docs: make Chinese README the default
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<!-- WEHUB_ZH_README -->
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> [!NOTE]
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> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
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> [English](./README.en.md) · [原始项目](https://github.com/ray-project/ray) · [上游 README](https://github.com/ray-project/ray/blob/HEAD/README.rst)
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> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
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.. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png
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.. image:: https://readthedocs.org/projects/ray/badge/?version=master
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:target: http://docs.ray.io/en/master/?badge=master
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.. image:: https://img.shields.io/badge/Ray-Join%20Slack-blue
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:target: https://www.ray.io/join-slack
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.. image:: https://img.shields.io/badge/Discuss-Ask%20Questions-blue
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:target: https://discuss.ray.io/
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.. image:: https://img.shields.io/twitter/follow/raydistributed.svg?style=social&logo=twitter
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:target: https://x.com/raydistributed
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.. image:: https://img.shields.io/badge/Get_started_for_free-3C8AE9?logo=data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8%2F9hAAAAAXNSR0IArs4c6QAAAERlWElmTU0AKgAAAAgAAYdpAAQAAAABAAAAGgAAAAAAA6ABAAMAAAABAAEAAKACAAQAAAABAAAAEKADAAQAAAABAAAAEAAAAAA0VXHyAAABKElEQVQ4Ea2TvWoCQRRGnWCVWChIIlikC9hpJdikSbGgaONbpAoY8gKBdAGfwkfwKQypLQ1sEGyMYhN1Pd%2B6A8PqwBZeOHt%2FvsvMnd3ZXBRFPQjBZ9K6OY8ZxF%2B0IYw9PW3qz8aY6lk92bZ%2BVqSI3oC9T7%2FyCVnrF1ngj93us%2B540sf5BrCDfw9b6jJ5lx%2FyjtGKBBXc3cnqx0INN4ImbI%2Bl%2BPnI8zWfFEr4chLLrWHCp9OO9j19Kbc91HX0zzzBO8EbLK2Iv4ZvNO3is3h6jb%2BCwO0iL8AaWqB7ILPTxq3kDypqvBuYuwswqo6wgYJbT8XxBPZ8KS1TepkFdC79TAHHce%2F7LbVioi3wEfTpmeKtPRGEeoldSP%2FOeoEftpP4BRbgXrYZefsAI%2BP9JU7ImyEAAAAASUVORK5CYII%3D
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:target: https://www.anyscale.com/ray-on-anyscale?utm_source=github&utm_medium=ray_readme&utm_campaign=get_started_badge
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Ray 是一个用于扩展 AI 与 Python 应用的统一框架。Ray 由核心分布式运行时和一组用于简化 ML 计算的 AI 库组成：
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.. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/what-is-ray-padded.svg
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..
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https://docs.google.com/drawings/d/1Pl8aCYOsZCo61cmp57c7Sja6HhIygGCvSZLi_AuBuqo/edit
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了解更多关于 `Ray AI Libraries`_ 的信息：
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- `Data`_：面向 ML 的可扩展数据集
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- `Train`_：分布式训练
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- `Tune`_：可扩展超参数调优
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- `RLlib`_：可扩展强化学习
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- `Serve`_：可扩展且可编程的 Serving
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或了解更多关于 `Ray Core`_ 及其核心抽象的信息：
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- `Tasks`_：在集群中执行的无状态函数。
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- `Actors`_：在集群中创建的有状态 worker 进程。
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- `Objects`_：可在整个集群中访问的不可变值。
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了解更多关于监控与调试的信息：
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- 使用 `Ray Dashboard <https://docs.ray.io/en/latest/ray-core/ray-dashboard.html>`__ 监控 Ray 应用与集群。
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- 使用 `Ray Distributed Debugger <https://docs.ray.io/en/latest/ray-observability/ray-distributed-debugger.html>`__ 调试 Ray 应用。
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Ray 可在任何机器、集群、云服务商和 Kubernetes 上运行，并提供不断增长的 `ecosystem of community integrations`_。
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使用以下命令安装 Ray：``pip install ray``。如需 nightly 构建的 wheel，请参阅 `Installation page <https://docs.ray.io/en/latest/ray-overview/installation.html>`__。
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.. _`Serve`: https://docs.ray.io/en/latest/serve/index.html
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.. _`Data`: https://docs.ray.io/en/latest/data/data.html
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.. _`Workflow`: https://docs.ray.io/en/latest/workflows/
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.. _`Train`: https://docs.ray.io/en/latest/train/train.html
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.. _`Tune`: https://docs.ray.io/en/latest/tune/index.html
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.. _`RLlib`: https://docs.ray.io/en/latest/rllib/index.html
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.. _`ecosystem of community integrations`: https://docs.ray.io/en/latest/ray-overview/ray-libraries.html
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为什么选择 Ray？
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--------
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当今的 ML 工作负载对计算的需求日益增加。尽管单机开发环境（例如你的笔记本电脑）十分便捷，但无法扩展以满足这些需求。
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Ray 提供了一种统一方式，可将 Python 与 AI 应用从笔记本电脑扩展到集群。
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借助 Ray，你可以无缝地将同一份代码从笔记本电脑扩展到集群。Ray 被设计为通用框架，意味着它能够高效运行任何类型的工作负载。如果你的应用使用 Python 编写，就可以用 Ray 进行扩展，无需其他基础设施。
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更多信息
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----------------
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- `Documentation`_
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- `Ray Architecture whitepaper`_
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- `Exoshuffle: large-scale data shuffle in Ray`_
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- `Ownership: a distributed futures system for fine-grained tasks`_
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- `RLlib paper`_
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- `Tune paper`_
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*较早的文档：*
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- `Ray paper`_
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- `Ray HotOS paper`_
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- `Ray Architecture v1 whitepaper`_
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.. _`Ray AI Libraries`: https://docs.ray.io/en/latest/ray-air/getting-started.html
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.. _`Ray Core`: https://docs.ray.io/en/latest/ray-core/walkthrough.html
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.. _`Tasks`: https://docs.ray.io/en/latest/ray-core/tasks.html
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.. _`Actors`: https://docs.ray.io/en/latest/ray-core/actors.html
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.. _`Objects`: https://docs.ray.io/en/latest/ray-core/objects.html
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.. _`Documentation`: http://docs.ray.io/en/latest/index.html
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.. _`Ray Architecture v1 whitepaper`: https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/preview
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.. _`Ray Architecture whitepaper`: https://docs.google.com/document/d/1tBw9A4j62ruI5omIJbMxly-la5w4q_TjyJgJL_jN2fI/preview
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.. _`Exoshuffle: large-scale data shuffle in Ray`: https://arxiv.org/abs/2203.05072
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.. _`Ownership: a distributed futures system for fine-grained tasks`: https://www.usenix.org/system/files/nsdi21-wang.pdf
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.. _`Ray paper`: https://arxiv.org/abs/1712.05889
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.. _`Ray HotOS paper`: https://arxiv.org/abs/1703.03924
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.. _`RLlib paper`: https://arxiv.org/abs/1712.09381
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.. _`Tune paper`: https://arxiv.org/abs/1807.05118
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参与贡献
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----------------
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.. list-table::
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:widths: 25 50 25 25
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:header-rows: 1
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* - 平台
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- 用途
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- 预计响应时间
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- 支持级别
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* - `Discourse Forum`_
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- 用于讨论开发与使用相关的问题。
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- < 1 day
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- Community
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* - `GitHub Issues`_
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- 用于报告 bug 和提交功能请求。
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- < 2 days
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- Ray OSS Team
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* - `Slack`_
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- 用于与其他 Ray 用户协作。
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- < 2 days
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- Community
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* - `StackOverflow`_
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- 用于咨询 Ray 的使用方法。
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- 3-5 days
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- Community
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* - `Meetup Group`_
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- 用于了解 Ray 项目与最佳实践。
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- Monthly
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- Ray DevRel
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* - `Twitter`_
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- 用于及时了解新功能。
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- Daily
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- Ray DevRel
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.. _`Discourse Forum`: https://discuss.ray.io/
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.. _`GitHub Issues`: https://github.com/ray-project/ray/issues
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.. _`StackOverflow`: https://stackoverflow.com/questions/tagged/ray
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.. _`Meetup Group`: https://www.meetup.com/Bay-Area-Ray-Meetup/
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.. _`Twitter`: https://x.com/raydistributed
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.. _`Slack`: https://www.ray.io/join-slack?utm_source=github&utm_medium=ray_readme&utm_campaign=getting_involved
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