ms-hydra-core-fork 1.6.0.dev1
A framework for elegantly configuring complex applications
License: MIT
Keywords: command-line configuration yaml tab-completion

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    <i>A framework for elegantly configuring complex applications.</i>
  </p>
  <p align="center">
    <i>Check the <a href="https://hydra.cc/">website</a> for more information,<br>
    or click the thumbnail below for a one-minute video introduction to Hydra.</i>
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### Releases

#### 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)
* [Twitter](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}
}
```