change: enable inconsistent-return-statements Pylint check

.pylintrc

@@ -91,7 +91,6 @@ disable=
  useless-object-inheritance, # TODO: Remove unnecessary imports
  cyclic-import, # TODO: Resolve cyclic imports
  no-self-use, # TODO: Convert methods to functions where appropriate
- inconsistent-return-statements, # TODO: Make returns consistent
  consider-merging-isinstance, # TODO: Merge isinstance where appropriate
  consider-using-in, # TODO: Consider merging comparisons with "in"
  too-many-public-methods, # TODO: Resolve

src/sagemaker/job.py

@@ -169,7 +169,7 @@ def _prepare_channel(
  input_mode=None,
  ):
  if not channel_uri:
- return
+ return None
  if not channel_name:
  raise ValueError(
  "Expected a channel name if a channel URI {} is specified".format(channel_uri)

src/sagemaker/local/data.py

@@ -38,15 +38,21 @@ def get_data_source_instance(data_source, sagemaker_session):
  sagemaker_session (:class:`sagemaker.session.Session`): a SageMaker Session to interact with
  S3 if required.
 
- Returns
+ Returns:
  :class:`sagemaker.local.data.DataSource`: an Instance of a Data Source
 
+ Raises:
+ ValueError: If parsed_uri scheme is neither `file` nor `s3`, raise an error.
+
  """
  parsed_uri = urlparse(data_source)
  if parsed_uri.scheme == "file":
  return LocalFileDataSource(parsed_uri.netloc + parsed_uri.path)
  if parsed_uri.scheme == "s3":
  return S3DataSource(parsed_uri.netloc, parsed_uri.path, sagemaker_session)
+ raise ValueError(
+ "data_source must be either file or s3. parsed_uri.scheme: {}".format(parsed_uri.scheme)
+ )
 
 
 def get_splitter_instance(split_type):

src/sagemaker/model.py

@@ -389,6 +389,7 @@ def deploy(
 
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def transformer(
  self,

src/sagemaker/pipeline.py

@@ -115,6 +115,7 @@ def deploy(
  )
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def _create_sagemaker_pipeline_model(self, instance_type):
  """Create a SageMaker Model Entity

src/sagemaker/predictor.py

@@ -392,6 +392,11 @@ def __call__(self, stream, content_type=CONTENT_TYPE_NPY):
  return np.load(BytesIO(stream.read()))
  finally:
  stream.close()
+ raise ValueError(
+ "content_type must be one of the following: CSV, JSON, NPY. content_type: {}".format(
+ content_type
+ )
+ )
 
 
 numpy_deserializer = _NumpyDeserializer()

src/sagemaker/rl/estimator.py

@@ -184,7 +184,8 @@ def create_model(
  MXNet was used as RL backend;
  * sagemaker.tensorflow.serving.Model - if image_name wasn't specified and
  TensorFlow was used as RL backend.
-
+ Raises:
+ ValueError: If image_name was not specified and framework enum is not valid.
  """
  base_args = dict(
  model_data=self.model_data,
@@ -230,6 +231,9 @@ def create_model(
  return MXNetModel(
  framework_version=self.framework_version, py_version=PYTHON_VERSION, **extended_args
  )
+ raise ValueError(
+ "An unknown RLFramework enum was passed in. framework: {}".format(self.framework)
+ )
 
  def train_image(self):
  """Return the Docker image to use for training.
@@ -399,6 +403,9 @@ def default_metric_definitions(cls, toolkit):
 
  Returns:
  list: metric definitions
+
+ Raises:
+ ValueError: If toolkit enum is not valid.
  """
  if toolkit is RLToolkit.COACH:
  return [
@@ -412,3 +419,4 @@ def default_metric_definitions(cls, toolkit):
  {"Name": "episode_reward_mean", "Regex": "episode_reward_mean: (%s)" % float_regex},
  {"Name": "episode_reward_max", "Regex": "episode_reward_max: (%s)" % float_regex},
  ]
+ raise ValueError("An unknown RLToolkit enum was passed in. toolkit: {}".format(toolkit))

.pylintrc

@@ -91,7 +91,6 @@ disable=
  useless-object-inheritance, # TODO: Remove unnecessary imports
  cyclic-import, # TODO: Resolve cyclic imports
  no-self-use, # TODO: Convert methods to functions where appropriate
- inconsistent-return-statements, # TODO: Make returns consistent
  consider-merging-isinstance, # TODO: Merge isinstance where appropriate
  consider-using-in, # TODO: Consider merging comparisons with "in"
  too-many-public-methods, # TODO: Resolve

src/sagemaker/job.py

@@ -169,7 +169,7 @@ def _prepare_channel(
  input_mode=None,
  ):
  if not channel_uri:
- return
+ return None
  if not channel_name:
  raise ValueError(
  "Expected a channel name if a channel URI {} is specified".format(channel_uri)

src/sagemaker/local/data.py

@@ -38,15 +38,21 @@ def get_data_source_instance(data_source, sagemaker_session):
  sagemaker_session (:class:`sagemaker.session.Session`): a SageMaker Session to interact with
  S3 if required.
 
- Returns
+ Returns:
  :class:`sagemaker.local.data.DataSource`: an Instance of a Data Source
 
+ Raises:
+ ValueError: If parsed_uri scheme is neither `file` nor `s3`, raise an error.
+
  """
  parsed_uri = urlparse(data_source)
  if parsed_uri.scheme == "file":
  return LocalFileDataSource(parsed_uri.netloc + parsed_uri.path)
  if parsed_uri.scheme == "s3":
  return S3DataSource(parsed_uri.netloc, parsed_uri.path, sagemaker_session)
+ raise ValueError(
+ "data_source must be either file or s3. parsed_uri.scheme: {}".format(parsed_uri.scheme)
+ )
 
 
 def get_splitter_instance(split_type):

src/sagemaker/model.py

@@ -389,6 +389,7 @@ def deploy(
 
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def transformer(
  self,

src/sagemaker/pipeline.py

@@ -115,6 +115,7 @@ def deploy(
  )
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def _create_sagemaker_pipeline_model(self, instance_type):
  """Create a SageMaker Model Entity

src/sagemaker/predictor.py

@@ -392,6 +392,11 @@ def __call__(self, stream, content_type=CONTENT_TYPE_NPY):
  return np.load(BytesIO(stream.read()))
  finally:
  stream.close()
+ raise ValueError(
+ "content_type must be one of the following: CSV, JSON, NPY. content_type: {}".format(
+ content_type
+ )
+ )
 
 
 numpy_deserializer = _NumpyDeserializer()

src/sagemaker/rl/estimator.py

@@ -184,7 +184,8 @@ def create_model(
  MXNet was used as RL backend;
  * sagemaker.tensorflow.serving.Model - if image_name wasn't specified and
  TensorFlow was used as RL backend.
-
+ Raises:
+ ValueError: If image_name was not specified and framework enum is not valid.
  """
  base_args = dict(
  model_data=self.model_data,
@@ -230,6 +231,9 @@ def create_model(
  return MXNetModel(
  framework_version=self.framework_version, py_version=PYTHON_VERSION, **extended_args
  )
+ raise ValueError(
+ "An unknown RLFramework enum was passed in. framework: {}".format(self.framework)
+ )
 
  def train_image(self):
  """Return the Docker image to use for training.
@@ -399,6 +403,9 @@ def default_metric_definitions(cls, toolkit):
 
  Returns:
  list: metric definitions
+
+ Raises:
+ ValueError: If toolkit enum is not valid.
  """
  if toolkit is RLToolkit.COACH:
  return [
@@ -412,3 +419,4 @@ def default_metric_definitions(cls, toolkit):
  {"Name": "episode_reward_mean", "Regex": "episode_reward_mean: (%s)" % float_regex},
  {"Name": "episode_reward_max", "Regex": "episode_reward_max: (%s)" % float_regex},
  ]
+ raise ValueError("An unknown RLToolkit enum was passed in. toolkit: {}".format(toolkit))

.pylintrc

@@ -91,7 +91,6 @@ disable=
  useless-object-inheritance, # TODO: Remove unnecessary imports
  cyclic-import, # TODO: Resolve cyclic imports
  no-self-use, # TODO: Convert methods to functions where appropriate
- inconsistent-return-statements, # TODO: Make returns consistent
  consider-merging-isinstance, # TODO: Merge isinstance where appropriate
  consider-using-in, # TODO: Consider merging comparisons with "in"
  too-many-public-methods, # TODO: Resolve

src/sagemaker/job.py

@@ -169,7 +169,7 @@ def _prepare_channel(
  input_mode=None,
  ):
  if not channel_uri:
- return
+ return None
  if not channel_name:
  raise ValueError(
  "Expected a channel name if a channel URI {} is specified".format(channel_uri)

src/sagemaker/local/data.py

@@ -38,15 +38,21 @@ def get_data_source_instance(data_source, sagemaker_session):
  sagemaker_session (:class:`sagemaker.session.Session`): a SageMaker Session to interact with
  S3 if required.
 
- Returns
+ Returns:
  :class:`sagemaker.local.data.DataSource`: an Instance of a Data Source
 
+ Raises:
+ ValueError: If parsed_uri scheme is neither `file` nor `s3`, raise an error.
+
  """
  parsed_uri = urlparse(data_source)
  if parsed_uri.scheme == "file":
  return LocalFileDataSource(parsed_uri.netloc + parsed_uri.path)
  if parsed_uri.scheme == "s3":
  return S3DataSource(parsed_uri.netloc, parsed_uri.path, sagemaker_session)
+ raise ValueError(
+ "data_source must be either file or s3. parsed_uri.scheme: {}".format(parsed_uri.scheme)
+ )
 
 
 def get_splitter_instance(split_type):

src/sagemaker/model.py

@@ -389,6 +389,7 @@ def deploy(
 
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def transformer(
  self,

src/sagemaker/pipeline.py

@@ -115,6 +115,7 @@ def deploy(
  )
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def _create_sagemaker_pipeline_model(self, instance_type):
  """Create a SageMaker Model Entity

src/sagemaker/predictor.py

@@ -392,6 +392,11 @@ def __call__(self, stream, content_type=CONTENT_TYPE_NPY):
  return np.load(BytesIO(stream.read()))
  finally:
  stream.close()
+ raise ValueError(
+ "content_type must be one of the following: CSV, JSON, NPY. content_type: {}".format(
+ content_type
+ )
+ )
 
 
 numpy_deserializer = _NumpyDeserializer()

src/sagemaker/rl/estimator.py

@@ -184,7 +184,8 @@ def create_model(
  MXNet was used as RL backend;
  * sagemaker.tensorflow.serving.Model - if image_name wasn't specified and
  TensorFlow was used as RL backend.
-
+ Raises:
+ ValueError: If image_name was not specified and framework enum is not valid.
  """
  base_args = dict(
  model_data=self.model_data,
@@ -230,6 +231,9 @@ def create_model(
  return MXNetModel(
  framework_version=self.framework_version, py_version=PYTHON_VERSION, **extended_args
  )
+ raise ValueError(
+ "An unknown RLFramework enum was passed in. framework: {}".format(self.framework)
+ )
 
  def train_image(self):
  """Return the Docker image to use for training.
@@ -399,6 +403,9 @@ def default_metric_definitions(cls, toolkit):
 
  Returns:
  list: metric definitions
+
+ Raises:
+ ValueError: If toolkit enum is not valid.
  """
  if toolkit is RLToolkit.COACH:
  return [
@@ -412,3 +419,4 @@ def default_metric_definitions(cls, toolkit):
  {"Name": "episode_reward_mean", "Regex": "episode_reward_mean: (%s)" % float_regex},
  {"Name": "episode_reward_max", "Regex": "episode_reward_max: (%s)" % float_regex},
  ]
+ raise ValueError("An unknown RLToolkit enum was passed in. toolkit: {}".format(toolkit))

.pylintrc

@@ -91,7 +91,6 @@ disable=
  useless-object-inheritance, # TODO: Remove unnecessary imports
  cyclic-import, # TODO: Resolve cyclic imports
  no-self-use, # TODO: Convert methods to functions where appropriate
- inconsistent-return-statements, # TODO: Make returns consistent
  consider-merging-isinstance, # TODO: Merge isinstance where appropriate
  consider-using-in, # TODO: Consider merging comparisons with "in"
  too-many-public-methods, # TODO: Resolve

src/sagemaker/job.py

@@ -169,7 +169,7 @@ def _prepare_channel(
  input_mode=None,
  ):
  if not channel_uri:
- return
+ return None
  if not channel_name:
  raise ValueError(
  "Expected a channel name if a channel URI {} is specified".format(channel_uri)

src/sagemaker/local/data.py

@@ -38,15 +38,21 @@ def get_data_source_instance(data_source, sagemaker_session):
  sagemaker_session (:class:`sagemaker.session.Session`): a SageMaker Session to interact with
  S3 if required.
 
- Returns
+ Returns:
  :class:`sagemaker.local.data.DataSource`: an Instance of a Data Source
 
+ Raises:
+ ValueError: If parsed_uri scheme is neither `file` nor `s3`, raise an error.
+
  """
  parsed_uri = urlparse(data_source)
  if parsed_uri.scheme == "file":
  return LocalFileDataSource(parsed_uri.netloc + parsed_uri.path)
  if parsed_uri.scheme == "s3":
  return S3DataSource(parsed_uri.netloc, parsed_uri.path, sagemaker_session)
+ raise ValueError(
+ "data_source must be either file or s3. parsed_uri.scheme: {}".format(parsed_uri.scheme)
+ )
 
 
 def get_splitter_instance(split_type):

src/sagemaker/model.py

@@ -389,6 +389,7 @@ def deploy(
 
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def transformer(
  self,

src/sagemaker/pipeline.py

@@ -115,6 +115,7 @@ def deploy(
  )
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def _create_sagemaker_pipeline_model(self, instance_type):
  """Create a SageMaker Model Entity

src/sagemaker/predictor.py

@@ -392,6 +392,11 @@ def __call__(self, stream, content_type=CONTENT_TYPE_NPY):
  return np.load(BytesIO(stream.read()))
  finally:
  stream.close()
+ raise ValueError(
+ "content_type must be one of the following: CSV, JSON, NPY. content_type: {}".format(
+ content_type
+ )
+ )
 
 
 numpy_deserializer = _NumpyDeserializer()

src/sagemaker/rl/estimator.py

@@ -184,7 +184,8 @@ def create_model(
  MXNet was used as RL backend;
  * sagemaker.tensorflow.serving.Model - if image_name wasn't specified and
  TensorFlow was used as RL backend.
-
+ Raises:
+ ValueError: If image_name was not specified and framework enum is not valid.
  """
  base_args = dict(
  model_data=self.model_data,
@@ -230,6 +231,9 @@ def create_model(
  return MXNetModel(
  framework_version=self.framework_version, py_version=PYTHON_VERSION, **extended_args
  )
+ raise ValueError(
+ "An unknown RLFramework enum was passed in. framework: {}".format(self.framework)
+ )
 
  def train_image(self):
  """Return the Docker image to use for training.
@@ -399,6 +403,9 @@ def default_metric_definitions(cls, toolkit):
 
  Returns:
  list: metric definitions
+
+ Raises:
+ ValueError: If toolkit enum is not valid.
  """
  if toolkit is RLToolkit.COACH:
  return [
@@ -412,3 +419,4 @@ def default_metric_definitions(cls, toolkit):
  {"Name": "episode_reward_mean", "Regex": "episode_reward_mean: (%s)" % float_regex},
  {"Name": "episode_reward_max", "Regex": "episode_reward_max: (%s)" % float_regex},
  ]
+ raise ValueError("An unknown RLToolkit enum was passed in. toolkit: {}".format(toolkit))

.pylintrc

@@ -91,7 +91,6 @@ disable=
  useless-object-inheritance, # TODO: Remove unnecessary imports
  cyclic-import, # TODO: Resolve cyclic imports
  no-self-use, # TODO: Convert methods to functions where appropriate
- inconsistent-return-statements, # TODO: Make returns consistent
  consider-merging-isinstance, # TODO: Merge isinstance where appropriate
  consider-using-in, # TODO: Consider merging comparisons with "in"
  too-many-public-methods, # TODO: Resolve

src/sagemaker/job.py

@@ -169,7 +169,7 @@ def _prepare_channel(
  input_mode=None,
  ):
  if not channel_uri:
- return
+ return None
  if not channel_name:
  raise ValueError(
  "Expected a channel name if a channel URI {} is specified".format(channel_uri)

src/sagemaker/local/data.py

@@ -38,15 +38,21 @@ def get_data_source_instance(data_source, sagemaker_session):
  sagemaker_session (:class:`sagemaker.session.Session`): a SageMaker Session to interact with
  S3 if required.
 
- Returns
+ Returns:
  :class:`sagemaker.local.data.DataSource`: an Instance of a Data Source
 
+ Raises:
+ ValueError: If parsed_uri scheme is neither `file` nor `s3`, raise an error.
+
  """
  parsed_uri = urlparse(data_source)
  if parsed_uri.scheme == "file":
  return LocalFileDataSource(parsed_uri.netloc + parsed_uri.path)
  if parsed_uri.scheme == "s3":
  return S3DataSource(parsed_uri.netloc, parsed_uri.path, sagemaker_session)
+ raise ValueError(
+ "data_source must be either file or s3. parsed_uri.scheme: {}".format(parsed_uri.scheme)
+ )
 
 
 def get_splitter_instance(split_type):

src/sagemaker/model.py

@@ -389,6 +389,7 @@ def deploy(
 
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def transformer(
  self,

src/sagemaker/pipeline.py

@@ -115,6 +115,7 @@ def deploy(
  )
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def _create_sagemaker_pipeline_model(self, instance_type):
  """Create a SageMaker Model Entity

src/sagemaker/predictor.py

@@ -392,6 +392,11 @@ def __call__(self, stream, content_type=CONTENT_TYPE_NPY):
  return np.load(BytesIO(stream.read()))
  finally:
  stream.close()
+ raise ValueError(
+ "content_type must be one of the following: CSV, JSON, NPY. content_type: {}".format(
+ content_type
+ )
+ )
 
 
 numpy_deserializer = _NumpyDeserializer()

src/sagemaker/rl/estimator.py

@@ -184,7 +184,8 @@ def create_model(
  MXNet was used as RL backend;
  * sagemaker.tensorflow.serving.Model - if image_name wasn't specified and
  TensorFlow was used as RL backend.
-
+ Raises:
+ ValueError: If image_name was not specified and framework enum is not valid.
  """
  base_args = dict(
  model_data=self.model_data,
@@ -230,6 +231,9 @@ def create_model(
  return MXNetModel(
  framework_version=self.framework_version, py_version=PYTHON_VERSION, **extended_args
  )
+ raise ValueError(
+ "An unknown RLFramework enum was passed in. framework: {}".format(self.framework)
+ )
 
  def train_image(self):
  """Return the Docker image to use for training.
@@ -399,6 +403,9 @@ def default_metric_definitions(cls, toolkit):
 
  Returns:
  list: metric definitions
+
+ Raises:
+ ValueError: If toolkit enum is not valid.
  """
  if toolkit is RLToolkit.COACH:
  return [
@@ -412,3 +419,4 @@ def default_metric_definitions(cls, toolkit):
  {"Name": "episode_reward_mean", "Regex": "episode_reward_mean: (%s)" % float_regex},
  {"Name": "episode_reward_max", "Regex": "episode_reward_max: (%s)" % float_regex},
  ]
+ raise ValueError("An unknown RLToolkit enum was passed in. toolkit: {}".format(toolkit))

.pylintrc

@@ -91,7 +91,6 @@ disable=
  useless-object-inheritance, # TODO: Remove unnecessary imports
  cyclic-import, # TODO: Resolve cyclic imports
  no-self-use, # TODO: Convert methods to functions where appropriate
- inconsistent-return-statements, # TODO: Make returns consistent
  consider-merging-isinstance, # TODO: Merge isinstance where appropriate
  consider-using-in, # TODO: Consider merging comparisons with "in"
  too-many-public-methods, # TODO: Resolve

src/sagemaker/job.py

@@ -169,7 +169,7 @@ def _prepare_channel(
  input_mode=None,
  ):
  if not channel_uri:
- return
+ return None
  if not channel_name:
  raise ValueError(
  "Expected a channel name if a channel URI {} is specified".format(channel_uri)

src/sagemaker/local/data.py

@@ -38,15 +38,21 @@ def get_data_source_instance(data_source, sagemaker_session):
  sagemaker_session (:class:`sagemaker.session.Session`): a SageMaker Session to interact with
  S3 if required.
 
- Returns
+ Returns:
  :class:`sagemaker.local.data.DataSource`: an Instance of a Data Source
 
+ Raises:
+ ValueError: If parsed_uri scheme is neither `file` nor `s3`, raise an error.
+
  """
  parsed_uri = urlparse(data_source)
  if parsed_uri.scheme == "file":
  return LocalFileDataSource(parsed_uri.netloc + parsed_uri.path)
  if parsed_uri.scheme == "s3":
  return S3DataSource(parsed_uri.netloc, parsed_uri.path, sagemaker_session)
+ raise ValueError(
+ "data_source must be either file or s3. parsed_uri.scheme: {}".format(parsed_uri.scheme)
+ )
 
 
 def get_splitter_instance(split_type):

src/sagemaker/model.py

@@ -389,6 +389,7 @@ def deploy(
 
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def transformer(
  self,

src/sagemaker/pipeline.py

@@ -115,6 +115,7 @@ def deploy(
  )
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def _create_sagemaker_pipeline_model(self, instance_type):
  """Create a SageMaker Model Entity

src/sagemaker/predictor.py

@@ -392,6 +392,11 @@ def __call__(self, stream, content_type=CONTENT_TYPE_NPY):
  return np.load(BytesIO(stream.read()))
  finally:
  stream.close()
+ raise ValueError(
+ "content_type must be one of the following: CSV, JSON, NPY. content_type: {}".format(
+ content_type
+ )
+ )
 
 
 numpy_deserializer = _NumpyDeserializer()

src/sagemaker/rl/estimator.py

@@ -184,7 +184,8 @@ def create_model(
  MXNet was used as RL backend;
  * sagemaker.tensorflow.serving.Model - if image_name wasn't specified and
  TensorFlow was used as RL backend.
-
+ Raises:
+ ValueError: If image_name was not specified and framework enum is not valid.
  """
  base_args = dict(
  model_data=self.model_data,
@@ -230,6 +231,9 @@ def create_model(
  return MXNetModel(
  framework_version=self.framework_version, py_version=PYTHON_VERSION, **extended_args
  )
+ raise ValueError(
+ "An unknown RLFramework enum was passed in. framework: {}".format(self.framework)
+ )
 
  def train_image(self):
  """Return the Docker image to use for training.
@@ -399,6 +403,9 @@ def default_metric_definitions(cls, toolkit):
 
  Returns:
  list: metric definitions
+
+ Raises:
+ ValueError: If toolkit enum is not valid.
  """
  if toolkit is RLToolkit.COACH:
  return [
@@ -412,3 +419,4 @@ def default_metric_definitions(cls, toolkit):
  {"Name": "episode_reward_mean", "Regex": "episode_reward_mean: (%s)" % float_regex},
  {"Name": "episode_reward_max", "Regex": "episode_reward_max: (%s)" % float_regex},
  ]
+ raise ValueError("An unknown RLToolkit enum was passed in. toolkit: {}".format(toolkit))

.pylintrc

@@ -91,7 +91,6 @@ disable=
  useless-object-inheritance, # TODO: Remove unnecessary imports
  cyclic-import, # TODO: Resolve cyclic imports
  no-self-use, # TODO: Convert methods to functions where appropriate
- inconsistent-return-statements, # TODO: Make returns consistent
  consider-merging-isinstance, # TODO: Merge isinstance where appropriate
  consider-using-in, # TODO: Consider merging comparisons with "in"
  too-many-public-methods, # TODO: Resolve

src/sagemaker/job.py

@@ -169,7 +169,7 @@ def _prepare_channel(
  input_mode=None,
  ):
  if not channel_uri:
- return
+ return None
  if not channel_name:
  raise ValueError(
  "Expected a channel name if a channel URI {} is specified".format(channel_uri)

src/sagemaker/local/data.py

@@ -38,15 +38,21 @@ def get_data_source_instance(data_source, sagemaker_session):
  sagemaker_session (:class:`sagemaker.session.Session`): a SageMaker Session to interact with
  S3 if required.
 
- Returns
+ Returns:
  :class:`sagemaker.local.data.DataSource`: an Instance of a Data Source
 
+ Raises:
+ ValueError: If parsed_uri scheme is neither `file` nor `s3`, raise an error.
+
  """
  parsed_uri = urlparse(data_source)
  if parsed_uri.scheme == "file":
  return LocalFileDataSource(parsed_uri.netloc + parsed_uri.path)
  if parsed_uri.scheme == "s3":
  return S3DataSource(parsed_uri.netloc, parsed_uri.path, sagemaker_session)
+ raise ValueError(
+ "data_source must be either file or s3. parsed_uri.scheme: {}".format(parsed_uri.scheme)
+ )
 
 
 def get_splitter_instance(split_type):

src/sagemaker/model.py

@@ -389,6 +389,7 @@ def deploy(
 
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def transformer(
  self,

src/sagemaker/pipeline.py

@@ -115,6 +115,7 @@ def deploy(
  )
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def _create_sagemaker_pipeline_model(self, instance_type):
  """Create a SageMaker Model Entity

src/sagemaker/predictor.py

@@ -392,6 +392,11 @@ def __call__(self, stream, content_type=CONTENT_TYPE_NPY):
  return np.load(BytesIO(stream.read()))
  finally:
  stream.close()
+ raise ValueError(
+ "content_type must be one of the following: CSV, JSON, NPY. content_type: {}".format(
+ content_type
+ )
+ )
 
 
 numpy_deserializer = _NumpyDeserializer()

src/sagemaker/rl/estimator.py

@@ -184,7 +184,8 @@ def create_model(
  MXNet was used as RL backend;
  * sagemaker.tensorflow.serving.Model - if image_name wasn't specified and
  TensorFlow was used as RL backend.
-
+ Raises:
+ ValueError: If image_name was not specified and framework enum is not valid.
  """
  base_args = dict(
  model_data=self.model_data,
@@ -230,6 +231,9 @@ def create_model(
  return MXNetModel(
  framework_version=self.framework_version, py_version=PYTHON_VERSION, **extended_args
  )
+ raise ValueError(
+ "An unknown RLFramework enum was passed in. framework: {}".format(self.framework)
+ )
 
  def train_image(self):
  """Return the Docker image to use for training.
@@ -399,6 +403,9 @@ def default_metric_definitions(cls, toolkit):
 
  Returns:
  list: metric definitions
+
+ Raises:
+ ValueError: If toolkit enum is not valid.
  """
  if toolkit is RLToolkit.COACH:
  return [
@@ -412,3 +419,4 @@ def default_metric_definitions(cls, toolkit):
  {"Name": "episode_reward_mean", "Regex": "episode_reward_mean: (%s)" % float_regex},
  {"Name": "episode_reward_max", "Regex": "episode_reward_max: (%s)" % float_regex},
  ]
+ raise ValueError("An unknown RLToolkit enum was passed in. toolkit: {}".format(toolkit))

.pylintrc

@@ -91,7 +91,6 @@ disable=
  useless-object-inheritance, # TODO: Remove unnecessary imports
  cyclic-import, # TODO: Resolve cyclic imports
  no-self-use, # TODO: Convert methods to functions where appropriate
- inconsistent-return-statements, # TODO: Make returns consistent
  consider-merging-isinstance, # TODO: Merge isinstance where appropriate
  consider-using-in, # TODO: Consider merging comparisons with "in"
  too-many-public-methods, # TODO: Resolve

src/sagemaker/job.py

@@ -169,7 +169,7 @@ def _prepare_channel(
  input_mode=None,
  ):
  if not channel_uri:
- return
+ return None
  if not channel_name:
  raise ValueError(
  "Expected a channel name if a channel URI {} is specified".format(channel_uri)

src/sagemaker/local/data.py

@@ -38,15 +38,21 @@ def get_data_source_instance(data_source, sagemaker_session):
  sagemaker_session (:class:`sagemaker.session.Session`): a SageMaker Session to interact with
  S3 if required.
 
- Returns
+ Returns:
  :class:`sagemaker.local.data.DataSource`: an Instance of a Data Source
 
+ Raises:
+ ValueError: If parsed_uri scheme is neither `file` nor `s3`, raise an error.
+
  """
  parsed_uri = urlparse(data_source)
  if parsed_uri.scheme == "file":
  return LocalFileDataSource(parsed_uri.netloc + parsed_uri.path)
  if parsed_uri.scheme == "s3":
  return S3DataSource(parsed_uri.netloc, parsed_uri.path, sagemaker_session)
+ raise ValueError(
+ "data_source must be either file or s3. parsed_uri.scheme: {}".format(parsed_uri.scheme)
+ )
 
 
 def get_splitter_instance(split_type):

src/sagemaker/model.py

@@ -389,6 +389,7 @@ def deploy(
 
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def transformer(
  self,

src/sagemaker/pipeline.py

@@ -115,6 +115,7 @@ def deploy(
  )
  if self.predictor_cls:
  return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
+ return None
 
  def _create_sagemaker_pipeline_model(self, instance_type):
  """Create a SageMaker Model Entity

src/sagemaker/predictor.py

@@ -392,6 +392,11 @@ def __call__(self, stream, content_type=CONTENT_TYPE_NPY):
  return np.load(BytesIO(stream.read()))
  finally:
  stream.close()
+ raise ValueError(
+ "content_type must be one of the following: CSV, JSON, NPY. content_type: {}".format(
+ content_type
+ )
+ )
 
 
 numpy_deserializer = _NumpyDeserializer()

src/sagemaker/rl/estimator.py

@@ -184,7 +184,8 @@ def create_model(
  MXNet was used as RL backend;
  * sagemaker.tensorflow.serving.Model - if image_name wasn't specified and
  TensorFlow was used as RL backend.
-
+ Raises:
+ ValueError: If image_name was not specified and framework enum is not valid.
  """
  base_args = dict(
  model_data=self.model_data,
@@ -230,6 +231,9 @@ def create_model(
  return MXNetModel(
  framework_version=self.framework_version, py_version=PYTHON_VERSION, **extended_args
  )
+ raise ValueError(
+ "An unknown RLFramework enum was passed in. framework: {}".format(self.framework)
+ )
 
  def train_image(self):
  """Return the Docker image to use for training.
@@ -399,6 +403,9 @@ def default_metric_definitions(cls, toolkit):
 
  Returns:
  list: metric definitions
+
+ Raises:
+ ValueError: If toolkit enum is not valid.
  """
  if toolkit is RLToolkit.COACH:
  return [
@@ -412,3 +419,4 @@ def default_metric_definitions(cls, toolkit):
  {"Name": "episode_reward_mean", "Regex": "episode_reward_mean: (%s)" % float_regex},
  {"Name": "episode_reward_max", "Regex": "episode_reward_max: (%s)" % float_regex},
  ]
+ raise ValueError("An unknown RLToolkit enum was passed in. toolkit: {}".format(toolkit))