Merge branch 'main' into fds-add-inner-dirichlet-partitioner

src/py/flwr/cli/new/new.py

@@ -28,6 +28,7 @@
 class MlFramework(str, Enum):
     """Available frameworks."""
 
+    NUMPY = "NumPy"
     PYTORCH = "PyTorch"
     TENSORFLOW = "TensorFlow"
 

src/py/flwr/cli/new/templates/app/code/client.numpy.py.tpl

@@ -0,0 +1,24 @@
+"""$project_name: A Flower / NumPy app."""
+
+import flwr as fl
+import numpy as np
+
+
+# Flower client, adapted from Pytorch quickstart example
+class FlowerClient(fl.client.NumPyClient):
+    def get_parameters(self, config):
+        return [np.ones((1, 1))]
+
+    def fit(self, parameters, config):
+        return ([np.ones((1, 1))], 1, {})
+
+    def evaluate(self, parameters, config):
+        return float(0.0), 1, {"accuracy": float(1.0)}
+
+
+def client_fn(cid: str):
+    return FlowerClient().to_client()
+
+
+# ClientApp for Flower-Next
+app = fl.client.ClientApp(client_fn=client_fn)

src/py/flwr/cli/new/templates/app/code/server.numpy.py.tpl

@@ -0,0 +1,12 @@
+"""$project_name: A Flower / NumPy app."""
+
+import flwr as fl
+
+# Configure the strategy
+strategy = fl.server.strategy.FedAvg()
+
+# Flower ServerApp
+app = fl.server.ServerApp(
+    config=fl.server.ServerConfig(num_rounds=1),
+    strategy=strategy,
+)

src/py/flwr/cli/new/templates/app/flower.toml.tpl

@@ -1,10 +1,10 @@
-[flower]
+[project]
 name = "$project_name"
 version = "1.0.0"
 description = ""
 license = "Apache-2.0"
 authors = ["The Flower Authors <hello@flower.ai>"]
 
-[components]
+[flower.components]
 serverapp = "$project_name.server:app"
 clientapp = "$project_name.client:app"

src/py/flwr/cli/new/templates/app/requirements.numpy.txt.tpl

@@ -0,0 +1,2 @@
+flwr>=1.8, <2.0
+numpy >= 1.21.0