experimental/cuda-ubi9/: hydra-core-1.3.2 metadata and description

A framework for elegantly configuring complex applications

author	Omry Yadan
author_email	omry@fb.com
classifiers	License :: OSI Approved :: MIT License Development Status :: 4 - Beta Programming Language :: Python :: 3.7 Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.9 Programming Language :: Python :: 3.10 Programming Language :: Python :: 3.11 Operating System :: POSIX :: Linux Operating System :: MacOS Operating System :: Microsoft :: Windows
description_content_type	text/markdown
keywords	command-line configuration yaml tab-completion
license	MIT
requires_dist	omegaconf <2.4,>=2.2 antlr4-python3-runtime ==4.9.* packaging importlib-resources ; python_version < "3.9"

File	Tox results	History
hydra_core-1.3.2-py3-none-any.whl Size 151 KB Type Python Wheel Python 3		Uploaded to experimental/cuda-ubi9 by experimental 2024-05-30 12:32:40

logo

A framework for elegantly configuring complex applications.

Check the website for more information,
or click the thumbnail below for a one-minute video introduction to Hydra.

Releases

Stable

Hydra 1.3 is the stable version of Hydra.

Documentation
Installation : pip install hydra-core --upgrade

See the NEWS.md file for a summary of recent changes to Hydra.

License

Hydra is licensed under MIT License.

Hydra Ecosystem

Check out these third-party libraries that build on Hydra's functionality:

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: user-friendly template combining Hydra with Pytorch-Lightning for ML experimentation.
hydra-torch: configen-generated configuration classes enabling type-safe PyTorch configuration for Hydra apps.
NVIDIA's DeepLearningExamples repository contains a Hydra Launcher plugin, the distributed_launcher, which makes use of the pytorch distributed.launch API.

Ask questions in Github Discussions or StackOverflow (Use the tag #fb-hydra or #omegaconf):

Check out the Meta AI blog post 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:

@Misc{Yadan2019Hydra,
  author =       {Omry Yadan},
  title =        {Hydra - A framework for elegantly configuring complex applications},
  howpublished = {Github},
  year =         {2019},
  url =          {https://github.com/facebookresearch/hydra}
}

devpi