Fix docstrings warnings.

doc/conf.py

@@ -68,7 +68,7 @@ def __getattr__(cls, name):
     html_theme = 'default'
 else:
     html_theme = 'haiku'
-html_static_path = ['_static']
+html_static_path = []
 htmlhelp_basename = '%sdoc' % project
 
 # Example configuration for intersphinx: refer to the Python standard library.

src/sagemaker/amazon/knn.py

@@ -60,6 +60,7 @@ def __init__(self, role, train_instance_count, train_instance_type, k, sample_si
         KNN are documented below.
         For further information on the AWS KNN algorithm,
         please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/knn.html
+
         Args:
             role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and
                 APIs that create Amazon SageMaker endpoints use this role to access

src/sagemaker/mxnet/estimator.py

@@ -58,15 +58,16 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, py_versio
                 For convenience, this accepts other types for keys and values, but ``str()`` will be called
                 to convert them before training.
             py_version (str): Python version you want to use for executing your model training code (default: 'py2').
-                              One of 'py2' or 'py3'.
+                One of 'py2' or 'py3'.
             framework_version (str): MXNet version you want to use for executing your model training code.
                 List of supported versions https://github.com/aws/sagemaker-python-sdk#mxnet-sagemaker-estimators
             image_name (str): If specified, the estimator will use this image for training and hosting, instead of
                 selecting the appropriate SageMaker official image based on framework_version and py_version. It can
                 be an ECR url or dockerhub image and tag.
-                    Examples:
-                        123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
-                        custom-image:latest.
+
+                Examples:
+                    123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
+                    custom-image:latest.
              distributions (dict): A dictionary with information on how to run distributed training
                 (default: None).
             **kwargs: Additional kwargs passed to the :class:`~sagemaker.estimator.Framework` constructor.
@@ -126,7 +127,7 @@ def _prepare_init_params_from_job_description(cls, job_details, model_channel_na
             model_channel_name (str): Name of the channel where pre-trained model data will be downloaded.
 
         Returns:
-             dictionary: The transformed init_params
+            dictionary: The transformed init_params
 
         """
         init_params = super(MXNet, cls)._prepare_init_params_from_job_description(job_details, model_channel_name)

src/sagemaker/session.py

@@ -325,7 +325,7 @@ def compile_model(self, input_model_config, output_model_config, role,
 
         Args:
             input_model_config (dict): the trained model and the Amazon S3 location where it is stored.
-            output_model_config (dict): - Identifies the Amazon S3 location where you want Amazon SageMaker Neo to save
+            output_model_config (dict): Identifies the Amazon S3 location where you want Amazon SageMaker Neo to save
                 the results of compilation job
             role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker Neo compilation jobs use this
                 role to access model artifacts. You must grant sufficient permissions to this role.
@@ -550,6 +550,7 @@ def create_model(self, name, role, container_defs, vpc_config=None,
         the inference code. Amazon SageMaker uses this information to deploy the
         model in Amazon SageMaker. This method can also be used to create a Model for an Inference Pipeline
         if you pass the list of container definitions through the containers parameter.
+
         Args:
             name (str): Name of the Amazon SageMaker ``Model`` to create.
             role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs
@@ -1177,6 +1178,7 @@ def logs_for_job(self, job_name, wait=False, poll=10):  # noqa: C901 - suppress
 
 def container_def(image, model_data_url=None, env=None):
     """Create a definition for executing a container as part of a SageMaker model.
+
     Args:
         image (str): Docker image to run for this container.
         model_data_url (str): S3 URI of data required by this container,
@@ -1286,19 +1288,22 @@ def __init__(self, s3_data, distribution='FullyReplicated', compression=None,
                 ``s3_data`` will be used to train. If 'ManifestFile' or 'AugmentedManifestFile', then ``s3_data``
                 defines a single s3 manifest file or augmented manifest file (respectively), listing the s3 data to
                 train on. Both the ManifestFile and AugmentedManifestFile formats are described in the SageMaker API
-                 documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/API_S3DataSource.html
+                documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/API_S3DataSource.html
             input_mode (str): Optional override for this channel's input mode (default: None). By default, channels will
                 use the input mode defined on ``sagemaker.estimator.EstimatorBase.input_mode``, but they will ignore
                 that setting if this parameter is set.
-                * None - Amazon SageMaker will use the input mode specified in the ``Estimator``.
-                * 'File' - Amazon SageMaker copies the training dataset from the S3 location to a local directory.
-                * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe.
+
+                    * None - Amazon SageMaker will use the input mode specified in the ``Estimator``.
+                    * 'File' - Amazon SageMaker copies the training dataset from the S3 location to a local directory.
+                    * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe.
+
             attribute_names (list[str]): A list of one or more attribute names to use that are found in a specified
                 AugmentedManifestFile.
             shuffle_config (ShuffleConfig): If specified this configuration enables shuffling on this channel. See the
                 SageMaker API documentation for more info:
                 https://docs.aws.amazon.com/sagemaker/latest/dg/API_ShuffleConfig.html
         """
+
         self.config = {
             'DataSource': {
                 'S3DataSource': {

src/sagemaker/sparkml/model.py

@@ -37,6 +37,7 @@ def __init__(self, endpoint, sagemaker_session=None):
         Initializes a SparkMLPredictor which should be used with SparkMLModel to perform predictions against SparkML
         models serialized via MLeap. The response is returned in text/csv format which is the default response format
         for SparkML Serving container.
+
         Args:
             endpoint (str): The name of the endpoint to perform inference on.
             sagemaker_session (sagemaker.session.Session): Session object which manages interactions with
@@ -56,7 +57,8 @@ class SparkMLModel(Model):
     """
 
     def __init__(self, model_data, role=None, spark_version=2.2, sagemaker_session=None, **kwargs):
-        """Initialize a SparkMLModel..
+        """Initialize a SparkMLModel.
+
         Args:
             model_data (str): The S3 location of a SageMaker model data ``.tar.gz`` file. For SparkML, this will be the
                 output that has been produced by the Spark job after serializing the Model via MLeap.

src/sagemaker/tensorflow/estimator.py

@@ -176,7 +176,8 @@ class TensorFlow(Framework):
     def __init__(self, training_steps=None, evaluation_steps=None, checkpoint_path=None, py_version='py2',
                  framework_version=None, model_dir=None, requirements_file='', image_name=None,
                  script_mode=False, distributions=None, **kwargs):
-        """Initialize an ``TensorFlow`` estimator.
+        """Initialize a ``TensorFlow`` estimator.
+
         Args:
             training_steps (int): Perform this many steps of training. `None`, the default means train forever.
             evaluation_steps (int): Perform this many steps of evaluation. `None`, the default means that evaluation
@@ -195,26 +196,36 @@ def __init__(self, training_steps=None, evaluation_steps=None, checkpoint_path=N
             image_name (str): If specified, the estimator will use this image for training and hosting, instead of
                 selecting the appropriate SageMaker official image based on framework_version and py_version. It can
                 be an ECR url or dockerhub image and tag.
-                    Examples:
-                        123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
-                        custom-image:latest.
+
+                Examples:
+                    123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
+                    custom-image:latest.
             script_mode (bool): If set to True will the estimator will use the Script Mode containers (default: False).
                 This will be ignored if py_version is set to 'py3'.
             distributions (dict): A dictionary with information on how to run distributed training
-                (default: None). Currently we support distributed training with parameter servers and MPI. To enable
-                parameter server use the following setup:
+                (default: None). Currently we support distributed training with parameter servers and MPI.
+                To enable parameter server use the following setup:
+
+                .. code:: python
+
+                    {
                         'parameter_server':
                         {
                             'enabled': True
                         }
                     }
+
                 To enable MPI:
+
+                .. code:: python
+
                     {
                         'mpi':
                         {
                             'enabled': True
                         }
                     }
+
             **kwargs: Additional kwargs passed to the Framework constructor.
         """
         if framework_version is None:
@@ -281,13 +292,15 @@ def fit(self, inputs=None, wait=True, logs=True, job_name=None, run_tensorboard_
         Args:
             inputs (str or dict or sagemaker.session.s3_input): Information about the training data.
                 This can be one of three types:
-                (str) - the S3 location where training data is saved.
-                (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple channels for
+
+                * (str) - the S3 location where training data is saved.
+                * (dict[str, str] or dict[str, sagemaker.session.s3_input]) - If using multiple channels for
                     training data, you can specify a dict mapping channel names
                     to strings or :func:`~sagemaker.session.s3_input` objects.
-                (sagemaker.session.s3_input) - channel configuration for S3 data sources that can provide
+                * (sagemaker.session.s3_input) - channel configuration for S3 data sources that can provide
                     additional information as well as the path to the training dataset.
                     See :func:`sagemaker.session.s3_input` for full details.
+
             wait (bool): Whether the call should wait until the job completes (default: True).
             logs (bool): Whether to show the logs produced by the job.
                 Only meaningful when wait is True (default: True).

src/sagemaker/tensorflow/model.py

@@ -21,9 +21,9 @@
 
 
 class TensorFlowPredictor(RealTimePredictor):
-    """A ``RealTimePredictor`` for inference against TensorFlow ``Endpoint``s.
+    """A ``RealTimePredictor`` for inference against TensorFlow endpoint.
 
-    This is able to serialize Python lists, dictionaries, and numpy arrays to multidimensional tensors for MXNet
+    This is able to serialize Python lists, dictionaries, and numpy arrays to multidimensional tensors for
     inference"""
     def __init__(self, endpoint_name, sagemaker_session=None):
         """Initialize an ``TensorFlowPredictor``.

src/sagemaker/workflow/airflow.py

@@ -447,8 +447,7 @@ def model_config_from_estimator(instance_type, estimator, task_id, task_type, ro
 
     Returns:
         dict: Model config that can be directly used by SageMakerModelOperator in Airflow. It can also be part
-        of the config used by SageMakerEndpointOperator.
-         SageMakerTransformOperator in Airflow.
+            of the config used by SageMakerEndpointOperator in Airflow.
     """
     update_estimator_from_task(estimator, task_id, task_type)
     if isinstance(estimator, sagemaker.estimator.Estimator):