[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-1108":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":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":45,"readmeContent":46,"aiSummary":47,"trendingCount":16,"starSnapshotCount":16,"syncStatus":48,"lastSyncTime":49,"discoverSource":50},1108,"ray","ray-project\u002Fray","ray-project","Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.","https:\u002F\u002Fray.io",null,"Python",42841,7669,480,2828,0,6,72,352,46,112,"Apache License 2.0",false,"master",[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,5,41,42,43,44],"data-science","deep-learning","deployment","distributed","hyperparameter-optimization","hyperparameter-search","large-language-models","llm","llm-inference","llm-serving","machine-learning","optimization","parallel","python","pytorch","reinforcement-learning","rllib","serving","tensorflow","2026-06-12 04:00:07",".. image:: https:\u002F\u002Fgithub.com\u002Fray-project\u002Fray\u002Fraw\u002Fmaster\u002Fdoc\u002Fsource\u002Fimages\u002Fray_header_logo.png\n\n.. image:: https:\u002F\u002Freadthedocs.org\u002Fprojects\u002Fray\u002Fbadge\u002F?version=master\n    :target: http:\u002F\u002Fdocs.ray.io\u002Fen\u002Fmaster\u002F?badge=master\n\n.. image:: https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FRay-Join%20Slack-blue\n    :target: https:\u002F\u002Fwww.ray.io\u002Fjoin-slack\n\n.. image:: https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDiscuss-Ask%20Questions-blue\n    :target: https:\u002F\u002Fdiscuss.ray.io\u002F\n\n.. image:: https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002Fraydistributed.svg?style=social&logo=twitter\n    :target: https:\u002F\u002Fx.com\u002Fraydistributed\n\n.. image:: https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGet_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\n   :target: https:\u002F\u002Fwww.anyscale.com\u002Fray-on-anyscale?utm_source=github&utm_medium=ray_readme&utm_campaign=get_started_badge\n\nRay is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute:\n\n.. image:: https:\u002F\u002Fgithub.com\u002Fray-project\u002Fray\u002Fraw\u002Fmaster\u002Fdoc\u002Fsource\u002Fimages\u002Fwhat-is-ray-padded.svg\n\n..\n  https:\u002F\u002Fdocs.google.com\u002Fdrawings\u002Fd\u002F1Pl8aCYOsZCo61cmp57c7Sja6HhIygGCvSZLi_AuBuqo\u002Fedit\n\nLearn more about `Ray AI Libraries`_:\n\n- `Data`_: Scalable Datasets for ML\n- `Train`_: Distributed Training\n- `Tune`_: Scalable Hyperparameter Tuning\n- `RLlib`_: Scalable Reinforcement Learning\n- `Serve`_: Scalable and Programmable Serving\n\nOr more about `Ray Core`_ and its key abstractions:\n\n- `Tasks`_: Stateless functions executed in the cluster.\n- `Actors`_: Stateful worker processes created in the cluster.\n- `Objects`_: Immutable values accessible across the cluster.\n\nLearn more about Monitoring and Debugging:\n\n- Monitor Ray apps and clusters with the `Ray Dashboard \u003Chttps:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fray-core\u002Fray-dashboard.html>`__.\n- Debug Ray apps with the `Ray Distributed Debugger \u003Chttps:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fray-observability\u002Fray-distributed-debugger.html>`__.\n\nRay runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing\n`ecosystem of community integrations`_.\n\nInstall Ray with: ``pip install ray``. For nightly wheels, see the\n`Installation page \u003Chttps:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fray-overview\u002Finstallation.html>`__.\n\n.. _`Serve`: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fserve\u002Findex.html\n.. _`Data`: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fdata\u002Fdataset.html\n.. _`Workflow`: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fworkflows\u002F\n.. _`Train`: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Ftrain\u002Ftrain.html\n.. _`Tune`: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Ftune\u002Findex.html\n.. _`RLlib`: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Frllib\u002Findex.html\n.. _`ecosystem of community integrations`: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fray-overview\u002Fray-libraries.html\n\n\nWhy Ray?\n--------\n\nToday's ML workloads are increasingly compute-intensive. As convenient as they are, single-node development environments such as your laptop cannot scale to meet these demands.\n\nRay is a unified way to scale Python and AI applications from a laptop to a cluster.\n\nWith Ray, you can seamlessly scale the same code from a laptop to a cluster. Ray is designed to be general-purpose, meaning that it can performantly run any kind of workload. If your application is written in Python, you can scale it with Ray, no other infrastructure required.\n\nMore Information\n----------------\n\n- `Documentation`_\n- `Ray Architecture whitepaper`_\n- `Exoshuffle: large-scale data shuffle in Ray`_\n- `Ownership: a distributed futures system for fine-grained tasks`_\n- `RLlib paper`_\n- `Tune paper`_\n\n*Older documents:*\n\n- `Ray paper`_\n- `Ray HotOS paper`_\n- `Ray Architecture v1 whitepaper`_\n\n.. _`Ray AI Libraries`: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fray-air\u002Fgetting-started.html\n.. _`Ray Core`: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fray-core\u002Fwalkthrough.html\n.. _`Tasks`: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fray-core\u002Ftasks.html\n.. _`Actors`: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fray-core\u002Factors.html\n.. _`Objects`: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Fray-core\u002Fobjects.html\n.. _`Documentation`: http:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Findex.html\n.. _`Ray Architecture v1 whitepaper`: https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c\u002Fpreview\n.. _`Ray Architecture whitepaper`: https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F1tBw9A4j62ruI5omIJbMxly-la5w4q_TjyJgJL_jN2fI\u002Fpreview\n.. _`Exoshuffle: large-scale data shuffle in Ray`: https:\u002F\u002Farxiv.org\u002Fabs\u002F2203.05072\n.. _`Ownership: a distributed futures system for fine-grained tasks`: https:\u002F\u002Fwww.usenix.org\u002Fsystem\u002Ffiles\u002Fnsdi21-wang.pdf\n.. _`Ray paper`: https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.05889\n.. _`Ray HotOS paper`: https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.03924\n.. _`RLlib paper`: https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.09381\n.. _`Tune paper`: https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.05118\n\nGetting Involved\n----------------\n\n.. list-table::\n   :widths: 25 50 25 25\n   :header-rows: 1\n\n   * - Platform\n     - Purpose\n     - Estimated Response Time\n     - Support Level\n   * - `Discourse Forum`_\n     - For discussions about development and questions about usage.\n     - \u003C 1 day\n     - Community\n   * - `GitHub Issues`_\n     - For reporting bugs and filing feature requests.\n     - \u003C 2 days\n     - Ray OSS Team\n   * - `Slack`_\n     - For collaborating with other Ray users.\n     - \u003C 2 days\n     - Community\n   * - `StackOverflow`_\n     - For asking questions about how to use Ray.\n     - 3-5 days\n     - Community\n   * - `Meetup Group`_\n     - For learning about Ray projects and best practices.\n     - Monthly\n     - Ray DevRel\n   * - `Twitter`_\n     - For staying up-to-date on new features.\n     - Daily\n     - Ray DevRel\n\n.. _`Discourse Forum`: https:\u002F\u002Fdiscuss.ray.io\u002F\n.. _`GitHub Issues`: https:\u002F\u002Fgithub.com\u002Fray-project\u002Fray\u002Fissues\n.. _`StackOverflow`: https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fray\n.. _`Meetup Group`: https:\u002F\u002Fwww.meetup.com\u002FBay-Area-Ray-Meetup\u002F\n.. _`Twitter`: https:\u002F\u002Fx.com\u002Fraydistributed\n.. _`Slack`: https:\u002F\u002Fwww.ray.io\u002Fjoin-slack?utm_source=github&utm_medium=ray_readme&utm_campaign=getting_involved\n","Ray 是一个用于扩展AI和Python应用程序的统一框架。它包含一个核心分布式运行时环境以及一系列简化机器学习计算的AI库。其核心功能包括支持分布式训练、超参数调优、强化学习等，并通过任务、演员和对象等抽象概念来管理分布式计算资源。Ray 适用于需要大规模并行处理的数据科学项目，如深度学习模型训练、超参数搜索及模型服务部署等场景。由于其强大的分布式计算能力和对多种后端的支持（包括本地机器、集群、云提供商和Kubernetes），Ray 成为了构建高性能AI应用的理想选择。",2,"2026-06-11 02:41:40","top_all"]