Format code examples (#2767)

doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb

@@ -484,7 +484,7 @@
     "    min_available_clients=10,  # Wait until all 10 clients are available\n",
     ")\n",
     "\n",
-    "# Specify the resources each of your clients need. By default, each \n",
+    "# Specify the resources each of your clients need. By default, each\n",
     "# client will be allocated 1x CPU and 0x CPUs\n",
     "client_resources = {\"num_cpus\": 1, \"num_gpus\": 0.0}\n",
     "if DEVICE.type == \"cuda\":\n",

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

@@ -73,10 +73,10 @@ def apply_transforms(batch):
 
 
 def train(
-        net: Net,
-        trainloader: torch.utils.data.DataLoader,
-        epochs: int,
-        device: torch.device,  # pylint: disable=no-member
+    net: Net,
+    trainloader: torch.utils.data.DataLoader,
+    epochs: int,
+    device: torch.device,  # pylint: disable=no-member
 ) -> None:
     """Train the network."""
     # Define loss and optimizer
@@ -110,9 +110,9 @@ def train(
 
 
 def test(
-        net: Net,
-        testloader: torch.utils.data.DataLoader,
-        device: torch.device,  # pylint: disable=no-member
+    net: Net,
+    testloader: torch.utils.data.DataLoader,
+    device: torch.device,  # pylint: disable=no-member
 ) -> Tuple[float, float]:
     """Validate the network on the entire test set."""
     # Define loss and metrics

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

@@ -24,10 +24,10 @@ class CifarClient(fl.client.NumPyClient):
     """Flower client implementing CIFAR-10 image classification using PyTorch."""
 
     def __init__(
-            self,
-            model: cifar.Net,
-            trainloader: DataLoader,
-            testloader: DataLoader,
+        self,
+        model: cifar.Net,
+        trainloader: DataLoader,
+        testloader: DataLoader,
     ) -> None:
         self.model = model
         self.trainloader = trainloader
@@ -61,15 +61,15 @@ def set_parameters(self, parameters: List[np.ndarray]) -> None:
             self.model.load_state_dict(state_dict, strict=True)
 
     def fit(
-            self, parameters: List[np.ndarray], config: Dict[str, str]
+        self, parameters: List[np.ndarray], config: Dict[str, str]
     ) -> Tuple[List[np.ndarray], int, Dict]:
         # Set model parameters, train model, return updated model parameters
         self.set_parameters(parameters)
         cifar.train(self.model, self.trainloader, epochs=1, device=DEVICE)
         return self.get_parameters(config={}), len(self.trainloader.dataset), {}
 
     def evaluate(
-            self, parameters: List[np.ndarray], config: Dict[str, str]
+        self, parameters: List[np.ndarray], config: Dict[str, str]
     ) -> Tuple[float, int, Dict]:
         # Set model parameters, evaluate model on local test dataset, return result
         self.set_parameters(parameters)

examples/quickstart-pytorch-lightning/client.py

@@ -10,6 +10,7 @@
 
 disable_progress_bar()
 
+
 class FlowerClient(fl.client.NumPyClient):
     def __init__(self, model, train_loader, val_loader, test_loader):
         self.model = model
@@ -55,7 +56,6 @@ def _set_parameters(model, parameters):
 
 
 def main() -> None:
-
     parser = argparse.ArgumentParser(description="Flower")
     parser.add_argument(
         "--node-id",

examples/quickstart-pytorch-lightning/mnist.py

@@ -86,16 +86,20 @@ def load_data(partition):
     # 60 % for the federated train and 20 % for the federated validation (both in fit)
     partition_train_valid = partition_full["train"].train_test_split(train_size=0.75)
     trainloader = DataLoader(
-        partition_train_valid["train"], batch_size=32,
-        shuffle=True, collate_fn=collate_fn, num_workers=1
+        partition_train_valid["train"],
+        batch_size=32,
+        shuffle=True,
+        collate_fn=collate_fn,
+        num_workers=1,
     )
     valloader = DataLoader(
-        partition_train_valid["test"], batch_size=32,
-        collate_fn=collate_fn, num_workers=1
+        partition_train_valid["test"],
+        batch_size=32,
+        collate_fn=collate_fn,
+        num_workers=1,
     )
     testloader = DataLoader(
-        partition_full["test"], batch_size=32,
-        collate_fn=collate_fn, num_workers=1
+        partition_full["test"], batch_size=32, collate_fn=collate_fn, num_workers=1
     )
     return trainloader, valloader, testloader
 

examples/quickstart-sklearn-tabular/client.py

@@ -68,4 +68,6 @@ def evaluate(self, parameters, config):  # type: ignore
             return loss, len(X_test), {"test_accuracy": accuracy}
 
     # Start Flower client
-    fl.client.start_client(server_address="0.0.0.0:8080", client=IrisClient().to_client())
+    fl.client.start_client(
+        server_address="0.0.0.0:8080", client=IrisClient().to_client()
+    )