SageMakerTuningOperator

Amazon

Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.

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Last Updated: Feb. 27, 2023

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.

Parameters

Documentation

Connections

Parameters

configRequiredThe configuration necessary to start a tuning job (templated). For details of the configuration parameter see SageMaker.Client.create_hyper_parameter_tuning_job()

aws_conn_idThe AWS connection ID to use.

wait_for_completionSet to True to wait until the tuning job finishes.

check_intervalIf wait is set to True, the time interval, in seconds, that this operation waits to check the status of the tuning job.

max_ingestion_timeIf 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.

Dict