doc: remove duplicate content from workflow readme (#759)

Author: eslesar-aws

README.rst

@@ -300,4 +300,4 @@ You can use Apache Airflow to author, schedule and monitor SageMaker workflow.
 
 For more information, see `SageMaker Workflow in Apache Airflow`_.
 
-.. _SageMaker Workflow in Apache Airflow: src/sagemaker/workflow/README.rst
+.. _SageMaker Workflow in Apache Airflow: https://sagemaker.readthedocs.io/en/stable/using_workflow.html

src/sagemaker/workflow/README.rst

@@ -10,157 +10,4 @@ is a platform that enables you to programmatically author, schedule, and monitor
 you can build a workflow for SageMaker training, hyperparameter tuning, batch transform and endpoint deployment.
 You can use any SageMaker deep learning framework or Amazon algorithms to perform above operations in Airflow.
 
-There are two ways to build a SageMaker workflow. Using Airflow SageMaker operators or using Airflow PythonOperator.
-
-1. SageMaker Operators: In Airflow 1.10.1, the SageMaker team contributed special operators for SageMaker operations.
-Each operator takes a configuration dictionary that defines the corresponding operation. We provide APIs to generate
-the configuration dictionary in the SageMaker Python SDK. Currently, the following SageMaker operators are supported:
-
-* ``SageMakerTrainingOperator``
-* ``SageMakerTuningOperator``
-* ``SageMakerModelOperator``
-* ``SageMakerTransformOperator``
-* ``SageMakerEndpointConfigOperator``
-* ``SageMakerEndpointOperator``
-
-2. PythonOperator: Airflow built-in operator that executes Python callables. You can use the PythonOperator to execute
-operations in the SageMaker Python SDK to create a SageMaker workflow.
-
-Using Airflow on AWS
-~~~~~~~~~~~~~~~~~~~~
-
-Turbine is an open-source AWS CloudFormation template that enables you to create an Airflow resource stack on AWS.
-You can get it here: https://github.com/villasv/aws-airflow-stack
-
-Using Airflow SageMaker Operators
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Starting with Airflow 1.10.1, you can use SageMaker operators in Airflow. All SageMaker operators take a configuration
-dictionary that can be generated by the SageMaker Python SDK. For example:
-
-.. code:: python
-
-    import sagemaker
-    from sagemaker.tensorflow import TensorFlow
-    from sagemaker.workflow.airflow import training_config, transform_config_from_estimator
-
-    estimator = TensorFlow(entry_point='tf_train.py',
-                           role='sagemaker-role',
-                           framework_version='1.11.0',
-                           training_steps=1000,
-                           evaluation_steps=100,
-                           train_instance_count=2,
-                           train_instance_type='ml.p2.xlarge')
-
-    # train_config specifies SageMaker training configuration
-    train_config = training_config(estimator=estimator,
-                                   inputs=your_training_data_s3_uri)
-
-    # trans_config specifies SageMaker batch transform configuration
-    # task_id specifies which operator the training job associatd with; task_type specifies whether the operator is a
-    # training operator or tuning operator
-    trans_config = transform_config_from_estimator(estimator=estimator,
-                                                   task_id='tf_training',
-                                                   task_type='training',
-                                                   instance_count=1,
-                                                   instance_type='ml.m4.xlarge',
-                                                   data=your_transform_data_s3_uri,
-                                                   content_type='text/csv')
-
-Now you can pass these configurations to the corresponding SageMaker operators and create the workflow:
-
-.. code:: python
-
-    import airflow
-    from airflow import DAG
-    from airflow.contrib.operators.sagemaker_training_operator import SageMakerTrainingOperator
-    from airflow.contrib.operators.sagemaker_transform_operator import SageMakerTransformOperator
-
-    default_args = {
-        'owner': 'airflow',
-        'start_date': airflow.utils.dates.days_ago(2),
-        'provide_context': True
-    }
-
-    dag = DAG('tensorflow_example', default_args=default_args,
-              schedule_interval='@once')
-
-    train_op = SageMakerTrainingOperator(
-        task_id='tf_training',
-        config=train_config,
-        wait_for_completion=True,
-        dag=dag)
-
-    transform_op = SageMakerTransformOperator(
-        task_id='tf_transform',
-        config=trans_config,
-        wait_for_completion=True,
-        dag=dag)
-
-    transform_op.set_upstream(train_op)
-
-Using Airflow Python Operator
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-`Airflow PythonOperator <https://airflow.apache.org/howto/operator.html?#pythonoperator>`_
-is a built-in operator that can execute any Python callable. If you want to build the SageMaker workflow in a more
-flexible way, write your python callables for SageMaker operations by using the SageMaker Python SDK.
-
-.. code:: python
-
-    from sagemaker.tensorflow import TensorFlow
-
-    # callable for SageMaker training in TensorFlow
-    def train(data, **context):
-        estimator = TensorFlow(entry_point='tf_train.py',
-                               role='sagemaker-role',
-                               framework_version='1.11.0',
-                               training_steps=1000,
-                               evaluation_steps=100,
-                               train_instance_count=2,
-                               train_instance_type='ml.p2.xlarge')
-        estimator.fit(data)
-        return estimator.latest_training_job.job_name
-
-    # callable for SageMaker batch transform
-    def transform(data, **context):
-        training_job = context['ti'].xcom_pull(task_ids='training')
-        estimator = TensorFlow.attach(training_job)
-        transformer = estimator.transformer(instance_count=1, instance_type='ml.c4.xlarge')
-        transformer.transform(data, content_type='text/csv')
-
-Then build your workflow by using the PythonOperator with the Python callables defined above:
-
-.. code:: python
-
-    import airflow
-    from airflow import DAG
-    from airflow.operators.python_operator import PythonOperator
-
-    default_args = {
-        'owner': 'airflow',
-        'start_date': airflow.utils.dates.days_ago(2),
-        'provide_context': True
-    }
-
-    dag = DAG('tensorflow_example', default_args=default_args,
-              schedule_interval='@once')
-
-    train_op = PythonOperator(
-        task_id='training',
-        python_callable=train,
-        op_args=[training_data_s3_uri],
-        provide_context=True,
-        dag=dag)
-
-    transform_op = PythonOperator(
-        task_id='transform',
-        python_callable=transform,
-        op_args=[transform_data_s3_uri],
-        provide_context=True,
-        dag=dag)
-
-    transform_op.set_upstream(train_op)
-
-A workflow that runs a SageMaker training job and a batch transform job is finished. You can customize your Python
-callables with the SageMaker Python SDK according to your needs, and build more flexible and powerful workflows.
+For information about using SageMaker Workflow, see https://sagemaker.readthedocs.io/en/stable/using_workflow.html.