blob: f9e883c8a559dc88b13a2b76331354b4e57a6627 [file] [log] [blame]
:mod:`airflow.providers.amazon.aws.operators.sagemaker_transform`
=================================================================
.. py:module:: airflow.providers.amazon.aws.operators.sagemaker_transform
Module Contents
---------------
.. py:class:: SageMakerTransformOperator(*, config: dict, wait_for_completion: bool = True, check_interval: int = 30, max_ingestion_time: Optional[int] = None, **kwargs)
Bases: :class:`airflow.providers.amazon.aws.operators.sagemaker_base.SageMakerBaseOperator`
Initiate a SageMaker transform job.
This operator returns The ARN of the model created in Amazon SageMaker.
: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
:py:meth:`SageMaker.Client.create_transform_job`
For details of the configuration parameter of model_config, See:
:py:meth:`SageMaker.Client.create_model`
:type config: dict
:param aws_conn_id: The AWS connection ID to use.
:type aws_conn_id: str
:param wait_for_completion: Set to True to wait until the transform job finishes.
:type wait_for_completion: bool
: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.
:type check_interval: int
:param max_ingestion_time: If wait is set to True, 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.
:type max_ingestion_time: int
.. method:: create_integer_fields(self)
Set fields which should be casted to integers.
.. method:: expand_role(self)
.. method:: execute(self, context)