airflow.providers.amazon.aws.operators.sagemaker_training¶
Module Contents¶
-
class
airflow.providers.amazon.aws.operators.sagemaker_training.SageMakerTrainingOperator(*, config: dict, wait_for_completion: bool = True, print_log: bool = True, check_interval: int = 30, max_ingestion_time: Optional[int] = None, check_if_job_exists: bool = True, action_if_job_exists: str = 'increment', **kwargs)[source]¶ Bases:
airflow.providers.amazon.aws.operators.sagemaker_base.SageMakerBaseOperatorInitiate a SageMaker training job.
This operator returns The ARN of the training job created in Amazon SageMaker.
- Parameters
config (dict) --
The configuration necessary to start a training job (templated).
For details of the configuration parameter see
SageMaker.Client.create_training_job()aws_conn_id (str) -- The AWS connection ID to use.
wait_for_completion (bool) -- If wait is set to True, the time interval, in seconds, that the operation waits to check the status of the training job.
print_log (bool) -- if the operator should print the cloudwatch log during training
check_interval (int) -- 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
max_ingestion_time (int) -- 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.
check_if_job_exists (bool) -- If set to true, then the operator will check whether a training job already exists for the name in the config.
action_if_job_exists (str) -- 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.