Add README for airflow

README.rst

@@ -38,6 +38,7 @@ Table of Contents
 10. `SageMaker Batch Transform <#sagemaker-batch-transform>`__
 11. `Secure Training and Inference with VPC <#secure-training-and-inference-with-vpc>`__
 12. `BYO Model <#byo-model>`__
+13. `SageMaker Workflow <#sagemaker-workflow>`__
 
 
 Installing the SageMaker Python SDK
@@ -706,3 +707,13 @@ After that, invoke the ``deploy()`` method on the ``Model``:
 This returns a predictor the same way an ``Estimator`` does when ``deploy()`` is called. You can now get inferences just like with any other model deployed on Amazon SageMaker.
 
 A full example is available in the `Amazon SageMaker examples repository <https://github.com/awslabs/amazon-sagemaker-examples/tree/master/advanced_functionality/mxnet_mnist_byom>`__.
+
+
+SageMaker Workflow
+------------------
+
+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

src/sagemaker/workflow/README.rst

@@ -0,0 +1,162 @@
+====================================
+SageMaker Workflow in Apache Airflow
+====================================
+
+Apache Airflow
+~~~~~~~~~~~~~~
+
+`Apache Airflow <https://airflow.apache.org/index.html>`_
+is a platform that enables you to programmatically author, schedule, and monitor workflows. Using Airflow,
+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 SageMaker workflow. Using Airflow SageMaker operators or using Airflow PythonOperator.
+
+1. SageMaker Operators: Since Airflow 1.10.1, we contributed special operators just for SageMaker operations.
+Each operator takes a configuration dictionary that defines the corresponding operation. And we provide APIs to
+generate the configuration dictionary in SageMaker Python SDK. Current supported SageMaker operators are:
+
+* ``SageMakerTrainingOperator``
+* ``SageMakerTuningOperator``
+* ``SageMakerModelOperator``
+* ``SageMakerTransformOperator``
+* ``SageMakerEndpointConfigOperator``
+* ``SageMakerEndpointOperator``
+
+2. PythonOperator: Airflow built-in operator that could execute Python callables. You could use SageMaker Python SDK to
+customize your workflow with PythonOperator.
+
+Using Airflow on AWS
+~~~~~~~~~~~~~~~~~~~~
+
+Turbine is an open source AWS CloudFormation template to create Airflow resources stack on AWS.
+You can get it here: https://github.com/villasv/aws-airflow-stack
+
+Using Airflow SageMaker Operators
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Starting from Airflow 1.10.1, you could use SageMaker operators in Airflow. All SageMaker operators take a
+configuration dictionary that can be easily generated by 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
+    trans_config = transform_config_from_estimator(estimator=estimator,
+                                                   instance_count=1,
+                                                   instance_type='ml.m4.xlarge',
+                                                   data=your_transform_data_s3_uri,
+                                                   content_type='text/csv')
+
+Now we can pass these configurations to related 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='training',
+        config=train_config,
+        wait_for_completion=True,
+        dag=dag)
+
+    transform_op = SageMakerTransformOperator(
+        task_id='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 callables. If you want to build the SageMaker workflow in a more
+flexible way,  you could write your python callables for SageMaker operations using SageMaker Python SDK. For example:
+
+.. 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 you could build your workflow using PythonOperator with 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 with SageMaker training and batch transform is finished! In this way, you could customize your Python
+callables with SageMaker Python SDK according to your needs and build more flexible and powerful workflow.