Welcome to RL-Insight's documentation! ======================== RL-Insight is a performance insight and observability toolkit for reinforcement learning (RL) systems. It focuses on analyzing offline cluster logs first, and is evolving towards full-stack online monitoring and visualization. -------------------------------------------- .. _Contents: .. toctree:: :maxdepth: 2 :caption: Overview Architecture RL Timeline quickstart GMM Expert Load visualization quickstart Memory Parser quickstart .. toctree:: :maxdepth: 2 :caption: Data Specification Data Specification and Format Guide .. toctree:: :maxdepth: 2 :caption: Utils MSTX Preprocessing .. toctree:: :maxdepth: 2 :caption: Developer Guides Extending Guide BaseClusterParser Interface DataRule Extending Guide Memory Parser Guide Contribution ------------- RL-Insight is free software; you can redistribute it and/or modify it under the terms of the Apache License 2.0. We welcome contributions. Join us on `GitHub `_ for discussions. Contributions from the community are welcome! Please check out our `project roadmap `_ and `good first issues `_ to see where you can contribute. Code Linting and Formatting ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ We use pre-commit to help improve code quality. To initialize pre-commit, run: .. code-block:: bash pip install pre-commit pre-commit install To resolve CI errors locally, you can also manually run pre-commit by: .. code-block:: bash pre-commit run Adding CI tests ^^^^^^^^^^^^^^^^^^^^^^^^ If possible, please add CI test(s) for your new feature: 1. Find the most relevant workflow yml file. 2. Add related path patterns to the ``paths`` section if not already included. 3. Minimize the workload of the test script(s) (see existing scripts for examples).