A framework for elegantly configuring complex applications.
Check the website for more information.
---------------------- ### Releases #### Development **Hydra 1.4** is the current development version of Hydra. - Hydra 1.4 is coming soon. Until the stable release is available, you can install Hydra from development releases on PyPI. - [Documentation](https://hydra.cc/docs/intro/) - Installation: `pip install --pre --upgrade hydra-core` - Supported Python versions: 3.10 through 3.14. #### Stable **Hydra 1.3** is the stable version of Hydra. - [Documentation](https://hydra.cc/docs/1.3/intro/) - Installation : `pip install hydra-core --upgrade` See the [NEWS.md](NEWS.md) file for a summary of recent changes to Hydra. ### License Hydra is licensed under [MIT License](LICENSE). ## Hydra Ecosystem #### Check out these third-party libraries that build on Hydra's functionality: * [hydra-zen](https://github.com/mit-ll-responsible-ai/hydra-zen): Pythonic utilities for working with Hydra. Dynamic config generation capabilities, enhanced config store features, a Python API for launching Hydra jobs, and more. * [lightning-hydra-template](https://github.com/ashleve/lightning-hydra-template): user-friendly template combining Hydra with [Pytorch-Lightning](https://github.com/Lightning-AI/lightning) for ML experimentation. * [hydra-torch](https://github.com/pytorch/hydra-torch): [configen](https://github.com/facebookresearch/hydra/tree/main/tools/configen)-generated configuration classes enabling type-safe PyTorch configuration for Hydra apps. * NVIDIA's DeepLearningExamples repository contains a Hydra Launcher plugin, the [distributed_launcher](https://github.com/NVIDIA/DeepLearningExamples/tree/9c34e35c218514b8607d7cf381d8a982a01175e9/Tools/PyTorch/TimeSeriesPredictionPlatform/distributed_launcher), which makes use of the pytorch [distributed.launch](https://pytorch.org/docs/stable/distributed.html#launch-utility) API. #### Ask questions in Github Discussions or StackOverflow (Use the tag #fb-hydra or #omegaconf): * [Github Discussions](https://github.com/facebookresearch/hydra/discussions) * [StackOverflow](https://stackexchange.com/filters/391828/hydra-questions) * [X](https://twitter.com/Hydra_Framework) Check out the Meta AI [blog post](https://ai.facebook.com/blog/reengineering-facebook-ais-deep-learning-platforms-for-interoperability/) to learn about how Hydra fits into Meta's efforts to reengineer deep learning platforms for interoperability. ### Citing Hydra If you use Hydra in your research please use the following BibTeX entry: ```BibTeX @Misc{Yadan2019Hydra, author = {Omry Yadan}, title = {Hydra - A framework for elegantly configuring complex applications}, howpublished = {Github}, year = {2019}, url = {https://github.com/facebookresearch/hydra} } ```