Formatting and sorting imports in Python files

.ci/tests/examples/is_success.py

@@ -1,6 +1,7 @@
-import pymongo
-from time import sleep
 import sys
+from time import sleep
+
+import pymongo
 
 N_ROUNDS = 3
 RETRIES= 6

.devcontainer/bin/init_venv.sh

@@ -9,5 +9,7 @@ python -m venv .venv
 .venv/bin/pip install \
     sphinx==4.4.0 \
     sphinx_press_theme==0.8.0 \
-    sphinx-autobuild==2021.3.14
+    sphinx-autobuild==2021.3.14 \
+    autopep8==1.5.7 \
+    isort==5.10.1
 .venv/bin/pip install -e fedn

.devcontainer/devcontainer.json.tpl

@@ -9,7 +9,8 @@
     "ms-azuretools.vscode-docker",
     "ms-python.python",
     "exiasr.hadolint",
-    "yzhang.markdown-all-in-one"
+    "yzhang.markdown-all-in-one",
+    "ms-python.isort"
   ],
   "mounts": [
     "source=/var/run/docker.sock,target=/var/run/docker.sock,type=bind,consistency=default",

.github/workflows/code-checks.yaml

@@ -0,0 +1,30 @@
+name: "code checks"
+
+on: push
+
+jobs:
+  code-checks:
+    runs-on: ubuntu-20.04
+    steps:
+      - name: checkout
+        uses: actions/checkout@v2
+
+      - name: init venv
+        run: .devcontainer/bin/init_venv.sh
+
+      - name: check Python imports
+        run: >
+          .venv/bin/isort . --check --diff 
+          --skip .venv
+          --skip .mnist-keras
+          --skip .mnist-pytorch
+
+      - name: check Python formatting
+        run: >
+          .venv/bin/autopep8 --recursive --diff
+          --exclude .venv
+          --exclude .mnist-keras
+          --exclude .mnist-pytorch
+          .
+
+      # TODO: add linting/formatting for all file types

.vscode/settings.json

@@ -0,0 +1,6 @@
+{
+    "editor.formatOnSave": true,
+    "editor.codeActionsOnSave": {
+        "source.organizeImports": true
+    },
+}

docs/source/conf.py

@@ -12,6 +12,7 @@
 #
 import os
 import sys
+
 sys.path.insert(0, os.path.abspath('../../fedn'))
 
 

examples/mnist-keras/bin/get_data

@@ -1,17 +1,21 @@
 #!./.mnist-keras/bin/python
+import os
+
 import fire
-import tensorflow as tf
 import numpy as np
-import os
+import tensorflow as tf
+
 
 def get_data(out_dir='data'):
     # Make dir if necessary
     if not os.path.exists(out_dir):
         os.mkdir(out_dir)
-    
+
     # Download data
     (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
-    np.savez(f'{out_dir}/mnist.npz', x_train=x_train, y_train=y_train, x_test=x_test, y_test=y_test)
+    np.savez(f'{out_dir}/mnist.npz', x_train=x_train,
+             y_train=y_train, x_test=x_test, y_test=y_test)
+
 
 if __name__ == '__main__':
-  fire.Fire(get_data)
+    fire.Fire(get_data)

examples/mnist-keras/bin/split_data

@@ -1,8 +1,10 @@
 #!./.mnist-keras/bin/python
 import os
-import numpy as np
 from math import floor
+
 import fire
+import numpy as np
+
 
 def splitset(dataset, parts):
     n = dataset.shape[0]
@@ -26,14 +28,15 @@ def split(dataset='data/mnist.npz', outdir='data', n_splits=2):
 
     # Make splits
     for i in range(n_splits):
-        subdir=f'{outdir}/clients/{str(i+1)}'
+        subdir = f'{outdir}/clients/{str(i+1)}'
         if not os.path.exists(subdir):
             os.mkdir(subdir)
         np.savez(f'{subdir}/mnist.npz',
-            x_train=data['x_train'][i],
-            y_train=data['y_train'][i],
-            x_test=data['x_test'][i],
-            y_test=data['y_test'][i])
+                 x_train=data['x_train'][i],
+                 y_train=data['y_train'][i],
+                 x_test=data['x_test'][i],
+                 y_test=data['y_test'][i])
+
 
 if __name__ == '__main__':
-  fire.Fire(split)
+    fire.Fire(split)

examples/mnist-keras/client/entrypoint

@@ -1,23 +1,27 @@
 #!./.mnist-keras/bin/python
-import tensorflow as tf
-import numpy as np
-from fedn.utils.kerashelper import KerasHelper
-import fire
 import json
-import docker
 import os
 
-NUM_CLASSES=10
+import docker
+import fire
+import numpy as np
+import tensorflow as tf
+
+from fedn.utils.kerashelper import KerasHelper
+
+NUM_CLASSES = 10
+
 
 def _get_data_path():
     # Figure out FEDn client number from container name
     client = docker.from_env()
     container = client.containers.get(os.environ['HOSTNAME'])
     number = container.name[-1]
-    
+
     # Return data path
     return f"/var/data/clients/{number}/mnist.npz"
 
+
 def _compile_model(img_rows=28, img_cols=28):
     # Set input shape
     input_shape = (img_rows, img_cols, 1)
@@ -30,10 +34,11 @@ def _compile_model(img_rows=28, img_cols=28):
     model.add(tf.keras.layers.Dense(32, activation='relu'))
     model.add(tf.keras.layers.Dense(NUM_CLASSES, activation='softmax'))
     model.compile(loss=tf.keras.losses.categorical_crossentropy,
-    	          optimizer=tf.keras.optimizers.Adam(),
-        	      metrics=['accuracy'])
+                  optimizer=tf.keras.optimizers.Adam(),
+                  metrics=['accuracy'])
     return model
 
+
 def _load_data(data_path, is_train=True):
     # Load data
     if data_path is None:
@@ -50,16 +55,18 @@ def _load_data(data_path, is_train=True):
 
     # Normalize
     X = X.astype('float32')
-    X = np.expand_dims(X,-1)
+    X = np.expand_dims(X, -1)
     X = X / 255
     y = tf.keras.utils.to_categorical(y, NUM_CLASSES)
 
     return X, y
 
+
 def init_seed(out_path='seed.npz'):
-	weights = _compile_model().get_weights()
-	helper = KerasHelper()
-	helper.save_model(weights, out_path)
+    weights = _compile_model().get_weights()
+    helper = KerasHelper()
+    helper.save_model(weights, out_path)
+
 
 def train(in_model_path, out_model_path, data_path=None, batch_size=32, epochs=1):
     # Load data
@@ -73,11 +80,12 @@ def train(in_model_path, out_model_path, data_path=None, batch_size=32, epochs=1
 
     # Train
     model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs)
-    
+
     # Save
     weights = model.get_weights()
     helper.save_model(weights, out_model_path)
 
+
 def validate(in_model_path, out_json_path, data_path=None):
     # Load data
     x_train, y_train = _load_data(data_path)
@@ -104,13 +112,14 @@ def validate(in_model_path, out_json_path, data_path=None):
     }
 
     # Save JSON
-    with open(out_json_path,"w") as fh:
+    with open(out_json_path, "w") as fh:
         fh.write(json.dumps(report))
 
+
 if __name__ == '__main__':
     fire.Fire({
         'init_seed': init_seed,
         'train': train,
         'validate': validate,
-        '_get_data_path': _get_data_path, # for testing
-    })
+        '_get_data_path': _get_data_path,  # for testing
+    })

examples/mnist-pytorch/bin/get_data

@@ -1,17 +1,22 @@
 #!./.mnist-pytorch/bin/python
+import os
+
 import fire
-import torchvision
 import numpy as np
-import os
+import torchvision
+
 
 def get_data(out_dir='data'):
     # Make dir if necessary
     if not os.path.exists(out_dir):
         os.mkdir(out_dir)
-    
+
     # Download data
-    torchvision.datasets.MNIST(root=f'{out_dir}/train', transform=torchvision.transforms.ToTensor, train=True, download=True)
-    torchvision.datasets.MNIST(root=f'{out_dir}/test', transform=torchvision.transforms.ToTensor, train=False, download=True)
+    torchvision.datasets.MNIST(
+        root=f'{out_dir}/train', transform=torchvision.transforms.ToTensor, train=True, download=True)
+    torchvision.datasets.MNIST(
+        root=f'{out_dir}/test', transform=torchvision.transforms.ToTensor, train=False, download=True)
+
 
 if __name__ == '__main__':
-  fire.Fire(get_data)
+    fire.Fire(get_data)

examples/mnist-pytorch/bin/split_data

@@ -1,9 +1,11 @@
 #!./.mnist-pytorch/bin/python
-import torchvision
-import torch
+import os
 from math import floor
+
 import fire
-import os
+import torch
+import torchvision
+
 
 def splitset(dataset, parts):
     n = dataset.shape[0]
@@ -20,8 +22,10 @@ def split(out_dir='data', n_splits=2):
         os.mkdir(f'{out_dir}/clients')
 
     # Load and convert to dict
-    train_data = torchvision.datasets.MNIST(root=f'{out_dir}/train', transform=torchvision.transforms.ToTensor, train=True)
-    test_data = torchvision.datasets.MNIST(root=f'{out_dir}/test', transform=torchvision.transforms.ToTensor, train=False)
+    train_data = torchvision.datasets.MNIST(
+        root=f'{out_dir}/train', transform=torchvision.transforms.ToTensor, train=True)
+    test_data = torchvision.datasets.MNIST(
+        root=f'{out_dir}/test', transform=torchvision.transforms.ToTensor, train=False)
     data = {
         'x_train': splitset(train_data.data, n_splits),
         'y_train': splitset(train_data.targets, n_splits),
@@ -31,7 +35,7 @@ def split(out_dir='data', n_splits=2):
 
     # Make splits
     for i in range(n_splits):
-        subdir=f'{out_dir}/clients/{str(i+1)}'
+        subdir = f'{out_dir}/clients/{str(i+1)}'
         if not os.path.exists(subdir):
             os.mkdir(subdir)
         torch.save({
@@ -40,7 +44,8 @@ def split(out_dir='data', n_splits=2):
             'x_test': data['x_test'][i],
             'y_test': data['y_test'][i],
         },
-        f'{subdir}/mnist.pt')
+            f'{subdir}/mnist.pt')
+
 
 if __name__ == '__main__':
-  fire.Fire(split)
+    fire.Fire(split)