[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-70805":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":27,"readmeContent":28,"aiSummary":29,"trendingCount":16,"starSnapshotCount":16,"syncStatus":30,"lastSyncTime":31,"discoverSource":32},70805,"hydra","facebookresearch\u002Fhydra","facebookresearch","Hydra is a framework for elegantly configuring complex applications","https:\u002F\u002Fhydra.cc",null,"Python",10434,860,126,281,0,6,27,77,18,102.01,"MIT License",false,"main",true,[],"2026-06-12 04:00:57","\u003Cp align=\"center\">\u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Ffacebookresearch\u002Fhydra\u002Fmain\u002Fwebsite\u002Fstatic\u002Fimg\u002FHydra-Readme-logo2.svg\" alt=\"logo\" width=\"70%\" \u002F>\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fhydra-core\u002F\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fhydra-core\" alt=\"PyPI\" \u002F>\u003C\u002Fa>\n  \u003Ca href=\"#\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Fhydra-core\" alt=\"PyPI - Python Version\" \u002F>\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fhydra\u002Factions\u002Fworkflows\u002Fcore_tests.yml\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Factions\u002Fworkflow\u002Fstatus\u002Ffacebookresearch\u002Fhydra\u002Fcore_tests.yml?branch=main\" alt=\"GitHub Actions build\" \u002F>\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fwww.pepy.tech\u002Fprojects\u002Fhydra-core?versions=0.11.*&versions=1.0.*&versions=1.1.*&versions=1.2.*&versions=1.3.*&versions=1.4.*\">\u003Cimg src=\"https:\u002F\u002Fpepy.tech\u002Fbadge\u002Fhydra-core\u002Fmonth\" alt=\"Downloads\" \u002F>\u003C\u002Fa>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"#\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fl\u002Fhydra-core\" alt=\"PyPI - License\" \u002F>\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fhydra-framework.zulipchat.com\u002F\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fzulip-join_chat-brightgreen.svg\" alt=\"Zulip chat\" \u002F>\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fpsf\u002Fblack\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fcode%20style-black-000000.svg\" alt=\"Code style: black\" \u002F>\u003C\u002Fa>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Ci>A framework for elegantly configuring complex applications.\u003C\u002Fi>\n\u003C\u002Fp>\n\u003Cp align=\"center\">\n  \u003Ci>Check the \u003Ca href=\"https:\u002F\u002Fhydra.cc\u002F\">website\u003C\u002Fa> for more information,\u003Cbr>\n  or click the thumbnail below for a one-minute video introduction to Hydra.\u003C\u002Fi>\n\u003C\u002Fp>\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Slc3gRQpnBI\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Ffacebookresearch\u002Fhydra\u002Fmain\u002Fwebsite\u002Fstatic\u002Fimg\u002FHydra-Readme-video-thumbnail.jpg\" alt=\"1 minute overview\" width=\"240\" height=\"180\" border=\"10\" \u002F>\u003C\u002Fa>\n\u003C\u002Fp>\n\n----------------------\n\n### Releases\n\n#### Development\n\n**Hydra 1.4** is the current development version of Hydra.\n- [Documentation](https:\u002F\u002Fhydra.cc\u002Fdocs\u002Fintro\u002F)\n- Installation from source : `pip install git+https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fhydra.git`\n- Supported Python versions: 3.10 through 3.14.\n\n#### Stable\n\n**Hydra 1.3** is the stable version of Hydra.\n- [Documentation](https:\u002F\u002Fhydra.cc\u002Fdocs\u002F1.3\u002Fintro\u002F)\n- Installation : `pip install hydra-core --upgrade`\n\nSee the [NEWS.md](NEWS.md) file for a summary of recent changes to Hydra.\n\n### License\nHydra is licensed under [MIT License](LICENSE).\n\n## Hydra Ecosystem\n\n#### Check out these third-party libraries that build on Hydra's functionality:\n* [hydra-zen](https:\u002F\u002Fgithub.com\u002Fmit-ll-responsible-ai\u002Fhydra-zen): Pythonic utilities for working with Hydra. Dynamic config generation capabilities, enhanced config store features, a Python API for launching Hydra jobs, and more.\n* [lightning-hydra-template](https:\u002F\u002Fgithub.com\u002Fashleve\u002Flightning-hydra-template): user-friendly template combining Hydra with [Pytorch-Lightning](https:\u002F\u002Fgithub.com\u002FLightning-AI\u002Flightning) for ML experimentation.\n* [hydra-torch](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fhydra-torch): [configen](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fhydra\u002Ftree\u002Fmain\u002Ftools\u002Fconfigen)-generated configuration classes enabling type-safe PyTorch configuration for Hydra apps.\n* NVIDIA's DeepLearningExamples repository contains a Hydra Launcher plugin, the [distributed_launcher](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples\u002Ftree\u002F9c34e35c218514b8607d7cf381d8a982a01175e9\u002FTools\u002FPyTorch\u002FTimeSeriesPredictionPlatform\u002Fdistributed_launcher), which makes use of the pytorch [distributed.launch](https:\u002F\u002Fpytorch.org\u002Fdocs\u002Fstable\u002Fdistributed.html#launch-utility) API.\n\n#### Ask questions in Github Discussions or StackOverflow (Use the tag #fb-hydra or #omegaconf):\n* [Github Discussions](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fhydra\u002Fdiscussions)\n* [StackOverflow](https:\u002F\u002Fstackexchange.com\u002Ffilters\u002F391828\u002Fhydra-questions)\n* [Twitter](https:\u002F\u002Ftwitter.com\u002FHydra_Framework)\n\nCheck out the Meta AI [blog post](https:\u002F\u002Fai.facebook.com\u002Fblog\u002Freengineering-facebook-ais-deep-learning-platforms-for-interoperability\u002F) to learn about how Hydra fits into Meta's efforts to reengineer deep learning platforms for interoperability.\n\n### Citing Hydra\nIf you use Hydra in your research please use the following BibTeX entry:\n```BibTeX\n@Misc{Yadan2019Hydra,\n  author =       {Omry Yadan},\n  title =        {Hydra - A framework for elegantly configuring complex applications},\n  howpublished = {Github},\n  year =         {2019},\n  url =          {https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fhydra}\n}\n```\n","Hydra 是一个用于复杂应用程序优雅配置的框架。其核心功能包括支持配置文件的结构化管理、动态覆盖以及命令行参数解析等，使得开发者能够更加灵活地管理和调整应用配置。采用Python语言编写，遵循MIT许可协议。Hydra特别适用于需要频繁调整参数的科研实验、机器学习项目或是任何需要高度可配置性的软件开发场景中，通过提供一套统一且强大的配置解决方案来简化这些过程。",2,"2026-06-11 03:34:16","high_star"]