:py:mod:`astronomer.providers.amazon.aws.operators.sagemaker`
=============================================================

.. py:module:: astronomer.providers.amazon.aws.operators.sagemaker


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   astronomer.providers.amazon.aws.operators.sagemaker.SageMakerProcessingOperatorAsync
   astronomer.providers.amazon.aws.operators.sagemaker.SageMakerTransformOperatorAsync
   astronomer.providers.amazon.aws.operators.sagemaker.SageMakerTrainingOperatorAsync



Functions
~~~~~~~~~

.. autoapisummary::

   astronomer.providers.amazon.aws.operators.sagemaker.serialize



.. py:function:: serialize(result)

   Serialize any objects coming from Sagemaker API response to json string


.. py:class:: SageMakerProcessingOperatorAsync(*, config, aws_conn_id = DEFAULT_CONN_ID, wait_for_completion = True, print_log = True, check_interval = CHECK_INTERVAL_SECOND, max_ingestion_time = None, action_if_job_exists = 'timestamp', **kwargs)

   Bases: :py:obj:`airflow.providers.amazon.aws.operators.sagemaker.SageMakerProcessingOperator`

   SageMakerProcessingOperatorAsync is used to analyze data and evaluate machine learning
   models on Amazon SageMaker. With SageMakerProcessingOperatorAsync, you can use a simplified, managed
   experience on SageMaker to run your data processing workloads, such as feature
   engineering, data validation, model evaluation, and model interpretation.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:SageMakerProcessingOperator`

   :param config: The configuration necessary to start a processing job (templated).
       For details of the configuration parameter see
       :ref:``SageMaker.Client.create_processing_job``
   :param aws_conn_id: The AWS connection ID to use.
   :param wait_for_completion: Even if wait is set to False, in async we will defer and
       the operation waits to check the status of the processing job.
   :param print_log: if the operator should print the cloudwatch log during processing
   :param check_interval: if wait is set to be true, this is the time interval
       in seconds which the operator will check the status of the processing job
   :param max_ingestion_time: The operation fails if the processing job
       doesn't finish within max_ingestion_time seconds. If you set this parameter to None,
       the operation does not timeout.
   :param action_if_job_exists: Behaviour if the job name already exists. Possible options are "increment"
       (default) and "fail".

   .. py:method:: execute(context)

      Creates processing job via sync hook `create_processing_job` and pass the
      control to trigger and polls for the status of the processing job in async


   .. py:method:: execute_complete(context, event = None)

      Callback for when the trigger fires - returns immediately.
      Relies on trigger to throw an exception, otherwise it assumes execution was
      successful.



.. py:class:: SageMakerTransformOperatorAsync(*, config, aws_conn_id = DEFAULT_CONN_ID, wait_for_completion = True, check_interval = CHECK_INTERVAL_SECOND, max_ingestion_time = None, check_if_job_exists = True, action_if_job_exists = 'timestamp', **kwargs)

   Bases: :py:obj:`airflow.providers.amazon.aws.operators.sagemaker.SageMakerTransformOperator`

   SageMakerTransformOperatorAsync starts a transform job and polls for the status asynchronously.
   A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon
   S3 location that you specify.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:``howto/operator:SageMakerTransformOperator``

   :param config: The configuration necessary to start a transform job (templated).

       If you need to create a SageMaker transform job based on an existed SageMaker model::

           config = transform_config

       If you need to create both SageMaker model and SageMaker Transform job::

           config = {
               'Model': model_config,
               'Transform': transform_config
           }

       For details of the configuration parameter of transform_config see
       :ref:``SageMaker.Client.create_transform_job``

       For details of the configuration parameter of model_config, See:
       :ref:``SageMaker.Client.create_model``

   :param aws_conn_id: The AWS connection ID to use.
   :param check_interval: If wait is set to True, the time interval, in seconds,
       that this operation waits to check the status of the transform job.
   :param max_ingestion_time: The operation fails if the transform job doesn't finish
    within max_ingestion_time seconds. If you set this parameter to None, the operation does not timeout.
   :param check_if_job_exists: If set to true, then the operator will check whether a transform job
       already exists for the name in the config.
   :param action_if_job_exists: Behaviour if the job name already exists. Possible options are "increment"
       (default) and "fail".
       This is only relevant if check_if_job_exists is True.

   .. py:method:: execute(context)

      Creates transform job via sync hook `create_transform_job` and pass the
      control to trigger and polls for the status of the transform job in async


   .. py:method:: execute_complete(context, event)

      Callback for when the trigger fires - returns immediately.
      Relies on trigger to throw an exception, otherwise it assumes execution was
      successful.



.. py:class:: SageMakerTrainingOperatorAsync(*, config, aws_conn_id = DEFAULT_CONN_ID, wait_for_completion = True, print_log = True, check_interval = CHECK_INTERVAL_SECOND, max_ingestion_time = None, check_if_job_exists = True, action_if_job_exists = 'timestamp', **kwargs)

   Bases: :py:obj:`airflow.providers.amazon.aws.operators.sagemaker.SageMakerTrainingOperator`

   SageMakerTrainingOperatorAsync starts a model training job and polls for the status asynchronously.
   After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location
   that you specify.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:``howto/operator:SageMakerTrainingOperator``

   :param config: The configuration necessary to start a training job (templated).
       For details of the configuration parameter see ``SageMaker.Client.create_training_job``
   :param aws_conn_id: The AWS connection ID to use.
   :param print_log: if the operator should print the cloudwatch log during training
   :param check_interval: if wait is set to be true, this is the time interval
       in seconds which the operator will check the status of the training job
   :param max_ingestion_time: The operation fails if the training job
       doesn't finish within max_ingestion_time seconds. If you set this parameter to None,
       the operation does not timeout.
   :param check_if_job_exists: If set to true, then the operator will check whether a training job
       already exists for the name in the config.
   :param action_if_job_exists: Behaviour if the job name already exists. Possible options are "increment"
       (default) and "fail".
       This is only relevant if check_if_job_exists is True.

   .. py:method:: execute(context)

      Creates SageMaker training job via sync hook `create_training_job` and pass the
      control to trigger and polls for the status of the training job in async


   .. py:method:: execute_complete(context, event)

      Callback for when the trigger fires - returns immediately.
      Relies on trigger to throw an exception, otherwise it assumes execution was
      successful.