doc: add missing classes to API docs

doc/algorithm.rst

@@ -0,0 +1,7 @@
+Algorithm Estimator
+-------------------
+
+.. automodule:: sagemaker.algorithm
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/automl.rst

@@ -0,0 +1,12 @@
+AutoML
+------
+
+.. automodule:: sagemaker.automl.automl
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: sagemaker.automl.candidate_estimator
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/index.rst

@@ -16,19 +16,42 @@ Overview
 
     overview
 
-The SageMaker Python SDK consists of a few primary classes:
+The SageMaker Python SDK consists of a variety classes:
+
+Training:
 
 .. toctree::
-    :maxdepth: 2
+    :maxdepth: 1
 
     estimators
+    algorithm
     tuner
+    parameter
+    automl
+    processing
+    analytics
+
+Inference:
+
+.. toctree::
+    :maxdepth: 1
+
     model
-    pipeline
+    multi_data_model
     predictors
     transformer
+    pipeline
+    model_monitor
+
+Utility:
+
+.. toctree::
+    :maxdepth: 1
+
     session
-    analytics
+    inputs
+    network
+    s3
 
 *****
 MXNet
@@ -175,5 +198,3 @@ SageMaker APIs to export configurations for creating and managing Airflow workfl
     :maxdepth: 2
 
     sagemaker.workflow.airflow
-
-

doc/inputs.rst

@@ -0,0 +1,7 @@
+Inputs
+------
+
+.. automodule:: sagemaker.inputs
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/model_monitor.rst

@@ -0,0 +1,27 @@
+Model Monitor
+-------------
+
+.. automodule:: sagemaker.model_monitor.model_monitoring
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: sagemaker.model_monitor.monitoring_files
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: sagemaker.model_monitor.dataset_format
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: sagemaker.model_monitor.data_capture_config
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: sagemaker.model_monitor.cron_expression_generator
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/multi_data_model.rst

@@ -0,0 +1,7 @@
+MultiDataModel
+--------------
+
+.. automodule:: sagemaker.multidatamodel
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/network.rst

@@ -0,0 +1,7 @@
+Network Configuration
+---------------------
+
+.. automodule:: sagemaker.network
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/parameter.rst

@@ -0,0 +1,7 @@
+Parameters
+----------
+
+.. automodule:: sagemaker.parameter
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/processing.rst

@@ -0,0 +1,7 @@
+Processing
+----------
+
+.. automodule:: sagemaker.processing
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/s3.rst

@@ -0,0 +1,7 @@
+S3 Utilities
+------------
+
+.. automodule:: sagemaker.s3
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/session.rst

@@ -4,4 +4,4 @@ Session
 .. automodule:: sagemaker.session
     :members:
     :undoc-members:
-    :show-inheritance:
+    :show-inheritance:

doc/using_tf.rst

@@ -93,7 +93,7 @@ For example, if you want to use a boolean hyperparameter, specify ``type`` as ``
 
 For a complete example of a TensorFlow training script, see `mnist.py <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_distributed_mnist/mnist.py>`__.
 
-   
+
 Adapting your local TensorFlow script
 -------------------------------------
 
@@ -181,7 +181,7 @@ Required arguments
 
   - ``str``: An S3 URI, for example ``s3://my-bucket/my-training-data``, which indicates the dataset's location.
   - ``dict[str, str]``: A dictionary mapping channel names to S3 locations, for example ``{'train': 's3://my-bucket/my-training-data/train', 'test': 's3://my-bucket/my-training-data/test'}``
-  - ``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 `the API docs <https://sagemaker.readthedocs.io/en/stable/session.html#sagemaker.session.s3_input>`_ for full details.
+  - ``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 `the API docs <https://sagemaker.readthedocs.io/en/stable/inputs.html#sagemaker.inputs.s3_input>`_ for full details.
 
 Optional arguments
 ------------------
@@ -593,7 +593,7 @@ The following content formats are supported without custom intput and output han
 For detailed information about how TensorFlow Serving formats these data types for input and output, see :ref:`using_tf:TensorFlow Serving Input and Output`.
 
 You can also accept any custom data format by writing input and output functions, and include them in the ``inference.py`` file in your model.
-For information, see :ref:`using_tf:Create Python Scripts for Custom Input and Output Formats`. 
+For information, see :ref:`using_tf:Create Python Scripts for Custom Input and Output Formats`.
 
 
 TensorFlow Serving Input and Output

src/sagemaker/multidatamodel.py

@@ -253,13 +253,13 @@ def deploy(
         return None
 
     def add_model(self, model_data_source, model_data_path=None):
-        """Adds a model to the `MultiDataModel` by uploading or copying the model_data_source
+        """Adds a model to the ``MultiDataModel`` by uploading or copying the model_data_source
          artifact to the given S3 path model_data_path relative to model_data_prefix
 
         Args:
-        model_source: Valid local file path or S3 path of the trained model artifact
-        model_data_path: S3 path where the trained model artifact
-                should be uploaded relative to `self.model_data_prefix` path. (default: None).
+            model_source: Valid local file path or S3 path of the trained model artifact
+            model_data_path: S3 path where the trained model artifact
+                should be uploaded relative to ``self.model_data_prefix`` path. (default: None).
                 If None, then the model artifact is uploaded to a path relative to model_data_prefix
 
         Returns:

src/sagemaker/processing.py

@@ -686,7 +686,7 @@ def __init__(self, source, destination=None, output_name=None, s3_upload_mode="E
             source (str): The source for the output.
             destination (str): The destination of the output. If a destination
                 is not provided, one will be generated:
-                 "s3://<default-bucket-name>/<job-name>/output/<output-name>".
+                "s3://<default-bucket-name>/<job-name>/output/<output-name>".
             output_name (str): The name of the output. If a name
                 is not provided, one will be generated (eg. "output-1").
             s3_upload_mode (str): Valid options are "EndOfJob" or "Continuous".