doc: add some clarification to Processing docs

Author: laurenyu

doc/amazon_sagemaker_processing.rst

@@ -1,6 +1,6 @@
-##############################
+###########################
 Amazon SageMaker Processing
-##############################
+###########################
 
 
 Amazon SageMaker Processing allows you to run steps for data pre- or post-processing, feature engineering, data validation, or model evaluation workloads on Amazon SageMaker.
@@ -27,37 +27,38 @@ You can run notebooks on Amazon SageMaker that demonstrate end-to-end examples o
 Data Pre-Processing and Model Evaluation with Scikit-Learn
 ==================================================================
 
-You can run a Scikit-Learn script to do data processing on SageMaker using the `SKLearnProcessor`_ class.
-
-.. _SKLearnProcessor: https://sagemaker.readthedocs.io/en/stable/sagemaker.sklearn.html#sagemaker.sklearn.processing.SKLearnProcessor
+You can run a Scikit-Learn script to do data processing on SageMaker using the :class:`sagemaker.sklearn.processing.SKLearnProcessor` class.
 
 You first create a ``SKLearnProcessor``
 
 .. code:: python
 
     from sagemaker.sklearn.processing import SKLearnProcessor
 
-    sklearn_processor = SKLearnProcessor(framework_version='0.20.0',
-                                     role='[Your SageMaker-compatible IAM role]',
-                                     instance_type='ml.m5.xlarge',
-                                     instance_count=1)
+    sklearn_processor = SKLearnProcessor(
+        framework_version='0.20.0',
+        role='[Your SageMaker-compatible IAM role]',
+        instance_type='ml.m5.xlarge',
+        instance_count=1,
+    )
 
 Then you can run a Scikit-Learn script ``preprocessing.py`` in a processing job. In this example, our script takes one input from S3 and one command-line argument, processes the data, then splits the data into two datasets for output. When the job is finished, we can retrive the output from S3.
 
 .. code:: python
 
     from sagemaker.processing import ProcessingInput, ProcessingOutput
 
-    sklearn_processor.run(code='preprocessing.py',
-                      inputs=[ProcessingInput(
-                        source='s3://your-bucket/path/to/your/data,
-                        destination='/opt/ml/processing/input')],
-                      outputs=[ProcessingOutput(output_name='train_data',
-                                                source='/opt/ml/processing/train'),
-                               ProcessingOutput(output_name='test_data',
-                                                source='/opt/ml/processing/test')],
-                      arguments=['--train-test-split-ratio', '0.2']
-                     )
+    sklearn_processor.run(
+        code='preprocessing.py',
+        inputs=[
+            ProcessingInput(source='s3://your-bucket/path/to/your/data, destination='/opt/ml/processing/input'),
+        ],
+        outputs=[
+            ProcessingOutput(output_name='train_data', source='/opt/ml/processing/train'),
+            ProcessingOutput(output_name='test_data', source='/opt/ml/processing/test'),
+        ],
+        arguments=['--train-test-split-ratio', '0.2'],
+    )
 
     preprocessing_job_description = sklearn_processor.jobs[-1].describe()
 
@@ -69,31 +70,39 @@ For an in-depth look, please see the `Scikit-Learn Data Processing and Model Eva
 Data Pre-Processing with Spark
 ==============================
 
-You can use the `ScriptProcessor`_ class to run a script in a processing container, including your own container.
-
-.. _ScriptProcessor: https://sagemaker.readthedocs.io/en/stable/processing.html#sagemaker.processing.ScriptProcessor
+You can use the :class:`sagemaker.processing.ScriptProcessor` class to run a script in a processing container, including your own container.
 
 This example shows how you can run a processing job inside of a container that can run a Spark script called ``preprocess.py`` by invoking a command ``/opt/program/submit`` inside the container.
 
 .. code:: python
 
     from sagemaker.processing import ScriptProcessor, ProcessingInput
 
-    spark_processor = ScriptProcessor(base_job_name='spark-preprocessor',
-                                  image_uri='<ECR repository URI to your Spark processing image>',
-                                  command=['/opt/program/submit'],
-                                  role=role,
-                                  instance_count=2,
-                                  instance_type='ml.r5.xlarge',
-                                  max_runtime_in_seconds=1200,
-                                  env={'mode': 'python'})
-
-    spark_processor.run(code='preprocess.py',
-                    arguments=['s3_input_bucket', bucket,
-                               's3_input_key_prefix', input_prefix,
-                               's3_output_bucket', bucket,
-                               's3_output_key_prefix', input_preprocessed_prefix],
-                    logs=False)
+    spark_processor = ScriptProcessor(
+        base_job_name='spark-preprocessor',
+        image_uri='<ECR repository URI to your Spark processing image>',
+        command=['/opt/program/submit'],
+        role=role,
+        instance_count=2,
+        instance_type='ml.r5.xlarge',
+        max_runtime_in_seconds=1200,
+        env={'mode': 'python'},
+    )
+
+    spark_processor.run(
+        code='preprocess.py',
+        arguments=[
+            's3_input_bucket',
+            bucket,
+            's3_input_key_prefix',
+            input_prefix,
+            's3_output_bucket',
+            bucket,
+            's3_output_key_prefix',
+            input_preprocessed_prefix,
+        ],
+        logs=False,
+    )
 
 For an in-depth look, please see the `Feature Transformation with Spark`_ example notebook.
 
@@ -106,19 +115,19 @@ Learn More
 Processing class documentation
 ------------------------------
 
-- ``Processor``: https://sagemaker.readthedocs.io/en/stable/processing.html#sagemaker.processing.Processor
-- ``ScriptProcessor``: https://sagemaker.readthedocs.io/en/stable/processing.html#sagemaker.processing.ScriptProcessor
-- ``SKLearnProcessor``: https://sagemaker.readthedocs.io/en/stable/sagemaker.sklearn.html#sagemaker.sklearn.processing.SKLearnProcessor
-- ``ProcessingInput``: https://sagemaker.readthedocs.io/en/stable/processing.html#sagemaker.processing.ProcessingInput
-- ``ProcessingOutput``: https://sagemaker.readthedocs.io/en/stable/processing.html#sagemaker.processing.ProcessingOutput
-- ``ProcessingJob``: https://sagemaker.readthedocs.io/en/stable/processing.html#sagemaker.processing.ProcessingJob
+- :class:`sagemaker.processing.Processor`
+- :class:`sagemaker.processing.ScriptProcessor`
+- :class:`sagemaker.sklearn.processing.SKLearnProcessor`
+- :class:`sagemaker.processing.ProcessingInput`
+- :class:`sagemaker.processing.ProcessingOutput`
+- :class:`sagemaker.processing.ProcessingJob`
 
 
 Further documentation
 ---------------------
 
-- Processing class documentation: https://sagemaker.readthedocs.io/en/stable/processing.html
-- ​​AWS Documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/processing-job.html
-- AWS Notebook examples: https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker_processing
-- Processing API documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateProcessingJob.html
-- Processing container specification: https://docs.aws.amazon.com/sagemaker/latest/dg/build-your-own-processing-container.html
+- `Processing class documentation <https://sagemaker.readthedocs.io/en/stable/processing.html>`_
+- `AWS Documentation <https://docs.aws.amazon.com/sagemaker/latest/dg/processing-job.html>`_
+- `AWS Notebook examples <https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker_processing>`_
+- `Processing API documentation <https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateProcessingJob.html>`_
+- `Processing container specification <https://docs.aws.amazon.com/sagemaker/latest/dg/build-your-own-processing-container.html>`_

src/sagemaker/processing.py

@@ -269,7 +269,9 @@ def _normalize_outputs(self, outputs=None):
 
 
 class ScriptProcessor(Processor):
-    """Handles Amazon SageMaker processing tasks for jobs using a machine learning framework."""
+    """Handles Amazon SageMaker processing tasks for jobs using a machine learning framework,
+    which allows for providing a script to be run as part of the Processing Job.
+    """
 
     def __init__(
         self,