Experiment tracking in SageMaker Training Jobs, Processing Jobs, and Notebooks.
SageMaker Experiments is an AWS service for tracking machine learning Experiments. The SageMaker Experiments Python SDK is a high-level interface to this service that helps you track Experiment information using Python.
Experiment tracking powers the machine learning integrated development environment Amazon SageMaker Studio.
For detailed API reference please go to: Read the Docs
- Experiment: A collection of related Trials. Add Trials to an Experiment that you wish to compare together.
- Trial: A description of a multi-step machine learning workflow. Each step in the workflow is described by a Trial Component. There is no relationship between Trial Components such as ordering.
- Trial Component: A description of a single step in a machine learning workflow. For example data cleaning, feature extraction, model training, model evaluation, etc...
- Tracker: A Python context-manager for logging information about a single TrialComponent.
For more information see Amazon SageMaker Experiments - Organize, Track, and Compare Your Machine Learning Trainings
You can use this SDK to:
- Manage Experiments, Trials, and Trial Components within Python scripts, programs, and notebooks.
- Add tracking information to a SageMaker notebook, allowing you to model your notebook in SageMaker Experiments as a multi-step ML workflow.
- Record experiment information from inside your running SageMaker Training and Processing Jobs.
pip install sagemaker-experimentsSee: sagemaker-experiments in AWS Labs Amazon SageMaker Examples.
This library is licensed under the Apache 2.0 License.
Unit Tests
tox tests/unitIntegration Tests
To run the integration tests, the following prerequisites must be met:
- AWS account credentials are available in the environment for the boto3 client to use.
- The AWS account has an IAM role with SageMaker permissions.
tox tests/integ