:mod:`airflow.contrib.operators.sagemaker_training_operator`
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.. py:module:: airflow.contrib.operators.sagemaker_training_operator







Module Contents
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.. py:class:: SageMakerTrainingOperator(config, wait_for_completion=True, print_log=True, check_interval=30, max_ingestion_time=None, *args, **kwargs)

   Bases::class:`airflow.contrib.operators.sagemaker_base_operator.SageMakerBaseOperator`

   

   Initiate a SageMaker training job.

   This operator returns The ARN of the training job created in Amazon SageMaker.

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

       For details of the configuration parameter see :py:meth:`SageMaker.Client.create_training_job`
   :type config: dict
   :param aws_conn_id: The AWS connection ID to use.
   :type aws_conn_id: str
   :param wait_for_completion: If wait is set to True, the time interval, in seconds,
       that the operation waits to check the status of the training job.
   :type wait_for_completion: bool
   :param print_log: if the operator should print the cloudwatch log during training
   :type print_log: bool
   :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
   :type check_interval: int
   :param max_ingestion_time: If wait is set to True, 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.
   :type max_ingestion_time: int


   

   

   .. attribute:: integer_fields
      :annotation: = [['ResourceConfig', 'InstanceCount'], ['ResourceConfig', 'VolumeSizeInGB'], ['StoppingCondition', 'MaxRuntimeInSeconds']] 

      



   

   

   

   .. method:: expand_role(self)

      



   

   .. method:: execute(self, context)