doc: add link to framework-related parent classes to clarify **kwargs (#1180)

This commit also fixes some minor Sphinx syntax issues
in the docstrings.

Author: laurenyu

src/sagemaker/chainer/estimator.py

@@ -111,13 +111,20 @@ def __init__(
             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. ..
-                admonition:: Examples
+                py_version. It can be an ECR url or dockerhub image and tag.
+
+                Examples
+                    * ``123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0``
+                    * ``custom-image:latest``
 
-                    123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
-                    custom-image:latest.
             **kwargs: Additional kwargs passed to the
                 :class:`~sagemaker.estimator.Framework` constructor.
+
+        .. tip::
+
+            You can find additional parameters for initializing this class at
+            :class:`~sagemaker.estimator.Framework` and
+            :class:`~sagemaker.estimator.EstimatorBase`.
         """
         if framework_version is None:
             logger.warning(empty_framework_version_warning(CHAINER_VERSION, self.LATEST_VERSION))

src/sagemaker/chainer/model.py

@@ -1,4 +1,4 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Copyright 2018-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"). You
 # may not use this file except in compliance with the License. A copy of
@@ -93,8 +93,14 @@ def __init__(
             model_server_workers (int): Optional. The number of worker processes
                 used by the inference server. If None, server will use one
                 worker per vCPU.
-            **kwargs: Keyword arguments passed to the ``FrameworkModel``
-                initializer.
+            **kwargs: Keyword arguments passed to the
+                :class:`~sagemaker.model.FrameworkModel` initializer.
+
+        .. tip::
+
+            You can find additional parameters for initializing this class at
+            :class:`~sagemaker.model.FrameworkModel` and
+            :class:`~sagemaker.model.Model`.
         """
         super(ChainerModel, self).__init__(
             model_data, image, role, entry_point, predictor_cls=predictor_cls, **kwargs

src/sagemaker/mxnet/estimator.py

@@ -90,15 +90,22 @@ def __init__(
             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.
+                    * ``123412341234.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). To have parameter servers launched for training,
                 set this value to be ``{'parameter_server': {'enabled': True}}``.
             **kwargs: Additional kwargs passed to the
                 :class:`~sagemaker.estimator.Framework` constructor.
+
+        .. tip::
+
+            You can find additional parameters for initializing this class at
+            :class:`~sagemaker.estimator.Framework` and
+            :class:`~sagemaker.estimator.EstimatorBase`.
         """
         if framework_version is None:
             logger.warning(empty_framework_version_warning(MXNET_VERSION, self.LATEST_VERSION))
@@ -161,8 +168,10 @@ def create_model(
                 role from the Estimator will be used.
             vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
                 the model. Default: use subnets and security groups from this Estimator.
+
                 * 'Subnets' (list[str]): List of subnet ids.
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
+
             entry_point (str): Path (absolute or relative) to the local Python source file which
                 should be executed as the entry point to training. If not specified, the training
                 entry point is used.
@@ -175,10 +184,13 @@ def create_model(
             image_name (str): If specified, the estimator will use this image for 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.
-            **kwargs: Additional kwargs passed to the MXNetModel constructor.
+                    * ``123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0``
+                    * ``custom-image:latest``
+
+            **kwargs: Additional kwargs passed to the :class:`~sagemaker.mxnet.model.MXNetModel`
+                constructor.
 
         Returns:
             sagemaker.mxnet.model.MXNetModel: A SageMaker ``MXNetModel`` object.

src/sagemaker/mxnet/model.py

@@ -1,4 +1,4 @@
-# Copyright 2017-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Copyright 2017-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"). You
 # may not use this file except in compliance with the License. A copy of
@@ -96,6 +96,12 @@ def __init__(
                 worker per vCPU.
             **kwargs: Keyword arguments passed to the ``FrameworkModel``
                 initializer.
+
+        .. tip::
+
+            You can find additional parameters for initializing this class at
+            :class:`~sagemaker.model.FrameworkModel` and
+            :class:`~sagemaker.model.Model`.
         """
         super(MXNetModel, self).__init__(
             model_data, image, role, entry_point, predictor_cls=predictor_cls, **kwargs

src/sagemaker/pytorch/estimator.py

@@ -89,11 +89,19 @@ def __init__(
                 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.
+                    * ``123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0``
+                    * ``custom-image:latest``
+
             **kwargs: Additional kwargs passed to the :class:`~sagemaker.estimator.Framework`
                 constructor.
+
+        .. tip::
+
+            You can find additional parameters for initializing this class at
+            :class:`~sagemaker.estimator.Framework` and
+            :class:`~sagemaker.estimator.EstimatorBase`.
         """
         if framework_version is None:
             logger.warning(empty_framework_version_warning(PYTORCH_VERSION, PYTORCH_VERSION))
@@ -146,7 +154,8 @@ def create_model(
             dependencies (list[str]): A list of paths to directories (absolute or relative) with
                 any additional libraries that will be exported to the container.
                 If not specified, the dependencies from training are used.
-            **kwargs: Additional kwargs passed to the PyTorchModel constructor.
+            **kwargs: Additional kwargs passed to the :class:`~sagemaker.pytorch.model.PyTorchModel`
+                constructor.
 
         Returns:
             sagemaker.pytorch.model.PyTorchModel: A SageMaker ``PyTorchModel``

src/sagemaker/pytorch/model.py

@@ -1,4 +1,4 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Copyright 2018-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"). You
 # may not use this file except in compliance with the License. A copy of
@@ -97,6 +97,12 @@ def __init__(
                 worker per vCPU.
             **kwargs: Keyword arguments passed to the ``FrameworkModel``
                 initializer.
+
+        .. tip::
+
+            You can find additional parameters for initializing this class at
+            :class:`~sagemaker.model.FrameworkModel` and
+            :class:`~sagemaker.model.Model`.
         """
         super(PyTorchModel, self).__init__(
             model_data, image, role, entry_point, predictor_cls=predictor_cls, **kwargs

src/sagemaker/rl/estimator.py

@@ -131,6 +131,12 @@ def __init__(
                 don't use an Amazon algorithm.
             **kwargs: Additional kwargs passed to the
                 :class:`~sagemaker.estimator.Framework` constructor.
+
+        .. tip::
+
+            You can find additional parameters for initializing this class at
+            :class:`~sagemaker.estimator.Framework` and
+            :class:`~sagemaker.estimator.EstimatorBase`.
         """
         self._validate_images_args(toolkit, toolkit_version, framework, image_name)
 
@@ -174,8 +180,10 @@ def create_model(
                 role from the Estimator will be used.
             vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
                 the model. Default: use subnets and security groups from this Estimator.
+
                 * 'Subnets' (list[str]): List of subnet ids.
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
+
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point for MXNet
                 hosting. This should be compatible with Python 3.5 (default:
@@ -190,18 +198,20 @@ def create_model(
                 folders will be copied to SageMaker in the same folder where the
                 entry_point is copied. If the ```source_dir``` points to S3,
                 code will be uploaded and the S3 location will be used instead.
-            **kwargs: Additional kwargs passed to the FrameworkModel constructor.
+            **kwargs: Additional kwargs passed to the :class:`~sagemaker.model.FrameworkModel`
+                constructor.
 
         Returns:
             sagemaker.model.FrameworkModel: Depending on input parameters returns
                 one of the following:
 
-                * sagemaker.model.FrameworkModel - in case image_name was specified
+                * :class:`~sagemaker.model.FrameworkModel` - if ``image_name`` was specified
                     on the estimator;
-                * sagemaker.mxnet.MXNetModel - if image_name wasn't specified and
-                    MXNet was used as RL backend;
-                * sagemaker.tensorflow.serving.Model - if image_name wasn't specified and
-                    TensorFlow was used as RL backend.
+                * :class:`~sagemaker.mxnet.MXNetModel` - if ``image_name`` wasn't specified and
+                    MXNet was used as the RL backend;
+                * :class:`~sagemaker.tensorflow.serving.Model` - if ``image_name`` wasn't specified
+                    and TensorFlow was used as the RL backend.
+
         Raises:
             ValueError: If image_name was not specified and framework enum is not valid.
         """

src/sagemaker/sklearn/estimator.py

@@ -90,6 +90,12 @@ def __init__(
                     custom-image:latest.
             **kwargs: Additional kwargs passed to the
                 :class:`~sagemaker.estimator.Framework` constructor.
+
+        .. tip::
+
+            You can find additional parameters for initializing this class at
+            :class:`~sagemaker.estimator.Framework` and
+            :class:`~sagemaker.estimator.EstimatorBase`.
         """
         # SciKit-Learn does not support distributed training or training on GPU instance types.
         # Fail fast.

src/sagemaker/sklearn/model.py

@@ -1,4 +1,4 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Copyright 2018-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"). You
 # may not use this file except in compliance with the License. A copy of
@@ -96,6 +96,12 @@ def __init__(
                 worker per vCPU.
             **kwargs: Keyword arguments passed to the ``FrameworkModel``
                 initializer.
+
+        .. tip::
+
+            You can find additional parameters for initializing this class at
+            :class:`~sagemaker.model.FrameworkModel` and
+            :class:`~sagemaker.model.Model`.
         """
         super(SKLearnModel, self).__init__(
             model_data, image, role, entry_point, predictor_cls=predictor_cls, **kwargs

src/sagemaker/tensorflow/estimator.py

@@ -279,6 +279,12 @@ def __init__(
                     }
 
             **kwargs: Additional kwargs passed to the Framework constructor.
+
+        .. tip::
+
+            You can find additional parameters for initializing this class at
+            :class:`~sagemaker.estimator.Framework` and
+            :class:`~sagemaker.estimator.EstimatorBase`.
         """
         if framework_version is None:
             logger.warning(fw.empty_framework_version_warning(TF_VERSION, self.LATEST_VERSION))
@@ -551,8 +557,8 @@ def create_model(
                 If not specified and ``endpoint_type`` is 'tensorflow-serving', ``dependencies`` is
                 set to ``None``.
                 If ``endpoint_type`` is also ``None``, then the dependencies from training are used.
-            **kwargs: Additional kwargs passed to ``sagemaker.tensorflow.serving.Model`` constructor
-                and ``sagemaker.tensorflow.model.TensorFlowModel`` constructor.
+            **kwargs: Additional kwargs passed to :class:`~sagemaker.tensorflow.serving.Model`
+                and :class:`~sagemaker.tensorflow.model.TensorFlowModel` constructors.
 
         Returns:
             sagemaker.tensorflow.model.TensorFlowModel or sagemaker.tensorflow.serving.Model: A