Merge branch 'master' into pytorch-doc-update

Author: chuyang-deng

.pylintrc

@@ -92,7 +92,6 @@ disable=
     cyclic-import, # TODO: Resolve cyclic imports
     no-self-use, # TODO: Convert methods to functions where appropriate
     too-many-branches, # TODO: Simplify or ignore as appropriate
-    missing-docstring, # TODO: Fix missing docstring
 
 [REPORTS]
 # Set the output format. Available formats are text, parseable, colorized, msvs

doc/conf.py

@@ -10,6 +10,7 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
+"""Placeholder docstring"""
 from __future__ import absolute_import
 
 import os
@@ -22,6 +23,10 @@
 class Mock(MagicMock):
     @classmethod
     def __getattr__(cls, name):
+        """
+        Args:
+            name:
+        """
         if name == "__version__":
             return "1.4.0"
         else:

examples/cli/host/script.py

@@ -6,11 +6,13 @@
 
 
 def model_fn(model_dir):
-    """
-    Load the gluon model. Called once when hosting service starts.
+    """Load the gluon model. Called once when hosting service starts.
+
+    Args:
+        model_dir: The directory where model files are stored.
 
-    :param: model_dir The directory where model files are stored.
-    :return: a model (in this case a Gluon network)
+    Returns:
+        a model (in this case a Gluon network)
     """
     symbol = mx.sym.load("%s/model.json" % model_dir)
     outputs = mx.symbol.softmax(data=symbol, name="softmax_label")
@@ -22,14 +24,16 @@ def model_fn(model_dir):
 
 
 def transform_fn(net, data, input_content_type, output_content_type):
-    """
-    Transform a request using the Gluon model. Called once per request.
+    """Transform a request using the Gluon model. Called once per request.
+
+    Args:
+        net: The Gluon model.
+        data: The request payload.
+        input_content_type: The request content type.
+        output_content_type: The (desired) response content type.
 
-    :param net: The Gluon model.
-    :param data: The request payload.
-    :param input_content_type: The request content type.
-    :param output_content_type: The (desired) response content type.
-    :return: response payload and content type.
+    Returns:
+        response payload and content type.
     """
     # we can use content types to vary input/output handling, but
     # here we just assume json for both

examples/cli/train/script.py

@@ -12,6 +12,12 @@
 def train(channel_input_dirs, hyperparameters, **kwargs):
     # SageMaker passes num_cpus, num_gpus and other args we can use to tailor training to
     # the current container environment, but here we just use simple cpu context.
+    """
+    Args:
+        channel_input_dirs:
+        hyperparameters:
+        **kwargs:
+    """
     ctx = mx.cpu()
 
     # retrieve the hyperparameters we set in notebook (with some defaults)
@@ -80,6 +86,11 @@ def train(channel_input_dirs, hyperparameters, **kwargs):
 
 def save(net, model_dir):
     # save the model
+    """
+    Args:
+        net:
+        model_dir:
+    """
     y = net(mx.sym.var("data"))
     y.save("%s/model.json" % model_dir)
     net.collect_params().save("%s/model.params" % model_dir)
@@ -95,11 +106,21 @@ def define_network():
 
 
 def input_transformer(data, label):
+    """
+    Args:
+        data:
+        label:
+    """
     data = data.reshape((-1,)).astype(np.float32) / 255
     return data, label
 
 
 def get_train_data(data_dir, batch_size):
+    """
+    Args:
+        data_dir:
+        batch_size:
+    """
     return gluon.data.DataLoader(
         gluon.data.vision.MNIST(data_dir, train=True, transform=input_transformer),
         batch_size=batch_size,
@@ -109,6 +130,11 @@ def get_train_data(data_dir, batch_size):
 
 
 def get_val_data(data_dir, batch_size):
+    """
+    Args:
+        data_dir:
+        batch_size:
+    """
     return gluon.data.DataLoader(
         gluon.data.vision.MNIST(data_dir, train=False, transform=input_transformer),
         batch_size=batch_size,
@@ -117,6 +143,12 @@ def get_val_data(data_dir, batch_size):
 
 
 def test(ctx, net, val_data):
+    """
+    Args:
+        ctx:
+        net:
+        val_data:
+    """
     metric = mx.metric.Accuracy()
     for data, label in val_data:
         data = data.as_in_context(ctx)

setup.py

@@ -10,6 +10,7 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
+"""Placeholder docstring"""
 from __future__ import absolute_import
 
 import os
@@ -20,6 +21,10 @@
 
 
 def read(fname):
+    """
+    Args:
+        fname:
+    """
     return open(os.path.join(os.path.dirname(__file__), fname)).read()
 
 

src/sagemaker/__init__.py

@@ -10,6 +10,7 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
+"""Placeholder docstring"""
 from __future__ import absolute_import
 
 import pkg_resources