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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. 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.
Features
- Python context-manager for logging information about a single TrialComponent
- 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
- This library is licensed under the Apache 2.0 License
- AWS account credentials are available in the environment for the boto3 client to use
Project Samples
