Format code examples (#2877)

Co-authored-by: Taner Topal <taner@flower.dev>

examples/custom-metrics/client.py

@@ -68,4 +68,6 @@ def evaluate(self, parameters, config):
 
 
 # Start Flower client
-fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client())
+fl.client.start_client(
+    server_address="127.0.0.1:8080", client=FlowerClient().to_client()
+)

examples/embedded-devices/client_pytorch.py

@@ -30,12 +30,13 @@
     "--mnist",
     action="store_true",
     help="If you use Raspberry Pi Zero clients (which just have 512MB or RAM) use "
-         "MNIST",
+    "MNIST",
 )
 
 warnings.filterwarnings("ignore", category=UserWarning)
 NUM_CLIENTS = 50
 
+
 class Net(nn.Module):
     """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')."""
 
@@ -96,6 +97,7 @@ def prepare_dataset(use_mnist: bool):
         img_key = "img"
         norm = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
     pytorch_transforms = Compose([ToTensor(), norm])
+
     def apply_transforms(batch):
         """Apply transforms to the partition from FederatedDataset."""
         batch[img_key] = [pytorch_transforms(img) for img in batch[img_key]]
@@ -112,7 +114,7 @@ def apply_transforms(batch):
         validsets.append(partition["test"])
     testset = fds.load_full("test")
     testset = testset.with_transform(apply_transforms)
-    return trainsets,  validsets, testset
+    return trainsets, validsets, testset
 
 
 # Flower client, adapted from Pytorch quickstart/simulation example

examples/embedded-devices/client_tf.py

@@ -45,8 +45,10 @@ def prepare_dataset(use_mnist: bool):
         partition.set_format("numpy")
         # Divide data on each node: 90% train, 10% test
         partition = partition.train_test_split(test_size=0.1)
-        x_train, y_train = partition["train"][img_key] / 255.0, partition["train"][
-            "label"]
+        x_train, y_train = (
+            partition["train"][img_key] / 255.0,
+            partition["train"]["label"],
+        )
         x_test, y_test = partition["test"][img_key] / 255.0, partition["test"]["label"]
         partitions.append(((x_train, y_train), (x_test, y_test)))
     data_centralized = fds.load_full("test")
@@ -123,7 +125,9 @@ def main():
     # Start Flower client setting its associated data partition
     fl.client.start_client(
         server_address=args.server_address,
-        client=FlowerClient(trainset=trainset, valset=valset, use_mnist=use_mnist).to_client(),
+        client=FlowerClient(
+            trainset=trainset, valset=valset, use_mnist=use_mnist
+        ).to_client(),
     )
 
 

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

@@ -93,7 +93,9 @@ def evaluate(self, parameters, config):
             loss = test(net, testloader)
             return float(loss), len(testloader), {}
 
-    fl.client.start_client(server_address="127.0.0.1:8080", client=CifarClient().to_client())
+    fl.client.start_client(
+        server_address="127.0.0.1:8080", client=CifarClient().to_client()
+    )
 
 
 if __name__ == "__main__":

examples/quickstart-huggingface/client.py

@@ -108,7 +108,9 @@ def evaluate(self, parameters, config):
             return float(loss), len(testloader), {"accuracy": float(accuracy)}
 
     # Start client
-    fl.client.start_client(server_address="127.0.0.1:8080", client=IMDBClient().to_client())
+    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 +52,6 @@ def evaluate(
 
 
 # Start Flower client
-fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client())
+fl.client.start_client(
+    server_address="127.0.0.1:8080", client=FlowerClient().to_client()
+)

examples/quickstart-mlcube/client.py

@@ -44,7 +44,8 @@ def main():
     )
 
     fl.client.start_client(
-        server_address="0.0.0.0:8080", client=MLCubeClient(workspace=workspace).to_client()
+        server_address="0.0.0.0:8080",
+        client=MLCubeClient(workspace=workspace).to_client(),
     )
 
 

examples/quickstart-tabnet/client.py

@@ -79,4 +79,6 @@ def evaluate(self, parameters, config):
 
 
 # Start Flower client
-fl.client.start_client(server_address="127.0.0.1:8080", client=TabNetClient().to_client())
+fl.client.start_client(
+    server_address="127.0.0.1:8080", client=TabNetClient().to_client()
+)

examples/quickstart-tensorflow/client.py

@@ -52,4 +52,6 @@ def evaluate(self, parameters, config):
 
 
 # Start Flower client
-fl.client.start_client(server_address="127.0.0.1:8080", client=CifarClient().to_client())
+fl.client.start_client(
+    server_address="127.0.0.1:8080", client=CifarClient().to_client()
+)

examples/sklearn-logreg-mnist/client.py

@@ -62,4 +62,6 @@ def evaluate(self, parameters, config):  # type: ignore
             return loss, len(X_test), {"accuracy": accuracy}
 
     # Start Flower client
-    fl.client.start_client(server_address="0.0.0.0:8080", client=MnistClient().to_client())
+    fl.client.start_client(
+        server_address="0.0.0.0:8080", client=MnistClient().to_client()
+    )