Metadata-Version: 2.1
Name: sagemaker-experiments
Version: 0.1.4
Summary: Open source library for Experiment Tracking in SageMaker Jobs and Notebooks
Home-page: https://github.com/aws/sagemaker-experiment-tracking/
Author: Amazon Web Services
License: Apache License 2.0
Keywords: ML Amazon AWS AI Tensorflow MXNet
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Dist: boto3 (>=1.10.32)
Provides-Extra: test
Requires-Dist: tox (==3.13.1) ; extra == 'test'
Requires-Dist: flake8 ; extra == 'test'
Requires-Dist: pytest (==4.4.1) ; extra == 'test'
Requires-Dist: pytest-cov ; extra == 'test'
Requires-Dist: pytest-coverage ; extra == 'test'
Requires-Dist: pytest-rerunfailures ; extra == 'test'
Requires-Dist: pytest-xdist ; extra == 'test'

=====================
SageMaker Experiments
=====================
Experiment tracking in SageMaker Training Jobs, Processing Jobs, and Notebooks.

Overview
--------
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.

Concepts
--------

- 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 TrialComponent.
- TrialComponent: A description of a single step in a machine learning workflow. 
- Tracker: A Python context-manager for logging information about a single TrialComponent.

Using the SDK
-------------
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.

Examples
--------
See: `sagemaker-experiments <https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-experiments>`_ in `AWS Labs Amazon SageMaker Examples <https://github.com/awslabs/amazon-sagemaker-examples>`_. 

Installation
------------

``pip install sagemaker-experiments``.

License
-------
This library is licensed under the Apache 2.0 License. 


