fixed flake-8 errors

Signed-off-by: ParthM-GitHub <[email protected]>
securefederatedai · May 10, 2023 · 1fc8d3a · 1fc8d3a
1 parent d9173f1
commit 1fc8d3a
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Showing 14 changed files with 146 additions and 100 deletions.
diff --git a/openfl-tutorials/experimental/Global_DP/Workflow_Interface_Mnist_Implementation_1.py b/openfl-tutorials/experimental/Global_DP/Workflow_Interface_Mnist_Implementation_1.py
@@ -39,7 +39,7 @@
 # Fixing the seed for result repeatation: remove below to stop repeatable runs
 # ----------------------------------
 random_seed = 5495300300540669060
-g_device = torch.Generator(device='cuda')
+g_device = torch.Generator(device="cuda")
 # Uncomment the line below to use g_cpu if not using cuda
 # g_device = torch.Generator() # noqa: E800
 # NOTE: remove below to stop repeatable runs
@@ -601,7 +601,6 @@ def end(self):
 
 
 if __name__ == "__main__":
-
     argparser = argparse.ArgumentParser(description=__doc__)
     argparser.add_argument(
         "--config_path", help="Absolute path to the flow configuration file"
@@ -615,9 +614,7 @@ def end(self):
     args = argparser.parse_args()
 
     if torch.cuda.is_available():
-        device = torch.device(
-            "cuda:0"
-        )
+        device = torch.device("cuda:0")
     else:
         device = torch.device("cpu")
 
@@ -638,8 +635,9 @@ def end(self):
         "Guadalajara",
     ]
 
-    def callable_to_initialize_collaborator_private_attributes(index, n_collaborators,
-            batch_size, train_dataset, test_dataset):
+    def callable_to_initialize_collaborator_private_attributes(
+        index, n_collaborators, batch_size, train_dataset, test_dataset
+    ):
         train = deepcopy(train_dataset)
         test = deepcopy(test_dataset)
         train.data = train_dataset.data[index::n_collaborators]
@@ -648,8 +646,12 @@ def callable_to_initialize_collaborator_private_attributes(index, n_collaborator
         test.targets = test_dataset.targets[index::n_collaborators]
 
         return {
-            "train_loader": torch.utils.data.DataLoader(train, batch_size=batch_size, shuffle=True),
-            "test_loader": torch.utils.data.DataLoader(test, batch_size=batch_size, shuffle=True),
+            "train_loader": torch.utils.data.DataLoader(
+                train, batch_size=batch_size, shuffle=True
+            ),
+            "test_loader": torch.utils.data.DataLoader(
+                test, batch_size=batch_size, shuffle=True
+            ),
         }
 
     collaborators = []
@@ -659,14 +661,19 @@ def callable_to_initialize_collaborator_private_attributes(index, n_collaborator
                 name=collaborator_name,
                 private_attributes_callable=callable_to_initialize_collaborator_private_attributes,
                 # Set `num_gpus=0.1` to `num_gpus=0.0` in order to run this tutorial on CPU
-                num_cpus=0.0, num_gpus=0.1, # Assuming GPU(s) is available in the machine
-                index=idx, n_collaborators=len(collaborator_names),
-                batch_size=batch_size_train, train_dataset=mnist_train,
-                test_dataset=mnist_test
+                num_cpus=0.0,
+                num_gpus=0.1,  # Assuming GPU(s) is available in the machine
+                index=idx,
+                n_collaborators=len(collaborator_names),
+                batch_size=batch_size_train,
+                train_dataset=mnist_train,
+                test_dataset=mnist_test,
             )
         )
 
-    local_runtime = LocalRuntime(aggregator=aggregator, collaborators=collaborators, backend="ray")
+    local_runtime = LocalRuntime(
+        aggregator=aggregator, collaborators=collaborators, backend="ray"
+    )
     print(f"Local runtime collaborators = {local_runtime.collaborators}")
 
     top_model_accuracy = 0

diff --git a/openfl-tutorials/experimental/Global_DP/Workflow_Interface_Mnist_Implementation_2.py b/openfl-tutorials/experimental/Global_DP/Workflow_Interface_Mnist_Implementation_2.py
@@ -35,7 +35,7 @@
 
 random_seed = 5495300300540669060
 
-g_device = torch.Generator(device='cuda')
+g_device = torch.Generator(device="cuda")
 # Uncomment the line below to use g_cpu if not using cuda
 # g_device = torch.Generator() # noqa: E800
 # NOTE: remove below to stop repeatable runs
@@ -580,7 +580,6 @@ def end(self):
 
 
 if __name__ == "__main__":
-
     argparser = argparse.ArgumentParser(description=__doc__)
     argparser.add_argument(
         "--config_path", help="Absolute path to the flow configuration file."
@@ -594,9 +593,7 @@ def end(self):
     args = argparser.parse_args()
 
     if torch.cuda.is_available():
-        device = torch.device(
-            "cuda:0"
-        )
+        device = torch.device("cuda:0")
     else:
         device = torch.device("cpu")
 
@@ -617,8 +614,9 @@ def end(self):
         "Guadalajara",
     ]
 
-    def callable_to_initialize_collaborator_private_attributes(index, n_collaborators,
-            batch_size, train_dataset, test_dataset):
+    def callable_to_initialize_collaborator_private_attributes(
+        index, n_collaborators, batch_size, train_dataset, test_dataset
+    ):
         train = deepcopy(train_dataset)
         test = deepcopy(test_dataset)
         train.data = train_dataset.data[index::n_collaborators]
@@ -627,8 +625,12 @@ def callable_to_initialize_collaborator_private_attributes(index, n_collaborator
         test.targets = test_dataset.targets[index::n_collaborators]
 
         return {
-            "train_loader": torch.utils.data.DataLoader(train, batch_size=batch_size, shuffle=True),
-            "test_loader": torch.utils.data.DataLoader(test, batch_size=batch_size, shuffle=True),
+            "train_loader": torch.utils.data.DataLoader(
+                train, batch_size=batch_size, shuffle=True
+            ),
+            "test_loader": torch.utils.data.DataLoader(
+                test, batch_size=batch_size, shuffle=True
+            ),
         }
 
     collaborators = []
@@ -638,14 +640,19 @@ def callable_to_initialize_collaborator_private_attributes(index, n_collaborator
                 name=collaborator_name,
                 private_attributes_callable=callable_to_initialize_collaborator_private_attributes,
                 # Set `num_gpus=0.1` to `num_gpus=0.0` in order to run this tutorial on CPU
-                num_cpus=0.0, num_gpus=0.1, # Assuming GPU(s) is available in the machine
-                index=idx, n_collaborators=len(collaborator_names),
-                batch_size=batch_size_train, train_dataset=mnist_train,
-                test_dataset=mnist_test
+                num_cpus=0.0,
+                num_gpus=0.1,  # Assuming GPU(s) is available in the machine
+                index=idx,
+                n_collaborators=len(collaborator_names),
+                batch_size=batch_size_train,
+                train_dataset=mnist_train,
+                test_dataset=mnist_test,
             )
         )
 
-    local_runtime = LocalRuntime(aggregator=aggregator, collaborators=collaborators, backend="ray")
+    local_runtime = LocalRuntime(
+        aggregator=aggregator, collaborators=collaborators, backend="ray"
+    )
     print(f"Local runtime collaborators = {local_runtime.collaborators}")
 
     best_model = None

diff --git a/openfl-tutorials/experimental/Privacy_Meter/cifar10_PM.py b/openfl-tutorials/experimental/Privacy_Meter/cifar10_PM.py
@@ -146,7 +146,6 @@ def inference(network, test_loader, device):
 
 
 def optimizer_to_device(optimizer, device):
-
     """
     Sending the "torch.optim.Optimizer" object into the specified device
     for model training and inference
@@ -581,7 +580,6 @@ def end(self):
 
 
 if __name__ == "__main__":
-
     argparser = argparse.ArgumentParser(description=__doc__)
     argparser.add_argument(
         "--audit_dataset_ratio",
@@ -663,9 +661,7 @@ def end(self):
     collaborator_names = ["Portland", "Seattle"]
 
     if torch.cuda.is_available():
-        device = torch.device(
-            "cuda:0"
-        )
+        device = torch.device("cuda:0")
     else:
         device = torch.device("cpu")
 
@@ -693,9 +689,9 @@ def end(self):
     train_dataset.targets = Y[:train_dataset_size]
 
     test_dataset = deepcopy(cifar_test)
-    test_dataset.data = X[train_dataset_size:train_dataset_size + test_dataset_size]
+    test_dataset.data = X[train_dataset_size: train_dataset_size + test_dataset_size]
     test_dataset.targets = Y[
-        train_dataset_size:train_dataset_size + test_dataset_size
+        train_dataset_size: train_dataset_size + test_dataset_size
     ]
 
     population_dataset = deepcopy(cifar_test)
@@ -715,7 +711,8 @@ def end(self):
     # this function will be called before executing collaborator steps
     # which will return private attributes dictionary for each collaborator
     def callable_to_initialize_collaborator_private_attributes(
-            index, n_collaborators, train_ds, test_ds, population_ds, args):
+        index, n_collaborators, train_ds, test_ds, population_ds, args
+    ):
         # construct the training and test and population dataset
         local_train = deepcopy(train_ds)
         local_test = deepcopy(test_ds)
@@ -773,24 +770,30 @@ def callable_to_initialize_collaborator_private_attributes(
             ),
         }
 
-
     collaborators = []
     for idx, collab_name in enumerate(collaborator_names):
         collaborators.append(
             Collaborator(
                 name=collab_name,
                 private_attributes_callable=callable_to_initialize_collaborator_private_attributes,
                 # If 1 GPU is available in the machine
-                # Set `num_gpus=0.0` to `num_gpus=0.5` to run on GPU with ray backend with 2 collaborators
-                num_cpus=0.0, num_gpus=0.0,
-                index=idx, n_collaborators=len(collaborator_names),
-                train_ds=train_dataset, test_ds=test_dataset,
-                population_ds=population_dataset, args=args
+                # Set `num_gpus=0.0` to `num_gpus=0.5` to run on GPU
+                # with ray backend with 2 collaborators
+                num_cpus=0.0,
+                num_gpus=0.0,
+                index=idx,
+                n_collaborators=len(collaborator_names),
+                train_ds=train_dataset,
+                test_ds=test_dataset,
+                population_ds=population_dataset,
+                args=args,
             )
         )
 
     # Set backend='ray' to use ray-backend
-    local_runtime = LocalRuntime(aggregator=aggregator, collaborators=collaborators, backend="single_process")
+    local_runtime = LocalRuntime(
+        aggregator=aggregator, collaborators=collaborators, backend="single_process"
+    )
 
     print(f"Local runtime collaborators = {local_runtime.collaborators}")
 

diff --git a/openfl/experimental/interface/fl_spec.py b/openfl/experimental/interface/fl_spec.py
@@ -84,7 +84,8 @@ def run(self) -> None:
                         "\nLocalRuntime(...,backend='single_process')\n"
                         "\n or for more information about the original error,"
                         "\nPlease see the official Ray documentation"
-                        "\nhttps://docs.ray.io/en/releases-2.2.0/ray-core/objects/serialization.html"
+                        "\nhttps://docs.ray.io/en/releases-2.2.0/ray-core/\
+                        objects/serialization.html"
                     )
                     raise SerializationError(str(e) + msg)
                 else:

diff --git a/openfl/experimental/interface/participants.py b/openfl/experimental/interface/participants.py
@@ -45,20 +45,20 @@ class Collaborator(Participant):
     def __init__(self, name: str = "", private_attributes_callable: Callable = None,
                  num_cpus: int = 0, num_gpus: int = 0.0, **kwargs):
         """
-        Create collaborator object with custom resources and a callable 
+        Create collaborator object with custom resources and a callable
         function to assign private attributes
 
         Parameters:
         name (str): Name of the collaborator. default=""
 
         private_attributes_callable (Callable): A function which returns collaborator
-        private attributes for each collaborator. In case private_attributes are not 
+        private attributes for each collaborator. In case private_attributes are not
         required this can be omitted. default=None
 
         num_cpus (int): Specifies how many cores to use for the collaborator step exection.
         This will only be used if backend is set to ray. default=0
 
-        num_gpus (float): Specifies how many GPUs to use to accerlerate the collaborator 
+        num_gpus (float): Specifies how many GPUs to use to accerlerate the collaborator
         step exection. This will only be used if backend is set to ray. default=0
 
         kwargs (dict): Parameters required to call private_attributes_callable function.
@@ -84,15 +84,15 @@ def get_name(self) -> str:
 
     def initialize_private_attributes(self) -> None:
         """
-        initialize private attributes of Collaborator object by invoking 
+        initialize private attributes of Collaborator object by invoking
         the callable specified by user
         """
         if self.private_attributes_callable is not None:
             self.private_attributes = self.private_attributes_callable(**self.kwargs)
 
     def __set_collaborator_attrs_to_clone(self, clone: Any) -> None:
         """
-        Set collaborator private attributes to FLSpec clone before transitioning 
+        Set collaborator private attributes to FLSpec clone before transitioning
         from Aggregator step to collaborator steps
         """
         # set collaborator private attributes as
@@ -102,10 +102,10 @@ def __set_collaborator_attrs_to_clone(self, clone: Any) -> None:
 
     def __delete_collab_attrs_from_clone(self, clone: Any) -> None:
         """
-        Remove collaborator private attributes from FLSpec clone before 
+        Remove collaborator private attributes from FLSpec clone before
         transitioning from Collaborator step to Aggregator step
         """
-        # Update collaborator private attributes by taking latest 
+        # Update collaborator private attributes by taking latest
         # parameters from clone, then delete attributes from clone.
         for attr_name in self.private_attributes:
             if hasattr(clone, attr_name):
@@ -146,9 +146,8 @@ def __init__(self, name: str = "", private_attributes_callable: Callable = None,
 
     def initialize_private_attributes(self) -> None:
         """
-        initialize private attributes of Aggregator object by invoking 
+        initialize private attributes of Aggregator object by invoking
         the callable specified by user
         """
         if self.private_attributes_callable is not None:
             self.private_attributes = self.private_attributes_callable(**self.kwargs)
-