Initiate a SageMaker hyperparameter tuning job.

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Last Updated: May. 7, 2021

Access Instructions

Install the Amazon provider package into your Airflow environment.

Import the module into your DAG file and instantiate it with your desired params.


configdictThe configuration necessary to start a tuning job (templated).For details of the configuration parameter see :py:meth:`SageMaker.Client.create_hyper_parameter_tuning_job`No role entry for "py:meth" in module "docutils.parsers.rst.languages.en". Trying "py:meth" as canonical role name.Unknown interpreted text role "py:meth".
aws_conn_idstrThe AWS connection ID to use.
wait_for_completionboolSet to True to wait until the tuning job finishes.
check_intervalintIf wait is set to True, the time interval, in seconds, that this operation waits to check the status of the tuning job.
max_ingestion_timeintIf wait is set to True, the operation fails if the tuning job doesn't finish within max_ingestion_time seconds. If you set this parameter to None, the operation does not timeout.


Initiate a SageMaker hyperparameter tuning job.

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

Example DAGs

Improve this module by creating an example DAG.

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  1. Add an `example_dags` directory to the top-level source of the provider package with an empty `` file.
  2. Add your DAG to this directory. Be sure to include a well-written and descriptive docstring
  3. Create a pull request against the source code. Once the package gets released, your DAG will show up on the Registry.

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