docs-archive/apache-airflow-providers-amazon/3.4.0/_sources/operators/sagemaker.rst.txt

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Amazon SageMaker Operators
==========================

`Amazon SageMaker <https://docs.aws.amazon.com/sagemaker>`__ is a fully managed
machine learning service. With Amazon SageMaker, data scientists and developers
can quickly build and train machine learning models, and then deploy them into a
production-ready hosted environment.

Airflow provides operators to create and interact with SageMaker Jobs.

Prerequisite Tasks
------------------

.. include:: _partials/prerequisite_tasks.rst

Manage Amazon SageMaker Jobs
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. _howto/operator:SageMakerProcessingOperator:

Create an Amazon SageMaker Processing Job
"""""""""""""""""""""""""""""""""""""""""

To create an Amazon Sagemaker processing job to sanitize your dataset you can use
:class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerProcessingOperator`.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_sagemaker.py
    :language: python
    :dedent: 4
    :start-after: [START howto_operator_sagemaker_processing]
    :end-before: [END howto_operator_sagemaker_processing]


.. _howto/operator:SageMakerTrainingOperator:

Create an Amazon SageMaker Training Job
"""""""""""""""""""""""""""""""""""""""

To create an Amazon Sagemaker training job you can use
:class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerTrainingOperator`.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_sagemaker.py
    :language: python
    :dedent: 4
    :start-after: [START howto_operator_sagemaker_training]
    :end-before: [END howto_operator_sagemaker_training]

.. _howto/operator:SageMakerModelOperator:

Create an Amazon SageMaker Model
""""""""""""""""""""""""""""""""

To create an Amazon Sagemaker model you can use
:class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerModelOperator`.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_sagemaker.py
    :language: python
    :dedent: 4
    :start-after: [START howto_operator_sagemaker_model]
    :end-before: [END howto_operator_sagemaker_model]

.. _howto/operator:SageMakerTuningOperator:

Start a Hyperparameter Tuning Job
"""""""""""""""""""""""""""""""""

To start a hyperparameter tuning job for an Amazon Sagemaker model you can use
:class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerTuningOperator`.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_sagemaker.py
    :language: python
    :dedent: 4
    :start-after: [START howto_operator_sagemaker_tuning]
    :end-before: [END howto_operator_sagemaker_tuning]

.. _howto/operator:SageMakerDeleteModelOperator:

Delete an Amazon SageMaker Model
""""""""""""""""""""""""""""""""

To delete an Amazon Sagemaker model you can use
:class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerDeleteModelOperator`.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_sagemaker.py
    :language: python
    :dedent: 4
    :start-after: [START howto_operator_sagemaker_delete_model]
    :end-before: [END howto_operator_sagemaker_delete_model]

.. _howto/operator:SageMakerTransformOperator:

Create an Amazon SageMaker Transform Job
""""""""""""""""""""""""""""""""""""""""

To create an Amazon Sagemaker transform job you can use
:class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerTransformOperator`.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_sagemaker.py
    :language: python
    :dedent: 4
    :start-after: [START howto_operator_sagemaker_transform]
    :end-before: [END howto_operator_sagemaker_transform]

.. _howto/operator:SageMakerEndpointConfigOperator:

Create an Amazon SageMaker Endpoint Config Job
""""""""""""""""""""""""""""""""""""""""""""""

To create an Amazon Sagemaker endpoint config job you can use
:class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerEndpointConfigOperator`.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_sagemaker_endpoint.py
    :language: python
    :dedent: 4
    :start-after: [START howto_operator_sagemaker_endpoint_config]
    :end-before: [END howto_operator_sagemaker_endpoint_config]

.. _howto/operator:SageMakerEndpointOperator:

Create an Amazon SageMaker Endpoint Job
"""""""""""""""""""""""""""""""""""""""

To create an Amazon Sagemaker endpoint you can use
:class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerEndpointOperator`.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_sagemaker_endpoint.py
    :language: python
    :dedent: 4
    :start-after: [START howto_operator_sagemaker_endpoint]
    :end-before: [END howto_operator_sagemaker_endpoint]


Amazon SageMaker Sensors
^^^^^^^^^^^^^^^^^^^^^^^^

.. _howto/sensor:SageMakerTrainingSensor:

Amazon SageMaker Training Sensor
""""""""""""""""""""""""""""""""

To check the state of an Amazon Sagemaker training job until it reaches a terminal state
you can use :class:`~airflow.providers.amazon.aws.sensors.sagemaker.SageMakerTrainingSensor`.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_sagemaker.py
    :language: python
    :dedent: 4
    :start-after: [START howto_operator_sagemaker_training_sensor]
    :end-before: [END howto_operator_sagemaker_training_sensor]

.. _howto/sensor:SageMakerTransformSensor:

Amazon SageMaker Transform Sensor
"""""""""""""""""""""""""""""""""""

To check the state of an Amazon Sagemaker transform job until it reaches a terminal state
you can use :class:`~airflow.providers.amazon.aws.operators.sagemaker.SageMakerTransformOperator`.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_sagemaker.py
    :language: python
    :dedent: 4
    :start-after: [START howto_operator_sagemaker_transform_sensor]
    :end-before: [END howto_operator_sagemaker_transform_sensor]

.. _howto/sensor:SageMakerTuningSensor:

Amazon SageMaker Tuning Sensor
""""""""""""""""""""""""""""""

To check the state of an Amazon Sagemaker hyperparameter tuning job until it reaches a terminal state
you can use :class:`~airflow.providers.amazon.aws.sensors.sagemaker.SageMakerTuningSensor`.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_sagemaker.py
    :language: python
    :dedent: 4
    :start-after: [START howto_operator_sagemaker_tuning_sensor]
    :end-before: [END howto_operator_sagemaker_tuning_sensor]

.. _howto/sensor:SageMakerEndpointSensor:

Amazon SageMaker Endpoint Sensor
""""""""""""""""""""""""""""""""

To check the state of an Amazon Sagemaker hyperparameter tuning job until it reaches a terminal state
you can use :class:`~airflow.providers.amazon.aws.sensors.sagemaker.SageMakerEndpointSensor`.

.. exampleinclude:: /../../airflow/providers/amazon/aws/example_dags/example_sagemaker_endpoint.py
    :language: python
    :dedent: 4
    :start-after: [START howto_operator_sagemaker_endpoint_sensor]
    :end-before: [END howto_operator_sagemaker_endpoint_sensor]

Reference
^^^^^^^^^

For further information, look at:

* `Boto3 Library Documentation for Sagemaker <https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html>`__
* `Amazon SageMaker Developer Guide <https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html>`__