SageMakerTuningSensor

Amazon

Asks for the state of the tuning state until it reaches a terminal state. The sensor will error if the job errors, throwing a AirflowException containing the failure reason.

View Source

Last Updated: Oct. 22, 2020

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

job_namestrjob_name of the tuning instance to check the state of

Documentation

Asks for the state of the tuning state until it reaches a terminal state. The sensor will error if the job errors, throwing a AirflowException containing the failure reason.

Example DAGs

Improve this module by creating an example DAG.

View Source
  1. Add an `example_dags` directory to the top-level source of the provider package with an empty `__init__.py` 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.

Was this page helpful?