Creates an SageMaker experiment
Creates an 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.
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 Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS 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 update_experiment
API.
To get a list of all your experiments, call the
list_experiments API. To view an
experiment's properties, call the
describe_experiment API. To get a
list of all the trials associated with an experiment, call the
list_trials API. To create a trial call the
create_trial API.
sagemaker_create_experiment(ExperimentName, DisplayName, Description, Tags)
ExperimentName |
[required] The name of the experiment. The name must be unique in your AWS account and is not case-sensitive. |
DisplayName |
The name of the experiment as displayed. The name doesn't need to be
unique. If you don't specify |
Description |
The description of the experiment. |
Tags |
A list of tags to associate with the experiment. You can use
|
A list with the following syntax:
list( ExperimentArn = "string" )
svc$create_experiment(
ExperimentName = "string",
DisplayName = "string",
Description = "string",
Tags = list(
list(
Key = "string",
Value = "string"
)
)
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