Make examples use start_client(). (#2718)

examples/advanced-pytorch/client.py

@@ -150,9 +150,8 @@ def main() -> None:
             trainset = trainset.select(range(10))
             testset = testset.select(range(10))
         # Start Flower client
-        client = CifarClient(trainset, testset, device, args.model)
-
-        fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=client)
+        client = CifarClient(trainset, testset, device, args.model).to_client()
+        fl.client.start_client(server_address="127.0.0.1:8080", client=client)
 
 
 if __name__ == "__main__":

examples/advanced-tensorflow/client.py

@@ -106,9 +106,9 @@ def main() -> None:
         x_test, y_test = x_test[:10], y_test[:10]
 
     # Start Flower client
-    client = CifarClient(model, x_train, y_train, x_test, y_test)
+    client = CifarClient(model, x_train, y_train, x_test, y_test).to_client()
 
-    fl.client.start_numpy_client(
+    fl.client.start_client(
         server_address="127.0.0.1:8080",
         client=client,
         root_certificates=Path(".cache/certificates/ca.crt").read_bytes(),

examples/custom-metrics/client.py

@@ -68,4 +68,4 @@ def evaluate(self, parameters, config):
 
 
 # Start Flower client
-fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=FlowerClient())
+fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client())

examples/embedded-devices/client_pytorch.py

@@ -204,11 +204,11 @@ def main():
     trainsets, valsets, _ = prepare_dataset(use_mnist)
 
     # Start Flower client setting its associated data partition
-    fl.client.start_numpy_client(
+    fl.client.start_client(
         server_address=args.server_address,
         client=FlowerClient(
             trainset=trainsets[args.cid], valset=valsets[args.cid], use_mnist=use_mnist
-        ),
+        ).to_client(),
     )
 
 

examples/embedded-devices/client_tf.py

@@ -123,9 +123,9 @@ def main():
     trainset, valset = partitions[args.cid]
 
     # Start Flower client setting its associated data partition
-    fl.client.start_numpy_client(
+    fl.client.start_client(
         server_address=args.server_address,
-        client=FlowerClient(trainset=trainset, valset=valset, use_mnist=use_mnist),
+        client=FlowerClient(trainset=trainset, valset=valset, use_mnist=use_mnist).to_client(),
     )
 
 

examples/flower-in-30-minutes/tutorial.ipynb

@@ -776,7 +776,7 @@
     "\n",
     "        return FlowerClient(\n",
     "            trainloader=trainloaders[int(cid)], vallodaer=valloaders[int(cid)]\n",
-    "        )\n",
+    "        ).to_client()\n",
     "\n",
     "    return client_fn\n",
     "\n",

examples/mt-pytorch/client.py

@@ -35,8 +35,8 @@ def evaluate(self, parameters, config):
 
 
 # Start Flower client
-fl.client.start_numpy_client(
+fl.client.start_client(
     server_address="0.0.0.0:9092",  # "0.0.0.0:9093" for REST
-    client=FlowerClient(),
+    client=FlowerClient().to_client(),
     transport="grpc-rere",  # "rest" for REST
 )

examples/opacus/dp_cifar_client.py

@@ -28,7 +28,7 @@ def load_data():
 
 model = Net()
 trainloader, testloader, sample_rate = load_data()
-fl.client.start_numpy_client(
+fl.client.start_client(
     server_address="127.0.0.1:8080",
-    client=DPCifarClient(model, trainloader, testloader),
+    client=DPCifarClient(model, trainloader, testloader).to_client(),
 )

examples/opacus/dp_cifar_simulation.py

@@ -1,14 +1,14 @@
 import math
 from collections import OrderedDict
-from typing import Callable, Optional, Tuple
+from typing import Callable, Dict, Optional, Tuple
 
 import flwr as fl
 import numpy as np
 import torch
 import torchvision.transforms as transforms
-from opacus.dp_model_inspector import DPModelInspector
 from torch.utils.data import DataLoader
 from torchvision.datasets import CIFAR10
+from flwr.common.typing import Scalar
 
 from dp_cifar_main import DEVICE, PARAMS, DPCifarClient, Net, test
 
@@ -23,8 +23,6 @@ def client_fn(cid: str) -> fl.client.Client:
     # Load model.
     model = Net()
     # Check model is compatible with Opacus.
-    # inspector = DPModelInspector()
-    # print(f"Is the model valid? {inspector.validate(model)}")
 
     # Load data partition (divide CIFAR10 into NUM_CLIENTS distinct partitions, using 30% for validation).
     transform = transforms.Compose(
@@ -45,12 +43,14 @@ def client_fn(cid: str) -> fl.client.Client:
     client_trainloader = DataLoader(client_trainset, PARAMS["batch_size"])
     client_testloader = DataLoader(client_testset, PARAMS["batch_size"])
 
-    return DPCifarClient(model, client_trainloader, client_testloader)
+    return DPCifarClient(model, client_trainloader, client_testloader).to_client()
 
 
 # Define an evaluation function for centralized evaluation (using whole CIFAR10 testset).
 def get_evaluate_fn() -> Callable[[fl.common.NDArrays], Optional[Tuple[float, float]]]:
-    def evaluate(weights: fl.common.NDArrays) -> Optional[Tuple[float, float]]:
+    def evaluate(
+        server_round: int, parameters: fl.common.NDArrays, config: Dict[str, Scalar]
+    ):
         transform = transforms.Compose(
             [
                 transforms.ToTensor(),
@@ -63,7 +63,7 @@ def evaluate(weights: fl.common.NDArrays) -> Optional[Tuple[float, float]]:
         state_dict = OrderedDict(
             {
                 k: torch.tensor(np.atleast_1d(v))
-                for k, v in zip(model.state_dict().keys(), weights)
+                for k, v in zip(model.state_dict().keys(), parameters)
             }
         )
         model.load_state_dict(state_dict, strict=True)
@@ -82,7 +82,7 @@ def main() -> None:
         client_fn=client_fn,
         num_clients=NUM_CLIENTS,
         client_resources={"num_cpus": 1},
-        num_rounds=3,
+        config=fl.server.ServerConfig(num_rounds=3),
         strategy=fl.server.strategy.FedAvg(
             fraction_fit=0.1, fraction_evaluate=0.1, evaluate_fn=get_evaluate_fn()
         ),

examples/pytorch-federated-variational-autoencoder/client.py

@@ -93,7 +93,7 @@ def evaluate(self, parameters, config):
             loss = test(net, testloader)
             return float(loss), len(testloader), {}
 
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=CifarClient())
+    fl.client.start_client(server_address="127.0.0.1:8080", client=CifarClient().to_client())
 
 
 if __name__ == "__main__":

examples/pytorch-from-centralized-to-federated/client.py

@@ -93,8 +93,8 @@ def main() -> None:
     _ = model(next(iter(trainloader))["img"].to(DEVICE))
 
     # Start client
-    client = CifarClient(model, trainloader, testloader)
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=client)
+    client = CifarClient(model, trainloader, testloader).to_client()
+    fl.client.start_client(server_address="127.0.0.1:8080", client=client)
 
 
 if __name__ == "__main__":

examples/quickstart-fastai/client.py

@@ -43,7 +43,7 @@ def evaluate(self, parameters, config):
 
 
 # Start Flower client
-fl.client.start_numpy_client(
+fl.client.start_client(
     server_address="127.0.0.1:8080",
-    client=FlowerClient(),
+    client=FlowerClient().to_client(),
 )

examples/quickstart-huggingface/client.py

@@ -108,7 +108,7 @@ def evaluate(self, parameters, config):
             return float(loss), len(testloader), {"accuracy": float(accuracy)}
 
     # Start client
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=IMDBClient())
+    fl.client.start_client(server_address="127.0.0.1:8080", client=IMDBClient().to_client())
 
 
 if __name__ == "__main__":

examples/quickstart-jax/client.py

@@ -52,4 +52,4 @@ def evaluate(
 
 
 # Start Flower client
-fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=FlowerClient())
+fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client())

examples/quickstart-mlcube/client.py

@@ -43,8 +43,8 @@ def main():
         os.path.dirname(os.path.abspath(__file__)), "workspaces", workspace_name
     )
 
-    fl.client.start_numpy_client(
-        server_address="0.0.0.0:8080", client=MLCubeClient(workspace=workspace)
+    fl.client.start_client(
+        server_address="0.0.0.0:8080", client=MLCubeClient(workspace=workspace).to_client()
     )
 
 

examples/quickstart-pandas/client.py

@@ -59,7 +59,7 @@ def fit(
     X = dataset[column_names]
 
     # Start Flower client
-    fl.client.start_numpy_client(
+    fl.client.start_client(
         server_address="127.0.0.1:8080",
-        client=FlowerClient(X),
+        client=FlowerClient(X).to_client(),
     )

examples/quickstart-pytorch-lightning/client.py

@@ -72,8 +72,8 @@ def main() -> None:
     train_loader, val_loader, test_loader = mnist.load_data(node_id)
 
     # Flower client
-    client = FlowerClient(model, train_loader, val_loader, test_loader)
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=client)
+    client = FlowerClient(model, train_loader, val_loader, test_loader).to_client()
+    fl.client.start_client(server_address="127.0.0.1:8080", client=client)
 
 
 if __name__ == "__main__":

examples/quickstart-pytorch/client.py

@@ -132,7 +132,7 @@ def evaluate(self, parameters, config):
 
 
 # Start Flower client
-fl.client.start_numpy_client(
+fl.client.start_client(
     server_address="127.0.0.1:8080",
-    client=FlowerClient(),
+    client=FlowerClient().to_client(),
 )

examples/quickstart-tabnet/client.py

@@ -79,4 +79,4 @@ def evaluate(self, parameters, config):
 
 
 # Start Flower client
-fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=TabNetClient())
+fl.client.start_client(server_address="127.0.0.1:8080", client=TabNetClient().to_client())

examples/quickstart-tensorflow/client.py

@@ -52,4 +52,4 @@ def evaluate(self, parameters, config):
 
 
 # Start Flower client
-fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=CifarClient())
+fl.client.start_client(server_address="127.0.0.1:8080", client=CifarClient().to_client())

examples/simulation-pytorch/sim.ipynb

@@ -509,7 +509,7 @@
     "        valloader = DataLoader(valset.with_transform(apply_transforms), batch_size=32)\n",
     "\n",
     "        # Create and return client\n",
-    "        return FlowerClient(trainloader, valloader)\n",
+    "        return FlowerClient(trainloader, valloader).to_client()\n",
     "\n",
     "    return client_fn\n",
     "\n",

examples/simulation-pytorch/sim.py

@@ -104,7 +104,7 @@ def client_fn(cid: str) -> fl.client.Client:
         valset = valset.with_transform(apply_transforms)
 
         # Create and return client
-        return FlowerClient(trainset, valset)
+        return FlowerClient(trainset, valset).to_client()
 
     return client_fn
 

examples/simulation-tensorflow/sim.ipynb

@@ -189,7 +189,7 @@
     "        )\n",
     "\n",
     "        # Create and return client\n",
-    "        return FlowerClient(trainset, valset)\n",
+    "        return FlowerClient(trainset, valset).to_client()\n",
     "\n",
     "    return client_fn\n",
     "\n",

examples/simulation-tensorflow/sim.py

@@ -94,7 +94,7 @@ def client_fn(cid: str) -> fl.client.Client:
         )
 
         # Create and return client
-        return FlowerClient(trainset, valset)
+        return FlowerClient(trainset, valset).to_client()
 
     return client_fn
 

examples/sklearn-logreg-mnist/client.py

@@ -62,4 +62,4 @@ def evaluate(self, parameters, config):  # type: ignore
             return loss, len(X_test), {"accuracy": accuracy}
 
     # Start Flower client
-    fl.client.start_numpy_client(server_address="0.0.0.0:8080", client=MnistClient())
+    fl.client.start_client(server_address="0.0.0.0:8080", client=MnistClient().to_client())

examples/whisper-federated-finetuning/client.py

@@ -146,7 +146,7 @@ def client_fn(cid: str):
 
         return WhisperFlowerClient(
             full_train_dataset, num_classes, disable_tqdm, compile
-        )
+        ).to_client()
 
     return client_fn
 
@@ -174,7 +174,7 @@ def run_client():
         client_data_path=CLIENT_DATA,
     )
 
-    fl.client.start_numpy_client(
+    fl.client.start_client(
         server_address=f"{args.server_address}:8080", client=client_fn(args.cid)
     )
 

examples/xgboost-comprehensive/client.py

@@ -171,4 +171,4 @@ def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
 
 
 # Start Flower client
-fl.client.start_client(server_address="127.0.0.1:8080", client=XgbClient())
+fl.client.start_client(server_address="127.0.0.1:8080", client=XgbClient().to_client())

examples/xgboost-quickstart/client.py

@@ -173,4 +173,4 @@ def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
 
 
 # Start Flower client
-fl.client.start_client(server_address="127.0.0.1:8080", client=XgbClient())
+fl.client.start_client(server_address="127.0.0.1:8080", client=XgbClient().to_client())