updating docs

doc/source/example-jax-from-centralized-to-federated.rst

@@ -259,7 +259,7 @@ Having defined the federation process, we can run it.
 
         # Start Flower client
         client = FlowerClient(params, grad_fn, train_x, train_y, test_x, test_y)
-        fl.client.start_numpy_client(server_address="0.0.0.0:8080", client)
+        fl.client.start_client(server_address="0.0.0.0:8080", client.to_client())
 
     if __name__ == "__main__":
         main()

doc/source/example-pytorch-from-centralized-to-federated.rst

@@ -278,7 +278,7 @@ We included type annotations to give you a better understanding of the data type
             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_numpy_client()` by pointing it at the same IP adress we used in :code:`server.py`: 
+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 adress we used in :code:`server.py`: 
 
 .. code-block:: python
 
@@ -292,7 +292,7 @@ You load your data and model by using :code:`cifar.py`. Start :code:`CifarClient
 
         # Start client
         client = CifarClient(model, trainloader, testloader, num_examples)
-        fl.client.start_numpy_client(server_address="0.0.0.0:8080", client)
+        fl.client.start_client(server_address="0.0.0.0:8080", client.to_client())
 
 
     if __name__ == "__main__":

doc/source/how-to-run-simulations.rst

@@ -7,7 +7,7 @@ Run simulations
 
 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 <contributor-explanation-architecture.html#virtual-client-engine>`_ 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_numpy_client <ref-api-flwr.html#start-numpy-client>`_) in the sense that they can be configure by creating a class inheriting, for example, from `flwr.client.NumPyClient <ref-api-flwr.html#flwr.client.NumPyClient>`_ and therefore behave in an identical way. In addition to that, clients managed by the :code:`VirtualClientEngine` are:
+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 <ref-api-flwr.html#start-client>`_) in the sense that they can be configure by creating a class inheriting, for example, from `flwr.client.NumPyClient <ref-api-flwr.html#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.

doc/source/tutorial-quickstart-huggingface.rst

@@ -212,9 +212,9 @@ We can now start client instances using:
 
 .. code-block:: python
 
-    fl.client.start_numpy_client(
+    fl.client.start_client(
         server_address="127.0.0.1:8080", 
-        client=IMDBClient()
+        client=IMDBClient().to_client()
     )
 
 

doc/source/tutorial-quickstart-jax.rst

@@ -265,7 +265,7 @@ Having defined the federation process, we can run it.
 
         # Start Flower client
         client = FlowerClient(params, grad_fn, train_x, train_y, test_x, test_y)
-        fl.client.start_numpy_client(server_address="0.0.0.0:8080", client)
+        fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
 
     if __name__ == "__main__":
         main()

doc/source/tutorial-quickstart-pytorch.rst

@@ -191,10 +191,10 @@ to actually run this client:
 
 .. code-block:: python
 
-     fl.client.start_numpy_client(server_address="[::]:8080", client=CifarClient())
+     fl.client.start_client(server_address="[::]:8080", client=CifarClient())
 
 That's it for the client. We only have to implement :code:`Client` or
-:code:`NumPyClient` and call :code:`fl.client.start_client()` or :code:`fl.client.start_numpy_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:`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.

doc/source/tutorial-quickstart-scikitlearn.rst

@@ -145,10 +145,10 @@ to actually run this client:
 
 .. code-block:: python
 
-    fl.client.start_numpy_client("0.0.0.0:8080", client=MnistClient())
+    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()` or :code:`fl.client.start_numpy_client()`. 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:`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.

doc/source/tutorial-quickstart-tensorflow.rst

@@ -84,11 +84,11 @@ to actually run this client:
 
 .. code-block:: python
 
-     fl.client.start_numpy_client(server_address="[::]:8080", client=CifarClient())
+     fl.client.start_client(server_address="[::]:8080", client=CifarClient().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()` or :code:`fl.client.start_numpy_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:`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.

src/py/flwr/client/app.py

@@ -399,6 +399,10 @@ def start_numpy_client(
 ) -> None:
     """Start a Flower NumPyClient which connects to a gRPC server.
 
+    .. deprecated:: 1.7.0
+       Use flwr.client.start_client() instead and pass your client of NumPyClient type
+       by first exectuing its .to_client method.
+
     Parameters
     ----------
     server_address : str
@@ -456,12 +460,13 @@ def start_numpy_client(
     """
     warnings.warn(
         "flwr.client.start_numpy_client() is deprecated and will "
-        "be removed in a future version of Flower. Instead, pass "
-        "your client to `flwr.client.start_client()` by calling "
-        "first the `.to_client()` method as shown below: \n"
+        "be removed in a future version of Flower. Instead, use "
+        "`flwr.client.start_client()` by ensuring you first call "
+        "the `.to_client()` method as shown below: \n"
         "\tflwr.client.start_client(\n"
         "\t\tserver_address='<IP>:<PORT>',\n"
-        "\t\tclient=FlowerClient().to_client()\n"
+        "\t\tclient=FlowerClient().to_client()," 
+        " # <-- where FlowerClient is of type flwr.client.NumPyClient object\n"
         "\t)",
         DeprecationWarning,
         stacklevel=2,