0.0.1:8265 ." msgstr "``_ 에서 모든 것을 볼 수 있습니다." -#: ../../source/how-to-monitor-simulation.rst:119 +#: ../../source/how-to-monitor-simulation.rst:129 msgid "" "It's a Ray Dashboard. You can navigate to Metrics (on the left panel, the" " lowest option)." msgstr "Ray 대시보드입니다. 메트릭(왼쪽 패널의 가장 아래 옵션)으로 이동할 수 있습니다." -#: ../../source/how-to-monitor-simulation.rst:121 +#: ../../source/how-to-monitor-simulation.rst:132 msgid "" "Or alternatively, you can just see them in Grafana by clicking on the " "right-up corner, “View in Grafana”. Please note that the Ray dashboard is" @@ -7146,26 +7245,27 @@ msgstr "" "시뮬레이션 중에만 액세스할 수 있다는 점에 유의하세요. 시뮬레이션이 종료된 후에는 Grafana를 사용하여 메트릭을 탐색할 수만 " "있습니다. ``http://localhost:3000/``로 이동하여 Grafana를 시작할 수 있습니다." -#: ../../source/how-to-monitor-simulation.rst:123 +#: ../../source/how-to-monitor-simulation.rst:137 +#, fuzzy msgid "" "After you finish the visualization, stop Prometheus and Grafana. This is " -"important as they will otherwise block, for example port :code:`3000` on " -"your machine as long as they are running." +"important as they will otherwise block, for example port ``3000`` on your" +" machine as long as they are running." msgstr "" "시각화를 완료한 후에는 Prometheus와 Grafana를 중지합니다. 그렇지 않으면 실행 중인 동안 컴퓨터에서 포트 " ":code:`3000` 등을 차단하므로 이 작업이 중요합니다." -#: ../../source/how-to-monitor-simulation.rst:132 +#: ../../source/how-to-monitor-simulation.rst:147 msgid "Resource allocation" msgstr "리소스 할당" -#: ../../source/how-to-monitor-simulation.rst:134 +#: ../../source/how-to-monitor-simulation.rst:149 msgid "" "You must understand how the Ray library works to efficiently allocate " "system resources to simulation clients on your own." msgstr "Ray 라이브러리가 어떻게 작동하는지 이해해야 시뮬레이션 클라이언트에 시스템 리소스를 효율적으로 할당할 수 있습니다." -#: ../../source/how-to-monitor-simulation.rst:136 +#: ../../source/how-to-monitor-simulation.rst:152 msgid "" "Initially, the simulation (which Ray handles under the hood) starts by " "default with all the available resources on the system, which it shares " @@ -7178,21 +7278,21 @@ msgstr "" "클라이언트 간에 공유됩니다. 그렇다고 해서 모든 클라이언트에게 균등하게 분배하거나 모든 클라이언트에서 동시에 모델 학습이 이루어지는" " 것은 아닙니다. 이에 대한 자세한 내용은 이 블로그의 뒷부분에서 설명합니다. 다음을 실행하여 시스템 리소스를 확인할 수 있습니다:" -#: ../../source/how-to-monitor-simulation.rst:143 +#: ../../source/how-to-monitor-simulation.rst:164 msgid "In Google Colab, the result you see might be similar to this:" msgstr "Google Colab에서는 이와 유사한 결과가 표시될 수 있습니다:" -#: ../../source/how-to-monitor-simulation.rst:155 +#: ../../source/how-to-monitor-simulation.rst:175 msgid "" "However, you can overwrite the defaults. When starting a simulation, do " "the following (you don't need to overwrite all of them):" msgstr "그러나 기본값을 덮어쓸 수 있습니다. 시뮬레이션을 시작할 때 다음을 수행합니다(모두 덮어쓸 필요는 없음):" -#: ../../source/how-to-monitor-simulation.rst:175 +#: ../../source/how-to-monitor-simulation.rst:195 msgid "Let’s also specify the resource for a single client." msgstr "단일 클라이언트에 대한 리소스도 지정해 보겠습니다." -#: ../../source/how-to-monitor-simulation.rst:205 +#: ../../source/how-to-monitor-simulation.rst:225 msgid "" "Now comes the crucial part. Ray will start a new client only when it has " "all the required resources (such that they run in parallel) when the " @@ -7201,29 +7301,30 @@ msgstr "" "이제 중요한 부분이 나옵니다. Ray는 리소스가 허용하는 경우에만 필요한 모든 리소스가 있을 때(병렬로 실행되는 등) 새 " "클라이언트를 시작합니다." -#: ../../source/how-to-monitor-simulation.rst:207 +#: ../../source/how-to-monitor-simulation.rst:228 +#, fuzzy msgid "" "In the example above, only one client will be run, so your clients won't " -"run concurrently. Setting :code:`client_num_gpus = 0.5` would allow " -"running two clients and therefore enable them to run concurrently. Be " -"careful not to require more resources than available. If you specified " -":code:`client_num_gpus = 2`, the simulation wouldn't start (even if you " -"had 2 GPUs but decided to set 1 in :code:`ray_init_args`)." +"run concurrently. Setting ``client_num_gpus = 0.5`` would allow running " +"two clients and therefore enable them to run concurrently. Be careful not" +" to require more resources than available. If you specified " +"``client_num_gpus = 2``, the simulation wouldn't start (even if you had 2" +" GPUs but decided to set 1 in ``ray_init_args``)." msgstr "" "위의 예에서는 하나의 클라이언트만 실행되므로 클라이언트가 동시에 실행되지 않습니다. :code:`client_num_gpus = " "0.5` 를 설정하면 두 개의 클라이언트를 실행할 수 있으므로 동시에 실행할 수 있습니다. 사용 가능한 리소스보다 더 많은 리소스를" " 요구하지 않도록 주의하세요. :code:`client_num_gpus = 2`를 지정하면 시뮬레이션이 시작되지 않습니다(GPU가 " "2개이지만 :code:`ray_init_args`에서 1개를 설정한 경우에도 마찬가지입니다)." -#: ../../source/how-to-monitor-simulation.rst:212 ../../source/ref-faq.rst:2 +#: ../../source/how-to-monitor-simulation.rst:235 ../../source/ref-faq.rst:2 msgid "FAQ" msgstr "자주 묻는 질문" -#: ../../source/how-to-monitor-simulation.rst:214 +#: ../../source/how-to-monitor-simulation.rst:237 msgid "Q: I don't see any metrics logged." msgstr "질문: 기록된 메트릭이 보이지 않습니다." -#: ../../source/how-to-monitor-simulation.rst:216 +#: ../../source/how-to-monitor-simulation.rst:239 msgid "" "A: The timeframe might not be properly set. The setting is in the top " "right corner (\"Last 30 minutes\" by default). Please change the " @@ -7232,7 +7333,7 @@ msgstr "" "A: 기간이 제대로 설정되지 않았을 수 있습니다. 설정은 오른쪽 상단에 있습니다(기본값은 '지난 30분'). 시뮬레이션이 실행된 " "기간을 반영하도록 기간을 변경해 주세요." -#: ../../source/how-to-monitor-simulation.rst:218 +#: ../../source/how-to-monitor-simulation.rst:243 msgid "" "Q: I see “Grafana server not detected. Please make sure the Grafana " "server is running and refresh this page” after going to the Metrics tab " @@ -7241,38 +7342,41 @@ msgstr "" "질문: \"Grafana 서버가 감지되지 않았습니다. Ray 대시보드의 메트릭 탭으로 이동한 후 Grafana 서버가 실행 중인지 " "확인하고 이 페이지를 새로고침하세요.\"라는 메시지가 표시됩니다." -#: ../../source/how-to-monitor-simulation.rst:220 +#: ../../source/how-to-monitor-simulation.rst:246 msgid "" "A: You probably don't have Grafana running. Please check the running " "services" msgstr "A: Grafana가 실행되고 있지 않을 수 있습니다. 실행 중인 서비스를 확인하세요" -#: ../../source/how-to-monitor-simulation.rst:226 +#: ../../source/how-to-monitor-simulation.rst:252 +#, fuzzy msgid "" "Q: I see \"This site can't be reached\" when going to " -"``_." +"http://127.0.0.1:8265." msgstr "Q: ``_로 이동할 때 \"이 사이트에 연결할 수 없습니다.\"라는 메시지가 표시됩니다." -#: ../../source/how-to-monitor-simulation.rst:228 +#: ../../source/how-to-monitor-simulation.rst:254 msgid "" "A: Either the simulation has already finished, or you still need to start" " Prometheus." msgstr "A: 시뮬레이션이 이미 완료되었거나 아직 Prometheus를 시작해야 합니다." -#: ../../source/how-to-monitor-simulation.rst:232 +#: ../../source/how-to-monitor-simulation.rst:257 msgid "Resources" msgstr "리소스" -#: ../../source/how-to-monitor-simulation.rst:234 +#: ../../source/how-to-monitor-simulation.rst:259 +#, fuzzy msgid "" -"Ray Dashboard: ``_" +"Ray Dashboard: https://docs.ray.io/en/latest/ray-observability/getting-" +"started.html" msgstr "" "Ray 대시보드: ``_" -#: ../../source/how-to-monitor-simulation.rst:236 -msgid "Ray Metrics: ``_" +#: ../../source/how-to-monitor-simulation.rst:261 +#, fuzzy +msgid "Ray Metrics: https://docs.ray.io/en/latest/cluster/metrics.html" msgstr "Ray 메트릭: ``_" #: ../../source/how-to-run-simulations.rst:2 @@ -7302,16 +7406,17 @@ msgstr "" "architecture.html#virtual-client-engine>`_ 또는 VCE를 통해 이러한 시나리오를 수용할 수 " "있습니다." -#: ../../source/how-to-run-simulations.rst:10 +#: ../../source/how-to-run-simulations.rst:19 +#, fuzzy msgid "" -"The :code:`VirtualClientEngine` schedules, launches and manages `virtual`" -" clients. These clients are identical to `non-virtual` clients (i.e. the " +"The ``VirtualClientEngine`` schedules, launches and manages `virtual` " +"clients. These clients are identical to `non-virtual` clients (i.e. the " "ones you launch via the command `flwr.client.start_client `_) in the sense that they can be configure by " "creating a class inheriting, for example, from `flwr.client.NumPyClient " "`_ and therefore behave in an " "identical way. In addition to that, clients managed by the " -":code:`VirtualClientEngine` are:" +"``VirtualClientEngine`` are:" msgstr "" ":code:`VirtualClientEngine`은 `virtual` 클라이언트를 예약, 실행 및 관리합니다. 이러한 클라이언트는 " "`non-virtual` 클라이언트(예: `flwr.client.start_client `_에서 상속하는 클래스 생성으로 구성될 수 있으므로 동일한 " "방식으로 동작합니다. 그 외에도 :code:`VirtualClientEngine`에 의해 관리되는 클라이언트는 다음과 같습니다:" -#: ../../source/how-to-run-simulations.rst:12 +#: ../../source/how-to-run-simulations.rst:26 msgid "" "resource-aware: this means that each client gets assigned a portion of " "the compute and memory on your system. You as a user can control this at " @@ -7331,16 +7436,17 @@ msgstr "" "시뮬레이션을 시작할 때 이를 제어할 수 있으며, 이를 통해 Flower FL 시뮬레이션의 병렬 처리 정도를 제어할 수 있습니다. " "클라이언트당 리소스가 적을수록 동일한 하드웨어에서 더 많은 클라이언트를 동시에 실행할 수 있습니다." -#: ../../source/how-to-run-simulations.rst:13 +#: ../../source/how-to-run-simulations.rst:31 +#, fuzzy msgid "" "self-managed: this means that you as a user do not need to launch clients" -" manually, instead this gets delegated to :code:`VirtualClientEngine`'s " +" manually, instead this gets delegated to ``VirtualClientEngine``'s " "internals." msgstr "" "self-managed: 이는 사용자가 클라이언트를 수동으로 실행할 필요가 없으며, 대신 " ":code:`VirtualClientEngine`의 내부에 위임된다는 의미입니다." -#: ../../source/how-to-run-simulations.rst:14 +#: ../../source/how-to-run-simulations.rst:33 msgid "" "ephemeral: this means that a client is only materialized when it is " "required in the FL process (e.g. to do `fit() `_을 수행하기 위해). 객체는 나중에 소멸되어 할당된 리소스를 해제하고" " 다른 클라이언트가 참여할 수 있도록 허용합니다." -#: ../../source/how-to-run-simulations.rst:16 +#: ../../source/how-to-run-simulations.rst:38 +#, fuzzy msgid "" -"The :code:`VirtualClientEngine` implements `virtual` clients using `Ray " +"The ``VirtualClientEngine`` implements `virtual` clients using `Ray " "`_, an open-source framework for scalable Python " -"workloads. In particular, Flower's :code:`VirtualClientEngine` makes use " -"of `Actors `_ to " -"spawn `virtual` clients and run their workload." +"workloads. In particular, Flower's ``VirtualClientEngine`` makes use of " +"`Actors `_ to spawn " +"`virtual` clients and run their workload." msgstr "" ":code:`VirtualClientEngine`은 확장 가능한 파이썬 워크로드를 위한 오픈 소스 프레임워크인 `Ray " "`_를 사용하여 `virtual` 클라이언트를 구현합니다. 특히 Flower의 " ":code:`VirtualClientEngine`은 `Actors `_를 사용하여 `virtual` 클라이언트를 생성하고 해당 워크로드를 실행합니다." -#: ../../source/how-to-run-simulations.rst:20 +#: ../../source/how-to-run-simulations.rst:45 msgid "Launch your Flower simulation" msgstr "Flower 시뮬레이션 시작" -#: ../../source/how-to-run-simulations.rst:22 +#: ../../source/how-to-run-simulations.rst:47 msgid "" "Running Flower simulations still require you to define your client class," " a strategy, and utility functions to download and load (and potentially " @@ -7383,22 +7490,23 @@ msgstr "" "flwr.html#flwr.simulation.start_simulation>`_을 사용하면 되며, 최소한의 예시는 다음과 " "같습니다:" -#: ../../source/how-to-run-simulations.rst:44 +#: ../../source/how-to-run-simulations.rst:73 msgid "VirtualClientEngine resources" msgstr "VirtualClientEngine 리소스" -#: ../../source/how-to-run-simulations.rst:45 +#: ../../source/how-to-run-simulations.rst:75 +#, fuzzy msgid "" "By default the VCE has access to all system resources (i.e. all CPUs, all" " GPUs, etc) since that is also the default behavior when starting Ray. " "However, in some settings you might want to limit how many of your system" " resources are used for simulation. You can do this via the " -":code:`ray_init_args` input argument to :code:`start_simulation` which " -"the VCE internally passes to Ray's :code:`ray.init` command. For a " -"complete list of settings you can configure check the `ray.init " +"``ray_init_args`` input argument to ``start_simulation`` which the VCE " +"internally passes to Ray's ``ray.init`` command. For a complete list of " +"settings you can configure check the `ray.init " "`_" -" documentation. Do not set :code:`ray_init_args` if you want the VCE to " -"use all your system's CPUs and GPUs." +" documentation. Do not set ``ray_init_args`` if you want the VCE to use " +"all your system's CPUs and GPUs." msgstr "" "기본적으로 VCE는 모든 시스템 리소스(예: 모든 CPU, 모든 GPU 등)에 액세스할 수 있으며, 이는 Ray를 시작할 때의 기본" " 동작이기도 합니다. 그러나 일부 설정에서는 시뮬레이션에 사용되는 시스템 리소스의 수를 제한하고 싶을 수 있습니다. 이 설정은 " @@ -7408,20 +7516,21 @@ msgstr "" " 설명서를 확인하세요. VCE가 시스템의 모든 CPU와 GPU를 사용하도록 하려면 :code:`ray_init_args`를 설정하지" " 마세요." -#: ../../source/how-to-run-simulations.rst:62 +#: ../../source/how-to-run-simulations.rst:97 msgid "Assigning client resources" msgstr "클라이언트 리소스 할당" -#: ../../source/how-to-run-simulations.rst:63 +#: ../../source/how-to-run-simulations.rst:99 +#, fuzzy msgid "" -"By default the :code:`VirtualClientEngine` assigns a single CPU core (and" -" nothing else) to each virtual client. This means that if your system has" -" 10 cores, that many virtual clients can be concurrently running." +"By default the ``VirtualClientEngine`` assigns a single CPU core (and " +"nothing else) to each virtual client. This means that if your system has " +"10 cores, that many virtual clients can be concurrently running." msgstr "" "기본적으로 :code:`VirtualClientEngine`은 각 가상 클라이언트에 단일 CPU 코어를 할당합니다(그 외에는 " "아무것도 할당하지 않음). 즉, 시스템에 코어가 10개인 경우 그만큼 많은 가상 클라이언트를 동시에 실행할 수 있습니다." -#: ../../source/how-to-run-simulations.rst:65 +#: ../../source/how-to-run-simulations.rst:103 msgid "" "More often than not, you would probably like to adjust the resources your" " clients get assigned based on the complexity (i.e. compute and memory " @@ -7436,31 +7545,32 @@ msgstr "" "flwr.html#flwr.simulation.start_simulation>`_로 설정하여 이를 수행할 수 있습니다. Ray는 " "내부적으로 두 개의 키를 사용하여 워크로드(이 경우 Flower 클라이언트)를 스케줄링하고 스폰합니다:" -#: ../../source/how-to-run-simulations.rst:67 -msgid ":code:`num_cpus` indicates the number of CPU cores a client would get." +#: ../../source/how-to-run-simulations.rst:110 +#, fuzzy +msgid "``num_cpus`` indicates the number of CPU cores a client would get." msgstr ":code:`num_cpus`는 클라이언트에서 사용할 수 있는 CPU 코어 수를 나타냅니다." -#: ../../source/how-to-run-simulations.rst:68 -msgid "" -":code:`num_gpus` indicates the **ratio** of GPU memory a client gets " -"assigned." +#: ../../source/how-to-run-simulations.rst:111 +#, fuzzy +msgid "``num_gpus`` indicates the **ratio** of GPU memory a client gets assigned." msgstr ":code:`num_gpus`는 클라이언트에 할당되는 GPU 메모리의 **비율**을 나타냅니다." -#: ../../source/how-to-run-simulations.rst:70 +#: ../../source/how-to-run-simulations.rst:113 msgid "Let's see a few examples:" msgstr "몇 가지 예를 살펴보겠습니다:" -#: ../../source/how-to-run-simulations.rst:89 +#: ../../source/how-to-run-simulations.rst:132 +#, fuzzy msgid "" -"While the :code:`client_resources` can be used to control the degree of " +"While the ``client_resources`` can be used to control the degree of " "concurrency in your FL simulation, this does not stop you from running " "dozens, hundreds or even thousands of clients in the same round and " "having orders of magnitude more `dormant` (i.e. not participating in a " "round) clients. Let's say you want to have 100 clients per round but your" " system can only accommodate 8 clients concurrently. The " -":code:`VirtualClientEngine` will schedule 100 jobs to run (each " -"simulating a client sampled by the strategy) and then will execute them " -"in a resource-aware manner in batches of 8." +"``VirtualClientEngine`` will schedule 100 jobs to run (each simulating a " +"client sampled by the strategy) and then will execute them in a resource-" +"aware manner in batches of 8." msgstr "" "code:`client_resources`를 사용하여 FL 시뮬레이션의 동시성 정도를 제어할 수 있지만, 동일한 라운드에서 수십, " "수백 또는 수천 개의 클라이언트를 실행하고 훨씬 더 많은 '휴면'(즉, 라운드에 참여하지 않는) 클라이언트를 보유하는 것을 막을 " @@ -7468,7 +7578,7 @@ msgstr "" "code:`VirtualClientEngine`은 실행할 100개의 작업(각각 전략에서 샘플링한 클라이언트를 시뮬레이션)을 예약한 " "다음 리소스 인식 방식으로 8개씩 일괄적으로 실행합니다." -#: ../../source/how-to-run-simulations.rst:91 +#: ../../source/how-to-run-simulations.rst:140 msgid "" "To understand all the intricate details on how resources are used to " "schedule FL clients and how to define custom resources, please take a " @@ -7479,11 +7589,11 @@ msgstr "" "이해하려면 'Ray 문서 '를 참조하세요." -#: ../../source/how-to-run-simulations.rst:94 +#: ../../source/how-to-run-simulations.rst:145 msgid "Simulation examples" msgstr "시뮬레이션 예제" -#: ../../source/how-to-run-simulations.rst:96 +#: ../../source/how-to-run-simulations.rst:147 msgid "" "A few ready-to-run complete examples for Flower simulation in " "Tensorflow/Keras and PyTorch are provided in the `Flower repository " @@ -7492,7 +7602,7 @@ msgstr "" "Tensorflow/Keras와 파이토치에서 바로 실행할 수 있는 몇 가지 Flower 시뮬레이션 예제는 `Flower 레포지토리 " "`_에서 제공됩니다. Google Colab에서도 실행할 수 있습니다:" -#: ../../source/how-to-run-simulations.rst:98 +#: ../../source/how-to-run-simulations.rst:151 msgid "" "`Tensorflow/Keras Simulation " "`_: 100개의 클라이언트가 공동으로 MNIST에서 MLP 모델을 훈련합니다." -#: ../../source/how-to-run-simulations.rst:99 +#: ../../source/how-to-run-simulations.rst:154 msgid "" "`PyTorch Simulation `_: 100 clients collaboratively train a CNN model on " @@ -7511,28 +7621,29 @@ msgstr "" "파이토치 시뮬레이션 `_: 100개의 클라이언트가 공동으로 MNIST에서 CNN 모델을 훈련합니다." -#: ../../source/how-to-run-simulations.rst:104 +#: ../../source/how-to-run-simulations.rst:159 msgid "Multi-node Flower simulations" msgstr "멀티 노드 Flower 시뮬레이션" -#: ../../source/how-to-run-simulations.rst:106 +#: ../../source/how-to-run-simulations.rst:161 +#, fuzzy msgid "" -"Flower's :code:`VirtualClientEngine` allows you to run FL simulations " -"across multiple compute nodes. Before starting your multi-node simulation" -" ensure that you:" +"Flower's ``VirtualClientEngine`` allows you to run FL simulations across " +"multiple compute nodes. Before starting your multi-node simulation ensure" +" that you:" msgstr "" "Flower의 :code:`VirtualClientEngine`을 사용하면 여러 컴퓨팅 노드에서 FL 시뮬레이션을 실행할 수 " "있습니다. 멀티 노드 시뮬레이션을 시작하기 전에 다음 사항을 확인하세요:" -#: ../../source/how-to-run-simulations.rst:108 +#: ../../source/how-to-run-simulations.rst:164 msgid "Have the same Python environment in all nodes." msgstr "모든 노드에서 동일한 Python 환경을 유지합니다." -#: ../../source/how-to-run-simulations.rst:109 +#: ../../source/how-to-run-simulations.rst:165 msgid "Have a copy of your code (e.g. your entire repo) in all nodes." msgstr "모든 노드에 코드 사본(예: 전체 레포지토리)을 보관하세요." -#: ../../source/how-to-run-simulations.rst:110 +#: ../../source/how-to-run-simulations.rst:166 msgid "" "Have a copy of your dataset in all nodes (more about this in " ":ref:`simulation considerations `)" @@ -7540,78 +7651,82 @@ msgstr "" "모든 노드에 데이터 세트의 사본을 보유하세요(자세한 내용은 :ref:`simulation considerations " "`에서 확인하세요)" -#: ../../source/how-to-run-simulations.rst:111 +#: ../../source/how-to-run-simulations.rst:168 +#, fuzzy msgid "" -"Pass :code:`ray_init_args={\"address\"=\"auto\"}` to `start_simulation " -"`_ so the " -":code:`VirtualClientEngine` attaches to a running Ray instance." +"Pass ``ray_init_args={\"address\"=\"auto\"}`` to `start_simulation `_ so the " +"``VirtualClientEngine`` attaches to a running Ray instance." msgstr "" ":code:`ray_init_args={\"address\"=\"auto\"}`를 `start_simulation `_에 전달하여 " ":code:`VirtualClientEngine`이 실행 중인 Ray 인스턴스에 연결되도록 합니다." -#: ../../source/how-to-run-simulations.rst:112 +#: ../../source/how-to-run-simulations.rst:171 +#, fuzzy msgid "" -"Start Ray on you head node: on the terminal type :code:`ray start " -"--head`. This command will print a few lines, one of which indicates how " -"to attach other nodes to the head node." +"Start Ray on you head node: on the terminal type ``ray start --head``. " +"This command will print a few lines, one of which indicates how to attach" +" other nodes to the head node." msgstr "" "헤드 노드에서 Ray 시작: 터미널에서 :code:`ray start --head`를 입력합니다. 이 명령은 몇 줄을 출력하며, 그" " 중 하나는 다른 노드를 헤드 노드에 연결하는 방법을 나타냅니다." -#: ../../source/how-to-run-simulations.rst:113 +#: ../../source/how-to-run-simulations.rst:174 +#, fuzzy msgid "" "Attach other nodes to the head node: copy the command shown after " "starting the head and execute it on terminal of a new node: for example " -":code:`ray start --address='192.168.1.132:6379'`" +"``ray start --address='192.168.1.132:6379'``" msgstr "" "헤드 노드에 다른 노드 연결: 헤드를 시작한 후 표시된 명령어을 복사하여 새 노드의 터미널에서 실행합니다: 예: :code:`ray" " start --address='192.168.1.132:6379'`" -#: ../../source/how-to-run-simulations.rst:115 +#: ../../source/how-to-run-simulations.rst:178 msgid "" "With all the above done, you can run your code from the head node as you " "would if the simulation was running on a single node." msgstr "위의 모든 작업이 완료되면 단일 노드에서 시뮬레이션을 실행할 때와 마찬가지로 헤드 노드에서 코드를 실행할 수 있습니다." -#: ../../source/how-to-run-simulations.rst:117 +#: ../../source/how-to-run-simulations.rst:181 +#, fuzzy msgid "" "Once your simulation is finished, if you'd like to dismantle your cluster" -" you simply need to run the command :code:`ray stop` in each node's " -"terminal (including the head node)." +" you simply need to run the command ``ray stop`` in each node's terminal " +"(including the head node)." msgstr "" "시뮬레이션이 완료되면 클러스터를 해체하려면 각 노드(헤드 노드 포함)의 터미널에서 :code:`ray stop` 명령을 실행하기만 " "하면 됩니다." -#: ../../source/how-to-run-simulations.rst:120 +#: ../../source/how-to-run-simulations.rst:185 msgid "Multi-node simulation good-to-know" msgstr "멀티 노드 시뮬레이션에 대해 알아두면 좋은 사항" -#: ../../source/how-to-run-simulations.rst:122 +#: ../../source/how-to-run-simulations.rst:187 msgid "" "Here we list a few interesting functionality when running multi-node FL " "simulations:" msgstr "여기에서는 멀티 노드 FL 시뮬레이션을 실행할 때 흥미로운 몇 가지 기능을 나열합니다:" -#: ../../source/how-to-run-simulations.rst:124 +#: ../../source/how-to-run-simulations.rst:189 +#, fuzzy msgid "" -"User :code:`ray status` to check all nodes connected to your head node as" -" well as the total resources available to the " -":code:`VirtualClientEngine`." +"User ``ray status`` to check all nodes connected to your head node as " +"well as the total resources available to the ``VirtualClientEngine``." msgstr "" "사용자는 :code:`ray status`를 통해 헤드 노드에 연결된 모든 노드와 " ":code:`VirtualClientEngine`에 사용 가능한 총 리소스를 확인할 수 있습니다." -#: ../../source/how-to-run-simulations.rst:126 +#: ../../source/how-to-run-simulations.rst:192 +#, fuzzy msgid "" "When attaching a new node to the head, all its resources (i.e. all CPUs, " "all GPUs) will be visible by the head node. This means that the " -":code:`VirtualClientEngine` can schedule as many `virtual` clients as " -"that node can possible run. In some settings you might want to exclude " -"certain resources from the simulation. You can do this by appending " -"`--num-cpus=` and/or `--num-" -"gpus=` in any :code:`ray start` command (including " -"when starting the head)" +"``VirtualClientEngine`` can schedule as many `virtual` clients as that " +"node can possible run. In some settings you might want to exclude certain" +" resources from the simulation. You can do this by appending `--num-" +"cpus=` and/or `--num-gpus=` in " +"any ``ray start`` command (including when starting the head)" msgstr "" "새 노드를 헤드에 연결하면 해당 노드의 모든 리소스(즉, 모든 CPU, 모든 GPU)가 헤드 노드에 표시됩니다. 즉, " ":code:`VirtualClientEngine`은 해당 노드가 실행할 수 있는 만큼의 `가상` 클라이언트를 예약할 수 있습니다. " @@ -7619,17 +7734,17 @@ msgstr "" "포함)에 `--num-cpus=` 및/또는 `--num-" "gpus=`를 추가하여 이 작업을 수행하면 됩니다" -#: ../../source/how-to-run-simulations.rst:132 +#: ../../source/how-to-run-simulations.rst:202 msgid "Considerations for simulations" msgstr "시뮬레이션 시 고려 사항" -#: ../../source/how-to-run-simulations.rst:135 +#: ../../source/how-to-run-simulations.rst:206 msgid "" "We are actively working on these fronts so to make it trivial to run any " "FL workload with Flower simulation." msgstr "Flower 시뮬레이션으로 모든 FL 워크로드를 간편하게 실행할 수 있도록 이러한 측면에서 적극적으로 노력하고 있습니다." -#: ../../source/how-to-run-simulations.rst:138 +#: ../../source/how-to-run-simulations.rst:209 msgid "" "The current VCE allows you to run Federated Learning workloads in " "simulation mode whether you are prototyping simple scenarios on your " @@ -7644,30 +7759,32 @@ msgstr "" "동안, 아래에서는 Flower로 FL 파이프라인을 설계할 때 염두에 두어야 할 몇 가지 사항을 강조합니다. 또한 현재 구현에서 몇 " "가지 제한 사항을 강조합니다." -#: ../../source/how-to-run-simulations.rst:141 +#: ../../source/how-to-run-simulations.rst:217 msgid "GPU resources" msgstr "GPU 리소스" -#: ../../source/how-to-run-simulations.rst:143 +#: ../../source/how-to-run-simulations.rst:219 +#, fuzzy msgid "" "The VCE assigns a share of GPU memory to a client that specifies the key " -":code:`num_gpus` in :code:`client_resources`. This being said, Ray (used " +"``num_gpus`` in ``client_resources``. This being said, Ray (used " "internally by the VCE) is by default:" msgstr "" "VCE는 :code:`client_resources`에서 :code:`num_gpus` 키를 지정하는 클라이언트에 GPU 메모리 " "공유를 할당합니다. 즉, (VCE에서 내부적으로 사용하는) Ray가 기본적으로 사용됩니다:" -#: ../../source/how-to-run-simulations.rst:146 +#: ../../source/how-to-run-simulations.rst:222 +#, fuzzy msgid "" "not aware of the total VRAM available on the GPUs. This means that if you" -" set :code:`num_gpus=0.5` and you have two GPUs in your system with " -"different (e.g. 32GB and 8GB) VRAM amounts, they both would run 2 clients" -" concurrently." +" set ``num_gpus=0.5`` and you have two GPUs in your system with different" +" (e.g. 32GB and 8GB) VRAM amounts, they both would run 2 clients " +"concurrently." msgstr "" "GPU에서 사용 가능한 총 VRAM을 인식하지 못합니다. 즉, 시스템에 서로 다른(예: 32GB와 8GB) VRAM 용량을 가진 두" " 개의 GPU가 있고 :code:`num_gpus=0.5`를 설정하면 둘 다 동시에 2개의 클라이언트를 실행하게 됩니다." -#: ../../source/how-to-run-simulations.rst:147 +#: ../../source/how-to-run-simulations.rst:225 msgid "" "not aware of other unrelated (i.e. not created by the VCE) workloads are " "running on the GPU. Two takeaways from this are:" @@ -7675,7 +7792,7 @@ msgstr "" "관련 없는(즉, VCE에 의해 생성되지 않은) 다른 워크로드가 GPU에서 실행되고 있는지 알지 못합니다. 여기서 두 가지 시사점을 " "얻을 수 있습니다:" -#: ../../source/how-to-run-simulations.rst:149 +#: ../../source/how-to-run-simulations.rst:228 msgid "" "Your Flower server might need a GPU to evaluate the `global model` after " "aggregation (by instance when making use of the `evaluate method `_를 사용할 때)" -#: ../../source/how-to-run-simulations.rst:150 +#: ../../source/how-to-run-simulations.rst:231 +#, fuzzy msgid "" "If you want to run several independent Flower simulations on the same " "machine you need to mask-out your GPUs with " -":code:`CUDA_VISIBLE_DEVICES=\"\"` when launching your " -"experiment." +"``CUDA_VISIBLE_DEVICES=\"\"`` when launching your experiment." msgstr "" "동일한 머신에서 여러 개의 독립적인 Flower 시뮬레이션을 실행하려면, 실험을 시작할 때 " ":code:`CUDA_VISIBLE_DEVICES=\"\"`로 GPU를 마스킹해야 합니다." -#: ../../source/how-to-run-simulations.rst:153 +#: ../../source/how-to-run-simulations.rst:235 +#, fuzzy msgid "" -"In addition, the GPU resource limits passed to :code:`client_resources` " -"are not `enforced` (i.e. they can be exceeded) which can result in the " +"In addition, the GPU resource limits passed to ``client_resources`` are " +"not `enforced` (i.e. they can be exceeded) which can result in the " "situation of client using more VRAM than the ratio specified when " "starting the simulation." msgstr "" "또한 :code:`client_resources`에 전달된 GPU 리소스 제한이 '강제'되지 않아(즉, 초과할 수 있음) " "클라이언트가 시뮬레이션을 시작할 때 지정된 비율보다 더 많은 VRAM을 사용하는 상황이 발생할 수 있습니다." -#: ../../source/how-to-run-simulations.rst:156 +#: ../../source/how-to-run-simulations.rst:240 msgid "TensorFlow with GPUs" msgstr "GPU를 사용한 TensorFlow" -#: ../../source/how-to-run-simulations.rst:158 +#: ../../source/how-to-run-simulations.rst:242 msgid "" "When `using a GPU with TensorFlow " "`_ nearly your entire GPU memory of" @@ -7726,20 +7844,21 @@ msgstr "" "`_'를 통해 " "이 기본 동작을 비활성화할 수 있습니다." -#: ../../source/how-to-run-simulations.rst:160 +#: ../../source/how-to-run-simulations.rst:249 +#, fuzzy msgid "" "This would need to be done in the main process (which is where the server" " would run) and in each Actor created by the VCE. By means of " -":code:`actor_kwargs` we can pass the reserved key `\"on_actor_init_fn\"` " -"in order to specify a function to be executed upon actor initialization. " -"In this case, to enable GPU growth for TF workloads. It would look as " +"``actor_kwargs`` we can pass the reserved key `\"on_actor_init_fn\"` in " +"order to specify a function to be executed upon actor initialization. In " +"this case, to enable GPU growth for TF workloads. It would look as " "follows:" msgstr "" "이 작업은 메인 프로세스(서버가 실행되는 곳)와 VCE에서 생성한 각 액터에서 수행해야 합니다. " ":code:`actor_kwargs`를 통해 예약 키 `\"on_actor_init_fn\"`을 전달하여 액터 초기화 시 실행할 " "함수를 지정할 수 있습니다. 이 경우 TF 워크로드에 대한 GPU 증가를 활성화합니다. 다음과 같이 보입니다:" -#: ../../source/how-to-run-simulations.rst:179 +#: ../../source/how-to-run-simulations.rst:272 msgid "" "This is precisely the mechanism used in `Tensorflow/Keras Simulation " "`_ 예제에서 사용된 메커니즘입니다." -#: ../../source/how-to-run-simulations.rst:183 +#: ../../source/how-to-run-simulations.rst:276 msgid "Multi-node setups" msgstr "멀티 노드 설정" -#: ../../source/how-to-run-simulations.rst:185 +#: ../../source/how-to-run-simulations.rst:278 msgid "" "The VCE does not currently offer a way to control on which node a " "particular `virtual` client is executed. In other words, if more than a " @@ -7771,7 +7890,7 @@ msgstr "" "방식에 따라 모든 노드에 모든 데이터 세트 파티션의 복사본을 보유하거나 데이터 중복을 피하기 위해 데이터 세트 제공 메커니즘(예: " "nfs, 데이터베이스 사용)을 사용해야 할 수 있습니다." -#: ../../source/how-to-run-simulations.rst:187 +#: ../../source/how-to-run-simulations.rst:286 msgid "" "By definition virtual clients are `stateless` due to their ephemeral " "nature. A client state can be implemented as part of the Flower client " @@ -7804,17 +7923,17 @@ msgid "Model checkpointing" msgstr "모델 체크포인트" #: ../../source/how-to-save-and-load-model-checkpoints.rst:10 +#, fuzzy msgid "" "Model updates can be persisted on the server-side by customizing " -":code:`Strategy` methods. Implementing custom strategies is always an " -"option, but for many cases it may be more convenient to simply customize " -"an existing strategy. The following code example defines a new " -":code:`SaveModelStrategy` which customized the existing built-in " -":code:`FedAvg` strategy. In particular, it customizes " -":code:`aggregate_fit` by calling :code:`aggregate_fit` in the base class " -"(:code:`FedAvg`). It then continues to save returned (aggregated) weights" -" before it returns those aggregated weights to the caller (i.e., the " -"server):" +"``Strategy`` methods. Implementing custom strategies is always an option," +" but for many cases it may be more convenient to simply customize an " +"existing strategy. The following code example defines a new " +"``SaveModelStrategy`` which customized the existing built-in ``FedAvg`` " +"strategy. In particular, it customizes ``aggregate_fit`` by calling " +"``aggregate_fit`` in the base class (``FedAvg``). It then continues to " +"save returned (aggregated) weights before it returns those aggregated " +"weights to the caller (i.e., the server):" msgstr "" ":code:`Strategy` 메소드를 사용자 지정하여 서버 측에서 모델 업데이트를 지속할 수 있습니다. 사용자 지정 전략을 " "구현하는 것은 항상 옵션이지만 대부분의 경우 기존 전략을 간단히 사용자 지정하는 것이 더 편리할 수 있습니다. 다음 코드 예시는 " @@ -7823,11 +7942,11 @@ msgstr "" ":code:`aggregate_fit`을 사용자 지정합니다. 그런 다음 호출자(즉, 서버)에게 집계된 가중치를 반환하기 전에 " "반환된(집계된) 가중치를 계속 저장합니다:" -#: ../../source/how-to-save-and-load-model-checkpoints.rst:47 +#: ../../source/how-to-save-and-load-model-checkpoints.rst:53 msgid "Save and load PyTorch checkpoints" msgstr "파이토치 체크포인트 저장 및 로드" -#: ../../source/how-to-save-and-load-model-checkpoints.rst:49 +#: ../../source/how-to-save-and-load-model-checkpoints.rst:55 msgid "" "Similar to the previous example but with a few extra steps, we'll show " "how to store a PyTorch checkpoint we'll use the ``torch.save`` function. " @@ -7841,7 +7960,7 @@ msgstr "" "``ndarray``의 목록으로 변환되어야 하며, ``OrderedDict`` 클래스 구조에 따라 파이토치 " "``state_dict``로 변환됩니다." -#: ../../source/how-to-save-and-load-model-checkpoints.rst:85 +#: ../../source/how-to-save-and-load-model-checkpoints.rst:98 msgid "" "To load your progress, you simply append the following lines to your " "code. Note that this will iterate over all saved checkpoints and load the" @@ -7850,7 +7969,7 @@ msgstr "" "진행 상황을 로드하려면 코드에 다음 줄을 추가하기만 하면 됩니다. 이렇게 하면 저장된 모든 체크포인트를 반복하고 최신 체크포인트를 " "로드합니다:" -#: ../../source/how-to-save-and-load-model-checkpoints.rst:97 +#: ../../source/how-to-save-and-load-model-checkpoints.rst:111 msgid "" "Return/use this object of type ``Parameters`` wherever necessary, such as" " in the ``initial_parameters`` when defining a ``Strategy``." @@ -7871,22 +7990,22 @@ msgstr "" "Flower 0.19(및 다른 0.x 시리즈 릴리스)와 비교했을 때 기존 0.x 시리즈 프로젝트의 코드를 변경해야 하는 몇 가지 " "획기적인 변경 사항이 있습니다." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:8 -#: ../../source/how-to-upgrade-to-flower-next.rst:43 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:10 +#: ../../source/how-to-upgrade-to-flower-next.rst:63 msgid "Install update" msgstr "업데이트 설치" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:10 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:12 msgid "" "Here's how to update an existing installation to Flower 1.0 using either " "pip or Poetry:" msgstr "다음은 pip 또는 Poetry를 사용하여 기존 설치를 Flower 1.0으로 업데이트하는 방법입니다:" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:12 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:14 msgid "pip: add ``-U`` when installing." msgstr "pip: 설치할 때 ``-U``를 추가합니다." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:14 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:16 msgid "" "``python -m pip install -U flwr`` (when using ``start_server`` and " "``start_client``)" @@ -7894,13 +8013,13 @@ msgstr "" "``python -m pip install -U flwr``(``start_server`` 및 ``start_client``를 " "사용하는 경우)" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:15 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:17 msgid "" "``python -m pip install -U 'flwr[simulation]'`` (when using " "``start_simulation``)" msgstr "``python -m pip install -U 'flwr[simulation]'``(``start_simulation`` 사용 시)" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:17 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:19 msgid "" "Poetry: update the ``flwr`` dependency in ``pyproject.toml`` and then " "reinstall (don't forget to delete ``poetry.lock`` via ``rm poetry.lock`` " @@ -7910,11 +8029,11 @@ msgstr "" "설치하세요(``poetry 설치``를 실행하기 전에 ``rm poetry.lock``을 통해 ``poetry.lock``을 삭제하는" " 것을 잊지 마세요)." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:19 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:23 msgid "``flwr = \"^1.0.0\"`` (when using ``start_server`` and ``start_client``)" msgstr "``flwr = \"^1.0.0\"``(``start_server`` 및 ``start_client`` 사용 시)" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:20 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:24 msgid "" "``flwr = { version = \"^1.0.0\", extras = [\"simulation\"] }`` (when " "using ``start_simulation``)" @@ -7922,26 +8041,26 @@ msgstr "" "``flwr = { version = \"^1.0.0\", extras = [\"simulation\"] }`` " "(``start_simulation`` 사용 시)" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:24 -#: ../../source/how-to-upgrade-to-flower-next.rst:100 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:28 +#: ../../source/how-to-upgrade-to-flower-next.rst:120 msgid "Required changes" msgstr "필수 변경 사항" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:26 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:30 msgid "The following breaking changes require manual updates." msgstr "다음과 같은 주요 변경 사항에는 수동 업데이트가 필요합니다." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:29 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:33 msgid "General" msgstr "일반" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:31 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:35 msgid "" "Pass all arguments as keyword arguments (not as positional arguments). " "Here's an example:" msgstr "모든 전달인자를 위치 전달인자가 아닌 키워드 전달인자로 전달합니다. 다음은 예시입니다:" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:33 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:38 msgid "" "Flower 0.19 (positional arguments): ``start_client(\"127.0.0.1:8080\", " "FlowerClient())``" @@ -7949,7 +8068,7 @@ msgstr "" "Flower 0.19 (위치 전달인자): ``start_client(\"127.0.0.1:8080\", " "FlowerClient())``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:34 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:39 msgid "" "Flower 1.0 (keyword arguments): " "``start_client(server_address=\"127.0.0.1:8080\", " @@ -7958,12 +8077,12 @@ msgstr "" "Flower 1.0 (키워드 전달인자): ``start_client(server_address=\"127.0.0.1:8080\", " "client=FlowerClient())``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:37 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:43 #: ../../source/ref-api/flwr.client.Client.rst:2 msgid "Client" msgstr "클라이언트" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:39 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:45 msgid "" "Subclasses of ``NumPyClient``: change ``def get_parameters(self):``` to " "``def get_parameters(self, config):``" @@ -7971,7 +8090,7 @@ msgstr "" "``NumPyClient``의 서브클래스: ``def get_parameters(self):``를 ``def " "get_parameters(self, config):``로 변경합니다" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:40 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:47 msgid "" "Subclasses of ``Client``: change ``def get_parameters(self):``` to ``def " "get_parameters(self, ins: GetParametersIns):``" @@ -7979,11 +8098,11 @@ msgstr "" "``클라이언트``의 서브클래스: ``def get_parameters(self):``를 ``def " "get_parameters(self, ins: GetParametersIns):``로 변경합니다" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:43 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:51 msgid "Strategies / ``start_server`` / ``start_simulation``" msgstr "전략 / ``start_server`` / ``start_simulation``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:45 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:53 msgid "" "Pass ``ServerConfig`` (instead of a dictionary) to ``start_server`` and " "``start_simulation``. Here's an example:" @@ -7991,7 +8110,7 @@ msgstr "" "Dictionary 대신 ``ServerConfig``를 ``start_server`` 및 ``start_simulation``에 " "전달합니다. 다음은 예제입니다:" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:47 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:56 msgid "" "Flower 0.19: ``start_server(..., config={\"num_rounds\": 3, " "\"round_timeout\": 600.0}, ...)``" @@ -7999,7 +8118,7 @@ msgstr "" "Flower 0.19: ``start_server(..., config={\"num_rounds\": 3, " "\"round_timeout\": 600.0}, ...)``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:48 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:58 msgid "" "Flower 1.0: ``start_server(..., " "config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " @@ -8009,7 +8128,7 @@ msgstr "" "config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " "...)``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:50 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:61 msgid "" "Replace ``num_rounds=1`` in ``start_simulation`` with the new " "``config=ServerConfig(...)`` (see previous item)" @@ -8017,7 +8136,7 @@ msgstr "" "``start_simulation``의 ``num_rounds=1``을 새로운 ``config=ServerConfig(...)``로" " 바꿉니다(이전 항목 참조)" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:51 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:63 msgid "" "Remove ``force_final_distributed_eval`` parameter from calls to " "``start_server``. Distributed evaluation on all clients can be enabled by" @@ -8028,19 +8147,19 @@ msgstr "" "클라이언트에 대한 분산 평가는 마지막 훈련 라운드 후 평가를 위해 모든 클라이언트를 샘플링하도록 전략을 구성하여 활성화할 수 " "있습니다." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:52 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:66 msgid "Rename parameter/ndarray conversion functions:" msgstr "매개변수/ndarray 변환 함수의 이름을 바꿉니다:" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:54 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:68 msgid "``parameters_to_weights`` --> ``parameters_to_ndarrays``" msgstr "``parameters_to_weights`` --> ``parameters_to_ndarrays``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:55 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:69 msgid "``weights_to_parameters`` --> ``ndarrays_to_parameters``" msgstr "``weights_to_parameters`` --> ``ndarrays_to_parameters``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:57 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:71 msgid "" "Strategy initialization: if the strategy relies on the default values for" " ``fraction_fit`` and ``fraction_evaluate``, set ``fraction_fit`` and " @@ -8055,23 +8174,23 @@ msgstr "" "호출하여)는 이제 ``fraction_fit`` 및 ``fraction_evaluate``를 ``0.1``로 설정하여 FedAvg를" " 수동으로 초기화해야 합니다." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:58 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:77 msgid "Rename built-in strategy parameters (e.g., ``FedAvg``):" msgstr "기본 제공 전략 매개변수의 이름을 바꿉니다(예: ``FedAvg``):" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:60 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:79 msgid "``fraction_eval`` --> ``fraction_evaluate``" msgstr "``fraction_eval`` --> ``fraction_evaluate``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:61 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:80 msgid "``min_eval_clients`` --> ``min_evaluate_clients``" msgstr "``min_eval_clients`` --> ``min_evaluate_clients``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:62 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:81 msgid "``eval_fn`` --> ``evaluate_fn``" msgstr "``eval_fn`` --> ``evaluate_fn``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:64 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:83 msgid "" "Rename ``rnd`` to ``server_round``. This impacts multiple methods and " "functions, for example, ``configure_fit``, ``aggregate_fit``, " @@ -8081,11 +8200,11 @@ msgstr "" " ``aggregate_fit``, ``configure_evaluate``, ``aggregate_evaluate`` 및 " "``evaluate_fn``)에 영향을 미칩니다." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:65 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:86 msgid "Add ``server_round`` and ``config`` to ``evaluate_fn``:" msgstr "``server_round`` 및 ``config``를 ``evaluate_fn``에 추가합니다:" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:67 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:88 msgid "" "Flower 0.19: ``def evaluate(parameters: NDArrays) -> " "Optional[Tuple[float, Dict[str, Scalar]]]:``" @@ -8093,7 +8212,7 @@ msgstr "" "Flower 0.19: ``def evaluate(parameters: NDArrays) -> " "Optional[Tuple[float, Dict[str, Scalar]]]:``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:68 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:90 msgid "" "Flower 1.0: ``def evaluate(server_round: int, parameters: NDArrays, " "config: Dict[str, Scalar]) -> Optional[Tuple[float, Dict[str, " @@ -8103,11 +8222,11 @@ msgstr "" "config: Dict[str, Scalar]) -> Optional[Tuple[float, Dict[str, " "Scalar]]]:``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:71 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:94 msgid "Custom strategies" msgstr "사용자 정의 전략" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:73 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:96 msgid "" "The type of parameter ``failures`` has changed from " "``List[BaseException]`` to ``List[Union[Tuple[ClientProxy, FitRes], " @@ -8120,13 +8239,13 @@ msgstr "" "BaseException]]``(``aggregate_fit``에서) 및 ``List[Union[Tuple[ClientProxy]," " EvaluateRes], BaseException]]``(``aggregate_evaluate``)로 변경되었습니다" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:74 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:100 msgid "" "The ``Strategy`` method ``evaluate`` now receives the current round of " "federated learning/evaluation as the first parameter:" msgstr "이제 ``Strategy`` 메서드 ``evaluate``는 현재 federated 학습/평가 라운드를 첫 번째 파라미터로 받습니다:" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:76 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:103 msgid "" "Flower 0.19: ``def evaluate(self, parameters: Parameters) -> " "Optional[Tuple[float, Dict[str, Scalar]]]:``" @@ -8134,7 +8253,7 @@ msgstr "" "Flower 0.19: ``def evaluate(self, parameters: Parameters) -> " "Optional[Tuple[float, Dict[str, Scalar]]]:``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:77 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:105 msgid "" "Flower 1.0: ``def evaluate(self, server_round: int, parameters: " "Parameters) -> Optional[Tuple[float, Dict[str, Scalar]]]:``" @@ -8142,17 +8261,17 @@ msgstr "" "Flower 1.0: ``def evaluate(self, server_round: int, parameters: " "Parameters) -> Optional[Tuple[float, Dict[str, Scalar]]]:``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:80 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:109 msgid "Optional improvements" msgstr "선택적 개선 사항" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:82 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:111 msgid "" "Along with the necessary changes above, there are a number of potential " "improvements that just became possible:" msgstr "위의 필수 변경 사항과 함께 방금 가능한 여러 가지 잠재적 개선 사항이 있습니다:" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:84 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:114 msgid "" "Remove \"placeholder\" methods from subclasses of ``Client`` or " "``NumPyClient``. If you, for example, use server-side evaluation, then " @@ -8162,7 +8281,7 @@ msgstr "" "``Client`` 또는 ``NumPyClient``의 서브 클래스에서 \"placeholder\" 메서드를 제거합니다. 예를 들어" " 서버 측 평가를 사용하는 경우 ``evaluate``의 빈 자리 표시자 구현은 더 이상 필요하지 않습니다." -#: ../../source/how-to-upgrade-to-flower-1.0.rst:85 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:117 msgid "" "Configure the round timeout via ``start_simulation``: " "``start_simulation(..., config=flwr.server.ServerConfig(num_rounds=3, " @@ -8172,12 +8291,12 @@ msgstr "" "config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), " "...)``" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:89 -#: ../../source/how-to-upgrade-to-flower-next.rst:317 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:121 +#: ../../source/how-to-upgrade-to-flower-next.rst:348 msgid "Further help" msgstr "추가 도움말" -#: ../../source/how-to-upgrade-to-flower-1.0.rst:91 +#: ../../source/how-to-upgrade-to-flower-1.0.rst:123 msgid "" "Most official `Flower code examples " "`_ are already updated" @@ -8206,7 +8325,7 @@ msgstr "" " 시작한 사용자든 상관없이 기존 설정을 원활하게 전환하여 버전 1.8부터 Flower Next의 최신 기능 및 개선 사항을 활용할 " "수 있도록 도와드립니다." -#: ../../source/how-to-upgrade-to-flower-next.rst:9 +#: ../../source/how-to-upgrade-to-flower-next.rst:11 msgid "" "This guide shows how to reuse pre-``1.8`` Flower code with minimum code " "changes by using the *compatibility layer* in Flower Next. In another " @@ -8217,35 +8336,35 @@ msgstr "" " 재사용하는 방법을 보여줍니다. 다른 가이드에서는 순수한 Flower Next API로 Flower Next를 end-to-end로" " 실행하는 방법을 보여드리겠습니다." -#: ../../source/how-to-upgrade-to-flower-next.rst:13 +#: ../../source/how-to-upgrade-to-flower-next.rst:15 msgid "Let's dive in!" msgstr "자세히 알아봅시다!" -#: ../../source/how-to-upgrade-to-flower-next.rst:48 +#: ../../source/how-to-upgrade-to-flower-next.rst:68 msgid "" "Here's how to update an existing installation of Flower to Flower Next " "with ``pip``:" msgstr "기존에 설치된 Flower to Flower Next를 ``pip``으로 업데이트하는 방법은 다음과 같습니다:" -#: ../../source/how-to-upgrade-to-flower-next.rst:54 +#: ../../source/how-to-upgrade-to-flower-next.rst:74 msgid "or if you need Flower Next with simulation:" msgstr "또는 시뮬레이션이 포함된 Flower Next가 필요한 경우:" -#: ../../source/how-to-upgrade-to-flower-next.rst:61 +#: ../../source/how-to-upgrade-to-flower-next.rst:80 msgid "" "Ensure you set the following version constraint in your " "``requirements.txt``" msgstr "``requirements.txt``에서 다음 버전 제약 조건을 설정했는지 확인하세요" -#: ../../source/how-to-upgrade-to-flower-next.rst:71 +#: ../../source/how-to-upgrade-to-flower-next.rst:90 msgid "or ``pyproject.toml``:" msgstr "또는 ``pyproject.toml``:" -#: ../../source/how-to-upgrade-to-flower-next.rst:82 +#: ../../source/how-to-upgrade-to-flower-next.rst:101 msgid "Using Poetry" msgstr "Poetry 사용" -#: ../../source/how-to-upgrade-to-flower-next.rst:84 +#: ../../source/how-to-upgrade-to-flower-next.rst:103 msgid "" "Update the ``flwr`` dependency in ``pyproject.toml`` and then reinstall " "(don't forget to delete ``poetry.lock`` via ``rm poetry.lock`` before " @@ -8254,13 +8373,13 @@ msgstr "" "``pyproject.toml``에서 ``flwr`` 의존성를 업데이트한 다음 다시 설치하세요(``poetry install``을 " "실행하기 전에 ``rm poetry.lock``을 통해 ``poetry.lock``을 삭제하는 것을 잊지 마세요)." -#: ../../source/how-to-upgrade-to-flower-next.rst:86 +#: ../../source/how-to-upgrade-to-flower-next.rst:106 msgid "" "Ensure you set the following version constraint in your " "``pyproject.toml``:" msgstr "``pyproject.toml``에 다음 버전 제약 조건을 설정했는지 확인하세요:" -#: ../../source/how-to-upgrade-to-flower-next.rst:102 +#: ../../source/how-to-upgrade-to-flower-next.rst:122 msgid "" "In Flower Next, the *infrastructure* and *application layers* have been " "decoupled. Instead of starting a client in code via ``start_client()``, " @@ -8279,11 +8398,11 @@ msgstr "" "업데이트가 필요하지 않고 기존 방식과 Flower Next 방식 모두에서 프로젝트를 실행할 수 있는 non-breaking 변경 " "사항은 다음과 같습니다:" -#: ../../source/how-to-upgrade-to-flower-next.rst:109 +#: ../../source/how-to-upgrade-to-flower-next.rst:131 msgid "|clientapp_link|_" msgstr "|clientapp_link|_" -#: ../../source/how-to-upgrade-to-flower-next.rst:110 +#: ../../source/how-to-upgrade-to-flower-next.rst:133 msgid "" "Wrap your existing client with |clientapp_link|_ instead of launching it " "via |startclient_link|_. Here's an example:" @@ -8291,11 +8410,11 @@ msgstr "" "|clientapp_link|_를 통해 실행하는 대신 기존 클라이언트를 |clientapp_link|_로 래핑하세요. 다음은 " "예시입니다:" -#: ../../source/how-to-upgrade-to-flower-next.rst:132 +#: ../../source/how-to-upgrade-to-flower-next.rst:156 msgid "|serverapp_link|_" msgstr "|serverapp_link|_" -#: ../../source/how-to-upgrade-to-flower-next.rst:133 +#: ../../source/how-to-upgrade-to-flower-next.rst:158 msgid "" "Wrap your existing strategy with |serverapp_link|_ instead of starting " "the server via |startserver_link|_. Here's an example:" @@ -8303,11 +8422,11 @@ msgstr "" "서버를 시작하려면 |startserver_link|_를 통해 서버를 시작하는 대신 기존 전략을 |serverapp_link|_로 " "래핑하세요. 다음은 예시입니다:" -#: ../../source/how-to-upgrade-to-flower-next.rst:154 +#: ../../source/how-to-upgrade-to-flower-next.rst:179 msgid "Deployment" msgstr "배포" -#: ../../source/how-to-upgrade-to-flower-next.rst:155 +#: ../../source/how-to-upgrade-to-flower-next.rst:181 msgid "" "Run the ``SuperLink`` using |flowernext_superlink_link|_ before running, " "in sequence, |flowernext_clientapp_link|_ (2x) and " @@ -8318,13 +8437,13 @@ msgstr "" "|flowernext_clientapp_link|_(2회) 및 |flowernext_serverapp_link|_를 순서대로 " "실행합니다. 'client.py'와 'server.py'를 Python 스크립트로 실행할 필요는 없습니다." -#: ../../source/how-to-upgrade-to-flower-next.rst:158 +#: ../../source/how-to-upgrade-to-flower-next.rst:184 msgid "" "Here's an example to start the server without HTTPS (only for " "prototyping):" msgstr "다음은 HTTPS 없이 서버를 시작하는 예제입니다(프로토타이핑용으로만 사용):" -#: ../../source/how-to-upgrade-to-flower-next.rst:174 +#: ../../source/how-to-upgrade-to-flower-next.rst:200 msgid "" "Here's another example to start with HTTPS. Use the ``--ssl-ca-" "certfile``, ``--ssl-certfile``, and ``--ssl-keyfile`` command line " @@ -8334,11 +8453,11 @@ msgstr "" "다음은 HTTPS로 시작하는 또 다른 예제입니다. '`--ssl-ca-certfile``, '`--ssl-certfile``, " "'`--ssl-keyfile`` 명령줄 옵션을 사용하여 (CA 인증서, 서버 인증서 및 서버 개인 키)의 경로를 전달합니다." -#: ../../source/how-to-upgrade-to-flower-next.rst:201 +#: ../../source/how-to-upgrade-to-flower-next.rst:228 msgid "Simulation in CLI" msgstr "CLI 시뮬레이션" -#: ../../source/how-to-upgrade-to-flower-next.rst:202 +#: ../../source/how-to-upgrade-to-flower-next.rst:230 msgid "" "Wrap your existing client and strategy with |clientapp_link|_ and " "|serverapp_link|_, respectively. There is no need to use |startsim_link|_" @@ -8347,7 +8466,7 @@ msgstr "" "기존 클라이언트와 전략을 각각 |clientapp_link|_와 |serverapp_link|_로 래핑하세요. 더 이상 " "|startsim_link|_를 사용할 필요가 없습니다. 다음은 예시입니다:" -#: ../../source/how-to-upgrade-to-flower-next.rst:232 +#: ../../source/how-to-upgrade-to-flower-next.rst:263 msgid "" "Run |flower_simulation_link|_ in CLI and point to the ``server_app`` / " "``client_app`` object in the code instead of executing the Python script." @@ -8358,7 +8477,7 @@ msgstr "" "``server_app`` / ``client_app`` 개체를 가리키세요. 다음은 예제입니다(``server_app`` 및 " "``client_app`` 객체가 ``sim.py`` 모듈에 있다고 가정):" -#: ../../source/how-to-upgrade-to-flower-next.rst:249 +#: ../../source/how-to-upgrade-to-flower-next.rst:280 msgid "" "Set default resources for each |clientapp_link|_ using the ``--backend-" "config`` command line argument instead of setting the " @@ -8367,17 +8486,17 @@ msgstr "" "|startsim_link|_에서 ``client_resources`` 인수를 설정하는 대신 ``--backend-config`` " "명령줄 인수를 사용하여 각 |clientapp_link|_에 대한 기본 리소스를 설정하세요. 다음은 예시입니다:" -#: ../../source/how-to-upgrade-to-flower-next.rst:275 +#: ../../source/how-to-upgrade-to-flower-next.rst:304 msgid "Simulation in a Notebook" msgstr "Notebook에서 시뮬레이션" -#: ../../source/how-to-upgrade-to-flower-next.rst:276 +#: ../../source/how-to-upgrade-to-flower-next.rst:306 msgid "" "Run |runsim_link|_ in your notebook instead of |startsim_link|_. Here's " "an example:" msgstr "notebook에서 |startsim_link|_ 대신 |runsim_link|_를 실행하세요. 다음은 예시입니다:" -#: ../../source/how-to-upgrade-to-flower-next.rst:319 +#: ../../source/how-to-upgrade-to-flower-next.rst:350 msgid "" "Some official `Flower code examples `_ " "are already updated to Flower Next so they can serve as a reference for " @@ -8393,11 +8512,11 @@ msgstr "" "``Flower Discuss `_에 참여하여 질문에 대한 답변을 확인하거나 다른" " 사람들과 Flower Next로의 이동에 대해 공유하고 배울 수 있습니다." -#: ../../source/how-to-upgrade-to-flower-next.rst:325 +#: ../../source/how-to-upgrade-to-flower-next.rst:357 msgid "Important" msgstr "중요" -#: ../../source/how-to-upgrade-to-flower-next.rst:328 +#: ../../source/how-to-upgrade-to-flower-next.rst:359 msgid "" "As we continuously enhance Flower Next at a rapid pace, we'll be " "periodically updating this guide. Please feel free to share any feedback " @@ -8406,7 +8525,7 @@ msgstr "" "Flower Next는 빠른 속도로 지속적으로 개선되고 있으므로 이 가이드는 주기적으로 업데이트될 예정입니다. 피드백이 있으면 " "언제든지 공유해 주세요!" -#: ../../source/how-to-upgrade-to-flower-next.rst:334 +#: ../../source/how-to-upgrade-to-flower-next.rst:365 msgid "Happy migrating! 🚀" msgstr "행복한 마이그레이션! 🚀" @@ -8420,7 +8539,7 @@ msgid "" " interfaces may change in future versions.**" msgstr "**참고: 이 튜토리얼은 실험적인 기능을 다룹니다. 기능 및 인터페이스는 향후 버전에서 변경될 수 있습니다.**" -#: ../../source/how-to-use-built-in-mods.rst:6 +#: ../../source/how-to-use-built-in-mods.rst:7 msgid "" "In this tutorial, we will learn how to utilize built-in mods to augment " "the behavior of a ``ClientApp``. Mods (sometimes also called Modifiers) " @@ -8430,11 +8549,11 @@ msgstr "" "이 튜토리얼에서는 내장 모드를 활용하여 ``ClientApp``의 동작을 보강하는 방법을 배우겠습니다. " "Mods(Modifiers라고도 함)를 사용하면 ``ClientApp``에서 작업이 처리되기 전과 후에 작업을 수행할 수 있습니다." -#: ../../source/how-to-use-built-in-mods.rst:9 +#: ../../source/how-to-use-built-in-mods.rst:12 msgid "What are Mods?" msgstr "Mods란 무엇인가요?" -#: ../../source/how-to-use-built-in-mods.rst:11 +#: ../../source/how-to-use-built-in-mods.rst:14 msgid "" "A Mod is a callable that wraps around a ``ClientApp``. It can manipulate " "or inspect the incoming ``Message`` and the resulting outgoing " @@ -8443,41 +8562,41 @@ msgstr "" "Mod는 ``ClientApp``을 감싸는 콜러블입니다. 들어오는 ``Message``와 그 결과로 나가는 ``Message``를 " "조작하거나 검사할 수 있습니다. ``Mod``의 시그니처는 다음과 같습니다:" -#: ../../source/how-to-use-built-in-mods.rst:18 +#: ../../source/how-to-use-built-in-mods.rst:23 msgid "A typical mod function might look something like this:" msgstr "일반적인 mod 함수는 다음과 같은 모습일 수 있습니다:" -#: ../../source/how-to-use-built-in-mods.rst:31 +#: ../../source/how-to-use-built-in-mods.rst:36 msgid "Using Mods" msgstr "Mods 사용" -#: ../../source/how-to-use-built-in-mods.rst:33 +#: ../../source/how-to-use-built-in-mods.rst:38 msgid "To use mods in your ``ClientApp``, you can follow these steps:" msgstr "``ClientApp``에서 mods를 사용하려면 다음 단계를 따르세요:" -#: ../../source/how-to-use-built-in-mods.rst:36 +#: ../../source/how-to-use-built-in-mods.rst:41 msgid "1. Import the required mods" msgstr "1. 필요한 mods를 가져옵니다" -#: ../../source/how-to-use-built-in-mods.rst:38 +#: ../../source/how-to-use-built-in-mods.rst:43 msgid "First, import the built-in mod you intend to use:" msgstr "먼저 사용하려는 기본 제공 mod를 가져옵니다:" -#: ../../source/how-to-use-built-in-mods.rst:46 +#: ../../source/how-to-use-built-in-mods.rst:51 msgid "2. Define your client function" msgstr "2. 클라이언트 기능 정의" -#: ../../source/how-to-use-built-in-mods.rst:48 +#: ../../source/how-to-use-built-in-mods.rst:53 msgid "" "Define your client function (``client_fn``) that will be wrapped by the " "mod(s):" msgstr "mod(s)로 래핑할 클라이언트 함수('``client_fn``)를 정의합니다:" -#: ../../source/how-to-use-built-in-mods.rst:57 +#: ../../source/how-to-use-built-in-mods.rst:62 msgid "3. Create the ``ClientApp`` with mods" msgstr "3. mods로 ``ClientApp``을 생성합니다" -#: ../../source/how-to-use-built-in-mods.rst:59 +#: ../../source/how-to-use-built-in-mods.rst:64 msgid "" "Create your ``ClientApp`` and pass the mods as a list to the ``mods`` " "argument. The order in which you provide the mods matters:" @@ -8485,39 +8604,39 @@ msgstr "" "``ClientApp``을 생성하고 mods를 ``mods`` argument에 목록으로 전달합니다. mods를 제공하는 순서가 " "중요합니다:" -#: ../../source/how-to-use-built-in-mods.rst:72 +#: ../../source/how-to-use-built-in-mods.rst:78 msgid "Order of execution" msgstr "실행 순서" -#: ../../source/how-to-use-built-in-mods.rst:74 +#: ../../source/how-to-use-built-in-mods.rst:80 msgid "" "When the ``ClientApp`` runs, the mods are executed in the order they are " "provided in the list:" msgstr "``ClientApp``이 실행되면 목록에 제공된 순서대로 모드가 실행됩니다:" -#: ../../source/how-to-use-built-in-mods.rst:76 +#: ../../source/how-to-use-built-in-mods.rst:83 msgid "``example_mod_1`` (outermost mod)" msgstr "``example_mod_1``(가장 바깥쪽 mod)" -#: ../../source/how-to-use-built-in-mods.rst:77 +#: ../../source/how-to-use-built-in-mods.rst:84 msgid "``example_mod_2`` (next mod)" msgstr "``example_mod_2`` (다음 mod)" -#: ../../source/how-to-use-built-in-mods.rst:78 +#: ../../source/how-to-use-built-in-mods.rst:85 msgid "" "Message handler (core function that handles the incoming ``Message`` and " "returns the outgoing ``Message``)" msgstr "Message handler(들어오는 ``Message``를 처리하고 나가는 ``Message``를 반환하는 핵심 함수)" -#: ../../source/how-to-use-built-in-mods.rst:79 +#: ../../source/how-to-use-built-in-mods.rst:87 msgid "``example_mod_2`` (on the way back)" msgstr "``example_mod_2``(돌아가는 방법)" -#: ../../source/how-to-use-built-in-mods.rst:80 +#: ../../source/how-to-use-built-in-mods.rst:88 msgid "``example_mod_1`` (outermost mod on the way back)" msgstr "``example_mod_1``(돌아가는 방법에 가장 바깥쪽 모드)" -#: ../../source/how-to-use-built-in-mods.rst:82 +#: ../../source/how-to-use-built-in-mods.rst:90 msgid "" "Each mod has a chance to inspect and modify the incoming ``Message`` " "before passing it to the next mod, and likewise with the outgoing " @@ -8526,7 +8645,7 @@ msgstr "" "각 mod는 다음 mod로 전달하기 전에 들어오는 ``Message``를 검사하고 수정할 기회가 있으며, 스택 위로 반환하기 전에 " "나가는 ``Message``도 마찬가지로 검사하고 수정할 수 있습니다." -#: ../../source/how-to-use-built-in-mods.rst:87 +#: ../../source/how-to-use-built-in-mods.rst:97 msgid "" "By following this guide, you have learned how to effectively use mods to " "enhance your ``ClientApp``'s functionality. Remember that the order of " @@ -8535,7 +8654,7 @@ msgstr "" "이 가이드를 따라 mods를 효과적으로 사용하여 ``ClientApp``의 기능을 향상시키는 방법을 배웠습니다. mods 순서는 " "매우 중요하며 입력과 출력이 처리되는 방식에 영향을 미친다는 점을 기억하세요." -#: ../../source/how-to-use-built-in-mods.rst:89 +#: ../../source/how-to-use-built-in-mods.rst:101 msgid "Enjoy building a more robust and flexible ``ClientApp`` with mods!" msgstr "Mods를 통해 더욱 강력하고 유연한 ``ClientApp``을 구축해 보세요!" @@ -8543,7 +8662,7 @@ msgstr "Mods를 통해 더욱 강력하고 유연한 ``ClientApp``을 구축해 msgid "Use Differential Privacy" msgstr "차분 개인정보 보호 사용" -#: ../../source/how-to-use-differential-privacy.rst:3 +#: ../../source/how-to-use-differential-privacy.rst:4 msgid "" "This guide explains how you can utilize differential privacy in the " "Flower framework. If you are not yet familiar with differential privacy, " @@ -8552,7 +8671,7 @@ msgstr "" "이 가이드에서는 Flower 프레임워크에서 차분 개인정보 보호 기능을 활용하는 방법을 설명합니다. 차분 개인정보 보호에 대해 아직 " "익숙하지 않은 경우 :doc:`explanation-differential-privacy`를 참조하세요." -#: ../../source/how-to-use-differential-privacy.rst:7 +#: ../../source/how-to-use-differential-privacy.rst:10 msgid "" "Differential Privacy in Flower is in a preview phase. If you plan to use " "these features in a production environment with sensitive data, feel free" @@ -8562,7 +8681,7 @@ msgstr "" "Flower의 차분 개인정보 보호는 현재 프리뷰 단계에 있습니다. 민감한 데이터가 있는 프로덕션 환경에서 이러한 기능을 사용할 " "계획이라면 언제든지 문의하여 요구 사항을 논의하고 이러한 기능을 가장 잘 사용하는 방법에 대한 안내를 받으세요." -#: ../../source/how-to-use-differential-privacy.rst:12 +#: ../../source/how-to-use-differential-privacy.rst:17 #, fuzzy msgid "" "This approach consists of two separate phases: clipping of the updates " @@ -8573,7 +8692,7 @@ msgstr "" "이 접근 방식은 업데이트 클리핑과 집계된 모델에 노이즈 추가라는 두 가지 단계로 구성됩니다. 클리핑 단계의 경우, Flower " "프레임워크는 클리핑을 서버 측에서 수행할지 클라이언트 측에서 수행할지 결정할 수 있도록 했습니다." -#: ../../source/how-to-use-differential-privacy.rst:15 +#: ../../source/how-to-use-differential-privacy.rst:21 msgid "" "**Server-side Clipping**: This approach has the advantage of the server " "enforcing uniform clipping across all clients' updates and reducing the " @@ -8585,7 +8704,7 @@ msgstr "" "값에 대한 통신 오버헤드를 줄일 수 있다는 장점이 있습니다. 하지만 모든 클라이언트에 대해 클리핑 작업을 수행해야 하기 때문에 " "서버의 계산 부하가 증가한다는 단점도 있습니다." -#: ../../source/how-to-use-differential-privacy.rst:16 +#: ../../source/how-to-use-differential-privacy.rst:26 msgid "" "**Client-side Clipping**: This approach has the advantage of reducing the" " computational overhead on the server. However, it also has the " @@ -8595,35 +8714,36 @@ msgstr "" "**Client-side Clipping**: 이 방식은 서버의 계산 오버헤드를 줄일 수 있다는 장점이 있습니다. 하지만 서버가 " "클리핑 프로세스에 대한 통제력이 떨어지기 때문에 centralized 제어가 부족하다는 단점도 있습니다." -#: ../../source/how-to-use-differential-privacy.rst:21 +#: ../../source/how-to-use-differential-privacy.rst:31 msgid "Server-side Clipping" msgstr "서버 측 클리핑" -#: ../../source/how-to-use-differential-privacy.rst:22 +#: ../../source/how-to-use-differential-privacy.rst:33 +#, fuzzy msgid "" -"For central DP with server-side clipping, there are two :code:`Strategy` " -"classes that act as wrappers around the actual :code:`Strategy` instance " -"(for example, :code:`FedAvg`). The two wrapper classes are " -":code:`DifferentialPrivacyServerSideFixedClipping` and " -":code:`DifferentialPrivacyServerSideAdaptiveClipping` for fixed and " -"adaptive clipping." +"For central DP with server-side clipping, there are two ``Strategy`` " +"classes that act as wrappers around the actual ``Strategy`` instance (for" +" example, ``FedAvg``). The two wrapper classes are " +"``DifferentialPrivacyServerSideFixedClipping`` and " +"``DifferentialPrivacyServerSideAdaptiveClipping`` for fixed and adaptive " +"clipping." msgstr "" "서버 측 클리핑이 있는 중앙 DP의 경우, 실제 :code:`Strategy` 인스턴스를 감싸는 래퍼 역할을 하는 두 개의 " ":code:`Strategy` 클래스가 있습니다(예: :code:`FedAvg`). 두 개의 래퍼 클래스는 고정 및 적응형 클리핑을" " 위한 :code:`DifferentialPrivacyServerSideFixedClipping`과 " ":code:`DifferentialPrivacyServerSideAdaptiveClipping`입니다." -#: ../../source/how-to-use-differential-privacy.rst:25 +#: ../../source/how-to-use-differential-privacy.rst:-1 msgid "server side clipping" msgstr "서버 측 클리핑" -#: ../../source/how-to-use-differential-privacy.rst:31 +#: ../../source/how-to-use-differential-privacy.rst:43 +#, fuzzy msgid "" -"The code sample below enables the :code:`FedAvg` strategy to use server-" -"side fixed clipping using the " -":code:`DifferentialPrivacyServerSideFixedClipping` wrapper class. The " -"same approach can be used with " -":code:`DifferentialPrivacyServerSideAdaptiveClipping` by adjusting the " +"The code sample below enables the ``FedAvg`` strategy to use server-side " +"fixed clipping using the ``DifferentialPrivacyServerSideFixedClipping`` " +"wrapper class. The same approach can be used with " +"``DifferentialPrivacyServerSideAdaptiveClipping`` by adjusting the " "corresponding input parameters." msgstr "" "아래 코드 샘플은 :code:`FedAvg` 전략이 " @@ -8632,19 +8752,20 @@ msgstr "" ":code:`DifferentialPrivacyServerSideAdaptiveClipping`과 동일한 접근 방식을 사용할 수 " "있습니다." -#: ../../source/how-to-use-differential-privacy.rst:52 +#: ../../source/how-to-use-differential-privacy.rst:64 msgid "Client-side Clipping" msgstr "클라이언트 측 클리핑" -#: ../../source/how-to-use-differential-privacy.rst:53 +#: ../../source/how-to-use-differential-privacy.rst:66 +#, fuzzy msgid "" "For central DP with client-side clipping, the server sends the clipping " "value to selected clients on each round. Clients can use existing Flower " -":code:`Mods` to perform the clipping. Two mods are available for fixed " -"and adaptive client-side clipping: :code:`fixedclipping_mod` and " -":code:`adaptiveclipping_mod` with corresponding server-side wrappers " -":code:`DifferentialPrivacyClientSideFixedClipping` and " -":code:`DifferentialPrivacyClientSideAdaptiveClipping`." +"``Mods`` to perform the clipping. Two mods are available for fixed and " +"adaptive client-side clipping: ``fixedclipping_mod`` and " +"``adaptiveclipping_mod`` with corresponding server-side wrappers " +"``DifferentialPrivacyClientSideFixedClipping`` and " +"``DifferentialPrivacyClientSideAdaptiveClipping``." msgstr "" "클라이언트 측 클리핑이 있는 중앙 DP의 경우 서버는 각 라운드마다 선택한 클라이언트에 클리핑 값을 보냅니다. 클라이언트는 기존 " "Flower :code:`Mods`를 사용하여 클리핑을 수행할 수 있습니다. 고정 및 적응형 클라이언트 측 클리핑에는 두 가지 " @@ -8652,31 +8773,33 @@ msgstr "" " 해당 서버 측 래퍼 :code:`DifferentialPrivacyClientSideFixedClipping` 및 " ":code:`DifferentialPrivacyClientSideAdaptiveClipping`이 있습니다." -#: ../../source/how-to-use-differential-privacy.rst:57 +#: ../../source/how-to-use-differential-privacy.rst:-1 msgid "client side clipping" msgstr "클라이언트 측 클리핑" -#: ../../source/how-to-use-differential-privacy.rst:63 +#: ../../source/how-to-use-differential-privacy.rst:78 +#, fuzzy msgid "" -"The code sample below enables the :code:`FedAvg` strategy to use " -"differential privacy with client-side fixed clipping using both the " -":code:`DifferentialPrivacyClientSideFixedClipping` wrapper class and, on " -"the client, :code:`fixedclipping_mod`:" +"The code sample below enables the ``FedAvg`` strategy to use differential" +" privacy with client-side fixed clipping using both the " +"``DifferentialPrivacyClientSideFixedClipping`` wrapper class and, on the " +"client, ``fixedclipping_mod``:" msgstr "" "아래 코드 샘플은 :code:`FedAvg` 전략이 클라이언트 측 고정 클리핑과 함께 차분 프라이버시를 사용할 수 있도록 " ":code:`DifferentialPrivacyClientSideFixedClipping` 래퍼 클래스와 클라이언트에서 " ":code:`fixedclipping_mod`를 모두 사용하도록 합니다:" -#: ../../source/how-to-use-differential-privacy.rst:80 +#: ../../source/how-to-use-differential-privacy.rst:97 +#, fuzzy msgid "" -"In addition to the server-side strategy wrapper, the :code:`ClientApp` " -"needs to configure the matching :code:`fixedclipping_mod` to perform the " -"client-side clipping:" +"In addition to the server-side strategy wrapper, the ``ClientApp`` needs " +"to configure the matching ``fixedclipping_mod`` to perform the client-" +"side clipping:" msgstr "" "서버 측 전략 래퍼 외에도 클라이언트 측 클리핑을 수행하려면 :code:`ClientApp`이 일치하는 " ":code:`fixedclipping_mod`를 구성해야 합니다:" -#: ../../source/how-to-use-differential-privacy.rst:97 +#: ../../source/how-to-use-differential-privacy.rst:115 msgid "" "To utilize local differential privacy (DP) and add noise to the client " "model parameters before transmitting them to the server in Flower, you " @@ -8686,15 +8809,16 @@ msgstr "" "로컬 차분 프라이버시(DP)를 활용하고 클라이언트 모델 파라미터를 서버로 전송하기 전에 노이즈를 추가하려면 `LocalDpMod`를" " 사용하면 됩니다. 클리핑 노멀 값, 감도, 엡실론, 델타 등의 하이퍼파라미터를 설정해야 합니다." -#: ../../source/how-to-use-differential-privacy.rst:99 +#: ../../source/how-to-use-differential-privacy.rst:-1 msgid "local DP mod" msgstr "로컬 DP mod" -#: ../../source/how-to-use-differential-privacy.rst:104 -msgid "Below is a code example that shows how to use :code:`LocalDpMod`:" +#: ../../source/how-to-use-differential-privacy.rst:125 +#, fuzzy +msgid "Below is a code example that shows how to use ``LocalDpMod``:" msgstr "다음은 :code:`LocalDpMod`를 사용하는 방법을 보여주는 코드 예시입니다:" -#: ../../source/how-to-use-differential-privacy.rst:122 +#: ../../source/how-to-use-differential-privacy.rst:140 msgid "" "Please note that the order of mods, especially those that modify " "parameters, is important when using multiple modifiers. Typically, " @@ -8704,11 +8828,11 @@ msgstr "" "여러 개의 수정자를 사용할 때는 수정자, 특히 매개변수를 수정하는 수정자의 순서가 중요하다는 점에 유의하세요. 일반적으로 차분 " "프라이버시(DP) 수정자는 매개변수에서 가장 마지막에 작동해야 합니다." -#: ../../source/how-to-use-differential-privacy.rst:125 +#: ../../source/how-to-use-differential-privacy.rst:145 msgid "Local Training using Privacy Engines" msgstr "Privacy Engines을 사용한 로컬 훈련" -#: ../../source/how-to-use-differential-privacy.rst:126 +#: ../../source/how-to-use-differential-privacy.rst:147 msgid "" "For ensuring data instance-level privacy during local model training on " "the client side, consider leveraging privacy engines such as Opacus and " @@ -8730,54 +8854,57 @@ msgid "Use strategies" msgstr "전략 사용하기" #: ../../source/how-to-use-strategies.rst:4 +#, fuzzy msgid "" "Flower allows full customization of the learning process through the " -":code:`Strategy` abstraction. A number of built-in strategies are " -"provided in the core framework." +"``Strategy`` abstraction. A number of built-in strategies are provided in" +" the core framework." msgstr "" "Flower는 :code:`Strategy` abstraction를 통해 학습 과정을 완전히 사용자 정의할 수 있습니다. 핵심 " "프레임워크에는 여러 가지 기본 제공 전략이 제공됩니다." -#: ../../source/how-to-use-strategies.rst:6 +#: ../../source/how-to-use-strategies.rst:7 msgid "" "There are three ways to customize the way Flower orchestrates the " "learning process on the server side:" msgstr "서버 측에서 Flower가 학습 과정을 조율하는 방식을 사용자 지정하는 방법에는 세 가지가 있습니다:" -#: ../../source/how-to-use-strategies.rst:8 -msgid "Use an existing strategy, for example, :code:`FedAvg`" +#: ../../source/how-to-use-strategies.rst:10 +#, fuzzy +msgid "Use an existing strategy, for example, ``FedAvg``" msgstr "기존 전략(예: :code:`FedAvg`)을 사용합니다" -#: ../../source/how-to-use-strategies.rst:9 -#: ../../source/how-to-use-strategies.rst:40 +#: ../../source/how-to-use-strategies.rst:11 +#: ../../source/how-to-use-strategies.rst:43 msgid "Customize an existing strategy with callback functions" msgstr "콜백 함수로 기존 전략 사용자 지정" -#: ../../source/how-to-use-strategies.rst:10 -#: ../../source/how-to-use-strategies.rst:87 +#: ../../source/how-to-use-strategies.rst:12 +#: ../../source/how-to-use-strategies.rst:99 msgid "Implement a novel strategy" msgstr "새로운 전략 구현" -#: ../../source/how-to-use-strategies.rst:14 +#: ../../source/how-to-use-strategies.rst:15 msgid "Use an existing strategy" msgstr "기존 전략 사용" -#: ../../source/how-to-use-strategies.rst:16 +#: ../../source/how-to-use-strategies.rst:17 msgid "" "Flower comes with a number of popular federated learning strategies " "built-in. A built-in strategy can be instantiated as follows:" msgstr "Flower에는 여러 가지 인기 있는 연합 학습 전략이 기본으로 제공됩니다. 기본 제공 전략은 다음과 같이 인스턴스화할 수 있습니다:" -#: ../../source/how-to-use-strategies.rst:25 +#: ../../source/how-to-use-strategies.rst:27 +#, fuzzy msgid "" "This creates a strategy with all parameters left at their default values " -"and passes it to the :code:`start_server` function. It is usually " -"recommended to adjust a few parameters during instantiation:" +"and passes it to the ``start_server`` function. It is usually recommended" +" to adjust a few parameters during instantiation:" msgstr "" "이렇게 하면 모든 매개변수가 기본값으로 유지된 전략이 생성되어 :code:`start_server` 함수에 전달됩니다. 일반적으로 " "인스턴스화 중에 몇 가지 매개변수를 조정하는 것이 좋습니다:" -#: ../../source/how-to-use-strategies.rst:42 +#: ../../source/how-to-use-strategies.rst:45 msgid "" "Existing strategies provide several ways to customize their behaviour. " "Callback functions allow strategies to call user-provided code during " @@ -8786,58 +8913,59 @@ msgstr "" "기존 전략은 동작을 사용자 지정하는 여러 가지 방법을 제공합니다. 콜백 함수를 사용하면 전략이 실행 중에 사용자가 제공한 코드를 " "호출할 수 있습니다." -#: ../../source/how-to-use-strategies.rst:45 +#: ../../source/how-to-use-strategies.rst:49 msgid "Configuring client fit and client evaluate" msgstr "클라이언트 적합성 및 클라이언트 평가 구성" -#: ../../source/how-to-use-strategies.rst:47 +#: ../../source/how-to-use-strategies.rst:51 +#, fuzzy msgid "" "The server can pass new configuration values to the client each round by " -"providing a function to :code:`on_fit_config_fn`. The provided function " -"will be called by the strategy and must return a dictionary of " -"configuration key values pairs that will be sent to the client. It must " -"return a dictionary of arbitrary configuration values :code:`client.fit`" -" and :code:`client.evaluate` functions during each round of federated " -"learning." +"providing a function to ``on_fit_config_fn``. The provided function will " +"be called by the strategy and must return a dictionary of configuration " +"key values pairs that will be sent to the client. It must return a " +"dictionary of arbitrary configuration values ``client.fit`` and " +"``client.evaluate`` functions during each round of federated learning." msgstr "" "서버는 매 라운드마다 새로운 설정 값을 클라이언트에 전달하기 위해 :code:`on_fit_config_fn`에 함수를 제공할 수 " "있습니다. 제공된 함수는 전략에 의해 호출되며 클라이언트에 전송될 구성 키 값 쌍의 dictionary를 반환해야 합니다. 연합 " "학습의 각 라운드 동안 임의의 구성 값 dictionary인 :code:`client.fit` 및 " ":code:`client.evaluate` 함수를 반환해야 합니다." -#: ../../source/how-to-use-strategies.rst:75 +#: ../../source/how-to-use-strategies.rst:84 #, fuzzy msgid "" -"The :code:`on_fit_config_fn` can be used to pass arbitrary configuration " +"The ``on_fit_config_fn`` can be used to pass arbitrary configuration " "values from server to client, and potentially change these values each " "round, for example, to adjust the learning rate. The client will receive " -"the dictionary returned by the :code:`on_fit_config_fn` in its own " -":code:`client.fit()` function." +"the dictionary returned by the ``on_fit_config_fn`` in its own " +"``client.fit()`` function." msgstr "" ":code:`on_fit_config_fn`은 서버에서 클라이언트로 임의의 구성 값을 전달하고, 예를 들어 학습 속도를 조정하기 " "위해 매 라운드마다 이 값을 잠재적으로 변경하는 데 사용할 수 있습니다. 클라이언트는 자체 :code:`client.fit()` " "함수에서 :code:`on_fit_config_fn`이 반환한 dictionary를 받습니다." -#: ../../source/how-to-use-strategies.rst:78 +#: ../../source/how-to-use-strategies.rst:89 +#, fuzzy msgid "" -"Similar to :code:`on_fit_config_fn`, there is also " -":code:`on_evaluate_config_fn` to customize the configuration sent to " -":code:`client.evaluate()`" +"Similar to ``on_fit_config_fn``, there is also ``on_evaluate_config_fn`` " +"to customize the configuration sent to ``client.evaluate()``" msgstr "" ":code:`on_fit_config_fn`과 유사하게, :code:`client.evaluate()`로 전송되는 구성을 사용자 " "지정하는 :code:`on_evaluate_config_fn`도 있습니다" -#: ../../source/how-to-use-strategies.rst:81 +#: ../../source/how-to-use-strategies.rst:93 msgid "Configuring server-side evaluation" msgstr "서버 측 평가 구성" -#: ../../source/how-to-use-strategies.rst:83 +#: ../../source/how-to-use-strategies.rst:95 +#, fuzzy msgid "" "Server-side evaluation can be enabled by passing an evaluation function " -"to :code:`evaluate_fn`." +"to ``evaluate_fn``." msgstr "서버 측 평가는 :code:`evaluate_fn`에 평가 함수를 전달하여 활성화할 수 있습니다." -#: ../../source/how-to-use-strategies.rst:89 +#: ../../source/how-to-use-strategies.rst:101 msgid "" "Writing a fully custom strategy is a bit more involved, but it provides " "the most flexibility. Read the `Implementing Strategies ` | :doc:`iOS `" -#: ../../source/index.rst:64 +#: ../../source/index.rst:70 msgid "We also made video tutorials for PyTorch:" msgstr "파이토치용 동영상 튜토리얼도 만들었습니다:" -#: ../../source/index.rst:69 +#: ../../source/index.rst:75 msgid "And TensorFlow:" msgstr "그리고 TensorFlow도:" -#: ../../source/index.rst:77 +#: ../../source/index.rst:83 msgid "" "Problem-oriented how-to guides show step-by-step how to achieve a " "specific goal." msgstr "문제 중심의 방법 가이드는 특정 목표를 달성하는 방법을 단계별로 보여줍니다." -#: ../../source/index.rst:110 +#: ../../source/index.rst:116 msgid "" "Understanding-oriented concept guides explain and discuss key topics and " "underlying ideas behind Flower and collaborative AI." msgstr "이해 중심의 개념 가이드에서는 Flower와 협업 AI의 주요 주제와 기본 아이디어를 설명하고 토론합니다." -#: ../../source/index.rst:121 +#: ../../source/index.rst:128 msgid "References" msgstr "참조" -#: ../../source/index.rst:123 +#: ../../source/index.rst:130 msgid "Information-oriented API reference and other reference material." msgstr "정보 지향 API 참조 및 기타 참고 자료." -#: ../../source/index.rst:132::1 +#: ../../source/index.rst:139::1 msgid ":py:obj:`flwr `\\" msgstr ":py:obj:`flwr `\\" -#: ../../source/index.rst:132::1 flwr:1 of +#: ../../source/index.rst:139::1 flwr:1 of msgid "Flower main package." msgstr "Flower 메인 패키지." -#: ../../source/index.rst:149 +#: ../../source/index.rst:155 msgid "Contributor docs" msgstr "기여자 문서" -#: ../../source/index.rst:151 +#: ../../source/index.rst:157 msgid "" "The Flower community welcomes contributions. The following docs are " "intended to help along the way." @@ -9232,17 +9360,16 @@ msgstr "Flower SuperNode" msgid "flower-server-app" msgstr "flower 서버 프로그램" -#: ../../source/ref-api-cli.rst:49 +#: ../../source/ref-api-cli.rst:50 msgid "" -"Note that since version :code:`1.11.0`, :code:`flower-server-app` no " -"longer supports passing a reference to a `ServerApp` attribute. Instead, " -"you need to pass the path to Flower app via the argument :code:`--app`. " -"This is the path to a directory containing a `pyproject.toml`. You can " -"create a valid Flower app by executing :code:`flwr new` and following the" -" prompt." +"Note that since version ``1.11.0``, ``flower-server-app`` no longer " +"supports passing a reference to a `ServerApp` attribute. Instead, you " +"need to pass the path to Flower app via the argument ``--app``. This is " +"the path to a directory containing a `pyproject.toml`. You can create a " +"valid Flower app by executing ``flwr new`` and following the prompt." msgstr "" -#: ../../source/ref-api-cli.rst:62 +#: ../../source/ref-api-cli.rst:64 #, fuzzy msgid "flower-superexec" msgstr "flower 초연결" @@ -11909,7 +12036,7 @@ msgstr "인코딩" msgid "The encoding in which to encode the string." msgstr "문자열을 인코딩합니다." -#: flwr.common.EventType.encode:5 of +#: flwr.common.EventType.encode:9 of msgid "errors" msgstr "오류" @@ -12103,7 +12230,7 @@ msgstr "" "문자열이 접미사 문자열로 끝나고 해당 접미사가 비어 있지 않으면 문자열[:-len(suffix)]을 반환합니다. 그렇지 않으면 원본" " 문자열의 복사본을 반환합니다." -#: flwr.common.EventType.replace:3 of +#: flwr.common.EventType.replace:5 of msgid "count" msgstr "카운트" @@ -12143,7 +12270,7 @@ msgid "" "strings and the original string." msgstr "구분 기호를 찾을 수 없는 경우 빈 문자열 2개와 원래 문자열을 포함하는 3-tuple을 반환합니다." -#: flwr.common.EventType.rsplit:3 flwr.common.EventType.split:3 of +#: flwr.common.EventType.rsplit:7 flwr.common.EventType.split:7 of msgid "sep" msgstr "sep" @@ -12160,7 +12287,7 @@ msgstr "" "None(기본값)으로 설정하면 모든 공백 문자(\\\\n \\\\r \\\\t \\\\f 및 공백 포함)를 분할하고 결과에서 빈 " "문자열을 삭제합니다." -#: flwr.common.EventType.rsplit:9 flwr.common.EventType.split:9 of +#: flwr.common.EventType.rsplit:11 flwr.common.EventType.split:11 of msgid "maxsplit" msgstr "maxsplit" @@ -12206,7 +12333,7 @@ msgid "" "remaining cased characters have lower case." msgstr "보다 구체적으로, 단어는 대문자로 시작하고 나머지 모든 대소문자는 소문자로 표기합니다." -#: flwr.common.EventType.translate:3 of +#: flwr.common.EventType.translate:5 of msgid "table" msgstr "table" @@ -21905,12 +22032,15 @@ msgid "" "`_." msgstr "" -#: ../../source/ref-example-projects.rst:10 -msgid "" -"The following examples are available as standalone projects. Quickstart " -"TensorFlow/Keras ---------------------------" +#: ../../source/ref-example-projects.rst:9 +msgid "The following examples are available as standalone projects." msgstr "" +#: ../../source/ref-example-projects.rst:12 +#, fuzzy +msgid "Quickstart TensorFlow/Keras" +msgstr "빠른 시작 튜토리얼" + #: ../../source/ref-example-projects.rst:14 msgid "" "The TensorFlow/Keras quickstart example shows CIFAR-10 image " @@ -21924,77 +22054,77 @@ msgid "" "tensorflow>`_" msgstr "" -#: ../../source/ref-example-projects.rst:18 +#: ../../source/ref-example-projects.rst:19 msgid ":doc:`Quickstart TensorFlow (Tutorial) `" msgstr "" -#: ../../source/ref-example-projects.rst:19 +#: ../../source/ref-example-projects.rst:20 msgid "" "`Quickstart TensorFlow (Blog Post) `_" msgstr "" -#: ../../source/ref-example-projects.rst:23 -#: ../../source/tutorial-quickstart-pytorch.rst:5 +#: ../../source/ref-example-projects.rst:24 +#: ../../source/tutorial-quickstart-pytorch.rst:4 msgid "Quickstart PyTorch" msgstr "" -#: ../../source/ref-example-projects.rst:25 +#: ../../source/ref-example-projects.rst:26 msgid "" "The PyTorch quickstart example shows CIFAR-10 image classification with a" " simple Convolutional Neural Network:" msgstr "" -#: ../../source/ref-example-projects.rst:28 +#: ../../source/ref-example-projects.rst:29 msgid "" "`Quickstart PyTorch (Code) " "`_" msgstr "" -#: ../../source/ref-example-projects.rst:29 +#: ../../source/ref-example-projects.rst:31 msgid ":doc:`Quickstart PyTorch (Tutorial) `" msgstr "" -#: ../../source/ref-example-projects.rst:33 +#: ../../source/ref-example-projects.rst:34 msgid "PyTorch: From Centralized To Federated" msgstr "" -#: ../../source/ref-example-projects.rst:35 +#: ../../source/ref-example-projects.rst:36 msgid "" "This example shows how a regular PyTorch project can be federated using " "Flower:" msgstr "" -#: ../../source/ref-example-projects.rst:37 +#: ../../source/ref-example-projects.rst:38 msgid "" "`PyTorch: From Centralized To Federated (Code) " "`_" msgstr "" -#: ../../source/ref-example-projects.rst:38 +#: ../../source/ref-example-projects.rst:40 msgid "" ":doc:`PyTorch: From Centralized To Federated (Tutorial) `" msgstr "" -#: ../../source/ref-example-projects.rst:42 +#: ../../source/ref-example-projects.rst:44 msgid "Federated Learning on Raspberry Pi and Nvidia Jetson" msgstr "" -#: ../../source/ref-example-projects.rst:44 +#: ../../source/ref-example-projects.rst:46 msgid "" "This example shows how Flower can be used to build a federated learning " "system that run across Raspberry Pi and Nvidia Jetson:" msgstr "" -#: ../../source/ref-example-projects.rst:46 +#: ../../source/ref-example-projects.rst:49 msgid "" "`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) " "`_" msgstr "" -#: ../../source/ref-example-projects.rst:47 +#: ../../source/ref-example-projects.rst:51 msgid "" "`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) " "`_" @@ -22010,20 +22140,20 @@ msgstr "" msgid ":fa:`eye,mr-1` Can Flower run on Jupyter Notebooks / Google Colab?" msgstr "" -#: ../../source/ref-faq.rst:8 +#: ../../source/ref-faq.rst:9 msgid "" "Yes, it can! Flower even comes with a few under-the-hood optimizations to" " make it work even better on Colab. Here's a quickstart example:" msgstr "" -#: ../../source/ref-faq.rst:10 +#: ../../source/ref-faq.rst:11 msgid "" "`Flower simulation PyTorch " "`_" msgstr "" -#: ../../source/ref-faq.rst:11 +#: ../../source/ref-faq.rst:12 msgid "" "`Flower simulation TensorFlow/Keras " "`_" @@ -22046,20 +22176,20 @@ msgstr "" msgid ":fa:`eye,mr-1` Does Flower support federated learning on Android devices?" msgstr "" -#: ../../source/ref-faq.rst:19 +#: ../../source/ref-faq.rst:20 msgid "" "Yes, it does. Please take a look at our `blog post " "`_ or check out the code examples:" msgstr "" -#: ../../source/ref-faq.rst:21 +#: ../../source/ref-faq.rst:22 msgid "" "`Android Kotlin example `_" msgstr "" -#: ../../source/ref-faq.rst:22 +#: ../../source/ref-faq.rst:23 msgid "`Android Java example `_" msgstr "" @@ -22067,50 +22197,50 @@ msgstr "" msgid ":fa:`eye,mr-1` Can I combine federated learning with blockchain?" msgstr "" -#: ../../source/ref-faq.rst:26 +#: ../../source/ref-faq.rst:27 msgid "" "Yes, of course. A list of available examples using Flower within a " "blockchain environment is available here:" msgstr "" -#: ../../source/ref-faq.rst:28 +#: ../../source/ref-faq.rst:30 msgid "`FLock: A Decentralised AI Training Platform `_." msgstr "" -#: ../../source/ref-faq.rst:29 +#: ../../source/ref-faq.rst:30 msgid "Contribute to on-chain training the model and earn rewards." msgstr "" -#: ../../source/ref-faq.rst:30 +#: ../../source/ref-faq.rst:31 msgid "Local blockchain with federated learning simulation." msgstr "" -#: ../../source/ref-faq.rst:31 +#: ../../source/ref-faq.rst:32 msgid "" "`Flower meets Nevermined GitHub Repository `_." msgstr "" -#: ../../source/ref-faq.rst:32 +#: ../../source/ref-faq.rst:33 msgid "" "`Flower meets Nevermined YouTube video " "`_." msgstr "" -#: ../../source/ref-faq.rst:33 +#: ../../source/ref-faq.rst:34 msgid "" "`Flower meets KOSMoS `_." msgstr "" -#: ../../source/ref-faq.rst:34 +#: ../../source/ref-faq.rst:35 msgid "" "`Flower meets Talan blog post `_ ." msgstr "" -#: ../../source/ref-faq.rst:35 +#: ../../source/ref-faq.rst:36 msgid "" "`Flower meets Talan GitHub Repository " "`_ ." @@ -22316,28 +22446,28 @@ msgid "" "app using Flower." msgstr "" -#: ../../source/tutorial-quickstart-android.rst:5 +#: ../../source/tutorial-quickstart-android.rst:4 msgid "Quickstart Android" msgstr "" -#: ../../source/tutorial-quickstart-android.rst:10 +#: ../../source/tutorial-quickstart-android.rst:9 msgid "" "Let's build a federated learning system using TFLite and Flower on " "Android!" msgstr "" -#: ../../source/tutorial-quickstart-android.rst:12 +#: ../../source/tutorial-quickstart-android.rst:11 msgid "" "Please refer to the `full code example " "`_ to learn " "more." msgstr "" -#: ../../source/tutorial-quickstart-fastai.rst:5 +#: ../../source/tutorial-quickstart-fastai.rst:4 msgid "Quickstart fastai" msgstr "" -#: ../../source/tutorial-quickstart-fastai.rst:7 +#: ../../source/tutorial-quickstart-fastai.rst:6 msgid "" "In this federated learning tutorial we will learn how to train a " "SqueezeNet model on MNIST using Flower and fastai. It is recommended to " @@ -22345,24 +22475,24 @@ msgid "" " `." msgstr "" -#: ../../source/tutorial-quickstart-fastai.rst:12 -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12 +#: ../../source/tutorial-quickstart-fastai.rst:10 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:11 msgid "Then, clone the code example directly from GitHub:" msgstr "" -#: ../../source/tutorial-quickstart-fastai.rst:20 +#: ../../source/tutorial-quickstart-fastai.rst:18 msgid "" "This will create a new directory called `quickstart-fastai` containing " "the following files:" msgstr "" -#: ../../source/tutorial-quickstart-fastai.rst:33 -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:33 +#: ../../source/tutorial-quickstart-fastai.rst:31 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:32 #, fuzzy msgid "Next, activate your environment, then run:" msgstr "그 후 가상 환경을 활성화합니다:" -#: ../../source/tutorial-quickstart-fastai.rst:43 +#: ../../source/tutorial-quickstart-fastai.rst:41 msgid "" "This example by default runs the Flower Simulation Engine, creating a " "federation of 10 nodes using `FedAvg `_ of this tutorial in ``examples/quickstart-fasai`` " @@ -22405,11 +22535,11 @@ msgid "" "with 🤗 HuggingFace Transformers in order to fine-tune an LLM." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:5 +#: ../../source/tutorial-quickstart-huggingface.rst:4 msgid "Quickstart 🤗 Transformers" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:7 +#: ../../source/tutorial-quickstart-huggingface.rst:6 msgid "" "In this federated learning tutorial we will learn how to train a large " "language model (LLM) on the `IMDB " @@ -22419,7 +22549,7 @@ msgid "" "`." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:14 +#: ../../source/tutorial-quickstart-huggingface.rst:12 msgid "" "Let's use ``flwr new`` to create a complete Flower+🤗 Hugging Face " "project. It will generate all the files needed to run, by default with " @@ -22428,74 +22558,74 @@ msgid "" "|iidpartitioner|_." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:20 -#: ../../source/tutorial-quickstart-mlx.rst:19 -#: ../../source/tutorial-quickstart-pytorch.rst:19 -#: ../../source/tutorial-quickstart-tensorflow.rst:20 +#: ../../source/tutorial-quickstart-huggingface.rst:17 +#: ../../source/tutorial-quickstart-mlx.rst:17 +#: ../../source/tutorial-quickstart-pytorch.rst:18 +#: ../../source/tutorial-quickstart-tensorflow.rst:18 msgid "" "Now that we have a rough idea of what this example is about, let's get " "started. First, install Flower in your new environment:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:28 +#: ../../source/tutorial-quickstart-huggingface.rst:25 msgid "" "Then, run the command below. You will be prompted to select one of the " "available templates (choose ``HuggingFace``), give a name to your " "project, and type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:36 -#: ../../source/tutorial-quickstart-mlx.rst:35 -#: ../../source/tutorial-quickstart-pytorch.rst:35 -#: ../../source/tutorial-quickstart-tensorflow.rst:36 +#: ../../source/tutorial-quickstart-huggingface.rst:33 +#: ../../source/tutorial-quickstart-mlx.rst:32 +#: ../../source/tutorial-quickstart-pytorch.rst:34 +#: ../../source/tutorial-quickstart-tensorflow.rst:34 msgid "" "After running it you'll notice a new directory with your project name has" " been created. It should have the following structure:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:50 -#: ../../source/tutorial-quickstart-mlx.rst:49 -#: ../../source/tutorial-quickstart-pytorch.rst:49 -#: ../../source/tutorial-quickstart-tensorflow.rst:50 +#: ../../source/tutorial-quickstart-huggingface.rst:47 +#: ../../source/tutorial-quickstart-mlx.rst:46 +#: ../../source/tutorial-quickstart-pytorch.rst:48 +#: ../../source/tutorial-quickstart-tensorflow.rst:48 msgid "" "If you haven't yet installed the project and its dependencies, you can do" " so by:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:58 -#: ../../source/tutorial-quickstart-pytorch.rst:57 -#: ../../source/tutorial-quickstart-tensorflow.rst:58 +#: ../../source/tutorial-quickstart-huggingface.rst:54 +#: ../../source/tutorial-quickstart-pytorch.rst:55 +#: ../../source/tutorial-quickstart-tensorflow.rst:55 msgid "To run the project, do:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:106 +#: ../../source/tutorial-quickstart-huggingface.rst:102 msgid "You can also run the project with GPU as follows:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:113 +#: ../../source/tutorial-quickstart-huggingface.rst:109 msgid "" "This will use the default arguments where each ``ClientApp`` will use 2 " "CPUs and at most 4 ``ClientApp``\\s will run in a given GPU." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:124 -#: ../../source/tutorial-quickstart-mlx.rst:114 -#: ../../source/tutorial-quickstart-pytorch.rst:113 +#: ../../source/tutorial-quickstart-huggingface.rst:120 +#: ../../source/tutorial-quickstart-mlx.rst:110 +#: ../../source/tutorial-quickstart-pytorch.rst:111 msgid "" "What follows is an explanation of each component in the project you just " "created: dataset partition, the model, defining the ``ClientApp`` and " "defining the ``ServerApp``." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:130 -#: ../../source/tutorial-quickstart-mlx.rst:120 -#: ../../source/tutorial-quickstart-pytorch.rst:119 -#: ../../source/tutorial-quickstart-tensorflow.rst:116 +#: ../../source/tutorial-quickstart-huggingface.rst:124 +#: ../../source/tutorial-quickstart-mlx.rst:114 +#: ../../source/tutorial-quickstart-pytorch.rst:115 +#: ../../source/tutorial-quickstart-tensorflow.rst:112 #, fuzzy msgid "The Data" msgstr "Metadata" -#: ../../source/tutorial-quickstart-huggingface.rst:132 +#: ../../source/tutorial-quickstart-huggingface.rst:126 msgid "" "This tutorial uses |flowerdatasets|_ to easily download and partition the" " `IMDB `_ dataset. In " @@ -22508,14 +22638,14 @@ msgid "" "their data partition." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:178 -#: ../../source/tutorial-quickstart-mlx.rst:164 -#: ../../source/tutorial-quickstart-pytorch.rst:157 -#: ../../source/tutorial-quickstart-tensorflow.rst:145 +#: ../../source/tutorial-quickstart-huggingface.rst:171 +#: ../../source/tutorial-quickstart-mlx.rst:155 +#: ../../source/tutorial-quickstart-pytorch.rst:150 +#: ../../source/tutorial-quickstart-tensorflow.rst:139 msgid "The Model" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:180 +#: ../../source/tutorial-quickstart-huggingface.rst:173 msgid "" "We will leverage 🤗 Hugging Face to federate the training of language " "models over multiple clients using Flower. More specifically, we will " @@ -22525,13 +22655,13 @@ msgid "" "larger GPUs, feel free to use larger models!" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:193 +#: ../../source/tutorial-quickstart-huggingface.rst:185 msgid "" "Note that here, ``model_name`` is a string that will be loaded from the " "``Context`` in the ClientApp and ServerApp." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:196 +#: ../../source/tutorial-quickstart-huggingface.rst:188 msgid "" "In addition to loading the pretrained model weights and architecture, we " "also include two utility functions to perform both training (i.e. " @@ -22544,15 +22674,15 @@ msgid "" "perform local training or evaluation:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:239 -#: ../../source/tutorial-quickstart-mlx.rst:210 -#: ../../source/tutorial-quickstart-pytorch.rst:234 -#: ../../source/tutorial-quickstart-tensorflow.rst:176 +#: ../../source/tutorial-quickstart-huggingface.rst:228 +#: ../../source/tutorial-quickstart-mlx.rst:199 +#: ../../source/tutorial-quickstart-pytorch.rst:224 +#: ../../source/tutorial-quickstart-tensorflow.rst:168 #, fuzzy msgid "The ClientApp" msgstr "클라이언트앱" -#: ../../source/tutorial-quickstart-huggingface.rst:241 +#: ../../source/tutorial-quickstart-huggingface.rst:230 msgid "" "The main changes we have to make to use 🤗 Hugging Face with Flower will " "be found in the ``get_weights()`` and ``set_weights()`` functions. Under " @@ -22565,8 +22695,8 @@ msgid "" "them to an existing PyTorch model. Doing this in fairly easy in PyTorch." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:254 -#: ../../source/tutorial-quickstart-pytorch.rst:245 +#: ../../source/tutorial-quickstart-huggingface.rst:241 +#: ../../source/tutorial-quickstart-pytorch.rst:234 msgid "" "The specific implementation of ``get_weights()`` and ``set_weights()`` " "depends on the type of models you use. The ones shown below work for a " @@ -22574,8 +22704,8 @@ msgid "" "have more exotic model architectures." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:269 -#: ../../source/tutorial-quickstart-pytorch.rst:261 +#: ../../source/tutorial-quickstart-huggingface.rst:257 +#: ../../source/tutorial-quickstart-pytorch.rst:250 msgid "" "The rest of the functionality is directly inspired by the centralized " "case. The ``fit()`` method in the client trains the model using the local" @@ -22583,7 +22713,7 @@ msgid "" "model received on a held-out validation set that the client might have:" msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:296 +#: ../../source/tutorial-quickstart-huggingface.rst:283 msgid "" "Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " "defined above by means of a ``client_fn()`` callback. Note that the " @@ -22594,15 +22724,15 @@ msgid "" "additional hyperparameters in ``pyproject.toml`` and access them here." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:330 -#: ../../source/tutorial-quickstart-mlx.rst:376 -#: ../../source/tutorial-quickstart-pytorch.rst:321 -#: ../../source/tutorial-quickstart-tensorflow.rst:245 +#: ../../source/tutorial-quickstart-huggingface.rst:316 +#: ../../source/tutorial-quickstart-mlx.rst:361 +#: ../../source/tutorial-quickstart-pytorch.rst:307 +#: ../../source/tutorial-quickstart-tensorflow.rst:232 #, fuzzy msgid "The ServerApp" msgstr "Flower 서버앱" -#: ../../source/tutorial-quickstart-huggingface.rst:332 +#: ../../source/tutorial-quickstart-huggingface.rst:318 msgid "" "To construct a ``ServerApp`` we define a ``server_fn()`` callback with an" " identical signature to that of ``client_fn()`` but the return type is " @@ -22613,13 +22743,13 @@ msgid "" "value defined in the ``pyproject.toml``." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:371 +#: ../../source/tutorial-quickstart-huggingface.rst:356 msgid "" "Congratulations! You've successfully built and run your first federated " "learning system for an LLM." msgstr "" -#: ../../source/tutorial-quickstart-huggingface.rst:376 +#: ../../source/tutorial-quickstart-huggingface.rst:361 msgid "" "Check the source code of the extended version of this tutorial in " "|quickstart_hf_link|_ in the Flower GitHub repository. For a " @@ -22633,11 +22763,11 @@ msgid "" "using Flower to train a neural network on MNIST." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:5 +#: ../../source/tutorial-quickstart-ios.rst:4 msgid "Quickstart iOS" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:10 +#: ../../source/tutorial-quickstart-ios.rst:9 msgid "" "In this tutorial we will learn how to train a Neural Network on MNIST " "using Flower and CoreML on iOS devices." @@ -22651,13 +22781,13 @@ msgid "" "implementation in iOS, it is recommended to use Xcode as our IDE." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:15 +#: ../../source/tutorial-quickstart-ios.rst:17 msgid "" "Our example consists of one Python *server* and two iPhone *clients* that" " all have the same model." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:17 +#: ../../source/tutorial-quickstart-ios.rst:20 msgid "" "*Clients* are responsible for generating individual weight updates for " "the model based on their local datasets. These updates are then sent to " @@ -22666,24 +22796,24 @@ msgid "" "each *client*. A complete cycle of weight updates is called a *round*." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:21 +#: ../../source/tutorial-quickstart-ios.rst:26 msgid "" "Now that we have a rough idea of what is going on, let's get started to " "setup our Flower server environment. We first need to install Flower. You" " can do this by using pip:" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:27 +#: ../../source/tutorial-quickstart-ios.rst:33 msgid "Or Poetry:" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:34 -#: ../../source/tutorial-quickstart-scikitlearn.rst:40 -#: ../../source/tutorial-quickstart-xgboost.rst:55 +#: ../../source/tutorial-quickstart-ios.rst:40 +#: ../../source/tutorial-quickstart-scikitlearn.rst:43 +#: ../../source/tutorial-quickstart-xgboost.rst:65 msgid "Flower Client" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:36 +#: ../../source/tutorial-quickstart-ios.rst:42 msgid "" "Now that we have all our dependencies installed, let's run a simple " "distributed training using CoreML as our local training pipeline and " @@ -22692,91 +22822,90 @@ msgid "" "the Swift SDK. The client implementation can be seen below:" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:72 +#: ../../source/tutorial-quickstart-ios.rst:80 msgid "" -"Let's create a new application project in Xcode and add :code:`flwr` as a" -" dependency in your project. For our application, we will store the logic" -" of our app in :code:`FLiOSModel.swift` and the UI elements in " -":code:`ContentView.swift`. We will focus more on :code:`FLiOSModel.swift`" -" in this quickstart. Please refer to the `full code example " +"Let's create a new application project in Xcode and add ``flwr`` as a " +"dependency in your project. For our application, we will store the logic " +"of our app in ``FLiOSModel.swift`` and the UI elements in " +"``ContentView.swift``. We will focus more on ``FLiOSModel.swift`` in this" +" quickstart. Please refer to the `full code example " "`_ to learn more " "about the app." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:75 -msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:" +#: ../../source/tutorial-quickstart-ios.rst:86 +msgid "Import Flower and CoreML related packages in ``FLiOSModel.swift``:" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:83 +#: ../../source/tutorial-quickstart-ios.rst:94 msgid "" "Then add the mlmodel to the project simply by drag-and-drop, the mlmodel " "will be bundled inside the application during deployment to your iOS " "device. We need to pass the url to access mlmodel and run CoreML machine " "learning processes, it can be retrieved by calling the function " -":code:`Bundle.main.url`. For the MNIST dataset, we need to preprocess it " -"into :code:`MLBatchProvider` object. The preprocessing is done inside " -":code:`DataLoader.swift`." +"``Bundle.main.url``. For the MNIST dataset, we need to preprocess it into" +" ``MLBatchProvider`` object. The preprocessing is done inside " +"``DataLoader.swift``." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:99 +#: ../../source/tutorial-quickstart-ios.rst:112 msgid "" "Since CoreML does not allow the model parameters to be seen before " "training, and accessing the model parameters during or after the training" " can only be done by specifying the layer name, we need to know this " "information beforehand, through looking at the model specification, which" " are written as proto files. The implementation can be seen in " -":code:`MLModelInspect`." +"``MLModelInspect``." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:102 +#: ../../source/tutorial-quickstart-ios.rst:118 msgid "" "After we have all of the necessary information, let's create our Flower " "client." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:117 +#: ../../source/tutorial-quickstart-ios.rst:133 msgid "" "Then start the Flower gRPC client and start communicating to the server " -"by passing our Flower client to the function :code:`startFlwrGRPC`." +"by passing our Flower client to the function ``startFlwrGRPC``." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:124 +#: ../../source/tutorial-quickstart-ios.rst:141 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -"call the provided :code:`MLFlwrClient` and call :code:`startFlwrGRPC()`. " -"The attribute :code:`hostname` and :code:`port` tells the client which " -"server to connect to. This can be done by entering the hostname and port " -"in the application before clicking the start button to start the " -"federated learning process." +"That's it for the client. We only have to implement ``Client`` or call " +"the provided ``MLFlwrClient`` and call ``startFlwrGRPC()``. The attribute" +" ``hostname`` and ``port`` tells the client which server to connect to. " +"This can be done by entering the hostname and port in the application " +"before clicking the start button to start the federated learning process." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:129 -#: ../../source/tutorial-quickstart-scikitlearn.rst:167 -#: ../../source/tutorial-quickstart-xgboost.rst:341 +#: ../../source/tutorial-quickstart-ios.rst:148 +#: ../../source/tutorial-quickstart-scikitlearn.rst:179 +#: ../../source/tutorial-quickstart-xgboost.rst:358 msgid "Flower Server" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:131 +#: ../../source/tutorial-quickstart-ios.rst:150 msgid "" "For simple workloads we can start a Flower server and leave all the " "configuration possibilities at their default values. In a file named " -":code:`server.py`, import Flower and start the server:" +"``server.py``, import Flower and start the server:" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:142 -#: ../../source/tutorial-quickstart-scikitlearn.rst:230 +#: ../../source/tutorial-quickstart-ios.rst:161 +#: ../../source/tutorial-quickstart-scikitlearn.rst:254 msgid "Train the model, federated!" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:144 -#: ../../source/tutorial-quickstart-xgboost.rst:567 +#: ../../source/tutorial-quickstart-ios.rst:163 +#: ../../source/tutorial-quickstart-xgboost.rst:590 msgid "" "With both client and server ready, we can now run everything and see " "federated learning in action. FL systems usually have a server and " "multiple clients. We therefore have to start the server first:" msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:152 +#: ../../source/tutorial-quickstart-ios.rst:171 msgid "" "Once the server is running we can start the clients in different " "terminals. Build and run the client through your Xcode, one through Xcode" @@ -22786,12 +22915,12 @@ msgid "" "simulator-or-on-a-device>`_." msgstr "" -#: ../../source/tutorial-quickstart-ios.rst:156 +#: ../../source/tutorial-quickstart-ios.rst:177 msgid "" "Congratulations! You've successfully built and run your first federated " "learning system in your ios device. The full `source code " "`_ for this " -"example can be found in :code:`examples/ios`." +"example can be found in ``examples/ios``." msgstr "" #: ../../source/tutorial-quickstart-jax.rst:-1 @@ -22800,11 +22929,11 @@ msgid "" "with Jax to train a linear regression model on a scikit-learn dataset." msgstr "" -#: ../../source/tutorial-quickstart-jax.rst:5 +#: ../../source/tutorial-quickstart-jax.rst:4 msgid "Quickstart JAX" msgstr "" -#: ../../source/tutorial-quickstart-jax.rst:10 +#: ../../source/tutorial-quickstart-jax.rst:9 msgid "" "This tutorial will show you how to use Flower to build a federated " "version of an existing JAX workload. We are using JAX to train a linear " @@ -22825,38 +22954,41 @@ msgstr "" " 튜토리얼`을 기반으로 centralized 학습 접근 방식을 구축합니다. 그런 다음 centralized 트레이닝 코드를 기반으로" " federated 방식으로 트레이닝을 실행합니다." -#: ../../source/tutorial-quickstart-jax.rst:16 +#: ../../source/tutorial-quickstart-jax.rst:20 +#, fuzzy msgid "" "Before we start building our JAX example, we need install the packages " -":code:`jax`, :code:`jaxlib`, :code:`scikit-learn`, and :code:`flwr`:" +"``jax``, ``jaxlib``, ``scikit-learn``, and ``flwr``:" msgstr "" "JAX 예제 빌드를 시작하기 전에 :code:`jax`, :code:`jaxlib`, :code:`scikit-learn`, " ":code:`flwr` 패키지를 설치해야 합니다:" -#: ../../source/tutorial-quickstart-jax.rst:24 +#: ../../source/tutorial-quickstart-jax.rst:28 msgid "Linear Regression with JAX" msgstr "JAX를 사용한 선형 회귀" -#: ../../source/tutorial-quickstart-jax.rst:26 +#: ../../source/tutorial-quickstart-jax.rst:30 +#, fuzzy msgid "" "We begin with a brief description of the centralized training code based " -"on a :code:`Linear Regression` model. If you want a more in-depth " -"explanation of what's going on then have a look at the official `JAX " -"documentation `_." +"on a ``Linear Regression`` model. If you want a more in-depth explanation" +" of what's going on then have a look at the official `JAX documentation " +"`_." msgstr "" "먼저 :code:`선형 회귀` 모델을 기반으로 하는 중앙 집중식 훈련 코드에 대한 간략한 설명부터 시작하겠습니다. 더 자세한 설명을" " 원하시면 공식 `JAX 문서 `_를 참조하세요." -#: ../../source/tutorial-quickstart-jax.rst:29 +#: ../../source/tutorial-quickstart-jax.rst:34 +#, fuzzy msgid "" -"Let's create a new file called :code:`jax_training.py` with all the " +"Let's create a new file called ``jax_training.py`` with all the " "components required for a traditional (centralized) linear regression " -"training. First, the JAX packages :code:`jax` and :code:`jaxlib` need to " -"be imported. In addition, we need to import :code:`sklearn` since we use " -":code:`make_regression` for the dataset and :code:`train_test_split` to " -"split the dataset into a training and test set. You can see that we do " -"not yet import the :code:`flwr` package for federated learning. This will" -" be done later." +"training. First, the JAX packages ``jax`` and ``jaxlib`` need to be " +"imported. In addition, we need to import ``sklearn`` since we use " +"``make_regression`` for the dataset and ``train_test_split`` to split the" +" dataset into a training and test set. You can see that we do not yet " +"import the ``flwr`` package for federated learning. This will be done " +"later." msgstr "" "전통적인(중앙 집중식) 선형 회귀 훈련에 필요한 모든 구성 요소가 포함된 :code:`jax_training.py`라는 새 파일을 " "생성해 보겠습니다. 먼저, JAX 패키지인 :code:`jax`와 :code:`jaxlib`를 가져와야 합니다. 또한 데이터 세트에" @@ -22864,55 +22996,58 @@ msgstr "" ":code:`train_test_split`을 사용하므로 :code:`sklearn`을 가져와야 합니다. 연합 학습을 위해 아직 " ":code:`flwr` 패키지를 가져오지 않은 것을 볼 수 있습니다. 이 작업은 나중에 수행됩니다." -#: ../../source/tutorial-quickstart-jax.rst:43 -msgid "" -"The :code:`load_data()` function loads the mentioned training and test " -"sets." +#: ../../source/tutorial-quickstart-jax.rst:51 +#, fuzzy +msgid "The ``load_data()`` function loads the mentioned training and test sets." msgstr "code:`load_data()` 함수는 앞서 언급한 트레이닝 및 테스트 세트를 로드합니다." -#: ../../source/tutorial-quickstart-jax.rst:53 +#: ../../source/tutorial-quickstart-jax.rst:63 +#, fuzzy msgid "" -"The model architecture (a very simple :code:`Linear Regression` model) is" -" defined in :code:`load_model()`." +"The model architecture (a very simple ``Linear Regression`` model) is " +"defined in ``load_model()``." msgstr "모델 아키텍처(매우 간단한 :code:`선형 회귀` 모델)는 :code:`load_model()`에 정의되어 있습니다." -#: ../../source/tutorial-quickstart-jax.rst:65 +#: ../../source/tutorial-quickstart-jax.rst:73 +#, fuzzy msgid "" -"We now need to define the training (function :code:`train()`), which " -"loops over the training set and measures the loss (function " -":code:`loss_fn()`) for each batch of training examples. The loss function" -" is separate since JAX takes derivatives with a :code:`grad()` function " -"(defined in the :code:`main()` function and called in :code:`train()`)." +"We now need to define the training (function ``train()``), which loops " +"over the training set and measures the loss (function ``loss_fn()``) for " +"each batch of training examples. The loss function is separate since JAX " +"takes derivatives with a ``grad()`` function (defined in the ``main()`` " +"function and called in ``train()``)." msgstr "" "이제 훈련 집합을 반복하고 각 훈련 예제 배치에 대해 손실을 측정하는(함수 :code:`loss_fn()`) 훈련(함수 " ":code:`train()`)을 정의해야 합니다. JAX는 :code:`grad()` 함수(:code:`main()` 함수에 " "정의되고 :code:`train()`에서 호출됨)로 파생물을 취하므로 손실 함수는 분리되어 있습니다." -#: ../../source/tutorial-quickstart-jax.rst:83 +#: ../../source/tutorial-quickstart-jax.rst:95 +#, fuzzy msgid "" -"The evaluation of the model is defined in the function " -":code:`evaluation()`. The function takes all test examples and measures " -"the loss of the linear regression model." +"The evaluation of the model is defined in the function ``evaluation()``. " +"The function takes all test examples and measures the loss of the linear " +"regression model." msgstr "" "모델의 평가는 :code:`evaluation()` 함수에 정의되어 있습니다. 이 함수는 모든 테스트 예제를 가져와 선형 회귀 " "모델의 손실을 측정합니다." -#: ../../source/tutorial-quickstart-jax.rst:94 +#: ../../source/tutorial-quickstart-jax.rst:107 +#, fuzzy msgid "" "Having defined the data loading, model architecture, training, and " "evaluation we can put everything together and train our model using JAX. " -"As already mentioned, the :code:`jax.grad()` function is defined in " -":code:`main()` and passed to :code:`train()`." +"As already mentioned, the ``jax.grad()`` function is defined in " +"``main()`` and passed to ``train()``." msgstr "" "데이터 로딩, 모델 아키텍처, 훈련 및 평가를 정의했으므로 이제 모든 것을 종합하여 JAX를 사용 모델을 훈련할 수 있습니다. 이미" " 언급했듯이 :code:`jax.grad()` 함수는 :code:`main()`에 정의되어 :code:`train()`에 " "전달됩니다." -#: ../../source/tutorial-quickstart-jax.rst:111 +#: ../../source/tutorial-quickstart-jax.rst:126 msgid "You can now run your (centralized) JAX linear regression workload:" msgstr "이제 (중앙 집중식) JAX 선형 회귀 워크로드를 실행할 수 있습니다:" -#: ../../source/tutorial-quickstart-jax.rst:117 +#: ../../source/tutorial-quickstart-jax.rst:132 msgid "" "So far this should all look fairly familiar if you've used JAX before. " "Let's take the next step and use what we've built to create a simple " @@ -22921,49 +23056,51 @@ msgstr "" "지금까지는 JAX를 사용해 본 적이 있다면 이 모든 것이 상당히 익숙해 보일 것입니다. 다음 단계로 넘어가서 우리가 구축한 것을 " "사용하여 하나의 서버와 두 개의 클라이언트로 구성된 간단한 연합 학습 시스템을 만들어 보겠습니다." -#: ../../source/tutorial-quickstart-jax.rst:121 +#: ../../source/tutorial-quickstart-jax.rst:137 msgid "JAX meets Flower" msgstr "JAX와 Flower의 만남" -#: ../../source/tutorial-quickstart-jax.rst:123 +#: ../../source/tutorial-quickstart-jax.rst:139 +#, fuzzy msgid "" "The concept of federating an existing workload is always the same and " "easy to understand. We have to start a *server* and then use the code in " -":code:`jax_training.py` for the *clients* that are connected to the " -"*server*. The *server* sends model parameters to the clients. The " -"*clients* run the training and update the parameters. The updated " -"parameters are sent back to the *server*, which averages all received " -"parameter updates. This describes one round of the federated learning " -"process, and we repeat this for multiple rounds." +"``jax_training.py`` for the *clients* that are connected to the *server*." +" The *server* sends model parameters to the clients. The *clients* run " +"the training and update the parameters. The updated parameters are sent " +"back to the *server*, which averages all received parameter updates. This" +" describes one round of the federated learning process, and we repeat " +"this for multiple rounds." msgstr "" "기존 워크로드를 연합하는 개념은 항상 동일하고 이해하기 쉽습니다. 서버*를 시작한 다음 *서버*에 연결된 *클라이언트*에 대해 " ":code:`jax_training.py`의 코드를 사용해야 합니다. *서버*는 모델 파라미터를 클라이언트로 전송합니다. " "클라이언트는 학습을 실행하고 파라미터를 업데이트합니다. 업데이트된 파라미터는 *서버*로 다시 전송되며, 수신된 모든 파라미터 " "업데이트의 평균을 구합니다. 이는 연합 학습 프로세스의 한 라운드를 설명하며, 이 과정을 여러 라운드에 걸쳐 반복합니다." -#: ../../source/tutorial-quickstart-jax.rst:145 +#: ../../source/tutorial-quickstart-jax.rst:167 +#, fuzzy msgid "" -"Finally, we will define our *client* logic in :code:`client.py` and build" -" upon the previously defined JAX training in :code:`jax_training.py`. Our" -" *client* needs to import :code:`flwr`, but also :code:`jax` and " -":code:`jaxlib` to update the parameters on our JAX model:" +"Finally, we will define our *client* logic in ``client.py`` and build " +"upon the previously defined JAX training in ``jax_training.py``. Our " +"*client* needs to import ``flwr``, but also ``jax`` and ``jaxlib`` to " +"update the parameters on our JAX model:" msgstr "" "마지막으로, :code:`client.py`에서 *client* 로직을 정의하고 :code:`jax_training.py`에서 " "이전에 정의한 JAX 교육을 기반으로 빌드합니다. *클라이언트*는 :code:`flwr`을 가져와야 하며, JAX 모델의 파라미터를" " 업데이트하기 위해 :code:`jax` 및 :code:`jaxlib`도 가져와야 합니다:" -#: ../../source/tutorial-quickstart-jax.rst:160 +#: ../../source/tutorial-quickstart-jax.rst:182 +#, fuzzy msgid "" "Implementing a Flower *client* basically means implementing a subclass of" -" either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. " -"Our implementation will be based on :code:`flwr.client.NumPyClient` and " -"we'll call it :code:`FlowerClient`. :code:`NumPyClient` is slightly " -"easier to implement than :code:`Client` if you use a framework with good " -"NumPy interoperability (like JAX) because it avoids some of the " -"boilerplate that would otherwise be necessary. :code:`FlowerClient` needs" -" to implement four methods, two methods for getting/setting model " -"parameters, one method for training the model, and one method for testing" -" the model:" +" either ``flwr.client.Client`` or ``flwr.client.NumPyClient``. Our " +"implementation will be based on ``flwr.client.NumPyClient`` and we'll " +"call it ``FlowerClient``. ``NumPyClient`` is slightly easier to implement" +" than ``Client`` if you use a framework with good NumPy interoperability " +"(like JAX) because it avoids some of the boilerplate that would otherwise" +" be necessary. ``FlowerClient`` needs to implement four methods, two " +"methods for getting/setting model parameters, one method for training the" +" model, and one method for testing the model:" msgstr "" "Flower *클라이언트*를 구현한다는 것은 기본적으로 :code:`flwr.client.Client` 또는 " ":code:`flwr.client.NumPyClient`의 서브클래스를 구현하는 것을 의미합니다. 구현은 " @@ -22973,40 +23110,43 @@ msgstr "" "code:`FlowerClient`는 모델 매개변수를 가져오거나 설정하는 메서드 2개, 모델 학습을 위한 메서드 1개, 모델 " "테스트를 위한 메서드 1개 등 총 4개의 메서드를 구현해야 합니다:" -#: ../../source/tutorial-quickstart-jax.rst:165 -msgid ":code:`set_parameters (optional)`" +#: ../../source/tutorial-quickstart-jax.rst:194 +#, fuzzy +msgid "``set_parameters (optional)``" msgstr ":code:`set_parameters (선택사항)`" -#: ../../source/tutorial-quickstart-jax.rst:167 -msgid "transform parameters to NumPy :code:`ndarray`'s" +#: ../../source/tutorial-quickstart-jax.rst:193 +#, fuzzy +msgid "transform parameters to NumPy ``ndarray``'s" msgstr "매개 변수를 NumPy :code:`ndarray`로 변환" -#: ../../source/tutorial-quickstart-jax.rst:174 +#: ../../source/tutorial-quickstart-jax.rst:203 msgid "get the updated local model parameters and return them to the server" msgstr "업데이트된 로컬 모델 파라미터를 가져와 서버로 반환합니다" -#: ../../source/tutorial-quickstart-jax.rst:178 +#: ../../source/tutorial-quickstart-jax.rst:208 msgid "return the local loss to the server" msgstr "로컬 손실을 서버로 반환합니다" -#: ../../source/tutorial-quickstart-jax.rst:180 +#: ../../source/tutorial-quickstart-jax.rst:210 +#, fuzzy msgid "" "The challenging part is to transform the JAX model parameters from " -":code:`DeviceArray` to :code:`NumPy ndarray` to make them compatible with" -" `NumPyClient`." +"``DeviceArray`` to ``NumPy ndarray`` to make them compatible with " +"`NumPyClient`." msgstr "" "어려운 부분은 JAX 모델 매개변수를 :code:`DeviceArray`에서 :code:`NumPy ndarray`로 변환하여 " "`NumPyClient`와 호환되도록 하는 것입니다." -#: ../../source/tutorial-quickstart-jax.rst:182 +#: ../../source/tutorial-quickstart-jax.rst:213 +#, fuzzy msgid "" -"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make" -" use of the functions :code:`train()` and :code:`evaluate()` previously " -"defined in :code:`jax_training.py`. So what we really do here is we tell " -"Flower through our :code:`NumPyClient` subclass which of our already " -"defined functions to call for training and evaluation. We included type " -"annotations to give you a better understanding of the data types that get" -" passed around." +"The two ``NumPyClient`` methods ``fit`` and ``evaluate`` make use of the " +"functions ``train()`` and ``evaluate()`` previously defined in " +"``jax_training.py``. So what we really do here is we tell Flower through " +"our ``NumPyClient`` subclass which of our already defined functions to " +"call for training and evaluation. We included type annotations to give " +"you a better understanding of the data types that get passed around." msgstr "" "두 개의 :code:`NumPyClient` 메서드인 :code:`fit`과 :code:`evaluate`는 이전에 " ":code:`jax_training.py`에 정의된 함수 :code:`train()`과 :code:`evaluate()`를 " @@ -23014,11 +23154,11 @@ msgstr "" ":code:`NumPyClient` 서브클래스를 통해 Flower에게 알려주는 것입니다. 전달되는 데이터 유형을 더 잘 이해할 수 " "있도록 유형 type annotation을 포함했습니다." -#: ../../source/tutorial-quickstart-jax.rst:251 +#: ../../source/tutorial-quickstart-jax.rst:286 msgid "Having defined the federation process, we can run it." msgstr "연합 프로세스를 정의했으면 이제 실행할 수 있습니다." -#: ../../source/tutorial-quickstart-jax.rst:280 +#: ../../source/tutorial-quickstart-jax.rst:315 msgid "" "in each window (make sure that the server is still running before you do " "so) and see your JAX project run federated learning across two clients. " @@ -23027,7 +23167,7 @@ msgstr "" "를 입력하고(그 전에 서버가 계속 실행 중인지 확인하세요) 두 클라이언트에서 연합 학습을 실행하는 JAX 프로젝트를 확인합니다. " "축하합니다!" -#: ../../source/tutorial-quickstart-jax.rst:285 +#: ../../source/tutorial-quickstart-jax.rst:321 msgid "" "The source code of this example was improved over time and can be found " "here: `Quickstart JAX `_. 두 " "클라이언트가 동일한 데이터 세트를 로드하기 때문에 이 예제는 다소 단순화되어 있습니다." -#: ../../source/tutorial-quickstart-jax.rst:288 +#: ../../source/tutorial-quickstart-jax.rst:325 msgid "" "You're now prepared to explore this topic further. How about using a more" " sophisticated model or using a different dataset? How about adding more " @@ -23047,12 +23187,12 @@ msgstr "" "이제 이 주제를 더 자세히 살펴볼 준비가 되었습니다. 더 정교한 모델을 사용하거나 다른 데이터 집합을 사용해 보는 것은 어떨까요? " "클라이언트를 더 추가하는 것은 어떨까요?" -#: ../../source/tutorial-quickstart-mlx.rst:5 +#: ../../source/tutorial-quickstart-mlx.rst:4 #, fuzzy msgid "Quickstart MLX" msgstr "빠른 시작" -#: ../../source/tutorial-quickstart-mlx.rst:7 +#: ../../source/tutorial-quickstart-mlx.rst:6 msgid "" "In this federated learning tutorial we will learn how to train simple MLP" " on MNIST using Flower and MLX. It is recommended to create a virtual " @@ -23060,7 +23200,7 @@ msgid "" "how-to-set-up-a-virtual-env>`." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:12 +#: ../../source/tutorial-quickstart-mlx.rst:10 msgid "" "Let's use `flwr new` to create a complete Flower+MLX project. It will " "generate all the files needed to run, by default with the Simulation " @@ -23072,24 +23212,24 @@ msgid "" "api/flwr_datasets.partitioner.IidPartitioner.html#flwr_datasets.partitioner.IidPartitioner>`_." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:27 +#: ../../source/tutorial-quickstart-mlx.rst:25 msgid "" "Then, run the command below. You will be prompted to select of the " "available templates (choose ``MLX``), give a name to your project, and " "type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:57 +#: ../../source/tutorial-quickstart-mlx.rst:53 msgid "To run the project do:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:106 +#: ../../source/tutorial-quickstart-mlx.rst:102 msgid "" "You can also override the parameters defined in " "``[tool.flwr.app.config]`` section in the ``pyproject.toml`` like this:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:122 +#: ../../source/tutorial-quickstart-mlx.rst:116 msgid "" "We will use `Flower Datasets `_ to " "easily download and partition the `MNIST` dataset. In this example you'll" @@ -23100,20 +23240,20 @@ msgid "" "api/flwr_datasets.partitioner.html>`_ available in Flower Datasets:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:166 +#: ../../source/tutorial-quickstart-mlx.rst:157 msgid "" "We define the model as in the `centralized MLX example " "`_, it's a " "simple MLP:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:190 +#: ../../source/tutorial-quickstart-mlx.rst:180 msgid "" "We also define some utility functions to test our model and to iterate " "over batches." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:212 +#: ../../source/tutorial-quickstart-mlx.rst:201 msgid "" "The main changes we have to make to use `MLX` with `Flower` will be found" " in the ``get_params()`` and ``set_params()`` functions. Indeed, MLX " @@ -23122,17 +23262,17 @@ msgid "" "messages to work)." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:218 +#: ../../source/tutorial-quickstart-mlx.rst:206 msgid "The way MLX stores its parameters is as follows:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:231 +#: ../../source/tutorial-quickstart-mlx.rst:219 msgid "" "Therefore, to get our list of ``np.array`` objects, we need to extract " "each array and convert them into a NumPy array:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:240 +#: ../../source/tutorial-quickstart-mlx.rst:228 msgid "" "For the ``set_params()`` function, we perform the reverse operation. We " "receive a list of NumPy arrays and want to convert them into MLX " @@ -23140,24 +23280,24 @@ msgid "" "them to the `weight` and `bias` keys of each layer dict:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:255 +#: ../../source/tutorial-quickstart-mlx.rst:243 msgid "" "The rest of the functionality is directly inspired by the centralized " "case. The ``fit()`` method in the client trains the model using the local" " dataset:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:272 +#: ../../source/tutorial-quickstart-mlx.rst:259 msgid "" "Here, after updating the parameters, we perform the training as in the " "centralized case, and return the new parameters." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:275 +#: ../../source/tutorial-quickstart-mlx.rst:262 msgid "And for the ``evaluate()`` method of the client:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:285 +#: ../../source/tutorial-quickstart-mlx.rst:272 msgid "" "We also begin by updating the parameters with the ones sent by the " "server, and then we compute the loss and accuracy using the functions " @@ -23165,11 +23305,11 @@ msgid "" "the `MLP` model as well as other components such as the optimizer." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:290 +#: ../../source/tutorial-quickstart-mlx.rst:277 msgid "Putting everything together we have:" msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:344 +#: ../../source/tutorial-quickstart-mlx.rst:331 msgid "" "Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " "defined above by means of a ``client_fn()`` callback. Note that " @@ -23180,7 +23320,7 @@ msgid "" "method." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:378 +#: ../../source/tutorial-quickstart-mlx.rst:363 msgid "" "To construct a ``ServerApp``, we define a ``server_fn()`` callback with " "an identical signature to that of ``client_fn()``, but the return type is" @@ -23191,15 +23331,15 @@ msgid "" "``FedAvg`` strategy." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:402 -#: ../../source/tutorial-quickstart-pytorch.rst:360 -#: ../../source/tutorial-quickstart-tensorflow.rst:279 +#: ../../source/tutorial-quickstart-mlx.rst:386 +#: ../../source/tutorial-quickstart-pytorch.rst:344 +#: ../../source/tutorial-quickstart-tensorflow.rst:266 msgid "" "Congratulations! You've successfully built and run your first federated " "learning system." msgstr "" -#: ../../source/tutorial-quickstart-mlx.rst:407 +#: ../../source/tutorial-quickstart-mlx.rst:390 msgid "" "Check the `source code `_ of the extended version of this tutorial in ``examples" @@ -23212,15 +23352,15 @@ msgid "" "with Pandas to perform Federated Analytics." msgstr "" -#: ../../source/tutorial-quickstart-pandas.rst:5 +#: ../../source/tutorial-quickstart-pandas.rst:4 msgid "Quickstart Pandas" msgstr "" -#: ../../source/tutorial-quickstart-pandas.rst:10 +#: ../../source/tutorial-quickstart-pandas.rst:9 msgid "Let's build a federated analytics system using Pandas and Flower!" msgstr "" -#: ../../source/tutorial-quickstart-pandas.rst:12 +#: ../../source/tutorial-quickstart-pandas.rst:11 msgid "" "Please refer to the `full code example " "`_ " @@ -23233,7 +23373,7 @@ msgid "" "with PyTorch to train a CNN model on MNIST." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:7 +#: ../../source/tutorial-quickstart-pytorch.rst:6 msgid "" "In this federated learning tutorial we will learn how to train a " "Convolutional Neural Network on CIFAR-10 using Flower and PyTorch. It is " @@ -23241,7 +23381,7 @@ msgid "" ":doc:`virtualenv `." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:12 +#: ../../source/tutorial-quickstart-pytorch.rst:11 msgid "" "Let's use `flwr new` to create a complete Flower+PyTorch project. It will" " generate all the files needed to run, by default with the Flower " @@ -23253,14 +23393,14 @@ msgid "" "api/flwr_datasets.partitioner.IidPartitioner.html#flwr_datasets.partitioner.IidPartitioner>`_." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:27 +#: ../../source/tutorial-quickstart-pytorch.rst:26 msgid "" "Then, run the command below. You will be prompted to select one of the " "available templates (choose ``PyTorch``), give a name to your project, " "and type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:121 +#: ../../source/tutorial-quickstart-pytorch.rst:117 msgid "" "This tutorial uses `Flower Datasets `_ " "to easily download and partition the `CIFAR-10` dataset. In this example " @@ -23274,13 +23414,13 @@ msgid "" " that correspond to their data partition." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:159 +#: ../../source/tutorial-quickstart-pytorch.rst:152 msgid "" "We defined a simple Convolutional Neural Network (CNN), but feel free to " "replace it with a more sophisticated model if you'd like:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:184 +#: ../../source/tutorial-quickstart-pytorch.rst:177 msgid "" "In addition to defining the model architecture, we also include two " "utility functions to perform both training (i.e. ``train()``) and " @@ -23293,7 +23433,7 @@ msgid "" "training or evaluation:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:236 +#: ../../source/tutorial-quickstart-pytorch.rst:226 msgid "" "The main changes we have to make to use `PyTorch` with `Flower` will be " "found in the ``get_weights()`` and ``set_weights()`` functions. In " @@ -23303,7 +23443,7 @@ msgid "" "PyTorch model. Doing this in fairly easy in PyTorch." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:294 +#: ../../source/tutorial-quickstart-pytorch.rst:282 msgid "" "Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " "defined above by means of a ``client_fn()`` callback. Note that the " @@ -23314,7 +23454,7 @@ msgid "" "additioinal hyperparameters in ``pyproject.toml`` and access them here." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:323 +#: ../../source/tutorial-quickstart-pytorch.rst:309 msgid "" "To construct a ``ServerApp`` we define a ``server_fn()`` callback with an" " identical signature to that of ``client_fn()`` but the return type is " @@ -23328,31 +23468,31 @@ msgid "" "``pyproject.toml``." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:365 +#: ../../source/tutorial-quickstart-pytorch.rst:348 msgid "" "Check the `source code `_ of the extended version of this tutorial in " "``examples/quickstart-pytorch`` in the Flower GitHub repository." msgstr "" -#: ../../source/tutorial-quickstart-pytorch.rst:372 -#: ../../source/tutorial-quickstart-tensorflow.rst:295 +#: ../../source/tutorial-quickstart-pytorch.rst:354 +#: ../../source/tutorial-quickstart-tensorflow.rst:278 #, fuzzy msgid "Video tutorial" msgstr "튜토리얼" -#: ../../source/tutorial-quickstart-pytorch.rst:376 +#: ../../source/tutorial-quickstart-pytorch.rst:358 msgid "" "The video shown below shows how to setup a PyTorch + Flower project using" " our previously recommended APIs. A new video tutorial will be released " "that shows the new APIs (as the content above does)" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:4 msgid "Quickstart PyTorch Lightning" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:7 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:6 msgid "" "In this federated learning tutorial we will learn how to train an " "AutoEncoder model on MNIST using Flower and PyTorch Lightning. It is " @@ -23360,13 +23500,13 @@ msgid "" ":doc:`virtualenv `." msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:20 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:19 msgid "" "This will create a new directory called `quickstart-pytorch-lightning` " "containing the following files:" msgstr "" -#: ../../source/tutorial-quickstart-pytorch-lightning.rst:43 +#: ../../source/tutorial-quickstart-pytorch-lightning.rst:42 msgid "" "By default, Flower Simulation Engine will be started and it will create a" " federation of 4 nodes using `FedAvg `_ of this tutorial in ``examples" @@ -23396,14 +23536,14 @@ msgid "" "with scikit-learn to train a linear regression model." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:5 +#: ../../source/tutorial-quickstart-scikitlearn.rst:4 msgid "Quickstart scikit-learn" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:10 +#: ../../source/tutorial-quickstart-scikitlearn.rst:9 msgid "" -"In this tutorial, we will learn how to train a :code:`Logistic " -"Regression` model on MNIST using Flower and scikit-learn." +"In this tutorial, we will learn how to train a ``Logistic Regression`` " +"model on MNIST using Flower and scikit-learn." msgstr "" #: ../../source/tutorial-quickstart-scikitlearn.rst:12 @@ -23412,13 +23552,13 @@ msgid "" "within this :doc:`virtualenv `." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:14 +#: ../../source/tutorial-quickstart-scikitlearn.rst:15 msgid "" "Our example consists of one *server* and two *clients* all having the " "same model." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:16 +#: ../../source/tutorial-quickstart-scikitlearn.rst:17 msgid "" "*Clients* are responsible for generating individual model parameter " "updates for the model based on their local datasets. These updates are " @@ -23428,189 +23568,190 @@ msgid "" "called a *round*." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:20 +#: ../../source/tutorial-quickstart-scikitlearn.rst:23 msgid "" "Now that we have a rough idea of what is going on, let's get started. We " "first need to install Flower. You can do this by running:" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:26 +#: ../../source/tutorial-quickstart-scikitlearn.rst:30 msgid "Since we want to use scikit-learn, let's go ahead and install it:" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:32 +#: ../../source/tutorial-quickstart-scikitlearn.rst:36 msgid "Or simply install all dependencies using Poetry:" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:42 +#: ../../source/tutorial-quickstart-scikitlearn.rst:45 msgid "" "Now that we have all our dependencies installed, let's run a simple " "distributed training with two clients and one server. However, before " "setting up the client and server, we will define all functionalities that" -" we need for our federated learning setup within :code:`utils.py`. The " -":code:`utils.py` contains different functions defining all the machine " +" we need for our federated learning setup within ``utils.py``. The " +"``utils.py`` contains different functions defining all the machine " "learning basics:" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:45 -msgid ":code:`get_model_parameters()`" -msgstr "" +#: ../../source/tutorial-quickstart-scikitlearn.rst:51 +#, fuzzy +msgid "``get_model_parameters()``" +msgstr "모델 매개변수." -#: ../../source/tutorial-quickstart-scikitlearn.rst:46 -msgid "Returns the parameters of a :code:`sklearn` LogisticRegression model" +#: ../../source/tutorial-quickstart-scikitlearn.rst:52 +msgid "Returns the parameters of a ``sklearn`` LogisticRegression model" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:47 -msgid ":code:`set_model_params()`" +#: ../../source/tutorial-quickstart-scikitlearn.rst:53 +msgid "``set_model_params()``" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:48 -msgid "Sets the parameters of a :code:`sklearn` LogisticRegression model" +#: ../../source/tutorial-quickstart-scikitlearn.rst:54 +msgid "Sets the parameters of a ``sklearn`` LogisticRegression model" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:49 -msgid ":code:`set_initial_params()`" +#: ../../source/tutorial-quickstart-scikitlearn.rst:56 +msgid "``set_initial_params()``" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:50 +#: ../../source/tutorial-quickstart-scikitlearn.rst:56 msgid "Initializes the model parameters that the Flower server will ask for" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:52 +#: ../../source/tutorial-quickstart-scikitlearn.rst:58 msgid "" -"Please check out :code:`utils.py` `here " +"Please check out ``utils.py`` `here " "`_ for more details. The pre-defined functions are used in" -" the :code:`client.py` and imported. The :code:`client.py` also requires " -"to import several packages such as Flower and scikit-learn:" +" the ``client.py`` and imported. The ``client.py`` also requires to " +"import several packages such as Flower and scikit-learn:" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:67 +#: ../../source/tutorial-quickstart-scikitlearn.rst:75 msgid "" "Prior to local training, we need to load the MNIST dataset, a popular " "image classification dataset of handwritten digits for machine learning, " "and partition the dataset for FL. This can be conveniently achieved using" " `Flower Datasets `_. The " -":code:`FederatedDataset.load_partition()` method loads the partitioned " -"training set for each partition ID defined in the :code:`--partition-id` " +"``FederatedDataset.load_partition()`` method loads the partitioned " +"training set for each partition ID defined in the ``--partition-id`` " "argument." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:95 +#: ../../source/tutorial-quickstart-scikitlearn.rst:106 msgid "" "Next, the logistic regression model is defined and initialized with " -":code:`utils.set_initial_params()`." +"``utils.set_initial_params()``." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:107 +#: ../../source/tutorial-quickstart-scikitlearn.rst:119 msgid "" "The Flower server interacts with clients through an interface called " -":code:`Client`. When the server selects a particular client for training," -" it sends training instructions over the network. The client receives " -"those instructions and calls one of the :code:`Client` methods to run " -"your code (i.e., to fit the logistic regression we defined earlier)." +"``Client``. When the server selects a particular client for training, it " +"sends training instructions over the network. The client receives those " +"instructions and calls one of the ``Client`` methods to run your code " +"(i.e., to fit the logistic regression we defined earlier)." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:113 +#: ../../source/tutorial-quickstart-scikitlearn.rst:124 msgid "" -"Flower provides a convenience class called :code:`NumPyClient` which " -"makes it easier to implement the :code:`Client` interface when your " -"workload uses scikit-learn. Implementing :code:`NumPyClient` usually " -"means defining the following methods (:code:`set_parameters` is optional " -"though):" +"Flower provides a convenience class called ``NumPyClient`` which makes it" +" easier to implement the ``Client`` interface when your workload uses " +"scikit-learn. Implementing ``NumPyClient`` usually means defining the " +"following methods (``set_parameters`` is optional though):" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:119 +#: ../../source/tutorial-quickstart-scikitlearn.rst:130 msgid "return the model weight as a list of NumPy ndarrays" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:120 -msgid ":code:`set_parameters` (optional)" -msgstr "" +#: ../../source/tutorial-quickstart-scikitlearn.rst:132 +#, fuzzy +msgid "``set_parameters`` (optional)" +msgstr ":code:`set_parameters (선택사항)`" -#: ../../source/tutorial-quickstart-scikitlearn.rst:121 +#: ../../source/tutorial-quickstart-scikitlearn.rst:132 msgid "" "update the local model weights with the parameters received from the " "server" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:122 -msgid "is directly imported with :code:`utils.set_model_params()`" +#: ../../source/tutorial-quickstart-scikitlearn.rst:133 +msgid "is directly imported with ``utils.set_model_params()``" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:124 +#: ../../source/tutorial-quickstart-scikitlearn.rst:135 msgid "set the local model weights" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:125 +#: ../../source/tutorial-quickstart-scikitlearn.rst:136 msgid "train the local model" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:126 +#: ../../source/tutorial-quickstart-scikitlearn.rst:137 #, fuzzy msgid "return the updated local model weights" msgstr "현재 로컬 모델 파라미터를 반환합니다." -#: ../../source/tutorial-quickstart-scikitlearn.rst:128 +#: ../../source/tutorial-quickstart-scikitlearn.rst:139 msgid "test the local model" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:130 +#: ../../source/tutorial-quickstart-scikitlearn.rst:141 msgid "The methods can be implemented in the following way:" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:153 +#: ../../source/tutorial-quickstart-scikitlearn.rst:163 msgid "" -"We can now create an instance of our class :code:`MnistClient` and add " -"one line to actually run this client:" +"We can now create an instance of our class ``MnistClient`` and add one " +"line to actually run this client:" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:160 +#: ../../source/tutorial-quickstart-scikitlearn.rst:170 msgid "" -"That's it for the client. We only have to implement :code:`Client` or " -":code:`NumPyClient` and call :code:`fl.client.start_client()`. If you " -"implement a client of type :code:`NumPyClient` you'll need to first call " -"its :code:`to_client()` method. The string :code:`\"0.0.0.0:8080\"` tells" -" the client which server to connect to. In our case we can run the server" -" and the client on the same machine, therefore we use " -":code:`\"0.0.0.0:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we pass to the client." +"That's it for the client. We only have to implement ``Client`` or " +"``NumPyClient`` and call ``fl.client.start_client()``. If you implement a" +" client of type ``NumPyClient`` you'll need to first call its " +"``to_client()`` method. The string ``\"0.0.0.0:8080\"`` tells the client " +"which server to connect to. In our case we can run the server and the " +"client on the same machine, therefore we use ``\"0.0.0.0:8080\"``. If we " +"run a truly federated workload with the server and clients running on " +"different machines, all that needs to change is the ``server_address`` we" +" pass to the client." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:169 +#: ../../source/tutorial-quickstart-scikitlearn.rst:181 msgid "" "The following Flower server is a little bit more advanced and returns an " "evaluation function for the server-side evaluation. First, we import " "again all required libraries such as Flower and scikit-learn." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:172 -msgid ":code:`server.py`, import Flower and start the server:" +#: ../../source/tutorial-quickstart-scikitlearn.rst:185 +msgid "``server.py``, import Flower and start the server:" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:185 +#: ../../source/tutorial-quickstart-scikitlearn.rst:198 msgid "" -"The number of federated learning rounds is set in :code:`fit_round()` and" -" the evaluation is defined in :code:`get_evaluate_fn()`. The evaluation " -"function is called after each federated learning round and gives you " -"information about loss and accuracy. Note that we also make use of Flower" -" Datasets here to load the test split of the MNIST dataset for server-" -"side evaluation." +"The number of federated learning rounds is set in ``fit_round()`` and the" +" evaluation is defined in ``get_evaluate_fn()``. The evaluation function " +"is called after each federated learning round and gives you information " +"about loss and accuracy. Note that we also make use of Flower Datasets " +"here to load the test split of the MNIST dataset for server-side " +"evaluation." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:213 +#: ../../source/tutorial-quickstart-scikitlearn.rst:228 msgid "" -"The :code:`main` contains the server-side parameter initialization " -":code:`utils.set_initial_params()` as well as the aggregation strategy " -":code:`fl.server.strategy:FedAvg()`. The strategy is the default one, " +"The ``main`` contains the server-side parameter initialization " +"``utils.set_initial_params()`` as well as the aggregation strategy " +"``fl.server.strategy:FedAvg()``. The strategy is the default one, " "federated averaging (or FedAvg), with two clients and evaluation after " "each federated learning round. The server can be started with the command" -" :code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " -"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))`." +" ``fl.server.start_server(server_address=\"0.0.0.0:8080\", " +"strategy=strategy, config=fl.server.ServerConfig(num_rounds=3))``." msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:232 +#: ../../source/tutorial-quickstart-scikitlearn.rst:256 msgid "" "With both client and server ready, we can now run everything and see " "federated learning in action. Federated learning systems usually have a " @@ -23618,33 +23759,33 @@ msgid "" "first:" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:239 -#: ../../source/tutorial-quickstart-xgboost.rst:575 +#: ../../source/tutorial-quickstart-scikitlearn.rst:264 +#: ../../source/tutorial-quickstart-xgboost.rst:598 msgid "" "Once the server is running we can start the clients in different " "terminals. Open a new terminal and start the first client:" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:246 -#: ../../source/tutorial-quickstart-xgboost.rst:582 +#: ../../source/tutorial-quickstart-scikitlearn.rst:271 +#: ../../source/tutorial-quickstart-xgboost.rst:605 msgid "Open another terminal and start the second client:" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:252 -#: ../../source/tutorial-quickstart-xgboost.rst:588 +#: ../../source/tutorial-quickstart-scikitlearn.rst:277 +#: ../../source/tutorial-quickstart-xgboost.rst:611 msgid "" "Each client will have its own dataset. You should now see how the " "training does in the very first terminal (the one that started the " "server):" msgstr "" -#: ../../source/tutorial-quickstart-scikitlearn.rst:286 +#: ../../source/tutorial-quickstart-scikitlearn.rst:311 msgid "" "Congratulations! You've successfully built and run your first federated " "learning system. The full `source code " "`_ for this example can be found in :code:`examples/sklearn-logreg-" -"mnist`." +"mnist>`_ for this example can be found in ``examples/sklearn-logreg-" +"mnist``." msgstr "" #: ../../source/tutorial-quickstart-tensorflow.rst:-1 @@ -23653,11 +23794,11 @@ msgid "" "with TensorFlow to train a CNN model on CIFAR-10." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:5 +#: ../../source/tutorial-quickstart-tensorflow.rst:4 msgid "Quickstart TensorFlow" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:7 +#: ../../source/tutorial-quickstart-tensorflow.rst:6 msgid "" "In this tutorial we will learn how to train a Convolutional Neural " "Network on CIFAR-10 using the Flower framework and TensorFlow. First of " @@ -23665,7 +23806,7 @@ msgid "" " within a :doc:`virtualenv `." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:13 +#: ../../source/tutorial-quickstart-tensorflow.rst:11 msgid "" "Let's use `flwr new` to create a complete Flower+TensorFlow project. It " "will generate all the files needed to run, by default with the Flower " @@ -23677,14 +23818,14 @@ msgid "" "api/flwr_datasets.partitioner.IidPartitioner.html#flwr_datasets.partitioner.IidPartitioner>`_." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:28 +#: ../../source/tutorial-quickstart-tensorflow.rst:26 msgid "" "Then, run the command below. You will be prompted to select one of the " "available templates (choose ``TensorFlow``), give a name to your project," " and type in your developer name:" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:118 +#: ../../source/tutorial-quickstart-tensorflow.rst:114 msgid "" "This tutorial uses `Flower Datasets `_ " "to easily download and partition the `CIFAR-10` dataset. In this example " @@ -23698,14 +23839,14 @@ msgid "" " correspond to their data partition." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:147 +#: ../../source/tutorial-quickstart-tensorflow.rst:141 msgid "" "Next, we need a model. We defined a simple Convolutional Neural Network " "(CNN), but feel free to replace it with a more sophisticated model if " "you'd like:" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:178 +#: ../../source/tutorial-quickstart-tensorflow.rst:170 msgid "" "With `TensorFlow`, we can use the built-in ``get_weights()`` and " "``set_weights()`` functions, which simplifies the implementation with " @@ -23716,7 +23857,7 @@ msgid "" "set that the client might have:" msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:212 +#: ../../source/tutorial-quickstart-tensorflow.rst:203 msgid "" "Finally, we can construct a ``ClientApp`` using the ``FlowerClient`` " "defined above by means of a ``client_fn()`` callback. Note that the " @@ -23728,7 +23869,7 @@ msgid "" "``pyproject.toml`` and access them here." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:247 +#: ../../source/tutorial-quickstart-tensorflow.rst:234 msgid "" "To construct a ``ServerApp`` we define a ``server_fn()`` callback with an" " identical signature to that of ``client_fn()`` but the return type is " @@ -23740,13 +23881,13 @@ msgid "" "the global model to federate." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:284 +#: ../../source/tutorial-quickstart-tensorflow.rst:270 msgid "" "Check the source code of the extended version of this tutorial in " "|quickstart_tf_link|_ in the Flower GitHub repository." msgstr "" -#: ../../source/tutorial-quickstart-tensorflow.rst:299 +#: ../../source/tutorial-quickstart-tensorflow.rst:282 msgid "" "The video shown below shows how to setup a TensorFlow + Flower project " "using our previously recommended APIs. A new video tutorial will be " @@ -23759,15 +23900,15 @@ msgid "" "with XGBoost to train classification models on trees." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:5 +#: ../../source/tutorial-quickstart-xgboost.rst:4 msgid "Quickstart XGBoost" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:14 +#: ../../source/tutorial-quickstart-xgboost.rst:13 msgid "Federated XGBoost" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:16 +#: ../../source/tutorial-quickstart-xgboost.rst:15 msgid "" "EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient " "implementation of gradient-boosted decision tree (**GBDT**), that " @@ -23777,18 +23918,18 @@ msgid "" "concurrently, unlike the sequential approach taken by GBDT." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:20 +#: ../../source/tutorial-quickstart-xgboost.rst:21 msgid "" "Often, for tabular data on medium-sized datasets with fewer than 10k " "training examples, XGBoost surpasses the results of deep learning " "techniques." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:23 +#: ../../source/tutorial-quickstart-xgboost.rst:25 msgid "Why federated XGBoost?" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:25 +#: ../../source/tutorial-quickstart-xgboost.rst:27 msgid "" "Indeed, as the demand for data privacy and decentralized learning grows, " "there's an increasing requirement to implement federated XGBoost systems " @@ -23796,7 +23937,7 @@ msgid "" "detection." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:27 +#: ../../source/tutorial-quickstart-xgboost.rst:31 msgid "" "Federated learning ensures that raw data remains on the local device, " "making it an attractive approach for sensitive domains where data " @@ -23805,10 +23946,10 @@ msgid "" "solution for these specific challenges." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:30 +#: ../../source/tutorial-quickstart-xgboost.rst:36 msgid "" "In this tutorial we will learn how to train a federated XGBoost model on " -"HIGGS dataset using Flower and :code:`xgboost` package. We use a simple " +"HIGGS dataset using Flower and ``xgboost`` package. We use a simple " "example (`full code xgboost-quickstart " "`_)" " with two *clients* and one *server* to demonstrate how federated XGBoost" @@ -23817,30 +23958,30 @@ msgid "" "comprehensive>`_) to run various experiments." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:37 +#: ../../source/tutorial-quickstart-xgboost.rst:46 msgid "Environment Setup" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:39 +#: ../../source/tutorial-quickstart-xgboost.rst:48 msgid "" "First of all, it is recommended to create a virtual environment and run " "everything within a :doc:`virtualenv `." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:41 +#: ../../source/tutorial-quickstart-xgboost.rst:51 msgid "" "We first need to install Flower and Flower Datasets. You can do this by " "running :" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:47 +#: ../../source/tutorial-quickstart-xgboost.rst:57 msgid "" -"Since we want to use :code:`xgboost` package to build up XGBoost trees, " -"let's go ahead and install :code:`xgboost`:" +"Since we want to use ``xgboost`` package to build up XGBoost trees, let's" +" go ahead and install ``xgboost``:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:57 +#: ../../source/tutorial-quickstart-xgboost.rst:67 msgid "" "*Clients* are responsible for generating individual weight-updates for " "the model based on their local datasets. Now that we have all our " @@ -23848,219 +23989,215 @@ msgid "" "clients and one server." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:60 +#: ../../source/tutorial-quickstart-xgboost.rst:71 msgid "" -"In a file called :code:`client.py`, import xgboost, Flower, Flower " -"Datasets and other related functions:" +"In a file called ``client.py``, import xgboost, Flower, Flower Datasets " +"and other related functions:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:87 +#: ../../source/tutorial-quickstart-xgboost.rst:99 msgid "Dataset partition and hyper-parameter selection" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:89 +#: ../../source/tutorial-quickstart-xgboost.rst:101 msgid "" "Prior to local training, we require loading the HIGGS dataset from Flower" " Datasets and conduct data partitioning for FL:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:102 +#: ../../source/tutorial-quickstart-xgboost.rst:115 msgid "" "In this example, we split the dataset into 30 partitions with uniform " -"distribution (:code:`IidPartitioner(num_partitions=30)`). Then, we load " -"the partition for the given client based on :code:`partition_id`:" +"distribution (``IidPartitioner(num_partitions=30)``). Then, we load the " +"partition for the given client based on ``partition_id``:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:121 +#: ../../source/tutorial-quickstart-xgboost.rst:135 msgid "" "After that, we do train/test splitting on the given partition (client's " -"local data), and transform data format for :code:`xgboost` package." +"local data), and transform data format for ``xgboost`` package." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:134 +#: ../../source/tutorial-quickstart-xgboost.rst:149 msgid "" -"The functions of :code:`train_test_split` and " -":code:`transform_dataset_to_dmatrix` are defined as below:" +"The functions of ``train_test_split`` and " +"``transform_dataset_to_dmatrix`` are defined as below:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:158 +#: ../../source/tutorial-quickstart-xgboost.rst:174 msgid "Finally, we define the hyper-parameters used for XGBoost training." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:174 +#: ../../source/tutorial-quickstart-xgboost.rst:190 msgid "" -"The :code:`num_local_round` represents the number of iterations for local" -" tree boost. We use CPU for the training in default. One can shift it to " -"GPU by setting :code:`tree_method` to :code:`gpu_hist`. We use AUC as " -"evaluation metric." +"The ``num_local_round`` represents the number of iterations for local " +"tree boost. We use CPU for the training in default. One can shift it to " +"GPU by setting ``tree_method`` to ``gpu_hist``. We use AUC as evaluation " +"metric." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:181 +#: ../../source/tutorial-quickstart-xgboost.rst:195 msgid "Flower client definition for XGBoost" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:183 +#: ../../source/tutorial-quickstart-xgboost.rst:197 msgid "" "After loading the dataset we define the Flower client. We follow the " -"general rule to define :code:`XgbClient` class inherited from " -":code:`fl.client.Client`." +"general rule to define ``XgbClient`` class inherited from " +"``fl.client.Client``." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:205 +#: ../../source/tutorial-quickstart-xgboost.rst:219 msgid "" -"All required parameters defined above are passed to :code:`XgbClient`'s " +"All required parameters defined above are passed to ``XgbClient``'s " "constructor." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:207 +#: ../../source/tutorial-quickstart-xgboost.rst:221 msgid "" -"Then, we override :code:`get_parameters`, :code:`fit` and " -":code:`evaluate` methods insides :code:`XgbClient` class as follows." +"Then, we override ``get_parameters``, ``fit`` and ``evaluate`` methods " +"insides ``XgbClient`` class as follows." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:221 +#: ../../source/tutorial-quickstart-xgboost.rst:236 msgid "" "Unlike neural network training, XGBoost trees are not started from a " -"specified random weights. In this case, we do not use " -":code:`get_parameters` and :code:`set_parameters` to initialise model " -"parameters for XGBoost. As a result, let's return an empty tensor in " -":code:`get_parameters` when it is called by the server at the first " -"round." +"specified random weights. In this case, we do not use ``get_parameters`` " +"and ``set_parameters`` to initialise model parameters for XGBoost. As a " +"result, let's return an empty tensor in ``get_parameters`` when it is " +"called by the server at the first round." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:262 +#: ../../source/tutorial-quickstart-xgboost.rst:278 msgid "" -"In :code:`fit`, at the first round, we call :code:`xgb.train()` to build " -"up the first set of trees. From the second round, we load the global " -"model sent from server to new build Booster object, and then update model" -" weights on local training data with function :code:`local_boost` as " -"follows:" +"In ``fit``, at the first round, we call ``xgb.train()`` to build up the " +"first set of trees. From the second round, we load the global model sent " +"from server to new build Booster object, and then update model weights on" +" local training data with function ``local_boost`` as follows:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:281 +#: ../../source/tutorial-quickstart-xgboost.rst:298 msgid "" -"Given :code:`num_local_round`, we update trees by calling " -":code:`bst_input.update` method. After training, the last " -":code:`N=num_local_round` trees will be extracted to send to the server." +"Given ``num_local_round``, we update trees by calling " +"``bst_input.update`` method. After training, the last " +"``N=num_local_round`` trees will be extracted to send to the server." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:313 +#: ../../source/tutorial-quickstart-xgboost.rst:330 msgid "" -"In :code:`evaluate`, after loading the global model, we call " -":code:`bst.eval_set` function to conduct evaluation on valid set. The AUC" -" value will be returned." +"In ``evaluate``, after loading the global model, we call ``bst.eval_set``" +" function to conduct evaluation on valid set. The AUC value will be " +"returned." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:316 +#: ../../source/tutorial-quickstart-xgboost.rst:333 msgid "" -"Now, we can create an instance of our class :code:`XgbClient` and add one" -" line to actually run this client:" +"Now, we can create an instance of our class ``XgbClient`` and add one " +"line to actually run this client:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:332 +#: ../../source/tutorial-quickstart-xgboost.rst:350 msgid "" -"That's it for the client. We only have to implement :code:`Client` and " -"call :code:`fl.client.start_client()`. The string :code:`\"[::]:8080\"` " -"tells the client which server to connect to. In our case we can run the " -"server and the client on the same machine, therefore we use " -":code:`\"[::]:8080\"`. If we run a truly federated workload with the " -"server and clients running on different machines, all that needs to " -"change is the :code:`server_address` we point the client at." +"That's it for the client. We only have to implement ``Client`` and call " +"``fl.client.start_client()``. The string ``\"[::]:8080\"`` tells the " +"client which server to connect to. In our case we can run the server and " +"the client on the same machine, therefore we use ``\"[::]:8080\"``. If we" +" run a truly federated workload with the server and clients running on " +"different machines, all that needs to change is the ``server_address`` we" +" point the client at." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:343 +#: ../../source/tutorial-quickstart-xgboost.rst:360 msgid "" "These updates are then sent to the *server* which will aggregate them to " "produce a better model. Finally, the *server* sends this improved version" " of the model back to each *client* to finish a complete FL round." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:346 +#: ../../source/tutorial-quickstart-xgboost.rst:364 msgid "" -"In a file named :code:`server.py`, import Flower and FedXgbBagging from " -":code:`flwr.server.strategy`." +"In a file named ``server.py``, import Flower and FedXgbBagging from " +"``flwr.server.strategy``." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:348 +#: ../../source/tutorial-quickstart-xgboost.rst:367 msgid "We first define a strategy for XGBoost bagging aggregation." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:380 +#: ../../source/tutorial-quickstart-xgboost.rst:401 msgid "" -"We use two clients for this example. An " -":code:`evaluate_metrics_aggregation` function is defined to collect and " -"wighted average the AUC values from clients. The :code:`config_func` " -"function is to return the current FL round number to client's " -":code:`fit()` and :code:`evaluate()` methods." +"We use two clients for this example. An ``evaluate_metrics_aggregation`` " +"function is defined to collect and wighted average the AUC values from " +"clients. The ``config_func`` function is to return the current FL round " +"number to client's ``fit()`` and ``evaluate()`` methods." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:384 +#: ../../source/tutorial-quickstart-xgboost.rst:406 msgid "Then, we start the server:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:396 +#: ../../source/tutorial-quickstart-xgboost.rst:418 msgid "Tree-based bagging aggregation" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:398 +#: ../../source/tutorial-quickstart-xgboost.rst:420 msgid "" "You must be curious about how bagging aggregation works. Let's look into " "the details." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:400 +#: ../../source/tutorial-quickstart-xgboost.rst:422 msgid "" -"In file :code:`flwr.server.strategy.fedxgb_bagging.py`, we define " -":code:`FedXgbBagging` inherited from :code:`flwr.server.strategy.FedAvg`." -" Then, we override the :code:`aggregate_fit`, :code:`aggregate_evaluate` " -"and :code:`evaluate` methods as follows:" +"In file ``flwr.server.strategy.fedxgb_bagging.py``, we define " +"``FedXgbBagging`` inherited from ``flwr.server.strategy.FedAvg``. Then, " +"we override the ``aggregate_fit``, ``aggregate_evaluate`` and " +"``evaluate`` methods as follows:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:496 +#: ../../source/tutorial-quickstart-xgboost.rst:519 msgid "" -"In :code:`aggregate_fit`, we sequentially aggregate the clients' XGBoost " -"trees by calling :code:`aggregate()` function:" +"In ``aggregate_fit``, we sequentially aggregate the clients' XGBoost " +"trees by calling ``aggregate()`` function:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:555 +#: ../../source/tutorial-quickstart-xgboost.rst:579 msgid "" "In this function, we first fetch the number of trees and the number of " "parallel trees for the current and previous model by calling " -":code:`_get_tree_nums`. Then, the fetched information will be aggregated." -" After that, the trees (containing model weights) are aggregated to " +"``_get_tree_nums``. Then, the fetched information will be aggregated. " +"After that, the trees (containing model weights) are aggregated to " "generate a new tree model." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:560 +#: ../../source/tutorial-quickstart-xgboost.rst:584 msgid "" "After traversal of all clients' models, a new global model is generated, " "followed by the serialisation, and sending back to each client." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:565 +#: ../../source/tutorial-quickstart-xgboost.rst:588 msgid "Launch Federated XGBoost!" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:641 +#: ../../source/tutorial-quickstart-xgboost.rst:664 msgid "" "Congratulations! You've successfully built and run your first federated " -"XGBoost system. The AUC values can be checked in " -":code:`metrics_distributed`. One can see that the average AUC increases " -"over FL rounds." +"XGBoost system. The AUC values can be checked in ``metrics_distributed``." +" One can see that the average AUC increases over FL rounds." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:646 +#: ../../source/tutorial-quickstart-xgboost.rst:668 msgid "" "The full `source code `_ for this example can be found in :code:`examples" -"/xgboost-quickstart`." +"/xgboost-quickstart/>`_ for this example can be found in ``examples" +"/xgboost-quickstart``." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:650 +#: ../../source/tutorial-quickstart-xgboost.rst:673 msgid "Comprehensive Federated XGBoost" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:652 +#: ../../source/tutorial-quickstart-xgboost.rst:675 msgid "" "Now that you have known how federated XGBoost work with Flower, it's time" " to run some more comprehensive experiments by customising the " @@ -24073,11 +24210,11 @@ msgid "" "client cohorts in a resource-aware manner. Let's take a look!" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:659 +#: ../../source/tutorial-quickstart-xgboost.rst:685 msgid "Cyclic training" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:661 +#: ../../source/tutorial-quickstart-xgboost.rst:687 msgid "" "In addition to bagging aggregation, we offer a cyclic training scheme, " "which performs FL in a client-by-client fashion. Instead of aggregating " @@ -24087,178 +24224,176 @@ msgid "" "for next round's boosting." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:665 -msgid "" -"To do this, we first customise a :code:`ClientManager` in " -":code:`server_utils.py`:" +#: ../../source/tutorial-quickstart-xgboost.rst:693 +msgid "To do this, we first customise a ``ClientManager`` in ``server_utils.py``:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:705 +#: ../../source/tutorial-quickstart-xgboost.rst:733 msgid "" -"The customised :code:`ClientManager` samples all available clients in " -"each FL round based on the order of connection to the server. Then, we " -"define a new strategy :code:`FedXgbCyclic` in " -":code:`flwr.server.strategy.fedxgb_cyclic.py`, in order to sequentially " +"The customised ``ClientManager`` samples all available clients in each FL" +" round based on the order of connection to the server. Then, we define a " +"new strategy ``FedXgbCyclic`` in " +"``flwr.server.strategy.fedxgb_cyclic.py``, in order to sequentially " "select only one client in given round and pass the received model to next" " client." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:746 +#: ../../source/tutorial-quickstart-xgboost.rst:775 msgid "" -"Unlike the original :code:`FedAvg`, we don't perform aggregation here. " +"Unlike the original ``FedAvg``, we don't perform aggregation here. " "Instead, we just make a copy of the received client model as global model" -" by overriding :code:`aggregate_fit`." +" by overriding ``aggregate_fit``." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:749 +#: ../../source/tutorial-quickstart-xgboost.rst:778 msgid "" -"Also, the customised :code:`configure_fit` and :code:`configure_evaluate`" -" methods ensure the clients to be sequentially selected given FL round:" +"Also, the customised ``configure_fit`` and ``configure_evaluate`` methods" +" ensure the clients to be sequentially selected given FL round:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:813 +#: ../../source/tutorial-quickstart-xgboost.rst:840 msgid "Customised data partitioning" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:815 +#: ../../source/tutorial-quickstart-xgboost.rst:842 msgid "" -"In :code:`dataset.py`, we have a function :code:`instantiate_partitioner`" -" to instantiate the data partitioner based on the given " -":code:`num_partitions` and :code:`partitioner_type`. Currently, we " -"provide four supported partitioner type to simulate the uniformity/non-" -"uniformity in data quantity (uniform, linear, square, exponential)." +"In ``dataset.py``, we have a function ``instantiate_partitioner`` to " +"instantiate the data partitioner based on the given ``num_partitions`` " +"and ``partitioner_type``. Currently, we provide four supported " +"partitioner type to simulate the uniformity/non-uniformity in data " +"quantity (uniform, linear, square, exponential)." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:846 +#: ../../source/tutorial-quickstart-xgboost.rst:873 msgid "Customised centralised/distributed evaluation" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:848 +#: ../../source/tutorial-quickstart-xgboost.rst:875 msgid "" "To facilitate centralised evaluation, we define a function in " -":code:`server_utils.py`:" +"``server_utils.py``:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:880 +#: ../../source/tutorial-quickstart-xgboost.rst:907 msgid "" "This function returns a evaluation function which instantiates a " -":code:`Booster` object and loads the global model weights to it. The " -"evaluation is conducted by calling :code:`eval_set()` method, and the " -"tested AUC value is reported." +"``Booster`` object and loads the global model weights to it. The " +"evaluation is conducted by calling ``eval_set()`` method, and the tested " +"AUC value is reported." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:883 +#: ../../source/tutorial-quickstart-xgboost.rst:911 msgid "" "As for distributed evaluation on the clients, it's same as the quick-" -"start example by overriding the :code:`evaluate()` method insides the " -":code:`XgbClient` class in :code:`client_utils.py`." +"start example by overriding the ``evaluate()`` method insides the " +"``XgbClient`` class in ``client_utils.py``." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:887 +#: ../../source/tutorial-quickstart-xgboost.rst:916 msgid "Flower simulation" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:888 +#: ../../source/tutorial-quickstart-xgboost.rst:918 msgid "" -"We also provide an example code (:code:`sim.py`) to use the simulation " +"We also provide an example code (``sim.py``) to use the simulation " "capabilities of Flower to simulate federated XGBoost training on either a" " single machine or a cluster of machines." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:922 +#: ../../source/tutorial-quickstart-xgboost.rst:954 msgid "" -"After importing all required packages, we define a :code:`main()` " -"function to perform the simulation process:" +"After importing all required packages, we define a ``main()`` function to" +" perform the simulation process:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:977 +#: ../../source/tutorial-quickstart-xgboost.rst:1010 msgid "" "We first load the dataset and perform data partitioning, and the pre-" -"processed data is stored in a :code:`list`. After the simulation begins, " -"the clients won't need to pre-process their partitions again." +"processed data is stored in a ``list``. After the simulation begins, the " +"clients won't need to pre-process their partitions again." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:980 +#: ../../source/tutorial-quickstart-xgboost.rst:1014 msgid "Then, we define the strategies and other hyper-parameters:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1031 +#: ../../source/tutorial-quickstart-xgboost.rst:1065 msgid "" "After that, we start the simulation by calling " -":code:`fl.simulation.start_simulation`:" +"``fl.simulation.start_simulation``:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1051 +#: ../../source/tutorial-quickstart-xgboost.rst:1085 msgid "" -"One of key parameters for :code:`start_simulation` is :code:`client_fn` " -"which returns a function to construct a client. We define it as follows:" +"One of key parameters for ``start_simulation`` is ``client_fn`` which " +"returns a function to construct a client. We define it as follows:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1094 +#: ../../source/tutorial-quickstart-xgboost.rst:1126 msgid "Arguments parser" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1096 +#: ../../source/tutorial-quickstart-xgboost.rst:1128 msgid "" -"In :code:`utils.py`, we define the arguments parsers for clients, server " -"and simulation, allowing users to specify different experimental " -"settings. Let's first see the sever side:" +"In ``utils.py``, we define the arguments parsers for clients, server and " +"simulation, allowing users to specify different experimental settings. " +"Let's first see the sever side:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1142 +#: ../../source/tutorial-quickstart-xgboost.rst:1175 msgid "" "This allows user to specify training strategies / the number of total " "clients / FL rounds / participating clients / clients for evaluation, and" -" evaluation fashion. Note that with :code:`--centralised-eval`, the sever" -" will do centralised evaluation and all functionalities for client " +" evaluation fashion. Note that with ``--centralised-eval``, the sever " +"will do centralised evaluation and all functionalities for client " "evaluation will be disabled." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1146 +#: ../../source/tutorial-quickstart-xgboost.rst:1180 msgid "Then, the argument parser on client side:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1200 +#: ../../source/tutorial-quickstart-xgboost.rst:1234 msgid "" "This defines various options for client data partitioning. Besides, " "clients also have an option to conduct evaluation on centralised test set" -" by setting :code:`--centralised-eval`, as well as an option to perform " -"scaled learning rate based on the number of clients by setting :code" -":`--scaled-lr`." +" by setting ``--centralised-eval``, as well as an option to perform " +"scaled learning rate based on the number of clients by setting " +"``--scaled-lr``." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1204 +#: ../../source/tutorial-quickstart-xgboost.rst:1239 msgid "We also have an argument parser for simulation:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1282 +#: ../../source/tutorial-quickstart-xgboost.rst:1317 msgid "This integrates all arguments for both client and server sides." msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1285 +#: ../../source/tutorial-quickstart-xgboost.rst:1320 msgid "Example commands" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1287 +#: ../../source/tutorial-quickstart-xgboost.rst:1322 msgid "" "To run a centralised evaluated experiment with bagging strategy on 5 " "clients with exponential distribution for 50 rounds, we first start the " "server as below:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1294 +#: ../../source/tutorial-quickstart-xgboost.rst:1329 msgid "Then, on each client terminal, we start the clients:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1300 +#: ../../source/tutorial-quickstart-xgboost.rst:1335 msgid "To run the same experiment with Flower simulation:" msgstr "" -#: ../../source/tutorial-quickstart-xgboost.rst:1306 +#: ../../source/tutorial-quickstart-xgboost.rst:1341 msgid "" "The full `code `_ for this comprehensive example can be found in" -" :code:`examples/xgboost-comprehensive`." +" ``examples/xgboost-comprehensive``." msgstr "" #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:9 @@ -25619,7 +25754,7 @@ msgstr "" " 수도 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:41 -msgid "|b8714c45b74b4d8fb008e2ebb3bc1d44|" +msgid "|4627c2bb6cc443ae9e079f81f33c9dd9|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:109 @@ -25636,7 +25771,7 @@ msgstr "" " 바둑과 같은 게임을 하는 것일 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:53 -msgid "|75f1561efcfd422ea67d28d1513120dc|" +msgid "|131af8322dc5466b827afd24be98f8c0|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:111 @@ -25659,7 +25794,7 @@ msgstr "" "부르리는 것을 듣는 스마트 스피커에서 비롯됩니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:67 -msgid "|6a1f51b235304558a9bdaaabfc93b8d2|" +msgid "|f92920b87f3a40179bf7ddd0b6144c53|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:113 @@ -25677,7 +25812,7 @@ msgstr "" "있습니다. 하지만 여러 조직이 모두 같은 작업을 위해 데이터를 생성하는 것일 수도 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:79 -msgid "|35e70dab1fb544af9aa3a9c09c4f9797|" +msgid "|d62da263071d45a496f543e41fce3a19|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:115 @@ -25695,7 +25830,7 @@ msgstr "" "서버는 데이터 센터 어딘가에 있을 수도 있고 클라우드 어딘가에 있을 수도 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:91 -msgid "|d7efb5705dd3467f991ed23746824a07|" +msgid "|ad851971645b4e1fbf8d15bcc0b2ee11|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:117 @@ -25712,7 +25847,7 @@ msgstr "" " 우리가 기본적으로 사용해 온 머신러닝 방법입니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:103 -msgid "|94e7b021c7b540bfbedf7f082a41ff87|" +msgid "|929e9a6de6b34edb8488e644e2bb5221|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:119 @@ -25734,7 +25869,7 @@ msgstr "" "트래픽을 분석하는 것이 있습니다. 이러한 사례에서 모든 데이터는 자연스럽게 중앙 서버에 존재합니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:138 -msgid "|a80714782dde439ab73936518f91fc3c|" +msgid "|404cf9c9e8d64784a55646c0f9479cbc|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:173 @@ -25751,7 +25886,7 @@ msgstr "" "좋은 모델을 훈련하기에 충분하지 않을 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:150 -msgid "|c62080ca6197473da57d191c8225a9d9|" +msgid "|b021ff9d25814458b1e631f8985a648b|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:175 @@ -25918,7 +26053,7 @@ msgstr "" "체크포인트에서 모델 매개변수를 초기화합니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:210 -msgid "|21a8f1e6a5b14a7bbb8559979d0e8a2b|" +msgid "|e6ca84e1df244f238288a768352678e5|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:307 @@ -25945,7 +26080,7 @@ msgstr "" "개의 연결 노드만 사용합니다. 그 이유는 점점 더 많은 클라이언트 노드를 선택하면 학습의 효율성이 감소하기 때문입니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:225 -msgid "|c310f2a22f7b4917bf42775aae7a1c09|" +msgid "|39c2422082554a21963baffb33a0d057|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:309 @@ -25972,7 +26107,7 @@ msgstr "" "데이터에서 한 단계 정도로 짧거나 몇 단계(mini-batches)에 불과할 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:240 -msgid "|a0c5b43401194535a8460bcf02e65f9a|" +msgid "|07ecf5fcd6814e88906accec6fa0fbfb|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:311 @@ -25998,7 +26133,7 @@ msgstr "" "보냅니다. 보내는 모델 업데이트는 전체 모델 파라미터거나 로컬 교육 중에 누적된 그레디언트(gradient)일 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:255 -msgid "|aabfdbd5564e41a790f8ea93cc21a444|" +msgid "|57e78c0ca8a94ba5a64a04b1f2280e55|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:313 @@ -26047,7 +26182,7 @@ msgstr "" "많은 영향을 미칩니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:273 -msgid "|c9cc8f160fa647b09e742fe4dc8edb54|" +msgid "|9819b40e59ee40a4921e1244e8c99bac|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:315 @@ -26166,7 +26301,7 @@ msgstr "" "사용자는 모든 워크로드, 머신러닝 프레임워크 및 모든 프로그래밍 언어를 통합할 수 있습니다." #: ../../source/tutorial-series-what-is-federated-learning.ipynb:334 -msgid "|7e83aad011cd4907b2f02f907c6922e9|" +msgid "|797bf279c4894b5ead31dc9b0534ed62|" msgstr "" #: ../../source/tutorial-series-what-is-federated-learning.ipynb:340 @@ -28388,3 +28523,683 @@ msgstr "" #~ msgid "|fbf2da0da3cc4f8ab3b3eff852d80c41|" #~ msgstr "" +#~ msgid "" +#~ "Some quickstart examples may have " +#~ "limitations or requirements that prevent " +#~ "them from running on every environment." +#~ " For more information, please see " +#~ "`Limitations`_." +#~ msgstr "" + +#~ msgid "" +#~ "Change the application code. For " +#~ "example, change the ``seed`` in " +#~ "``quickstart_docker/task.py`` to ``43`` and " +#~ "save it:" +#~ msgstr "" + +#~ msgid ":code:`fit`" +#~ msgstr ":code:`fit`" + +#~ msgid "" +#~ "Note that since version :code:`1.11.0`, " +#~ ":code:`flower-server-app` no longer " +#~ "supports passing a reference to a " +#~ "`ServerApp` attribute. Instead, you need " +#~ "to pass the path to Flower app " +#~ "via the argument :code:`--app`. This is" +#~ " the path to a directory containing" +#~ " a `pyproject.toml`. You can create a" +#~ " valid Flower app by executing " +#~ ":code:`flwr new` and following the " +#~ "prompt." +#~ msgstr "" + +#~ msgid "" +#~ "The following examples are available as" +#~ " standalone projects. Quickstart TensorFlow/Keras" +#~ " ---------------------------" +#~ msgstr "" + +#~ msgid "" +#~ "Let's create a new application project" +#~ " in Xcode and add :code:`flwr` as " +#~ "a dependency in your project. For " +#~ "our application, we will store the " +#~ "logic of our app in " +#~ ":code:`FLiOSModel.swift` and the UI elements" +#~ " in :code:`ContentView.swift`. We will " +#~ "focus more on :code:`FLiOSModel.swift` in " +#~ "this quickstart. Please refer to the " +#~ "`full code example " +#~ "`_ to " +#~ "learn more about the app." +#~ msgstr "" + +#~ msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:" +#~ msgstr "" + +#~ msgid "" +#~ "Then add the mlmodel to the " +#~ "project simply by drag-and-drop, " +#~ "the mlmodel will be bundled inside " +#~ "the application during deployment to " +#~ "your iOS device. We need to pass" +#~ " the url to access mlmodel and " +#~ "run CoreML machine learning processes, " +#~ "it can be retrieved by calling the" +#~ " function :code:`Bundle.main.url`. For the " +#~ "MNIST dataset, we need to preprocess " +#~ "it into :code:`MLBatchProvider` object. The" +#~ " preprocessing is done inside " +#~ ":code:`DataLoader.swift`." +#~ msgstr "" + +#~ msgid "" +#~ "Since CoreML does not allow the " +#~ "model parameters to be seen before " +#~ "training, and accessing the model " +#~ "parameters during or after the training" +#~ " can only be done by specifying " +#~ "the layer name, we need to know" +#~ " this information beforehand, through " +#~ "looking at the model specification, " +#~ "which are written as proto files. " +#~ "The implementation can be seen in " +#~ ":code:`MLModelInspect`." +#~ msgstr "" + +#~ msgid "" +#~ "Then start the Flower gRPC client " +#~ "and start communicating to the server" +#~ " by passing our Flower client to " +#~ "the function :code:`startFlwrGRPC`." +#~ msgstr "" + +#~ msgid "" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` or " +#~ "call the provided :code:`MLFlwrClient` and " +#~ "call :code:`startFlwrGRPC()`. The attribute " +#~ ":code:`hostname` and :code:`port` tells the" +#~ " client which server to connect to." +#~ " This can be done by entering " +#~ "the hostname and port in the " +#~ "application before clicking the start " +#~ "button to start the federated learning" +#~ " process." +#~ msgstr "" + +#~ msgid "" +#~ "For simple workloads we can start " +#~ "a Flower server and leave all the" +#~ " configuration possibilities at their " +#~ "default values. In a file named " +#~ ":code:`server.py`, import Flower and start " +#~ "the server:" +#~ msgstr "" + +#~ msgid "" +#~ "Congratulations! You've successfully built and" +#~ " run your first federated learning " +#~ "system in your ios device. The " +#~ "full `source code " +#~ "`_ for" +#~ " this example can be found in " +#~ ":code:`examples/ios`." +#~ msgstr "" + +#~ msgid "" +#~ "In this tutorial, we will learn " +#~ "how to train a :code:`Logistic " +#~ "Regression` model on MNIST using Flower" +#~ " and scikit-learn." +#~ msgstr "" + +#~ msgid "" +#~ "Now that we have all our " +#~ "dependencies installed, let's run a " +#~ "simple distributed training with two " +#~ "clients and one server. However, before" +#~ " setting up the client and server," +#~ " we will define all functionalities " +#~ "that we need for our federated " +#~ "learning setup within :code:`utils.py`. The" +#~ " :code:`utils.py` contains different functions" +#~ " defining all the machine learning " +#~ "basics:" +#~ msgstr "" + +#~ msgid ":code:`get_model_parameters()`" +#~ msgstr "" + +#~ msgid "Returns the parameters of a :code:`sklearn` LogisticRegression model" +#~ msgstr "" + +#~ msgid ":code:`set_model_params()`" +#~ msgstr "" + +#~ msgid "Sets the parameters of a :code:`sklearn` LogisticRegression model" +#~ msgstr "" + +#~ msgid ":code:`set_initial_params()`" +#~ msgstr "" + +#~ msgid "" +#~ "Please check out :code:`utils.py` `here " +#~ "`_ for more details. " +#~ "The pre-defined functions are used " +#~ "in the :code:`client.py` and imported. " +#~ "The :code:`client.py` also requires to " +#~ "import several packages such as Flower" +#~ " and scikit-learn:" +#~ msgstr "" + +#~ msgid "" +#~ "Prior to local training, we need " +#~ "to load the MNIST dataset, a " +#~ "popular image classification dataset of " +#~ "handwritten digits for machine learning, " +#~ "and partition the dataset for FL. " +#~ "This can be conveniently achieved using" +#~ " `Flower Datasets `_." +#~ " The :code:`FederatedDataset.load_partition()` method" +#~ " loads the partitioned training set " +#~ "for each partition ID defined in " +#~ "the :code:`--partition-id` argument." +#~ msgstr "" + +#~ msgid "" +#~ "Next, the logistic regression model is" +#~ " defined and initialized with " +#~ ":code:`utils.set_initial_params()`." +#~ msgstr "" + +#~ msgid "" +#~ "The Flower server interacts with clients" +#~ " through an interface called " +#~ ":code:`Client`. When the server selects " +#~ "a particular client for training, it " +#~ "sends training instructions over the " +#~ "network. The client receives those " +#~ "instructions and calls one of the " +#~ ":code:`Client` methods to run your code" +#~ " (i.e., to fit the logistic " +#~ "regression we defined earlier)." +#~ msgstr "" + +#~ msgid "" +#~ "Flower provides a convenience class " +#~ "called :code:`NumPyClient` which makes it " +#~ "easier to implement the :code:`Client` " +#~ "interface when your workload uses " +#~ "scikit-learn. Implementing :code:`NumPyClient` " +#~ "usually means defining the following " +#~ "methods (:code:`set_parameters` is optional " +#~ "though):" +#~ msgstr "" + +#~ msgid ":code:`set_parameters` (optional)" +#~ msgstr "" + +#~ msgid "is directly imported with :code:`utils.set_model_params()`" +#~ msgstr "" + +#~ msgid "" +#~ "We can now create an instance of" +#~ " our class :code:`MnistClient` and add " +#~ "one line to actually run this " +#~ "client:" +#~ msgstr "" + +#~ msgid "" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` or " +#~ ":code:`NumPyClient` and call " +#~ ":code:`fl.client.start_client()`. If you implement" +#~ " a client of type :code:`NumPyClient` " +#~ "you'll need to first call its " +#~ ":code:`to_client()` method. The string " +#~ ":code:`\"0.0.0.0:8080\"` tells the client " +#~ "which server to connect to. In our" +#~ " case we can run the server and" +#~ " the client on the same machine, " +#~ "therefore we use :code:`\"0.0.0.0:8080\"`. If" +#~ " we run a truly federated workload" +#~ " with the server and clients running" +#~ " on different machines, all that " +#~ "needs to change is the " +#~ ":code:`server_address` we pass to the " +#~ "client." +#~ msgstr "" + +#~ msgid ":code:`server.py`, import Flower and start the server:" +#~ msgstr "" + +#~ msgid "" +#~ "The number of federated learning rounds" +#~ " is set in :code:`fit_round()` and " +#~ "the evaluation is defined in " +#~ ":code:`get_evaluate_fn()`. The evaluation function" +#~ " is called after each federated " +#~ "learning round and gives you information" +#~ " about loss and accuracy. Note that" +#~ " we also make use of Flower " +#~ "Datasets here to load the test " +#~ "split of the MNIST dataset for " +#~ "server-side evaluation." +#~ msgstr "" + +#~ msgid "" +#~ "The :code:`main` contains the server-" +#~ "side parameter initialization " +#~ ":code:`utils.set_initial_params()` as well as " +#~ "the aggregation strategy " +#~ ":code:`fl.server.strategy:FedAvg()`. The strategy is" +#~ " the default one, federated averaging " +#~ "(or FedAvg), with two clients and " +#~ "evaluation after each federated learning " +#~ "round. The server can be started " +#~ "with the command " +#~ ":code:`fl.server.start_server(server_address=\"0.0.0.0:8080\", " +#~ "strategy=strategy, " +#~ "config=fl.server.ServerConfig(num_rounds=3))`." +#~ msgstr "" + +#~ msgid "" +#~ "Congratulations! You've successfully built and" +#~ " run your first federated learning " +#~ "system. The full `source code " +#~ "`_ for this example can " +#~ "be found in :code:`examples/sklearn-logreg-" +#~ "mnist`." +#~ msgstr "" + +#~ msgid "" +#~ "In this tutorial we will learn how" +#~ " to train a federated XGBoost model" +#~ " on HIGGS dataset using Flower and" +#~ " :code:`xgboost` package. We use a " +#~ "simple example (`full code xgboost-" +#~ "quickstart `_) with two *clients* " +#~ "and one *server* to demonstrate how " +#~ "federated XGBoost works, and then we " +#~ "dive into a more complex example " +#~ "(`full code xgboost-comprehensive " +#~ "`_) to run various experiments." +#~ msgstr "" + +#~ msgid "" +#~ "Since we want to use :code:`xgboost` " +#~ "package to build up XGBoost trees, " +#~ "let's go ahead and install " +#~ ":code:`xgboost`:" +#~ msgstr "" + +#~ msgid "" +#~ "In a file called :code:`client.py`, " +#~ "import xgboost, Flower, Flower Datasets " +#~ "and other related functions:" +#~ msgstr "" + +#~ msgid "" +#~ "In this example, we split the " +#~ "dataset into 30 partitions with uniform" +#~ " distribution (:code:`IidPartitioner(num_partitions=30)`)." +#~ " Then, we load the partition for " +#~ "the given client based on " +#~ ":code:`partition_id`:" +#~ msgstr "" + +#~ msgid "" +#~ "After that, we do train/test splitting" +#~ " on the given partition (client's " +#~ "local data), and transform data format" +#~ " for :code:`xgboost` package." +#~ msgstr "" + +#~ msgid "" +#~ "The functions of :code:`train_test_split` and" +#~ " :code:`transform_dataset_to_dmatrix` are defined " +#~ "as below:" +#~ msgstr "" + +#~ msgid "" +#~ "The :code:`num_local_round` represents the " +#~ "number of iterations for local tree " +#~ "boost. We use CPU for the training" +#~ " in default. One can shift it " +#~ "to GPU by setting :code:`tree_method` to" +#~ " :code:`gpu_hist`. We use AUC as " +#~ "evaluation metric." +#~ msgstr "" + +#~ msgid "" +#~ "After loading the dataset we define " +#~ "the Flower client. We follow the " +#~ "general rule to define :code:`XgbClient` " +#~ "class inherited from :code:`fl.client.Client`." +#~ msgstr "" + +#~ msgid "" +#~ "All required parameters defined above " +#~ "are passed to :code:`XgbClient`'s constructor." +#~ msgstr "" + +#~ msgid "" +#~ "Then, we override :code:`get_parameters`, " +#~ ":code:`fit` and :code:`evaluate` methods " +#~ "insides :code:`XgbClient` class as follows." +#~ msgstr "" + +#~ msgid "" +#~ "Unlike neural network training, XGBoost " +#~ "trees are not started from a " +#~ "specified random weights. In this case," +#~ " we do not use :code:`get_parameters` " +#~ "and :code:`set_parameters` to initialise model" +#~ " parameters for XGBoost. As a result," +#~ " let's return an empty tensor in " +#~ ":code:`get_parameters` when it is called " +#~ "by the server at the first round." +#~ msgstr "" + +#~ msgid "" +#~ "In :code:`fit`, at the first round, " +#~ "we call :code:`xgb.train()` to build up" +#~ " the first set of trees. From " +#~ "the second round, we load the " +#~ "global model sent from server to " +#~ "new build Booster object, and then " +#~ "update model weights on local training" +#~ " data with function :code:`local_boost` as" +#~ " follows:" +#~ msgstr "" + +#~ msgid "" +#~ "Given :code:`num_local_round`, we update trees" +#~ " by calling :code:`bst_input.update` method. " +#~ "After training, the last " +#~ ":code:`N=num_local_round` trees will be " +#~ "extracted to send to the server." +#~ msgstr "" + +#~ msgid "" +#~ "In :code:`evaluate`, after loading the " +#~ "global model, we call :code:`bst.eval_set` " +#~ "function to conduct evaluation on valid" +#~ " set. The AUC value will be " +#~ "returned." +#~ msgstr "" + +#~ msgid "" +#~ "Now, we can create an instance of" +#~ " our class :code:`XgbClient` and add " +#~ "one line to actually run this " +#~ "client:" +#~ msgstr "" + +#~ msgid "" +#~ "That's it for the client. We only" +#~ " have to implement :code:`Client` and " +#~ "call :code:`fl.client.start_client()`. The string" +#~ " :code:`\"[::]:8080\"` tells the client " +#~ "which server to connect to. In our" +#~ " case we can run the server and" +#~ " the client on the same machine, " +#~ "therefore we use :code:`\"[::]:8080\"`. If " +#~ "we run a truly federated workload " +#~ "with the server and clients running " +#~ "on different machines, all that needs" +#~ " to change is the :code:`server_address`" +#~ " we point the client at." +#~ msgstr "" + +#~ msgid "" +#~ "In a file named :code:`server.py`, " +#~ "import Flower and FedXgbBagging from " +#~ ":code:`flwr.server.strategy`." +#~ msgstr "" + +#~ msgid "" +#~ "We use two clients for this " +#~ "example. An :code:`evaluate_metrics_aggregation` " +#~ "function is defined to collect and " +#~ "wighted average the AUC values from " +#~ "clients. The :code:`config_func` function is" +#~ " to return the current FL round " +#~ "number to client's :code:`fit()` and " +#~ ":code:`evaluate()` methods." +#~ msgstr "" + +#~ msgid "" +#~ "In file :code:`flwr.server.strategy.fedxgb_bagging.py`," +#~ " we define :code:`FedXgbBagging` inherited " +#~ "from :code:`flwr.server.strategy.FedAvg`. Then, we" +#~ " override the :code:`aggregate_fit`, " +#~ ":code:`aggregate_evaluate` and :code:`evaluate` " +#~ "methods as follows:" +#~ msgstr "" + +#~ msgid "" +#~ "In :code:`aggregate_fit`, we sequentially " +#~ "aggregate the clients' XGBoost trees by" +#~ " calling :code:`aggregate()` function:" +#~ msgstr "" + +#~ msgid "" +#~ "In this function, we first fetch " +#~ "the number of trees and the number" +#~ " of parallel trees for the current" +#~ " and previous model by calling " +#~ ":code:`_get_tree_nums`. Then, the fetched " +#~ "information will be aggregated. After " +#~ "that, the trees (containing model " +#~ "weights) are aggregated to generate a" +#~ " new tree model." +#~ msgstr "" + +#~ msgid "" +#~ "Congratulations! You've successfully built and" +#~ " run your first federated XGBoost " +#~ "system. The AUC values can be " +#~ "checked in :code:`metrics_distributed`. One " +#~ "can see that the average AUC " +#~ "increases over FL rounds." +#~ msgstr "" + +#~ msgid "" +#~ "The full `source code " +#~ "`_ for this example can be" +#~ " found in :code:`examples/xgboost-quickstart`." +#~ msgstr "" + +#~ msgid "" +#~ "To do this, we first customise a" +#~ " :code:`ClientManager` in :code:`server_utils.py`:" +#~ msgstr "" + +#~ msgid "" +#~ "The customised :code:`ClientManager` samples " +#~ "all available clients in each FL " +#~ "round based on the order of " +#~ "connection to the server. Then, we " +#~ "define a new strategy :code:`FedXgbCyclic` " +#~ "in :code:`flwr.server.strategy.fedxgb_cyclic.py`, in " +#~ "order to sequentially select only one" +#~ " client in given round and pass " +#~ "the received model to next client." +#~ msgstr "" + +#~ msgid "" +#~ "Unlike the original :code:`FedAvg`, we " +#~ "don't perform aggregation here. Instead, " +#~ "we just make a copy of the " +#~ "received client model as global model" +#~ " by overriding :code:`aggregate_fit`." +#~ msgstr "" + +#~ msgid "" +#~ "Also, the customised :code:`configure_fit` and" +#~ " :code:`configure_evaluate` methods ensure the" +#~ " clients to be sequentially selected " +#~ "given FL round:" +#~ msgstr "" + +#~ msgid "" +#~ "In :code:`dataset.py`, we have a " +#~ "function :code:`instantiate_partitioner` to " +#~ "instantiate the data partitioner based " +#~ "on the given :code:`num_partitions` and " +#~ ":code:`partitioner_type`. Currently, we provide " +#~ "four supported partitioner type to " +#~ "simulate the uniformity/non-uniformity in " +#~ "data quantity (uniform, linear, square, " +#~ "exponential)." +#~ msgstr "" + +#~ msgid "" +#~ "To facilitate centralised evaluation, we " +#~ "define a function in :code:`server_utils.py`:" +#~ msgstr "" + +#~ msgid "" +#~ "This function returns a evaluation " +#~ "function which instantiates a :code:`Booster`" +#~ " object and loads the global model" +#~ " weights to it. The evaluation is " +#~ "conducted by calling :code:`eval_set()` " +#~ "method, and the tested AUC value " +#~ "is reported." +#~ msgstr "" + +#~ msgid "" +#~ "As for distributed evaluation on the " +#~ "clients, it's same as the quick-" +#~ "start example by overriding the " +#~ ":code:`evaluate()` method insides the " +#~ ":code:`XgbClient` class in :code:`client_utils.py`." +#~ msgstr "" + +#~ msgid "" +#~ "We also provide an example code " +#~ "(:code:`sim.py`) to use the simulation " +#~ "capabilities of Flower to simulate " +#~ "federated XGBoost training on either a" +#~ " single machine or a cluster of " +#~ "machines." +#~ msgstr "" + +#~ msgid "" +#~ "After importing all required packages, " +#~ "we define a :code:`main()` function to" +#~ " perform the simulation process:" +#~ msgstr "" + +#~ msgid "" +#~ "We first load the dataset and " +#~ "perform data partitioning, and the " +#~ "pre-processed data is stored in a " +#~ ":code:`list`. After the simulation begins, " +#~ "the clients won't need to pre-" +#~ "process their partitions again." +#~ msgstr "" + +#~ msgid "" +#~ "After that, we start the simulation " +#~ "by calling :code:`fl.simulation.start_simulation`:" +#~ msgstr "" + +#~ msgid "" +#~ "One of key parameters for " +#~ ":code:`start_simulation` is :code:`client_fn` which" +#~ " returns a function to construct a" +#~ " client. We define it as follows:" +#~ msgstr "" + +#~ msgid "" +#~ "In :code:`utils.py`, we define the " +#~ "arguments parsers for clients, server " +#~ "and simulation, allowing users to " +#~ "specify different experimental settings. Let's" +#~ " first see the sever side:" +#~ msgstr "" + +#~ msgid "" +#~ "This allows user to specify training " +#~ "strategies / the number of total " +#~ "clients / FL rounds / participating " +#~ "clients / clients for evaluation, and" +#~ " evaluation fashion. Note that with " +#~ ":code:`--centralised-eval`, the sever will " +#~ "do centralised evaluation and all " +#~ "functionalities for client evaluation will " +#~ "be disabled." +#~ msgstr "" + +#~ msgid "" +#~ "This defines various options for client" +#~ " data partitioning. Besides, clients also" +#~ " have an option to conduct evaluation" +#~ " on centralised test set by setting" +#~ " :code:`--centralised-eval`, as well as " +#~ "an option to perform scaled learning " +#~ "rate based on the number of " +#~ "clients by setting :code:`--scaled-lr`." +#~ msgstr "" + +#~ msgid "" +#~ "The full `code " +#~ "`_ for this comprehensive " +#~ "example can be found in :code:`examples" +#~ "/xgboost-comprehensive`." +#~ msgstr "" + +#~ msgid "|b8714c45b74b4d8fb008e2ebb3bc1d44|" +#~ msgstr "" + +#~ msgid "|75f1561efcfd422ea67d28d1513120dc|" +#~ msgstr "" + +#~ msgid "|6a1f51b235304558a9bdaaabfc93b8d2|" +#~ msgstr "" + +#~ msgid "|35e70dab1fb544af9aa3a9c09c4f9797|" +#~ msgstr "" + +#~ msgid "|d7efb5705dd3467f991ed23746824a07|" +#~ msgstr "" + +#~ msgid "|94e7b021c7b540bfbedf7f082a41ff87|" +#~ msgstr "" + +#~ msgid "|a80714782dde439ab73936518f91fc3c|" +#~ msgstr "" + +#~ msgid "|c62080ca6197473da57d191c8225a9d9|" +#~ msgstr "" + +#~ msgid "|21a8f1e6a5b14a7bbb8559979d0e8a2b|" +#~ msgstr "" + +#~ msgid "|c310f2a22f7b4917bf42775aae7a1c09|" +#~ msgstr "" + +#~ msgid "|a0c5b43401194535a8460bcf02e65f9a|" +#~ msgstr "" + +#~ msgid "|aabfdbd5564e41a790f8ea93cc21a444|" +#~ msgstr "" + +#~ msgid "|c9cc8f160fa647b09e742fe4dc8edb54|" +#~ msgstr "" + +#~ msgid "|7e83aad011cd4907b2f02f907c6922e9|" +#~ msgstr "" + diff --git a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po index 1f8c40d304e7..a3a0f0668c7c 100644 --- a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po +++ b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po @@ -7,7 +7,7 @@ msgid "" msgstr "" "Project-Id-Version: Flower main\n" "Report-Msgid-Bugs-To: \n" -"POT-Creation-Date: 2024-09-25 00:30+0000\n" +"POT-Creation-Date: 2024-09-26 00:29+0000\n" "PO-Revision-Date: 2024-05-25 11:09+0000\n" "Last-Translator: Gustavo Bertoli \n" "Language: pt_BR\n" @@ -62,22 +62,22 @@ msgid "" "or not by reading the Flower source code." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:22 +#: ../../source/contributor-explanation-public-and-private-apis.rst:23 msgid "Flower public API" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:24 +#: ../../source/contributor-explanation-public-and-private-apis.rst:25 msgid "Flower has a well-defined public API. Let's look at this in more detail." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:28 +#: ../../source/contributor-explanation-public-and-private-apis.rst:29 msgid "" "Every component that is reachable by recursively following " "``__init__.__all__`` starting from the root package (``flwr``) is part of" " the public API." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:30 +#: ../../source/contributor-explanation-public-and-private-apis.rst:32 msgid "" "If you want to determine whether a component " "(class/function/generator/...) is part of the public API or not, you need" @@ -85,13 +85,13 @@ msgid "" "src/py/flwr`` to look at the Python sub-packages contained ``flwr``:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:43 +#: ../../source/contributor-explanation-public-and-private-apis.rst:46 msgid "" "Contrast this with the definition of ``__all__`` in the root " "``src/py/flwr/__init__.py``:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:55 +#: ../../source/contributor-explanation-public-and-private-apis.rst:59 msgid "" "You can see that ``flwr`` has six subpackages (``cli``, ``client``, " "``common``, ``proto``, ``server``, ``simulation``), but only four of them" @@ -99,7 +99,7 @@ msgid "" "``simulation``)." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:57 +#: ../../source/contributor-explanation-public-and-private-apis.rst:63 msgid "" "What does this mean? It means that ``client``, ``common``, ``server`` and" " ``simulation`` are part of the public API, but ``cli`` and ``proto`` are" @@ -110,21 +110,21 @@ msgid "" "even be removed completely." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:62 +#: ../../source/contributor-explanation-public-and-private-apis.rst:70 msgid "Therefore, as a Flower user:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:64 +#: ../../source/contributor-explanation-public-and-private-apis.rst:72 msgid "``from flwr import client`` ✅ Ok, you're importing a public API." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:65 +#: ../../source/contributor-explanation-public-and-private-apis.rst:73 msgid "" "``from flwr import proto`` ❌ Not recommended, you're importing a private " "API." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:67 +#: ../../source/contributor-explanation-public-and-private-apis.rst:75 msgid "" "What about components that are nested deeper in the hierarchy? Let's look" " at Flower strategies to see another typical pattern. Flower strategies " @@ -133,7 +133,7 @@ msgid "" "``src/py/flwr/server/strategy/__init__.py``:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:81 +#: ../../source/contributor-explanation-public-and-private-apis.rst:91 msgid "" "What's notable here is that all strategies are implemented in dedicated " "modules (e.g., ``fedavg.py``). In ``__init__.py``, we *import* the " @@ -145,33 +145,33 @@ msgid "" "the public API (as long as we update the import path in ``__init__.py``)." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:86 +#: ../../source/contributor-explanation-public-and-private-apis.rst:99 msgid "Therefore:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:88 +#: ../../source/contributor-explanation-public-and-private-apis.rst:101 msgid "" "``from flwr.server.strategy import FedAvg`` ✅ Ok, you're importing a " "class that is part of the public API." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:89 +#: ../../source/contributor-explanation-public-and-private-apis.rst:103 msgid "" "``from flwr.server.strategy import fedavg`` ❌ Not recommended, you're " "importing a private module." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:91 +#: ../../source/contributor-explanation-public-and-private-apis.rst:106 msgid "" "This approach is also implemented in the tooling that automatically " "builds API reference docs." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:94 +#: ../../source/contributor-explanation-public-and-private-apis.rst:110 msgid "Flower public API of private packages" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:96 +#: ../../source/contributor-explanation-public-and-private-apis.rst:112 msgid "" "We also use this to define the public API of private subpackages. Public," " in this context, means the API that other ``flwr`` subpackages should " @@ -179,14 +179,14 @@ msgid "" "not exported via ``src/py/flwr/server/__init__.py``'s ``__all__``)." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:100 +#: ../../source/contributor-explanation-public-and-private-apis.rst:117 msgid "" "Still, the private sub-package ``flwr.server.driver`` defines a " "\"public\" API using ``__all__`` in " "``src/py/flwr/server/driver/__init__.py``:" msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:114 +#: ../../source/contributor-explanation-public-and-private-apis.rst:132 msgid "" "The interesting part is that both ``GrpcDriver`` and ``InMemoryDriver`` " "are never used by Flower framework users, only by other parts of the " @@ -198,7 +198,7 @@ msgid "" "``InMemoryDriver`` class definition)." msgstr "" -#: ../../source/contributor-explanation-public-and-private-apis.rst:117 +#: ../../source/contributor-explanation-public-and-private-apis.rst:140 msgid "" "This is because ``flwr.server.driver`` defines a public interface for " "other ``flwr`` subpackages. This allows codeowners of " @@ -237,16 +237,16 @@ msgstr "" "Antes de começarmos, precisamos encontrar alguns pré-requisitos em nosso " "ambiente de desenvolvimento local." -#: ../../source/contributor-how-to-build-docker-images.rst:12 +#: ../../source/contributor-how-to-build-docker-images.rst:13 #, fuzzy msgid "Clone the ``flower`` repository." msgstr "Clone o repositório do flower." -#: ../../source/contributor-how-to-build-docker-images.rst:18 +#: ../../source/contributor-how-to-build-docker-images.rst:19 msgid "Verify the Docker daemon is running." msgstr "Verifique que o serviço Docker está rodando." -#: ../../source/contributor-how-to-build-docker-images.rst:20 +#: ../../source/contributor-how-to-build-docker-images.rst:21 msgid "" "The build instructions that assemble the images are located in the " "respective Dockerfiles. You can find them in the subdirectories of " @@ -256,7 +256,7 @@ msgstr "" "respectivos Dockerfiles. Você pode encontrá-los nos subdiretórios " "``src/docker```." -#: ../../source/contributor-how-to-build-docker-images.rst:23 +#: ../../source/contributor-how-to-build-docker-images.rst:24 #, fuzzy msgid "" "Flower Docker images are configured via build arguments. Through build " @@ -276,146 +276,146 @@ msgstr "" "Todos os argumentos de compilação disponíveis para cada imagem estão " "listados em uma das tabelas abaixo." -#: ../../source/contributor-how-to-build-docker-images.rst:30 +#: ../../source/contributor-how-to-build-docker-images.rst:32 #, fuzzy msgid "Building the Base Image" msgstr "Construindo a imagem base" -#: ../../source/contributor-how-to-build-docker-images.rst:36 -#: ../../source/contributor-how-to-build-docker-images.rst:98 +#: ../../source/contributor-how-to-build-docker-images.rst:38 +#: ../../source/contributor-how-to-build-docker-images.rst:104 msgid "Build argument" msgstr "Argumento de compilação" -#: ../../source/contributor-how-to-build-docker-images.rst:37 -#: ../../source/contributor-how-to-build-docker-images.rst:99 +#: ../../source/contributor-how-to-build-docker-images.rst:39 +#: ../../source/contributor-how-to-build-docker-images.rst:105 msgid "Description" msgstr "Descrição" -#: ../../source/contributor-how-to-build-docker-images.rst:38 -#: ../../source/contributor-how-to-build-docker-images.rst:100 +#: ../../source/contributor-how-to-build-docker-images.rst:40 +#: ../../source/contributor-how-to-build-docker-images.rst:106 msgid "Required" msgstr "Necessário" -#: ../../source/contributor-how-to-build-docker-images.rst:39 -#: ../../source/contributor-how-to-build-docker-images.rst:101 -#: ../../source/docker/persist-superlink-state.rst:18 -#: ../../source/docker/pin-version.rst:11 +#: ../../source/contributor-how-to-build-docker-images.rst:41 +#: ../../source/contributor-how-to-build-docker-images.rst:107 +#: ../../source/docker/persist-superlink-state.rst:19 +#: ../../source/docker/pin-version.rst:12 #: ../../source/docker/set-environment-variables.rst:8 msgid "Example" msgstr "Exemplo" -#: ../../source/contributor-how-to-build-docker-images.rst:40 +#: ../../source/contributor-how-to-build-docker-images.rst:42 msgid "``DISTRO``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:41 +#: ../../source/contributor-how-to-build-docker-images.rst:43 #, fuzzy msgid "The Linux distribution to use as the base image." msgstr "O nome do repositório da imagem base." -#: ../../source/contributor-how-to-build-docker-images.rst:42 -#: ../../source/contributor-how-to-build-docker-images.rst:46 -#: ../../source/contributor-how-to-build-docker-images.rst:50 -#: ../../source/contributor-how-to-build-docker-images.rst:66 -#: ../../source/contributor-how-to-build-docker-images.rst:70 -#: ../../source/contributor-how-to-build-docker-images.rst:104 +#: ../../source/contributor-how-to-build-docker-images.rst:44 +#: ../../source/contributor-how-to-build-docker-images.rst:48 +#: ../../source/contributor-how-to-build-docker-images.rst:52 +#: ../../source/contributor-how-to-build-docker-images.rst:68 +#: ../../source/contributor-how-to-build-docker-images.rst:75 +#: ../../source/contributor-how-to-build-docker-images.rst:110 msgid "No" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:43 +#: ../../source/contributor-how-to-build-docker-images.rst:45 #, fuzzy msgid "``ubuntu``" msgstr "``UBUNTU_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:44 +#: ../../source/contributor-how-to-build-docker-images.rst:46 #, fuzzy msgid "``DISTRO_VERSION``" msgstr "``PIP_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:45 +#: ../../source/contributor-how-to-build-docker-images.rst:47 msgid "Version of the Linux distribution." msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:47 +#: ../../source/contributor-how-to-build-docker-images.rst:49 msgid ":substitution-code:`|ubuntu_version|`" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:48 +#: ../../source/contributor-how-to-build-docker-images.rst:50 msgid "``PYTHON_VERSION``" msgstr "``PYTHON_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:49 +#: ../../source/contributor-how-to-build-docker-images.rst:51 msgid "Version of ``python`` to be installed." msgstr "Versão do ``python`` a ser instalada." -#: ../../source/contributor-how-to-build-docker-images.rst:51 +#: ../../source/contributor-how-to-build-docker-images.rst:53 msgid "``3.11`` or ``3.11.1``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:52 +#: ../../source/contributor-how-to-build-docker-images.rst:54 msgid "``PIP_VERSION``" msgstr "``PIP_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:53 +#: ../../source/contributor-how-to-build-docker-images.rst:55 msgid "Version of ``pip`` to be installed." msgstr "Versão do ``pip`` a ser instalada." -#: ../../source/contributor-how-to-build-docker-images.rst:54 -#: ../../source/contributor-how-to-build-docker-images.rst:58 -#: ../../source/contributor-how-to-build-docker-images.rst:62 -#: ../../source/contributor-how-to-build-docker-images.rst:108 +#: ../../source/contributor-how-to-build-docker-images.rst:56 +#: ../../source/contributor-how-to-build-docker-images.rst:60 +#: ../../source/contributor-how-to-build-docker-images.rst:64 +#: ../../source/contributor-how-to-build-docker-images.rst:114 msgid "Yes" msgstr "Sim" -#: ../../source/contributor-how-to-build-docker-images.rst:55 +#: ../../source/contributor-how-to-build-docker-images.rst:57 msgid ":substitution-code:`|pip_version|`" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:56 +#: ../../source/contributor-how-to-build-docker-images.rst:58 msgid "``SETUPTOOLS_VERSION``" msgstr "``SETUPTOOLS_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:57 +#: ../../source/contributor-how-to-build-docker-images.rst:59 msgid "Version of ``setuptools`` to be installed." msgstr "Versão do ``setuptools`` a ser instalada." -#: ../../source/contributor-how-to-build-docker-images.rst:59 +#: ../../source/contributor-how-to-build-docker-images.rst:61 #, fuzzy msgid ":substitution-code:`|setuptools_version|`" msgstr "``SETUPTOOLS_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:60 +#: ../../source/contributor-how-to-build-docker-images.rst:62 msgid "``FLWR_VERSION``" msgstr "``FLWR_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:61 +#: ../../source/contributor-how-to-build-docker-images.rst:63 msgid "Version of Flower to be installed." msgstr "Versão do Flower a ser instalada." -#: ../../source/contributor-how-to-build-docker-images.rst:63 +#: ../../source/contributor-how-to-build-docker-images.rst:65 msgid ":substitution-code:`|stable_flwr_version|`" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:64 +#: ../../source/contributor-how-to-build-docker-images.rst:66 #, fuzzy msgid "``FLWR_PACKAGE``" msgstr "``FLWR_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:65 +#: ../../source/contributor-how-to-build-docker-images.rst:67 #, fuzzy msgid "The Flower package to be installed." msgstr "Versão do Flower a ser instalada." -#: ../../source/contributor-how-to-build-docker-images.rst:67 +#: ../../source/contributor-how-to-build-docker-images.rst:69 msgid "``flwr`` or ``flwr-nightly``" msgstr "" -#: ../../source/contributor-how-to-build-docker-images.rst:68 +#: ../../source/contributor-how-to-build-docker-images.rst:70 #, fuzzy msgid "``FLWR_VERSION_REF``" msgstr "``FLWR_VERSION``" -#: ../../source/contributor-how-to-build-docker-images.rst:69 +#: ../../source/contributor-how-to-build-docker-images.rst:71 msgid "" "A `direct reference " "`_." -#: ../../source/contributor-how-to-contribute-translations.rst:29 +#: ../../source/contributor-how-to-contribute-translations.rst:28 msgid "" "Once you are signed in to Weblate, you can navigate to the `Flower " "Framework project `_. Aqui, você deve ver os diferentes idiomas existentes " "que podem ser encontrados no site." -#: ../../source/contributor-how-to-contribute-translations.rst:34 +#: ../../source/contributor-how-to-contribute-translations.rst:32 msgid "" "Once you have selected the language you want to contribute to, you should" " see a similar interface to this:" @@ -585,7 +585,7 @@ msgstr "" "Uma vez que você tenha selecionado o idioma que deseja contribuir, você " "deve ver uma interface semelhante a esta:" -#: ../../source/contributor-how-to-contribute-translations.rst:39 +#: ../../source/contributor-how-to-contribute-translations.rst:37 msgid "" "The most straight forward option here is to click on the ``Translate`` " "button on the top right (in the ``Translation status`` section). This " @@ -597,12 +597,12 @@ msgstr "" "automaticamente para a interface de tradução de strings ainda não " "traduzidas." -#: ../../source/contributor-how-to-contribute-translations.rst:43 +#: ../../source/contributor-how-to-contribute-translations.rst:41 #, fuzzy msgid "This is what the interface looks like:" msgstr "É assim que a interface se parece:" -#: ../../source/contributor-how-to-contribute-translations.rst:47 +#: ../../source/contributor-how-to-contribute-translations.rst:45 msgid "" "You input your translation in the text box at the top and then, once you " "are happy with it, you either press ``Save and continue`` (to save the " @@ -619,7 +619,7 @@ msgstr "" "ou ``Skip`` (para ir para a próxima string não traduzida sem salvar nada " "na atual)." -#: ../../source/contributor-how-to-contribute-translations.rst:54 +#: ../../source/contributor-how-to-contribute-translations.rst:51 msgid "" "In order to help with the translations, you can see on the bottom the " "``Nearby strings``, the ``Comments`` (from other contributors), the " @@ -634,7 +634,7 @@ msgstr "" "(outras línguas), e o ``History`` (histórico) de traduções para esta " "string." -#: ../../source/contributor-how-to-contribute-translations.rst:59 +#: ../../source/contributor-how-to-contribute-translations.rst:56 msgid "" "On the right, under the ``String information`` section, you can also " "click the link under ``Source string location`` in order to view the " @@ -644,7 +644,7 @@ msgstr "" " link sob ``Source string location`` para visualizar a fonte do arquivo " "doc que contém a string." -#: ../../source/contributor-how-to-contribute-translations.rst:63 +#: ../../source/contributor-how-to-contribute-translations.rst:60 msgid "" "For more information about translating using Weblate, you can check out " "this `in-depth guide " @@ -654,12 +654,12 @@ msgstr "" "pode conferir este `guia detalhado " "`_." -#: ../../source/contributor-how-to-contribute-translations.rst:67 +#: ../../source/contributor-how-to-contribute-translations.rst:64 #, fuzzy msgid "Add new languages" msgstr "Adicionar novos idiomas" -#: ../../source/contributor-how-to-contribute-translations.rst:69 +#: ../../source/contributor-how-to-contribute-translations.rst:66 msgid "" "If you want to add a new language, you will first have to contact us, " "either on `Slack `_, or by opening an issue" @@ -682,20 +682,19 @@ msgid "" "extension. What is it? Read the following quote:" msgstr "" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:7 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:8 msgid "" "The Visual Studio Code Remote - Containers extension lets you use a " "Docker container as a fully-featured development environment. It allows " "you to open any folder inside (or mounted into) a container and take " "advantage of Visual Studio Code's full feature set. A " -":code:`devcontainer.json` file in your project tells VS Code how to " -"access (or create) a development container with a well-defined tool and " -"runtime stack. This container can be used to run an application or to " -"separate tools, libraries, or runtimes needed for working with a " -"codebase." +"``devcontainer.json`` file in your project tells VS Code how to access " +"(or create) a development container with a well-defined tool and runtime " +"stack. This container can be used to run an application or to separate " +"tools, libraries, or runtimes needed for working with a codebase." msgstr "" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:9 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:16 msgid "" "Workspace files are mounted from the local file system or copied or " "cloned into the container. Extensions are installed and run inside the " @@ -704,28 +703,28 @@ msgid "" " environment just by connecting to a different container." msgstr "" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:11 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:22 msgid "" "Source: `Official VSCode documentation " "`_" msgstr "" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:15 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:26 msgid "Getting started" msgstr "" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:17 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:28 msgid "" -"Configuring and setting up the :code:`Dockerfile` as well the " -"configuration for the devcontainer can be a bit more involved. The good " -"thing is you don't have to do it. Usually it should be enough to install " -"`Docker `_ on your system and " -"ensure its available on your command line. Additionally, install the " -"`VSCode Containers Extension `_ on your system and ensure its" +" available on your command line. Additionally, install the `VSCode " +"Containers Extension `_." msgstr "" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:19 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:35 msgid "" "Now you should be good to go. When starting VSCode, it will ask you to " "run in the container environment and - if you confirm - automatically " @@ -735,20 +734,20 @@ msgid "" "option *(Re)Open Folder in Container*." msgstr "" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:21 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:41 msgid "" "In some cases your setup might be more involved. For those cases consult " "the following sources:" msgstr "" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:23 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:44 msgid "" "`Developing inside a Container " "`_" msgstr "" -#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:24 +#: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:46 msgid "" "`Remote development in Containers " "`_" @@ -773,154 +772,154 @@ msgid "" "``poetry.lock`` (``rm poetry.lock``) before running ``poetry install``)." msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:12 +#: ../../source/contributor-how-to-install-development-versions.rst:14 msgid "" "``flwr = { version = \"1.0.0a0\", allow-prereleases = true }`` (without " "extras)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:13 +#: ../../source/contributor-how-to-install-development-versions.rst:15 msgid "" "``flwr = { version = \"1.0.0a0\", allow-prereleases = true, extras = " "[\"simulation\"] }`` (with extras)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:15 +#: ../../source/contributor-how-to-install-development-versions.rst:18 msgid "" "Install ``flwr`` from a local copy of the Flower source code via " "``pyproject.toml``:" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:17 +#: ../../source/contributor-how-to-install-development-versions.rst:20 msgid "``flwr = { path = \"../../\", develop = true }`` (without extras)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:18 +#: ../../source/contributor-how-to-install-development-versions.rst:21 msgid "" "``flwr = { path = \"../../\", develop = true, extras = [\"simulation\"] " "}`` (with extras)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:20 +#: ../../source/contributor-how-to-install-development-versions.rst:23 msgid "Install ``flwr`` from a local wheel file via ``pyproject.toml``:" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:22 +#: ../../source/contributor-how-to-install-development-versions.rst:25 msgid "" "``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\" }`` (without" " extras)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:23 +#: ../../source/contributor-how-to-install-development-versions.rst:26 msgid "" "``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\", extras = " "[\"simulation\"] }`` (with extras)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:25 +#: ../../source/contributor-how-to-install-development-versions.rst:29 msgid "" "Please refer to the Poetry documentation for further details: `Poetry " "Dependency Specification `_" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:28 +#: ../../source/contributor-how-to-install-development-versions.rst:33 msgid "Using pip (recommended on Colab)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:30 +#: ../../source/contributor-how-to-install-development-versions.rst:35 msgid "Install a ``flwr`` pre-release from PyPI:" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:32 +#: ../../source/contributor-how-to-install-development-versions.rst:37 msgid "``pip install -U --pre flwr`` (without extras)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:33 +#: ../../source/contributor-how-to-install-development-versions.rst:38 msgid "``pip install -U --pre 'flwr[simulation]'`` (with extras)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:35 +#: ../../source/contributor-how-to-install-development-versions.rst:40 msgid "" "Python packages can be installed from git repositories. Use one of the " "following commands to install the Flower directly from GitHub." msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:37 +#: ../../source/contributor-how-to-install-development-versions.rst:43 msgid "Install ``flwr`` from the default GitHub branch (``main``):" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:39 +#: ../../source/contributor-how-to-install-development-versions.rst:45 msgid "" "``pip install flwr@git+https://github.com/adap/flower.git`` (without " "extras)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:40 +#: ../../source/contributor-how-to-install-development-versions.rst:46 msgid "" "``pip install 'flwr[simulation]@git+https://github.com/adap/flower.git'``" " (with extras)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:42 +#: ../../source/contributor-how-to-install-development-versions.rst:49 msgid "Install ``flwr`` from a specific GitHub branch (``branch-name``):" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:44 +#: ../../source/contributor-how-to-install-development-versions.rst:51 msgid "" "``pip install flwr@git+https://github.com/adap/flower.git@branch-name`` " "(without extras)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:45 +#: ../../source/contributor-how-to-install-development-versions.rst:53 msgid "" "``pip install 'flwr[simulation]@git+https://github.com/adap/flower.git" "@branch-name'`` (with extras)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:49 +#: ../../source/contributor-how-to-install-development-versions.rst:57 msgid "Open Jupyter Notebooks on Google Colab" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:51 +#: ../../source/contributor-how-to-install-development-versions.rst:59 msgid "" "Open the notebook ``doc/source/tutorial-series-get-started-with-flower-" "pytorch.ipynb``:" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:53 +#: ../../source/contributor-how-to-install-development-versions.rst:61 msgid "" "https://colab.research.google.com/github/adap/flower/blob/main/doc/source" "/tutorial-series-get-started-with-flower-pytorch.ipynb" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:55 +#: ../../source/contributor-how-to-install-development-versions.rst:63 msgid "" "Open a development version of the same notebook from branch `branch-name`" " by changing ``main`` to ``branch-name`` (right after ``blob``):" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:57 +#: ../../source/contributor-how-to-install-development-versions.rst:66 msgid "" "https://colab.research.google.com/github/adap/flower/blob/branch-" "name/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:59 +#: ../../source/contributor-how-to-install-development-versions.rst:68 msgid "Install a `whl` on Google Colab:" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:61 +#: ../../source/contributor-how-to-install-development-versions.rst:70 msgid "" "In the vertical icon grid on the left hand side, select ``Files`` > " "``Upload to session storage``" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:62 +#: ../../source/contributor-how-to-install-development-versions.rst:72 msgid "Upload the whl (e.g., ``flwr-1.8.0-py3-none-any.whl``)" msgstr "" -#: ../../source/contributor-how-to-install-development-versions.rst:63 +#: ../../source/contributor-how-to-install-development-versions.rst:73 msgid "" "Change ``!pip install -q 'flwr[simulation]' torch torchvision " "matplotlib`` to ``!pip install -q 'flwr-1.8.0-py3-none-" @@ -937,25 +936,25 @@ msgid "" "change in the future." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:7 +#: ../../source/contributor-how-to-release-flower.rst:8 msgid "During the release" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:9 +#: ../../source/contributor-how-to-release-flower.rst:10 msgid "" "The version number of a release is stated in ``pyproject.toml``. To " "release a new version of Flower, the following things need to happen (in " "that order):" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:11 +#: ../../source/contributor-how-to-release-flower.rst:13 msgid "" "Run ``python3 src/py/flwr_tool/update_changelog.py `` in " "order to add every new change to the changelog (feel free to make manual " "changes to the changelog afterwards until it looks good)." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:12 +#: ../../source/contributor-how-to-release-flower.rst:16 msgid "" "Once the changelog has been updated with all the changes, run ``./dev" "/prepare-release-changelog.sh v``, where ```` " @@ -965,7 +964,7 @@ msgid "" "the contributors. Open a pull request with those changes." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:13 +#: ../../source/contributor-how-to-release-flower.rst:22 msgid "" "Once the pull request is merged, tag the release commit with the version " "number as soon as the PR is merged: ``git tag v`` (notice " @@ -974,100 +973,100 @@ msgid "" "artifacts and the relevant part of the changelog." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:14 +#: ../../source/contributor-how-to-release-flower.rst:26 msgid "Check the draft release on GitHub, and if everything is good, publish it." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:17 +#: ../../source/contributor-how-to-release-flower.rst:29 msgid "After the release" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:19 +#: ../../source/contributor-how-to-release-flower.rst:31 msgid "Create a pull request which contains the following changes:" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:21 +#: ../../source/contributor-how-to-release-flower.rst:33 msgid "Increase the minor version in ``pyproject.toml`` by one." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:22 +#: ../../source/contributor-how-to-release-flower.rst:34 msgid "Update all files which contain the current version number if necessary." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:23 +#: ../../source/contributor-how-to-release-flower.rst:35 msgid "Add a new ``Unreleased`` section in ``changelog.md``." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:25 +#: ../../source/contributor-how-to-release-flower.rst:37 msgid "" "Merge the pull request on the same day (i.e., before a new nightly " "release gets published to PyPI)." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:28 +#: ../../source/contributor-how-to-release-flower.rst:41 msgid "Publishing a pre-release" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:31 +#: ../../source/contributor-how-to-release-flower.rst:44 msgid "Pre-release naming" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:33 +#: ../../source/contributor-how-to-release-flower.rst:46 msgid "" "PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases" " MUST use one of the following naming patterns:" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:35 +#: ../../source/contributor-how-to-release-flower.rst:49 msgid "Alpha: ``MAJOR.MINOR.PATCHaN``" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:36 +#: ../../source/contributor-how-to-release-flower.rst:50 msgid "Beta: ``MAJOR.MINOR.PATCHbN``" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:37 +#: ../../source/contributor-how-to-release-flower.rst:51 msgid "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:39 +#: ../../source/contributor-how-to-release-flower.rst:53 msgid "Examples include:" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:41 +#: ../../source/contributor-how-to-release-flower.rst:55 msgid "``1.0.0a0``" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:42 +#: ../../source/contributor-how-to-release-flower.rst:56 msgid "``1.0.0b0``" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:43 +#: ../../source/contributor-how-to-release-flower.rst:57 msgid "``1.0.0rc0``" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:44 +#: ../../source/contributor-how-to-release-flower.rst:58 msgid "``1.0.0rc1``" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:46 +#: ../../source/contributor-how-to-release-flower.rst:60 msgid "" "This is in line with PEP-440 and the recommendations from the Python " "Packaging Authority (PyPA):" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:49 +#: ../../source/contributor-how-to-release-flower.rst:63 msgid "`PEP-440 `_" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:50 +#: ../../source/contributor-how-to-release-flower.rst:64 msgid "" "`PyPA Choosing a versioning scheme " "`_" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:52 +#: ../../source/contributor-how-to-release-flower.rst:67 msgid "" "Note that the approach defined by PyPA is not compatible with SemVer " "2.0.0 spec, for details consult the `Semantic Versioning Specification " @@ -1075,26 +1074,26 @@ msgid "" "11 on precedence)." msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:55 +#: ../../source/contributor-how-to-release-flower.rst:73 msgid "Pre-release classification" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:57 +#: ../../source/contributor-how-to-release-flower.rst:75 msgid "Should the next pre-release be called alpha, beta, or release candidate?" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:59 +#: ../../source/contributor-how-to-release-flower.rst:77 msgid "" "RC: feature complete, no known issues (apart from issues that are " "classified as \"won't fix\" for the next stable release) - if no issues " "surface this will become the next stable release" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:60 +#: ../../source/contributor-how-to-release-flower.rst:80 msgid "Beta: feature complete, allowed to have known issues" msgstr "" -#: ../../source/contributor-how-to-release-flower.rst:61 +#: ../../source/contributor-how-to-release-flower.rst:81 msgid "Alpha: not feature complete, allowed to have known issues" msgstr "" @@ -1110,19 +1109,19 @@ msgid "" "the instructions or choose your preferred setup." msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:9 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:10 msgid "Python Version" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:11 -#: ../../source/how-to-install-flower.rst:8 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:12 +#: ../../source/how-to-install-flower.rst:7 msgid "" "Flower requires at least `Python 3.9 `_, " "but `Python 3.10 `_ or above is " "recommended." msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:14 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:17 msgid "" "Due to a known incompatibility with `ray " "`_, we currently recommend utilizing at " @@ -1130,11 +1129,11 @@ msgid "" "simulations." msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:19 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:22 msgid "Virtualenv with Pyenv/Virtualenv" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:21 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:24 msgid "" "One of the recommended virtual environment is `pyenv " "`_/`virtualenv `_ for details." msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:23 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:29 msgid "" "Once Pyenv is set up, you can use it to install `Python Version 3.10 " "`_ or above:" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:29 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:36 msgid "Create the virtualenv with:" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:36 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:42 msgid "Activate the virtualenv by running the following command:" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:44 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:49 msgid "Virtualenv with Poetry" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:46 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:51 msgid "" "The Flower examples are based on `Poetry `_ to manage dependencies. After installing Poetry you " "simply create a virtual environment with:" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:52 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:58 msgid "" "If you open a new terminal you can activate the previously created " "virtual environment with the following command:" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:60 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:66 msgid "Virtualenv with Anaconda" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:62 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:68 msgid "" "If you prefer to use Anaconda for your virtual environment then install " "and setup the `conda `_ package. After setting it up you can " +"/user-guide/install/index.html>`_ package. After setting it up you can " "create a virtual environment with:" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:68 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:76 msgid "and activate the virtual environment with:" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:76 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:83 msgid "And then?" msgstr "" -#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:78 +#: ../../source/contributor-how-to-set-up-a-virtual-env.rst:85 msgid "" "As soon as you created your virtual environment you clone one of the " "`Flower examples `_." @@ -1203,11 +1202,11 @@ msgstr "" msgid "Write documentation" msgstr "" -#: ../../source/contributor-how-to-write-documentation.rst:6 +#: ../../source/contributor-how-to-write-documentation.rst:5 msgid "Project layout" msgstr "" -#: ../../source/contributor-how-to-write-documentation.rst:8 +#: ../../source/contributor-how-to-write-documentation.rst:7 msgid "" "The Flower documentation lives in the ``doc`` directory. The Sphinx-based" " documentation system supports both reStructuredText (``.rst`` files) and" @@ -1215,7 +1214,7 @@ msgid "" msgstr "" #: ../../source/contributor-how-to-write-documentation.rst:10 -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:169 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:193 msgid "" "Note that, in order to build the documentation locally (with ``poetry run" " make html``, like described below), `Pandoc " @@ -1223,20 +1222,20 @@ msgid "" "system." msgstr "" -#: ../../source/contributor-how-to-write-documentation.rst:14 +#: ../../source/contributor-how-to-write-documentation.rst:15 msgid "Edit an existing page" msgstr "" -#: ../../source/contributor-how-to-write-documentation.rst:16 +#: ../../source/contributor-how-to-write-documentation.rst:17 msgid "Edit an existing ``.rst`` (or ``.md``) file under ``doc/source/``" msgstr "" -#: ../../source/contributor-how-to-write-documentation.rst:17 +#: ../../source/contributor-how-to-write-documentation.rst:18 #: ../../source/contributor-how-to-write-documentation.rst:27 msgid "Compile the docs: ``cd doc``, then ``poetry run make html``" msgstr "" -#: ../../source/contributor-how-to-write-documentation.rst:18 +#: ../../source/contributor-how-to-write-documentation.rst:19 #: ../../source/contributor-how-to-write-documentation.rst:28 msgid "Open ``doc/build/html/index.html`` in the browser to check the result" msgstr "" @@ -1269,41 +1268,41 @@ msgid "" "the Flower codebase." msgstr "" -#: ../../source/contributor-ref-good-first-contributions.rst:11 +#: ../../source/contributor-ref-good-first-contributions.rst:9 msgid "Where to start" msgstr "" -#: ../../source/contributor-ref-good-first-contributions.rst:13 +#: ../../source/contributor-ref-good-first-contributions.rst:11 msgid "" "Until the Flower core library matures it will be easier to get PR's " "accepted if they only touch non-core areas of the codebase. Good " "candidates to get started are:" msgstr "" -#: ../../source/contributor-ref-good-first-contributions.rst:17 +#: ../../source/contributor-ref-good-first-contributions.rst:14 msgid "Documentation: What's missing? What could be expressed more clearly?" msgstr "" -#: ../../source/contributor-ref-good-first-contributions.rst:18 +#: ../../source/contributor-ref-good-first-contributions.rst:15 msgid "Baselines: See below." msgstr "" -#: ../../source/contributor-ref-good-first-contributions.rst:19 +#: ../../source/contributor-ref-good-first-contributions.rst:16 msgid "Examples: See below." msgstr "" -#: ../../source/contributor-ref-good-first-contributions.rst:23 +#: ../../source/contributor-ref-good-first-contributions.rst:19 msgid "Request for Flower Baselines" msgstr "" -#: ../../source/contributor-ref-good-first-contributions.rst:25 +#: ../../source/contributor-ref-good-first-contributions.rst:21 msgid "" "If you are not familiar with Flower Baselines, you should probably check-" "out our `contributing guide for baselines " "`_." msgstr "" -#: ../../source/contributor-ref-good-first-contributions.rst:27 +#: ../../source/contributor-ref-good-first-contributions.rst:25 msgid "" "You should then check out the open `issues " "`_" @@ -1312,7 +1311,7 @@ msgid "" "working on it!" msgstr "" -#: ../../source/contributor-ref-good-first-contributions.rst:31 +#: ../../source/contributor-ref-good-first-contributions.rst:30 msgid "" "Otherwise, if you don't find a baseline you'd like to work on, be sure to" " open a new issue with the baseline request template!" @@ -1353,30 +1352,30 @@ msgid "" "special case of the SecAgg+ protocol." msgstr "" -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:8 -msgid "The :code:`SecAgg+` abstraction" +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:9 +msgid "The ``SecAgg+`` abstraction" msgstr "" -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:10 -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:161 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:11 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:163 msgid "" "In this implementation, each client will be assigned with a unique index " "(int) for secure aggregation, and thus many python dictionaries used have" " keys of int type rather than ClientProxy type." msgstr "" -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:65 -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:198 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:67 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:204 msgid "" "The Flower server will execute and process received results in the " "following order:" msgstr "" -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:159 -msgid "The :code:`LightSecAgg` abstraction" +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:161 +msgid "The ``LightSecAgg`` abstraction" msgstr "" -#: ../../source/contributor-ref-secure-aggregation-protocols.rst:271 +#: ../../source/contributor-ref-secure-aggregation-protocols.rst:277 msgid "Types" msgstr "" @@ -1390,22 +1389,22 @@ msgid "" "are not used to contributing to GitHub projects." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:6 +#: ../../source/contributor-tutorial-contribute-on-github.rst:7 msgid "" "If you're familiar with how contributing on GitHub works, you can " "directly checkout our :doc:`getting started guide for contributors " "`." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:10 +#: ../../source/contributor-tutorial-contribute-on-github.rst:12 msgid "Setting up the repository" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:12 +#: ../../source/contributor-tutorial-contribute-on-github.rst:29 msgid "**Create a GitHub account and setup Git**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:13 +#: ../../source/contributor-tutorial-contribute-on-github.rst:15 msgid "" "Git is a distributed version control tool. This allows for an entire " "codebase's history to be stored and every developer's machine. It is a " @@ -1414,20 +1413,20 @@ msgid "" "started-with-git/set-up-git>`_ to set it up." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:16 +#: ../../source/contributor-tutorial-contribute-on-github.rst:21 msgid "" "GitHub, itself, is a code hosting platform for version control and " "collaboration. It allows for everyone to collaborate and work from " "anywhere on remote repositories." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:18 +#: ../../source/contributor-tutorial-contribute-on-github.rst:25 msgid "" "If you haven't already, you will need to create an account on `GitHub " "`_." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:20 +#: ../../source/contributor-tutorial-contribute-on-github.rst:28 msgid "" "The idea behind the generic Git and GitHub workflow boils down to this: " "you download code from a remote repository on GitHub, make changes " @@ -1435,19 +1434,19 @@ msgid "" "history back to GitHub." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:23 +#: ../../source/contributor-tutorial-contribute-on-github.rst:42 msgid "**Forking the Flower repository**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:24 +#: ../../source/contributor-tutorial-contribute-on-github.rst:32 msgid "" "A fork is a personal copy of a GitHub repository. To create one for " -"Flower, you must navigate to ``_ (while " +"Flower, you must navigate to https://github.com/adap/flower (while " "connected to your GitHub account) and click the ``Fork`` button situated " "on the top right of the page." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:29 +#: ../../source/contributor-tutorial-contribute-on-github.rst:38 msgid "" "You can change the name if you want, but this is not necessary as this " "version of Flower will be yours and will sit inside your own account " @@ -1455,11 +1454,11 @@ msgid "" " the top left corner that you are looking at your own version of Flower." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:34 +#: ../../source/contributor-tutorial-contribute-on-github.rst:59 msgid "**Cloning your forked repository**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:35 +#: ../../source/contributor-tutorial-contribute-on-github.rst:45 msgid "" "The next step is to download the forked repository on your machine to be " "able to make changes to it. On your forked repository page, you should " @@ -1467,27 +1466,27 @@ msgid "" "ability to copy the HTTPS link of the repository." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:41 +#: ../../source/contributor-tutorial-contribute-on-github.rst:52 msgid "" "Once you copied the \\, you can open a terminal on your machine, " "navigate to the place you want to download the repository to and type:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:47 +#: ../../source/contributor-tutorial-contribute-on-github.rst:59 msgid "" "This will create a ``flower/`` (or the name of your fork if you renamed " "it) folder in the current working directory." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:49 +#: ../../source/contributor-tutorial-contribute-on-github.rst:78 msgid "**Add origin**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:50 +#: ../../source/contributor-tutorial-contribute-on-github.rst:62 msgid "You can then go into the repository folder:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:56 +#: ../../source/contributor-tutorial-contribute-on-github.rst:68 msgid "" "And here we will need to add an origin to our repository. The origin is " "the \\ of the remote fork repository. To obtain it, we can do as " @@ -1495,27 +1494,27 @@ msgid "" "account and copying the link." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:61 +#: ../../source/contributor-tutorial-contribute-on-github.rst:75 msgid "" "Once the \\ is copied, we can type the following command in our " "terminal:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:68 +#: ../../source/contributor-tutorial-contribute-on-github.rst:102 msgid "**Add upstream**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:69 +#: ../../source/contributor-tutorial-contribute-on-github.rst:81 msgid "" "Now we will add an upstream address to our repository. Still in the same " "directory, we must run the following command:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:76 +#: ../../source/contributor-tutorial-contribute-on-github.rst:88 msgid "The following diagram visually explains what we did in the previous steps:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:80 +#: ../../source/contributor-tutorial-contribute-on-github.rst:92 msgid "" "The upstream is the GitHub remote address of the parent repository (in " "this case Flower), i.e. the one we eventually want to contribute to and " @@ -1524,17 +1523,17 @@ msgid "" "in our own account." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:84 +#: ../../source/contributor-tutorial-contribute-on-github.rst:97 msgid "" "To make sure our local version of the fork is up-to-date with the latest " "changes from the Flower repository, we can execute the following command:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:93 +#: ../../source/contributor-tutorial-contribute-on-github.rst:105 msgid "Setting up the coding environment" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:95 +#: ../../source/contributor-tutorial-contribute-on-github.rst:107 msgid "" "This can be achieved by following this :doc:`getting started guide for " "contributors ` (note " @@ -1542,158 +1541,158 @@ msgid "" "code and test it, you can finally start making changes!" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:100 +#: ../../source/contributor-tutorial-contribute-on-github.rst:113 msgid "Making changes" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:102 +#: ../../source/contributor-tutorial-contribute-on-github.rst:115 msgid "" "Before making any changes make sure you are up-to-date with your " "repository:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:108 +#: ../../source/contributor-tutorial-contribute-on-github.rst:121 msgid "And with Flower's repository:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:114 +#: ../../source/contributor-tutorial-contribute-on-github.rst:134 msgid "**Create a new branch**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:115 +#: ../../source/contributor-tutorial-contribute-on-github.rst:128 msgid "" "To make the history cleaner and easier to work with, it is good practice " "to create a new branch for each feature/project that needs to be " "implemented." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:118 +#: ../../source/contributor-tutorial-contribute-on-github.rst:131 msgid "" "To do so, just run the following command inside the repository's " "directory:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:124 +#: ../../source/contributor-tutorial-contribute-on-github.rst:136 msgid "**Make changes**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:125 +#: ../../source/contributor-tutorial-contribute-on-github.rst:137 msgid "Write great code and create wonderful changes using your favorite editor!" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:127 +#: ../../source/contributor-tutorial-contribute-on-github.rst:149 msgid "**Test and format your code**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:128 +#: ../../source/contributor-tutorial-contribute-on-github.rst:139 msgid "" "Don't forget to test and format your code! Otherwise your code won't be " "able to be merged into the Flower repository. This is done so the " "codebase stays consistent and easy to understand." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:131 +#: ../../source/contributor-tutorial-contribute-on-github.rst:143 msgid "To do so, we have written a few scripts that you can execute:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:140 +#: ../../source/contributor-tutorial-contribute-on-github.rst:162 msgid "**Stage changes**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:141 +#: ../../source/contributor-tutorial-contribute-on-github.rst:152 msgid "" "Before creating a commit that will update your history, you must specify " "to Git which files it needs to take into account." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:143 +#: ../../source/contributor-tutorial-contribute-on-github.rst:155 msgid "This can be done with:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:149 +#: ../../source/contributor-tutorial-contribute-on-github.rst:161 msgid "" "To check which files have been modified compared to the last version " "(last commit) and to see which files are staged for commit, you can use " -"the :code:`git status` command." +"the ``git status`` command." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:152 +#: ../../source/contributor-tutorial-contribute-on-github.rst:173 msgid "**Commit changes**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:153 +#: ../../source/contributor-tutorial-contribute-on-github.rst:165 msgid "" -"Once you have added all the files you wanted to commit using :code:`git " -"add`, you can finally create your commit using this command:" +"Once you have added all the files you wanted to commit using ``git add``," +" you can finally create your commit using this command:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:159 +#: ../../source/contributor-tutorial-contribute-on-github.rst:172 msgid "" "The \\ is there to explain to others what the commit " "does. It should be written in an imperative style and be concise. An " -"example would be :code:`git commit -m \"Add images to README\"`." +"example would be ``git commit -m \"Add images to README\"``." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:162 +#: ../../source/contributor-tutorial-contribute-on-github.rst:185 msgid "**Push the changes to the fork**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:163 +#: ../../source/contributor-tutorial-contribute-on-github.rst:176 msgid "" "Once we have committed our changes, we have effectively updated our local" " history, but GitHub has no way of knowing this unless we push our " "changes to our origin's remote address:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:170 +#: ../../source/contributor-tutorial-contribute-on-github.rst:184 msgid "" "Once this is done, you will see on the GitHub that your forked repo was " "updated with the changes you have made." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:174 +#: ../../source/contributor-tutorial-contribute-on-github.rst:188 msgid "Creating and merging a pull request (PR)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:176 +#: ../../source/contributor-tutorial-contribute-on-github.rst:226 msgid "**Create the PR**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:177 +#: ../../source/contributor-tutorial-contribute-on-github.rst:191 msgid "" "Once you have pushed changes, on the GitHub webpage of your repository " "you should see the following message:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:181 +#: ../../source/contributor-tutorial-contribute-on-github.rst:196 msgid "Otherwise you can always find this option in the ``Branches`` page." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:183 +#: ../../source/contributor-tutorial-contribute-on-github.rst:198 msgid "" "Once you click the ``Compare & pull request`` button, you should see " "something similar to this:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:187 +#: ../../source/contributor-tutorial-contribute-on-github.rst:203 msgid "At the top you have an explanation of which branch will be merged where:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:191 +#: ../../source/contributor-tutorial-contribute-on-github.rst:207 msgid "" "In this example you can see that the request is to merge the branch " "``doc-fixes`` from my forked repository to branch ``main`` from the " "Flower repository." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:193 +#: ../../source/contributor-tutorial-contribute-on-github.rst:210 msgid "" "The title should be changed to adhere to the :ref:`pr_title_format` " "guidelines, otherwise it won't be possible to merge the PR. So in this " "case, a correct title might be ``docs(framework:skip) Fix typos``." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:196 +#: ../../source/contributor-tutorial-contribute-on-github.rst:214 msgid "" "The input box in the middle is there for you to describe what your PR " "does and to link it to existing issues. We have placed comments (that " @@ -1701,167 +1700,167 @@ msgid "" "process." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:199 +#: ../../source/contributor-tutorial-contribute-on-github.rst:218 msgid "It is important to follow the instructions described in comments." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:201 +#: ../../source/contributor-tutorial-contribute-on-github.rst:220 msgid "" "At the bottom you will find the button to open the PR. This will notify " "reviewers that a new PR has been opened and that they should look over it" " to merge or to request changes." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:204 +#: ../../source/contributor-tutorial-contribute-on-github.rst:224 msgid "" "If your PR is not yet ready for review, and you don't want to notify " "anyone, you have the option to create a draft pull request:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:208 +#: ../../source/contributor-tutorial-contribute-on-github.rst:230 msgid "**Making new changes**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:209 +#: ../../source/contributor-tutorial-contribute-on-github.rst:229 msgid "" "Once the PR has been opened (as draft or not), you can still push new " "commits to it the same way we did before, by making changes to the branch" " associated with the PR." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:211 +#: ../../source/contributor-tutorial-contribute-on-github.rst:253 msgid "**Review the PR**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:212 +#: ../../source/contributor-tutorial-contribute-on-github.rst:233 msgid "" "Once the PR has been opened or once the draft PR has been marked as " "ready, a review from code owners will be automatically requested:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:216 +#: ../../source/contributor-tutorial-contribute-on-github.rst:238 msgid "" "Code owners will then look into the code, ask questions, request changes " "or validate the PR." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:218 +#: ../../source/contributor-tutorial-contribute-on-github.rst:241 msgid "Merging will be blocked if there are ongoing requested changes." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:222 +#: ../../source/contributor-tutorial-contribute-on-github.rst:245 msgid "" "To resolve them, just push the necessary changes to the branch associated" " with the PR:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:226 +#: ../../source/contributor-tutorial-contribute-on-github.rst:250 msgid "And resolve the conversation:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:230 +#: ../../source/contributor-tutorial-contribute-on-github.rst:254 msgid "" "Once all the conversations have been resolved, you can re-request a " "review." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:233 +#: ../../source/contributor-tutorial-contribute-on-github.rst:274 msgid "**Once the PR is merged**" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:234 +#: ../../source/contributor-tutorial-contribute-on-github.rst:256 msgid "" "If all the automatic tests have passed and reviewers have no more changes" " to request, they can approve the PR and merge it." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:238 +#: ../../source/contributor-tutorial-contribute-on-github.rst:261 msgid "" "Once it is merged, you can delete the branch on GitHub (a button should " "appear to do so) and also delete it locally by doing:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:245 +#: ../../source/contributor-tutorial-contribute-on-github.rst:269 msgid "Then you should update your forked repository by doing:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:254 +#: ../../source/contributor-tutorial-contribute-on-github.rst:277 msgid "Example of first contribution" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:257 +#: ../../source/contributor-tutorial-contribute-on-github.rst:280 msgid "Problem" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:259 +#: ../../source/contributor-tutorial-contribute-on-github.rst:282 msgid "" "For our documentation, we've started to use the `Diàtaxis framework " "`_." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:261 +#: ../../source/contributor-tutorial-contribute-on-github.rst:285 msgid "" "Our \"How to\" guides should have titles that continue the sentence \"How" " to …\", for example, \"How to upgrade to Flower 1.0\"." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:263 +#: ../../source/contributor-tutorial-contribute-on-github.rst:288 msgid "" "Most of our guides do not follow this new format yet, and changing their " "title is (unfortunately) more involved than one might think." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:265 +#: ../../source/contributor-tutorial-contribute-on-github.rst:291 msgid "" "This issue is about changing the title of a doc from present continuous " "to present simple." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:267 +#: ../../source/contributor-tutorial-contribute-on-github.rst:294 msgid "" "Let's take the example of \"Saving Progress\" which we changed to \"Save " "Progress\". Does this pass our check?" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:269 +#: ../../source/contributor-tutorial-contribute-on-github.rst:297 msgid "Before: \"How to saving progress\" ❌" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:271 +#: ../../source/contributor-tutorial-contribute-on-github.rst:299 msgid "After: \"How to save progress\" ✅" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:274 +#: ../../source/contributor-tutorial-contribute-on-github.rst:302 msgid "Solution" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:276 +#: ../../source/contributor-tutorial-contribute-on-github.rst:304 msgid "" "This is a tiny change, but it'll allow us to test your end-to-end setup. " "After cloning and setting up the Flower repo, here's what you should do:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:278 +#: ../../source/contributor-tutorial-contribute-on-github.rst:307 msgid "Find the source file in ``doc/source``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:279 +#: ../../source/contributor-tutorial-contribute-on-github.rst:308 msgid "" "Make the change in the ``.rst`` file (beware, the dashes under the title " "should be the same length as the title itself)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:280 +#: ../../source/contributor-tutorial-contribute-on-github.rst:310 msgid "" "Build the docs and `check the result `_" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:283 +#: ../../source/contributor-tutorial-contribute-on-github.rst:314 msgid "Rename file" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:285 +#: ../../source/contributor-tutorial-contribute-on-github.rst:316 msgid "" "You might have noticed that the file name still reflects the old wording." " If we just change the file, then we break all existing links to it - it " @@ -1869,68 +1868,68 @@ msgid "" "engine ranking." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:288 +#: ../../source/contributor-tutorial-contribute-on-github.rst:320 msgid "Here's how to change the file name:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:290 +#: ../../source/contributor-tutorial-contribute-on-github.rst:322 msgid "Change the file name to ``save-progress.rst``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:291 +#: ../../source/contributor-tutorial-contribute-on-github.rst:323 msgid "Add a redirect rule to ``doc/source/conf.py``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:293 +#: ../../source/contributor-tutorial-contribute-on-github.rst:325 msgid "" "This will cause a redirect from ``saving-progress.html`` to ``save-" "progress.html``, old links will continue to work." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:296 +#: ../../source/contributor-tutorial-contribute-on-github.rst:329 msgid "Apply changes in the index file" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:298 +#: ../../source/contributor-tutorial-contribute-on-github.rst:331 msgid "" "For the lateral navigation bar to work properly, it is very important to " "update the ``index.rst`` file as well. This is where we define the whole " "arborescence of the navbar." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:301 +#: ../../source/contributor-tutorial-contribute-on-github.rst:335 msgid "Find and modify the file name in ``index.rst``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:304 +#: ../../source/contributor-tutorial-contribute-on-github.rst:338 msgid "Open PR" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:306 +#: ../../source/contributor-tutorial-contribute-on-github.rst:340 msgid "" "Commit the changes (commit messages are always imperative: \"Do " "something\", in this case \"Change …\")" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:307 +#: ../../source/contributor-tutorial-contribute-on-github.rst:342 msgid "Push the changes to your fork" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:308 +#: ../../source/contributor-tutorial-contribute-on-github.rst:343 msgid "" "Open a PR (as shown above) with title ``docs(framework) Update how-to " "guide title``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:309 +#: ../../source/contributor-tutorial-contribute-on-github.rst:344 msgid "Wait for it to be approved!" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:310 +#: ../../source/contributor-tutorial-contribute-on-github.rst:345 msgid "Congrats! 🥳 You're now officially a Flower contributor!" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:314 +#: ../../source/contributor-tutorial-contribute-on-github.rst:348 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:573 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:1012 #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:811 @@ -1939,39 +1938,39 @@ msgstr "" msgid "Next steps" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:316 +#: ../../source/contributor-tutorial-contribute-on-github.rst:350 msgid "" "Once you have made your first PR, and want to contribute more, be sure to" " check out the following :" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:318 +#: ../../source/contributor-tutorial-contribute-on-github.rst:353 msgid "" ":doc:`Good first contributions `, where you should particularly look into the " -":code:`baselines` contributions." +"``baselines`` contributions." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:322 +#: ../../source/contributor-tutorial-contribute-on-github.rst:357 #: ../../source/fed/0000-20200102-fed-template.md:60 msgid "Appendix" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:327 +#: ../../source/contributor-tutorial-contribute-on-github.rst:362 msgid "PR title format" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:329 +#: ../../source/contributor-tutorial-contribute-on-github.rst:364 msgid "We enforce the following PR title format:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:335 +#: ../../source/contributor-tutorial-contribute-on-github.rst:370 msgid "" "(or ``(:skip) `` to ignore the PR in the " "changelog)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:337 +#: ../../source/contributor-tutorial-contribute-on-github.rst:372 msgid "" "Where ```` needs to be in ``{ci, fix, feat, docs, refactor, " "break}``, ```` should be in ``{framework, baselines, datasets, " @@ -1980,50 +1979,50 @@ msgid "" "verb in the imperative mood." msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:341 +#: ../../source/contributor-tutorial-contribute-on-github.rst:377 #, fuzzy msgid "Valid examples:" msgstr "Exemplo" -#: ../../source/contributor-tutorial-contribute-on-github.rst:343 +#: ../../source/contributor-tutorial-contribute-on-github.rst:379 msgid "``feat(framework) Add flwr build CLI command``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:344 +#: ../../source/contributor-tutorial-contribute-on-github.rst:380 msgid "``refactor(examples:skip) Improve quickstart-pytorch logging``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:345 +#: ../../source/contributor-tutorial-contribute-on-github.rst:381 msgid "``ci(*:skip) Enforce PR title format``" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:347 +#: ../../source/contributor-tutorial-contribute-on-github.rst:383 msgid "Invalid examples:" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:349 +#: ../../source/contributor-tutorial-contribute-on-github.rst:385 msgid "``feat(framework): Add flwr build CLI command`` (extra ``:``)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:350 +#: ../../source/contributor-tutorial-contribute-on-github.rst:386 msgid "" "``feat(*) Add flwr build CLI command`` (missing ``skip`` flag along with " "``*``)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:351 +#: ../../source/contributor-tutorial-contribute-on-github.rst:387 msgid "``feat(skip) Add flwr build CLI command`` (missing ````)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:352 +#: ../../source/contributor-tutorial-contribute-on-github.rst:388 msgid "``feat(framework) add flwr build CLI command`` (non capitalised verb)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:353 +#: ../../source/contributor-tutorial-contribute-on-github.rst:389 msgid "``feat(framework) Add flwr build CLI command.`` (dot at the end)" msgstr "" -#: ../../source/contributor-tutorial-contribute-on-github.rst:354 +#: ../../source/contributor-tutorial-contribute-on-github.rst:390 msgid "``Add flwr build CLI command.`` (missing ``()``)" msgstr "" @@ -2033,9 +2032,9 @@ msgstr "" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5 #: ../../source/docker/run-as-subprocess.rst:11 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:15 -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:22 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:12 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:16 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:18 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:13 #: ../../source/docker/tutorial-quickstart-docker.rst:11 msgid "Prerequisites" msgstr "" @@ -2058,16 +2057,16 @@ msgstr "" #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:12 msgid "" -"Flower uses :code:`pyproject.toml` to manage dependencies and configure " +"Flower uses ``pyproject.toml`` to manage dependencies and configure " "development tools (the ones which support it). Poetry is a build tool " "which supports `PEP 517 `_." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:18 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:17 msgid "Developer Machine Setup" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:21 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:20 msgid "Preliminaries" msgstr "" @@ -2085,94 +2084,93 @@ msgid "" "installation actions to add `brew` to your PATH." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:28 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:29 msgid "" "Install `xz` (to install different Python versions) and `pandoc` to build" -" the docs::" +" the docs:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:34 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:36 msgid "For Ubuntu" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:35 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:38 msgid "" "Ensure you system (Ubuntu 22.04+) is up-to-date, and you have all " -"necessary packages::" +"necessary packages:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:44 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:47 msgid "Create Flower Dev Environment" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:46 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:49 msgid "" -"1. Clone the `Flower repository `_ from " -"GitHub::" +"Clone the `Flower repository `_ from " +"GitHub:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:52 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:56 msgid "" "Let's create the Python environment for all-things Flower. If you wish to" -" use :code:`pyenv`, we provide two convenience scripts that you can use. " -"If you prefer using something else than :code:`pyenv`, create a new " -"environment, activate and skip to the last point where all packages are " -"installed." +" use ``pyenv``, we provide two convenience scripts that you can use. If " +"you prefer using something else than ``pyenv``, create a new environment," +" activate and skip to the last point where all packages are installed." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:54 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:61 msgid "" -"If you don't have :code:`pyenv` installed, the following script that will" -" install it, set it up, and create the virtual environment (with " -":code:`Python 3.9.20` by default)::" +"If you don't have ``pyenv`` installed, the following script that will " +"install it, set it up, and create the virtual environment (with ``Python " +"3.9.20`` by default):" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:58 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:68 msgid "" -"If you already have :code:`pyenv` installed (along with the :code:`pyenv-" -"virtualenv` plugin), you can use the following convenience script (with " -":code:`Python 3.9.20` by default)::" +"If you already have ``pyenv`` installed (along with the ``pyenv-" +"virtualenv`` plugin), you can use the following convenience script (with " +"``Python 3.9.20`` by default):" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:62 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:75 msgid "" -"3. Install the Flower package in development mode (think :code:`pip " -"install -e`) along with all necessary dependencies::" +"3. Install the Flower package in development mode (think ``pip install " +"-e``) along with all necessary dependencies:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:69 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:83 msgid "Convenience Scripts" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:71 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:85 msgid "" "The Flower repository contains a number of convenience scripts to make " -"recurring development tasks easier and less error-prone. See the " -":code:`/dev` subdirectory for a full list. The following scripts are " -"amongst the most important ones:" +"recurring development tasks easier and less error-prone. See the ``/dev``" +" subdirectory for a full list. The following scripts are amongst the most" +" important ones:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:77 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:90 msgid "Create/Delete Virtual Environment" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:85 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:98 msgid "Compile ProtoBuf Definitions" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:92 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:105 msgid "Auto-Format Code" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:99 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:112 msgid "Run Linters and Tests" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:106 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:119 msgid "Add a pre-commit hook" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:108 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:121 msgid "" "Developers may integrate a pre-commit hook into their workflow utilizing " "the `pre-commit `_ library. The pre-" @@ -2180,85 +2178,85 @@ msgid "" "``./dev/format.sh`` and ``./dev/test.sh`` scripts." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:110 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:125 msgid "There are multiple ways developers can use this:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:112 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:127 msgid "Install the pre-commit hook to your local git directory by simply running:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:118 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:133 msgid "" "Each ``git commit`` will trigger the execution of formatting and " "linting/test scripts." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:119 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:135 msgid "" "If in a hurry, bypass the hook using ``--no-verify`` with the ``git " -"commit`` command. ::" +"commit`` command." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:124 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142 msgid "" "For developers who prefer not to install the hook permanently, it is " "possible to execute a one-time check prior to committing changes by using" " the following command:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:130 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:149 msgid "" "This executes the formatting and linting checks/tests on all the files " "without modifying the default behavior of ``git commit``." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:133 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:153 msgid "Run Github Actions (CI) locally" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:135 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:155 msgid "" "Developers could run the full set of Github Actions workflows under their" " local environment by using `Act `_. " "Please refer to the installation instructions under the linked repository" -" and run the next command under Flower main cloned repository folder::" +" and run the next command under Flower main cloned repository folder:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:164 msgid "" "The Flower default workflow would run by setting up the required Docker " "machines underneath." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:147 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:168 msgid "Build Release" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:149 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:170 msgid "" "Flower uses Poetry to build releases. The necessary command is wrapped in" -" a simple script::" +" a simple script:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:154 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:177 msgid "" -"The resulting :code:`.whl` and :code:`.tar.gz` releases will be stored in" -" the :code:`/dist` subdirectory." +"The resulting ``.whl`` and ``.tar.gz`` releases will be stored in the " +"``/dist`` subdirectory." msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:159 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:181 msgid "Build Documentation" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:161 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:183 msgid "" "Flower's documentation uses `Sphinx `_. " "There's no convenience script to re-build the documentation yet, but it's" -" pretty easy::" +" pretty easy:" msgstr "" -#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:167 +#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:191 msgid "This will generate HTML documentation in ``doc/build/html``." msgstr "" @@ -2301,7 +2299,7 @@ msgid "" msgstr "" #: ../../source/docker/enable-tls.rst:23 -#: ../../source/docker/persist-superlink-state.rst:14 +#: ../../source/docker/persist-superlink-state.rst:15 msgid "" "If you later want to delete the directory, you can change the user ID " "back to the current user ID by running ``sudo chown -R $USER:$(id -gn) " @@ -2323,21 +2321,21 @@ msgstr "" msgid "Understanding the command" msgstr "" -#: ../../source/docker/enable-tls.rst:44 ../../source/docker/enable-tls.rst:91 +#: ../../source/docker/enable-tls.rst:45 ../../source/docker/enable-tls.rst:92 #: ../../source/docker/enable-tls.rst:125 #: ../../source/docker/tutorial-quickstart-docker.rst:66 #: ../../source/docker/tutorial-quickstart-docker.rst:103 -#: ../../source/docker/tutorial-quickstart-docker.rst:213 -#: ../../source/docker/tutorial-quickstart-docker.rst:300 +#: ../../source/docker/tutorial-quickstart-docker.rst:217 +#: ../../source/docker/tutorial-quickstart-docker.rst:305 msgid "``docker run``: This tells Docker to run a container from an image." msgstr "" -#: ../../source/docker/enable-tls.rst:45 ../../source/docker/enable-tls.rst:92 +#: ../../source/docker/enable-tls.rst:46 ../../source/docker/enable-tls.rst:93 #: ../../source/docker/enable-tls.rst:126 #: ../../source/docker/tutorial-quickstart-docker.rst:67 #: ../../source/docker/tutorial-quickstart-docker.rst:104 -#: ../../source/docker/tutorial-quickstart-docker.rst:214 -#: ../../source/docker/tutorial-quickstart-docker.rst:301 +#: ../../source/docker/tutorial-quickstart-docker.rst:218 +#: ../../source/docker/tutorial-quickstart-docker.rst:306 msgid "``--rm``: Remove the container once it is stopped or the command exits." msgstr "" @@ -2440,18 +2438,18 @@ msgstr "" msgid "the network." msgstr "" -#: ../../source/docker/enable-tls.rst:71 +#: ../../source/docker/enable-tls.rst:72 msgid "SuperNode" msgstr "" -#: ../../source/docker/enable-tls.rst:73 +#: ../../source/docker/enable-tls.rst:74 msgid "" "Assuming that the ``ca.crt`` certificate already exists locally, we can " "use the flag ``--volume`` to mount the local certificate into the " "container's ``/app/`` directory." msgstr "" -#: ../../source/docker/enable-tls.rst:78 +#: ../../source/docker/enable-tls.rst:79 msgid "" "If you're generating self-signed certificates and the ``ca.crt`` " "certificate doesn't exist on the SuperNode, you can copy it over after " @@ -2564,15 +2562,15 @@ msgstr "" msgid "Getting Started" msgstr "" -#: ../../source/docker/index.rst:20 +#: ../../source/docker/index.rst:19 msgid "Running in Production" msgstr "" -#: ../../source/docker/index.rst:29 +#: ../../source/docker/index.rst:28 msgid "Advanced Options" msgstr "" -#: ../../source/docker/index.rst:41 +#: ../../source/docker/index.rst:40 msgid "Run Flower using Docker Compose" msgstr "" @@ -2594,7 +2592,7 @@ msgid "" " on your host system and a name for the database file." msgstr "" -#: ../../source/docker/persist-superlink-state.rst:10 +#: ../../source/docker/persist-superlink-state.rst:11 msgid "" "By default, the SuperLink container runs with a non-root user called " "``app`` with the user ID ``49999``. It is recommended to create a new " @@ -2602,7 +2600,7 @@ msgid "" "the mounted directory has the proper permissions." msgstr "" -#: ../../source/docker/persist-superlink-state.rst:20 +#: ../../source/docker/persist-superlink-state.rst:21 msgid "" "In the example below, we create a new directory called ``state``, change " "the user ID and tell Docker via the flag ``--volume`` to mount the local " @@ -2611,7 +2609,7 @@ msgid "" "database file." msgstr "" -#: ../../source/docker/persist-superlink-state.rst:35 +#: ../../source/docker/persist-superlink-state.rst:36 msgid "" "As soon as the SuperLink starts, the file ``state.db`` is created in the " "``state`` directory on your host system. If the file already exists, the " @@ -2636,17 +2634,17 @@ msgid "" "by-digest-immutable-identifier>`_ of the image instead of the tag." msgstr "" -#: ../../source/docker/pin-version.rst:13 +#: ../../source/docker/pin-version.rst:14 msgid "" "The following command returns the current image digest referenced by the " ":substitution-code:`superlink:|stable_flwr_version|` tag:" msgstr "" -#: ../../source/docker/pin-version.rst:22 +#: ../../source/docker/pin-version.rst:23 msgid "This will output" msgstr "" -#: ../../source/docker/pin-version.rst:29 +#: ../../source/docker/pin-version.rst:30 msgid "Next, we can pin the digest when running a new SuperLink container:" msgstr "" @@ -2693,7 +2691,7 @@ msgid "" "``USER root`` directive within your Dockerfile." msgstr "" -#: ../../source/docker/run-as-root-user.rst:29 +#: ../../source/docker/run-as-root-user.rst:30 #, fuzzy msgid "SuperNode Dockerfile" msgstr "Construindo a imagem do servidor" @@ -2719,11 +2717,11 @@ msgid "" "done by extending the SuperNode image:" msgstr "" -#: ../../source/docker/run-as-subprocess.rst:16 +#: ../../source/docker/run-as-subprocess.rst:17 msgid "Dockerfile.supernode" msgstr "" -#: ../../source/docker/run-as-subprocess.rst:30 +#: ../../source/docker/run-as-subprocess.rst:31 msgid "" "Next, build the SuperNode Docker image by running the following command " "in the directory where Dockerfile is located:" @@ -2753,78 +2751,78 @@ msgid "" " Engine via Docker Compose." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:11 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:12 msgid "" "Some quickstart examples may have limitations or requirements that " "prevent them from running on every environment. For more information, " -"please see `Limitations`_." +"please see Limitations_." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:17 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:14 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:18 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:15 #: ../../source/docker/tutorial-quickstart-docker.rst:13 msgid "Before you start, make sure that:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:19 -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:26 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:16 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:20 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:22 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:17 #: ../../source/docker/tutorial-quickstart-docker.rst:15 msgid "The ``flwr`` CLI is :doc:`installed <../how-to-install-flower>` locally." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:20 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:17 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:21 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:18 #: ../../source/docker/tutorial-quickstart-docker.rst:16 #, fuzzy msgid "The Docker daemon is running." msgstr "Verifique que o serviço Docker está rodando." -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:21 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:18 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:22 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:19 msgid "Docker Compose is `installed `_." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:24 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:25 msgid "Run the Quickstart Example" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:26 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:27 msgid "" "Clone the quickstart example you like to run. For example, ``quickstart-" "pytorch``:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:34 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:35 msgid "" "Download the `compose.yml " "`_" " file into the example directory:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:41 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:44 msgid "Build and start the services using the following command:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:47 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:50 msgid "" "Append the following lines to the end of the ``pyproject.toml`` file and " "save it:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:49 -#: ../../source/docker/tutorial-quickstart-docker.rst:319 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:52 +#: ../../source/docker/tutorial-quickstart-docker.rst:324 msgid "pyproject.toml" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:58 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:61 msgid "" "You can customize the string that follows ``tool.flwr.federations.`` to " "fit your needs. However, please note that the string cannot contain a dot" " (``.``)." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:61 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:64 msgid "" "In this example, ``local-deployment`` has been used. Just remember to " "replace ``local-deployment`` with your chosen name in both the " @@ -2832,54 +2830,54 @@ msgid "" "command." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:65 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:68 #, fuzzy msgid "Run the example:" msgstr "Exemplo" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:71 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:74 msgid "Follow the logs of the SuperExec service:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:77 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:80 msgid "" "That is all it takes! You can monitor the progress of the run through the" " logs of the SuperExec." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:80 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:84 msgid "Run a Different Quickstart Example" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:82 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:86 msgid "" "To run a different quickstart example, such as ``quickstart-tensorflow``," " first, shut down the Docker Compose services of the current example:" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:89 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:93 msgid "After that, you can repeat the steps above." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:92 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:98 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:96 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:102 msgid "Limitations" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:97 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:101 msgid "Quickstart Example" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:99 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:103 msgid "quickstart-fastai" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:100 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:102 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:110 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:112 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:116 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:118 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:104 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:106 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:115 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:117 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:121 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:123 #: ../../source/ref-changelog.md:33 ../../source/ref-changelog.md:399 #: ../../source/ref-changelog.md:676 ../../source/ref-changelog.md:740 #: ../../source/ref-changelog.md:798 ../../source/ref-changelog.md:867 @@ -2887,71 +2885,71 @@ msgstr "" msgid "None" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:101 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:105 msgid "quickstart-huggingface" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:103 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:107 msgid "quickstart-jax" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:104 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:106 -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:120 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:108 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:110 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:125 msgid "" "The example has not yet been updated to work with the latest ``flwr`` " "version." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:105 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:109 msgid "quickstart-mlcube" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:107 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:111 msgid "quickstart-mlx" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:108 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:112 msgid "" "`Requires to run on macOS with Apple Silicon `_." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:109 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:114 msgid "quickstart-monai" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:111 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:116 msgid "quickstart-pandas" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:113 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:118 msgid "quickstart-pytorch-lightning" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:114 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:119 msgid "" "Requires an older pip version that is not supported by the Flower Docker " "images." msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:115 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:120 msgid "quickstart-pytorch" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:117 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:122 msgid "quickstart-sklearn-tabular" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:119 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:124 msgid "quickstart-tabnet" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:121 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:126 msgid "quickstart-tensorflow" msgstr "" -#: ../../source/docker/run-quickstart-examples-docker-compose.rst:122 +#: ../../source/docker/run-quickstart-examples-docker-compose.rst:127 msgid "Only runs on AMD64." msgstr "" @@ -2966,17 +2964,17 @@ msgid "" "environment variables for a container." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:3 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:2 msgid "Deploy Flower on Multiple Machines with Docker Compose" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:5 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:4 msgid "" "This guide will help you set up a Flower project on multiple machines " "using Docker Compose." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:8 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:7 msgid "" "You will learn how to run the Flower client and server components on two " "separate machines, with Flower configured to use TLS encryption and " @@ -2985,7 +2983,7 @@ msgid "" " to the :doc:`../explanation-flower-architecture` explainer page." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:15 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:13 msgid "" "This guide assumes you have completed the :doc:`tutorial-quickstart-" "docker-compose` tutorial. It is highly recommended that you follow and " @@ -2993,137 +2991,137 @@ msgid "" "guide." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:24 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:20 msgid "Before you begin, make sure you have the following prerequisites:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:28 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:23 msgid "The Docker daemon is running on your local machine and the remote machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:30 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:24 msgid "" "Docker Compose V2 is installed on both your local machine and the remote " "machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:32 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:25 msgid "You can connect to the remote machine from your local machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:33 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:26 msgid "Ports ``9091`` and ``9093`` are accessible on the remote machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:37 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:30 msgid "" "The guide uses the |quickstart_sklearn_tabular|_ example as an example " "project." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:40 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:32 msgid "" "If your project has a different name or location, please remember to " "adjust the commands/paths accordingly." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:45 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:21 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:36 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:22 #: ../../source/docker/tutorial-quickstart-docker.rst:19 msgid "Step 1: Set Up" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:47 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:38 msgid "Clone the Flower repository and change to the ``distributed`` directory:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:55 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:45 msgid "Get the IP address from the remote machine and save it for later." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:57 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:46 msgid "" "Use the ``certs.yml`` Compose file to generate your own self-signed " "certificates. If you have certificates, you can continue with Step 2." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:62 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:215 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:51 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:221 msgid "These certificates should be used only for development purposes." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:64 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:53 msgid "" "For production environments, you may have to use dedicated services to " "obtain your certificates." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:67 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:56 msgid "" "First, set the environment variables ``SUPERLINK_IP`` and " "``SUPEREXEC_IP`` with the IP address from the remote machine. For " "example, if the IP is ``192.168.2.33``, execute:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:76 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:65 msgid "Next, generate the self-signed certificates:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:84 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:72 msgid "Step 2: Copy the Server Compose Files" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:86 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:74 msgid "" "Use the method that works best for you to copy the ``server`` directory, " "the certificates, and your Flower project to the remote machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:89 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:77 msgid "For example, you can use ``scp`` to copy the directories:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:100 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:87 msgid "Step 3: Start the Flower Server Components" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:102 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:89 msgid "" "Log into the remote machine using ``ssh`` and run the following command " "to start the SuperLink and SuperExec services:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:115 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:102 msgid "" "The Path of the ``PROJECT_DIR`` should be relative to the location of the" " ``server`` Docker Compose files." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:118 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:105 msgid "Go back to your terminal on your local machine." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:122 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:108 msgid "Step 4: Start the Flower Client Components" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:124 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:110 msgid "" "On your local machine, run the following command to start the client " "components:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:135 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:120 msgid "" "The Path of the ``PROJECT_DIR`` should be relative to the location of the" " ``client`` Docker Compose files." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:140 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:124 msgid "Step 5: Run Your Flower Project" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:142 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:126 msgid "" "Specify the remote SuperExec IP addresses and the path to the root " "certificate in the ``[tool.flwr.federations.remote-superexec]`` table in " @@ -3131,35 +3129,35 @@ msgid "" "``remote-superexec``:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:147 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:130 msgid "examples/quickstart-sklearn-tabular/pyproject.toml" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:156 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:139 msgid "" "The Path of the ``root-certificates`` should be relative to the location " "of the ``pyproject.toml`` file." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:159 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:142 msgid "To run the project, execute:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:165 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:148 msgid "" "That's it! With these steps, you've set up Flower on two separate " "machines and are ready to start using it." msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:170 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:152 msgid "Step 6: Clean Up" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:172 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:154 msgid "Shut down the Flower client components:" msgstr "" -#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:179 +#: ../../source/docker/tutorial-deploy-on-multiple-machines.rst:161 msgid "Shut down the Flower server components and delete the SuperLink state:" msgstr "" @@ -3180,7 +3178,7 @@ msgid "" " understanding the basic workflow that uses the minimum configurations." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:31 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:32 #: ../../source/docker/tutorial-quickstart-docker.rst:21 msgid "Create a new Flower project (PyTorch):" msgstr "" @@ -3201,7 +3199,7 @@ msgstr "" msgid "Step 2: Start the SuperLink" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:60 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:62 #: ../../source/docker/tutorial-quickstart-docker.rst:52 msgid "Open your terminal and run:" msgstr "" @@ -3227,8 +3225,8 @@ msgstr "" #: ../../source/docker/tutorial-quickstart-docker.rst:71 #: ../../source/docker/tutorial-quickstart-docker.rst:108 -#: ../../source/docker/tutorial-quickstart-docker.rst:215 -#: ../../source/docker/tutorial-quickstart-docker.rst:304 +#: ../../source/docker/tutorial-quickstart-docker.rst:219 +#: ../../source/docker/tutorial-quickstart-docker.rst:309 msgid "" "``--network flwr-network``: Make the container join the network named " "``flwr-network``." @@ -3240,8 +3238,8 @@ msgstr "" #: ../../source/docker/tutorial-quickstart-docker.rst:73 #: ../../source/docker/tutorial-quickstart-docker.rst:110 -#: ../../source/docker/tutorial-quickstart-docker.rst:216 -#: ../../source/docker/tutorial-quickstart-docker.rst:306 +#: ../../source/docker/tutorial-quickstart-docker.rst:220 +#: ../../source/docker/tutorial-quickstart-docker.rst:311 msgid "" "``--detach``: Run the container in the background, freeing up the " "terminal." @@ -3357,13 +3355,13 @@ msgid "" "extends the ClientApp image and installs the required dependencies." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:148 +#: ../../source/docker/tutorial-quickstart-docker.rst:149 msgid "" "Create a ClientApp Dockerfile called ``Dockerfile.clientapp`` and paste " "the following code into it:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:150 +#: ../../source/docker/tutorial-quickstart-docker.rst:152 msgid "Dockerfile.clientapp" msgstr "" @@ -3445,7 +3443,7 @@ msgstr "" msgid "the default command run when the container is started." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:184 +#: ../../source/docker/tutorial-quickstart-docker.rst:186 msgid "" "Note that `flwr `__ is already installed " "in the ``flwr/clientapp`` base image, so only other package dependencies " @@ -3454,20 +3452,20 @@ msgid "" "after it has been copied into the Docker image (see line 5)." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:189 +#: ../../source/docker/tutorial-quickstart-docker.rst:192 msgid "" "Next, build the ClientApp Docker image by running the following command " "in the directory where the Dockerfile is located:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:198 +#: ../../source/docker/tutorial-quickstart-docker.rst:201 msgid "" "The image name was set as ``flwr_clientapp`` with the tag ``0.0.1``. " "Remember that these values are merely examples, and you can customize " "them according to your requirements." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:201 +#: ../../source/docker/tutorial-quickstart-docker.rst:205 msgid "Start the first ClientApp container:" msgstr "" @@ -3487,33 +3485,33 @@ msgstr "" msgid "``supernode-1:9094``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:222 +#: ../../source/docker/tutorial-quickstart-docker.rst:226 msgid "Start the second ClientApp container:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:233 +#: ../../source/docker/tutorial-quickstart-docker.rst:237 msgid "Step 5: Start the SuperExec" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:235 +#: ../../source/docker/tutorial-quickstart-docker.rst:239 msgid "" "The procedure for building and running a SuperExec image is almost " "identical to the ClientApp image." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:237 +#: ../../source/docker/tutorial-quickstart-docker.rst:242 msgid "" "Similar to the ClientApp image, you will need to create a Dockerfile that" " extends the SuperExec image and installs the required FAB dependencies." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:240 +#: ../../source/docker/tutorial-quickstart-docker.rst:245 msgid "" "Create a SuperExec Dockerfile called ``Dockerfile.superexec`` and paste " "the following code in:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:242 +#: ../../source/docker/tutorial-quickstart-docker.rst:248 msgid "Dockerfile.superexec" msgstr "" @@ -3543,13 +3541,13 @@ msgstr "" msgid "``flwr.superexec.deployment:executor`` executor to run the ServerApps." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:277 +#: ../../source/docker/tutorial-quickstart-docker.rst:283 msgid "" "Afterward, in the directory that holds the Dockerfile, execute this " "Docker command to build the SuperExec image:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:285 +#: ../../source/docker/tutorial-quickstart-docker.rst:290 msgid "Start the SuperExec container:" msgstr "" @@ -3563,7 +3561,7 @@ msgid "" "``http://localhost:9093``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:305 +#: ../../source/docker/tutorial-quickstart-docker.rst:310 msgid "``--name superexec``: Assign the name ``superexec`` to the container." msgstr "" @@ -3583,75 +3581,75 @@ msgstr "" msgid "connect to the SuperLink running on port ``9091``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:315 +#: ../../source/docker/tutorial-quickstart-docker.rst:320 msgid "Step 6: Run the Quickstart Project" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:317 +#: ../../source/docker/tutorial-quickstart-docker.rst:322 msgid "Add the following lines to the ``pyproject.toml``:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:326 +#: ../../source/docker/tutorial-quickstart-docker.rst:331 msgid "Run the ``quickstart-docker`` project by executing the command:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:332 +#: ../../source/docker/tutorial-quickstart-docker.rst:337 msgid "Follow the SuperExec logs to track the execution of the run:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:339 +#: ../../source/docker/tutorial-quickstart-docker.rst:344 msgid "Step 7: Update the Application" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:341 +#: ../../source/docker/tutorial-quickstart-docker.rst:346 msgid "" -"Change the application code. For example, change the ``seed`` in " +"Change the application code. For example, change the ``seed`` in " "``quickstart_docker/task.py`` to ``43`` and save it:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:344 +#: ../../source/docker/tutorial-quickstart-docker.rst:349 msgid "quickstart_docker/task.py" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:351 +#: ../../source/docker/tutorial-quickstart-docker.rst:356 msgid "Stop the current ClientApp containers:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:357 +#: ../../source/docker/tutorial-quickstart-docker.rst:362 #, fuzzy msgid "Rebuild the FAB and ClientApp image:" msgstr "Construindo a imagem base" -#: ../../source/docker/tutorial-quickstart-docker.rst:363 +#: ../../source/docker/tutorial-quickstart-docker.rst:368 msgid "Launch two new ClientApp containers based on the newly built image:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:378 +#: ../../source/docker/tutorial-quickstart-docker.rst:383 msgid "Run the updated project:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:385 +#: ../../source/docker/tutorial-quickstart-docker.rst:390 msgid "Step 8: Clean Up" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:387 +#: ../../source/docker/tutorial-quickstart-docker.rst:392 msgid "Remove the containers and the bridge network:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:401 -#: ../../source/docker/tutorial-quickstart-docker.rst:399 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:408 +#: ../../source/docker/tutorial-quickstart-docker.rst:404 msgid "Where to Go Next" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:401 +#: ../../source/docker/tutorial-quickstart-docker.rst:406 msgid ":doc:`enable-tls`" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:402 +#: ../../source/docker/tutorial-quickstart-docker.rst:407 msgid ":doc:`persist-superlink-state`" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker.rst:403 +#: ../../source/docker/tutorial-quickstart-docker.rst:408 msgid ":doc:`tutorial-quickstart-docker-compose`" msgstr "" @@ -3673,176 +3671,176 @@ msgid "" "configuration that best suits your project's needs." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:23 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:24 msgid "Clone the Docker Compose ``complete`` directory:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:37 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:38 msgid "" "Export the path of the newly created project. The path should be relative" " to the location of the Docker Compose files:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:44 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:45 msgid "" "Setting the ``PROJECT_DIR`` helps Docker Compose locate the " "``pyproject.toml`` file, allowing it to install dependencies in the " "SuperExec and SuperNode images correctly." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:48 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:49 msgid "Step 2: Run Flower in Insecure Mode" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:50 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:51 msgid "" "To begin, start Flower with the most basic configuration. In this setup, " "Flower will run without TLS and without persisting the state." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:55 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:56 msgid "" "Without TLS, the data sent between the services remains **unencrypted**. " "Use it only for development purposes." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:58 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:59 msgid "" "For production-oriented use cases, :ref:`enable TLS` for secure data" " transmission." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:68 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:179 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:70 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:184 msgid "``docker compose``: The Docker command to run the Docker Compose tool." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:69 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:180 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:71 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:185 msgid "" "``-f compose.yml``: Specify the YAML file that contains the basic Flower " "service definitions." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:70 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:185 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:72 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:190 msgid "" "``--build``: Rebuild the images for each service if they don't already " "exist." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:71 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:186 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:73 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:191 msgid "" "``-d``: Detach the containers from the terminal and run them in the " "background." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:74 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:76 msgid "Step 3: Run the Quickstart Project" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:76 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:78 msgid "" "Now that the Flower services have been started via Docker Compose, it is " "time to run the quickstart example." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:79 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:81 msgid "" "To ensure the ``flwr`` CLI connects to the SuperExec, you need to specify" " the SuperExec addresses in the ``pyproject.toml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:82 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:226 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:84 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:232 msgid "Add the following lines to the ``quickstart-compose/pyproject.toml``:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:84 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:228 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:86 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:234 msgid "quickstart-compose/pyproject.toml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:91 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:93 msgid "Execute the command to run the quickstart example:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:97 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:99 msgid "Monitor the SuperExec logs and wait for the summary to appear:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:104 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:106 msgid "Step 4: Update the Application" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:106 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:108 msgid "In the next step, change the application code." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:108 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:110 msgid "" "For example, go to the ``task.py`` file in the ``quickstart-" "compose/quickstart_compose/`` directory and add a ``print`` call in the " "``get_weights`` function:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:111 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:114 msgid "quickstart-compose/quickstart_compose/task.py" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:120 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:125 msgid "Rebuild and restart the services." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:124 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:129 msgid "" "If you have modified the dependencies listed in your ``pyproject.toml`` " "file, it is essential to rebuild images." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:127 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:132 msgid "If you haven't made any changes, you can skip this step." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:129 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:134 msgid "Run the following command to rebuild and restart the services:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:135 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:140 msgid "Run the updated quickstart example:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:142 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:147 msgid "In the SuperExec logs, you should find the ``Get weights`` line:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:159 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:164 msgid "Step 5: Persisting the SuperLink State" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:161 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:166 msgid "" "In this step, Flower services are configured to persist the state of the " "SuperLink service, ensuring that it maintains its state even after a " "restart." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:166 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:171 msgid "" "When working with Docker Compose on Linux, you may need to create the " "``state`` directory first and change its ownership to ensure proper " "access and permissions." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:169 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:174 msgid "" "For more information, consult the following page: :doc:`persist-" "superlink-state`." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:171 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:220 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:176 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:226 msgid "Run the command:" msgstr "" @@ -3863,17 +3861,17 @@ msgid "" "rules>`_." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:188 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:241 -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:369 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:193 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:247 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:375 msgid "Rerun the ``quickstart-compose`` project:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:194 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:199 msgid "Check the content of the ``state`` directory:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:201 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:206 msgid "" "You should see a ``state.db`` file in the ``state`` directory. If you " "restart the service, the state file will be used to restore the state " @@ -3881,119 +3879,119 @@ msgid "" "if the containers are stopped and started again." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:208 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:214 msgid "Step 6: Run Flower with TLS" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:210 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:216 msgid "" "To demonstrate how to enable TLS, generate self-signed certificates using" " the ``certs.yml`` Compose file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:217 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:223 msgid "" "For production environments, use a service like `Let's Encrypt " "`_ to obtain your certificates." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:235 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:241 msgid "Restart the services with TLS enabled:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:249 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:255 msgid "Step 7: Add another SuperNode" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:251 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:257 msgid "" "You can add more SuperNodes and ClientApps by duplicating their " "definitions in the ``compose.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:254 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:260 msgid "" "Just give each new SuperNode and ClientApp service a unique service name " "like ``supernode-3``, ``clientapp-3``, etc." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:257 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:263 msgid "In ``compose.yml``, add the following:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:259 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:265 msgid "compose.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:310 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:316 msgid "" "If you also want to enable TLS for the new SuperNodes, duplicate the " "SuperNode definition for each new SuperNode service in the ``with-" "tls.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:313 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:319 msgid "" "Make sure that the names of the services match with the one in the " "``compose.yml`` file." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:315 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:321 msgid "In ``with-tls.yml``, add the following:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:317 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:323 msgid "with-tls.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:339 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:345 msgid "Step 8: Persisting the SuperLink State and Enabling TLS" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:341 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:347 msgid "" "To run Flower with persisted SuperLink state and enabled TLS, a slight " "change in the ``with-state.yml`` file is required:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:344 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:350 msgid "Comment out the lines 2-4 and uncomment the lines 5-9:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:346 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:352 msgid "with-state.yml" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:363 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:369 msgid "Restart the services:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:377 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:383 msgid "Step 9: Merge Multiple Compose Files" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:379 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:385 msgid "" "You can merge multiple Compose files into a single file. For instance, if" " you wish to combine the basic configuration with the TLS configuration, " "execute the following command:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:387 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:394 msgid "" "This will merge the contents of ``compose.yml`` and ``with-tls.yml`` into" " a new file called ``my_compose.yml``." msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:391 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:398 msgid "Step 10: Clean Up" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:393 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:400 msgid "Remove all services and volumes:" msgstr "" -#: ../../source/docker/tutorial-quickstart-docker-compose.rst:403 +#: ../../source/docker/tutorial-quickstart-docker-compose.rst:410 msgid ":doc:`run-quickstart-examples-docker-compose`" msgstr "" @@ -4008,7 +4006,7 @@ msgid "" " `Docker Hub `__." msgstr "" -#: ../../source/docker/use-a-different-version.rst:9 +#: ../../source/docker/use-a-different-version.rst:10 msgid "" "When using Flower nightly, the SuperLink nightly image must be paired " "with the corresponding SuperNode and ServerApp nightly images released on" @@ -4032,31 +4030,31 @@ msgid "" "centralized-to-federated>`." msgstr "" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:9 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:10 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:12 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:12 msgid "Centralized Training" msgstr "" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:10 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:14 msgid "" "All files are revised based on :doc:`Example: PyTorch - From Centralized " "To Federated `. The only " -"thing to do is modifying the file called :code:`cifar.py`, revised part " -"is shown below:" +"thing to do is modifying the file called ``cifar.py``, revised part is " +"shown below:" msgstr "" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:13 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:18 msgid "" "The model architecture defined in class Net() is added with Batch " "Normalization layers accordingly." msgstr "" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:41 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:157 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:47 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:171 msgid "You can now run your machine learning workload:" msgstr "" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:47 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:53 msgid "" "So far this should all look fairly familiar if you've used PyTorch " "before. Let's take the next step and use what we've built to create a " @@ -4064,53 +4062,53 @@ msgid "" " and two clients." msgstr "" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:51 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:167 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:58 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:182 msgid "Federated Training" msgstr "" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:53 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:60 msgid "" "If you have read :doc:`Example: PyTorch - From Centralized To Federated " "`, the following parts are" -" easy to follow, only :code:`get_parameters` and :code:`set_parameters` " -"function in :code:`client.py` needed to revise. If not, please read the " -":doc:`Example: PyTorch - From Centralized To Federated `. first." +" easy to follow, only ``get_parameters`` and ``set_parameters`` function " +"in ``client.py`` needed to revise. If not, please read the :doc:`Example:" +" PyTorch - From Centralized To Federated `. first." msgstr "" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:56 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:66 msgid "" "Our example consists of one *server* and two *clients*. In FedBN, " -":code:`server.py` keeps unchanged, we can start the server directly." +"``server.py`` keeps unchanged, we can start the server directly." msgstr "" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:62 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:73 msgid "" -"Finally, we will revise our *client* logic by changing " -":code:`get_parameters` and :code:`set_parameters` in :code:`client.py`, " -"we will exclude batch normalization parameters from model parameter list " -"when sending to or receiving from the server." +"Finally, we will revise our *client* logic by changing ``get_parameters``" +" and ``set_parameters`` in ``client.py``, we will exclude batch " +"normalization parameters from model parameter list when sending to or " +"receiving from the server." msgstr "" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:85 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:102 msgid "Now, you can now open two additional terminal windows and run" msgstr "" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:91 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:108 msgid "" "in each window (make sure that the server is still running before you do " "so) and see your (previously centralized) PyTorch project run federated " "learning with FedBN strategy across two clients. Congratulations!" msgstr "" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:94 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:310 -#: ../../source/tutorial-quickstart-jax.rst:283 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:113 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:349 +#: ../../source/tutorial-quickstart-jax.rst:319 msgid "Next Steps" msgstr "" -#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:96 +#: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:115 msgid "" "The full source code for this example can be found `here " "`_." msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:15 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:18 msgid "" -"Let's create a new file called :code:`cifar.py` with all the components " +"Let's create a new file called ``cifar.py`` with all the components " "required for a traditional (centralized) training on CIFAR-10. First, all" -" required packages (such as :code:`torch` and :code:`torchvision`) need " -"to be imported. You can see that we do not import any package for " -"federated learning. You can keep all these imports as they are even when " -"we add the federated learning components at a later point." +" required packages (such as ``torch`` and ``torchvision``) need to be " +"imported. You can see that we do not import any package for federated " +"learning. You can keep all these imports as they are even when we add the" +" federated learning components at a later point." msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:32 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:36 msgid "" "As already mentioned we will use the CIFAR-10 dataset for this machine " "learning workload. The model architecture (a very simple Convolutional " -"Neural Network) is defined in :code:`class Net()`." +"Neural Network) is defined in ``class Net()``." msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:56 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:62 msgid "" -"The :code:`load_data()` function loads the CIFAR-10 training and test " -"sets. The :code:`transform` normalized the data after loading." +"The ``load_data()`` function loads the CIFAR-10 training and test sets. " +"The ``transform`` normalized the data after loading." msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:74 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:84 msgid "" -"We now need to define the training (function :code:`train()`) which loops" -" over the training set, measures the loss, backpropagates it, and then " +"We now need to define the training (function ``train()``) which loops " +"over the training set, measures the loss, backpropagates it, and then " "takes one optimizer step for each batch of training examples." msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:76 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:88 msgid "" -"The evaluation of the model is defined in the function :code:`test()`. " -"The function loops over all test samples and measures the loss of the " -"model based on the test dataset." +"The evaluation of the model is defined in the function ``test()``. The " +"function loops over all test samples and measures the loss of the model " +"based on the test dataset." msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:136 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:149 msgid "" "Having defined the data loading, model architecture, training, and " "evaluation we can put everything together and train our CNN on CIFAR-10." msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:163 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:177 msgid "" "So far, this should all look fairly familiar if you've used PyTorch " "before. Let's take the next step and use what we've built to create a " @@ -4196,7 +4194,7 @@ msgid "" "clients." msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:169 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:184 msgid "" "The simple machine learning project discussed in the previous section " "trains the model on a single dataset (CIFAR-10), we call this centralized" @@ -4207,162 +4205,161 @@ msgid "" "everything up from scratch. This can be a considerable effort." msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:173 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:191 msgid "" "However, with Flower you can evolve your pre-existing code into a " "federated learning setup without the need for a major rewrite." msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:175 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:194 msgid "" "The concept is easy to understand. We have to start a *server* and then " -"use the code in :code:`cifar.py` for the *clients* that are connected to " -"the *server*. The *server* sends model parameters to the clients. The " +"use the code in ``cifar.py`` for the *clients* that are connected to the " +"*server*. The *server* sends model parameters to the clients. The " "*clients* run the training and update the parameters. The updated " "parameters are sent back to the *server* which averages all received " "parameter updates. This describes one round of the federated learning " "process and we repeat this for multiple rounds." msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:181 -#: ../../source/tutorial-quickstart-jax.rst:129 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:201 +#: ../../source/tutorial-quickstart-jax.rst:147 msgid "" "Our example consists of one *server* and two *clients*. Let's set up " -":code:`server.py` first. The *server* needs to import the Flower package " -":code:`flwr`. Next, we use the :code:`start_server` function to start a " -"server and tell it to perform three rounds of federated learning." +"``server.py`` first. The *server* needs to import the Flower package " +"``flwr``. Next, we use the ``start_server`` function to start a server " +"and tell it to perform three rounds of federated learning." msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:191 -#: ../../source/tutorial-quickstart-jax.rst:139 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:215 +#: ../../source/tutorial-quickstart-jax.rst:161 msgid "We can already start the *server*:" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:197 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:221 msgid "" -"Finally, we will define our *client* logic in :code:`client.py` and build" -" upon the previously defined centralized training in :code:`cifar.py`. " -"Our *client* needs to import :code:`flwr`, but also :code:`torch` to " -"update the parameters on our PyTorch model:" +"Finally, we will define our *client* logic in ``client.py`` and build " +"upon the previously defined centralized training in ``cifar.py``. Our " +"*client* needs to import ``flwr``, but also ``torch`` to update the " +"parameters on our PyTorch model:" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:213 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:238 msgid "" "Implementing a Flower *client* basically means implementing a subclass of" -" either :code:`flwr.client.Client` or :code:`flwr.client.NumPyClient`. " -"Our implementation will be based on :code:`flwr.client.NumPyClient` and " -"we'll call it :code:`CifarClient`. :code:`NumPyClient` is slightly easier" -" to implement than :code:`Client` if you use a framework with good NumPy " -"interoperability (like PyTorch or TensorFlow/Keras) because it avoids " -"some of the boilerplate that would otherwise be necessary. " -":code:`CifarClient` needs to implement four methods, two methods for " -"getting/setting model parameters, one method for training the model, and " -"one method for testing the model:" +" either ``flwr.client.Client`` or ``flwr.client.NumPyClient``. Our " +"implementation will be based on ``flwr.client.NumPyClient`` and we'll " +"call it ``CifarClient``. ``NumPyClient`` is slightly easier to implement " +"than ``Client`` if you use a framework with good NumPy interoperability " +"(like PyTorch or TensorFlow/Keras) because it avoids some of the " +"boilerplate that would otherwise be necessary. ``CifarClient`` needs to " +"implement four methods, two methods for getting/setting model parameters," +" one method for training the model, and one method for testing the model:" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:218 -msgid ":code:`set_parameters`" -msgstr "" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:249 +#, fuzzy +msgid "``set_parameters``" +msgstr "``SETUPTOOLS_VERSION``" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:219 -#: ../../source/tutorial-quickstart-jax.rst:166 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:248 +#: ../../source/tutorial-quickstart-jax.rst:192 msgid "" "set the model parameters on the local model that are received from the " "server" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:220 -#: ../../source/tutorial-quickstart-jax.rst:168 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:249 +#: ../../source/tutorial-quickstart-jax.rst:194 msgid "" -"loop over the list of model parameters received as NumPy " -":code:`ndarray`'s (think list of neural network layers)" +"loop over the list of model parameters received as NumPy ``ndarray``'s " +"(think list of neural network layers)" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:221 -#: ../../source/tutorial-quickstart-jax.rst:169 -#: ../../source/tutorial-quickstart-scikitlearn.rst:118 -msgid ":code:`get_parameters`" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:252 +#: ../../source/tutorial-quickstart-jax.rst:197 +#: ../../source/tutorial-quickstart-scikitlearn.rst:129 +msgid "``get_parameters``" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:222 -#: ../../source/tutorial-quickstart-jax.rst:170 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:252 +#: ../../source/tutorial-quickstart-jax.rst:197 msgid "" -"get the model parameters and return them as a list of NumPy " -":code:`ndarray`'s (which is what :code:`flwr.client.NumPyClient` expects)" +"get the model parameters and return them as a list of NumPy ``ndarray``'s" +" (which is what ``flwr.client.NumPyClient`` expects)" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:223 -#: ../../source/tutorial-quickstart-jax.rst:171 -#: ../../source/tutorial-quickstart-scikitlearn.rst:123 -msgid ":code:`fit`" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:257 +#: ../../source/tutorial-quickstart-jax.rst:202 +#: ../../source/tutorial-quickstart-scikitlearn.rst:136 +msgid "``fit``" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:224 -#: ../../source/example-pytorch-from-centralized-to-federated.rst:228 -#: ../../source/tutorial-quickstart-jax.rst:172 -#: ../../source/tutorial-quickstart-jax.rst:176 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:255 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:260 +#: ../../source/tutorial-quickstart-jax.rst:200 +#: ../../source/tutorial-quickstart-jax.rst:205 msgid "" "update the parameters of the local model with the parameters received " "from the server" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:225 -#: ../../source/tutorial-quickstart-jax.rst:173 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:257 +#: ../../source/tutorial-quickstart-jax.rst:202 msgid "train the model on the local training set" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:226 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:258 msgid "get the updated local model weights and return them to the server" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:227 -#: ../../source/tutorial-quickstart-jax.rst:175 -#: ../../source/tutorial-quickstart-scikitlearn.rst:127 -msgid ":code:`evaluate`" +#: ../../source/example-pytorch-from-centralized-to-federated.rst:263 +#: ../../source/tutorial-quickstart-jax.rst:208 +#: ../../source/tutorial-quickstart-scikitlearn.rst:139 +msgid "``evaluate``" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:229 -#: ../../source/tutorial-quickstart-jax.rst:177 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:262 +#: ../../source/tutorial-quickstart-jax.rst:207 msgid "evaluate the updated model on the local test set" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:230 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:263 msgid "return the local loss and accuracy to the server" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:232 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:265 msgid "" -"The two :code:`NumPyClient` methods :code:`fit` and :code:`evaluate` make" -" use of the functions :code:`train()` and :code:`test()` previously " -"defined in :code:`cifar.py`. So what we really do here is we tell Flower " -"through our :code:`NumPyClient` subclass which of our already defined " -"functions to call for training and evaluation. We included type " -"annotations to give you a better understanding of the data types that get" -" passed around." +"The two ``NumPyClient`` methods ``fit`` and ``evaluate`` make use of the " +"functions ``train()`` and ``test()`` previously defined in ``cifar.py``. " +"So what we really do here is we tell Flower through our ``NumPyClient`` " +"subclass which of our already defined functions to call for training and " +"evaluation. We included type annotations to give you a better " +"understanding of the data types that get passed around." msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:280 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:315 msgid "" "All that's left to do it to define a function that loads both model and " -"data, creates a :code:`CifarClient`, and starts this client. You load " -"your data and model by using :code:`cifar.py`. Start :code:`CifarClient` " -"with the function :code:`fl.client.start_client()` by pointing it at the " -"same IP address we used in :code:`server.py`:" +"data, creates a ``CifarClient``, and starts this client. You load your " +"data and model by using ``cifar.py``. Start ``CifarClient`` with the " +"function ``fl.client.start_client()`` by pointing it at the same IP " +"address we used in ``server.py``:" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:301 -#: ../../source/tutorial-quickstart-jax.rst:274 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:338 +#: ../../source/tutorial-quickstart-jax.rst:309 msgid "And that's it. You can now open two additional terminal windows and run" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:307 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:344 msgid "" "in each window (make sure that the server is running before you do so) " "and see your (previously centralized) PyTorch project run federated " "learning across two clients. Congratulations!" msgstr "" -#: ../../source/example-pytorch-from-centralized-to-federated.rst:312 +#: ../../source/example-pytorch-from-centralized-to-federated.rst:351 msgid "" "The full source code for this example: `PyTorch: From Centralized To " "Federated (Code)