Access experiment data from a pipeline - Amazon SageMaker AI

Access experiment data from a pipeline

Note

SageMaker Experiments is a feature provided in Studio Classic only.

When you create a pipeline and specify pipeline_experiment_config, Pipelines creates the following SageMaker Experiments entities by default if they don't exist:

  • An experiment for the pipeline

  • A run group for every execution of the pipeline

  • A run for each SageMaker AI job created in a pipeline step

For information about how experiments are integrated with pipelines, see Amazon SageMaker Experiments Integration. For more information about SageMaker Experiments, see Amazon SageMaker Experiments in Studio Classic.

You can get to the list of runs associated with a pipeline from either the pipeline executions list or the experiments list.

To view the runs list from the pipeline executions list

  1. To view the pipeline executions list, follow the first five steps in the Studio Classic tab of View the details of a pipeline.

  2. On the top right of the screen, choose the Filter icon ( Funnel or filter icon representing data filtering or narrowing down options. ).

  3. Choose Experiment. If experiment integration wasn't deactivated when the pipeline was created, the experiment name is displayed in the executions list.

    Note

    Experiments integration was introduced in v2.41.0 of the Amazon SageMaker Python SDK. Pipelines created with an earlier version of the SDK aren't integrated with experiments by default.

  4. Select the experiment of your choice to view run groups and runs related to that experiment.

To view the runs list from the experiments list

  1. In the left sidebar of Studio Classic, choose the Home icon ( Black square icon representing a placeholder or empty image. ).

  2. Select Experiments from the menu.

  3. Use search bar or Filter icon ( Funnel or filter icon representing data filtering or narrowing down options. ) to filter the list to experiments created by a pipeline.

  4. Open an experiment name and view a list of runs created by the pipeline.