create_experiment

create_experiment(**kwargs)

Creates a SageMaker experiment . An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components , that produce a machine learning model.

Note

In the Studio UI, trials are referred to as run groups and trial components are referred to as runs .

The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.

When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.

You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.

To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API.

To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.

See also: AWS API Documentation

Request Syntax

response = client.create_experiment(
    ExperimentName='string',
    DisplayName='string',
    Description='string',
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)

Parameters

• ExperimentName (string) --

  [REQUIRED]

  The name of the experiment. The name must be unique in your Amazon Web Services account and is not case-sensitive.

• DisplayName (string) -- The name of the experiment as displayed. The name doesn't need to be unique. If you don't specify DisplayName , the value in ExperimentName is displayed.
• Description (string) -- The description of the experiment.
• Tags (list) --

  A list of tags to associate with the experiment. You can use Search API to search on the tags.

  • (dict) --

    A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

    You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

    For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

    • Key (string) -- [REQUIRED]

      The tag key. Tag keys must be unique per resource.

    • Value (string) -- [REQUIRED]

      The tag value.

Return type

dict

Returns

Response Syntax

{
    'ExperimentArn': 'string'
}

Response Structure

• (dict) --

  • ExperimentArn (string) --

    The Amazon Resource Name (ARN) of the experiment.

Exceptions

• SageMaker.Client.exceptions.ResourceLimitExceeded