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Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute:\n\n.. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/what-is-ray-padded.svg\n\n..\n  https://docs.google.com/drawings/d/1Pl8aCYOsZCo61cmp57c7Sja6HhIygGCvSZLi_AuBuqo/edit\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://docs.ray.io/en/latest/ray-core/ray-dashboard.html\u003e`__.\n- Debug Ray apps with the `Ray Distributed Debugger \u003chttps://docs.ray.io/en/latest/ray-observability/ray-distributed-debugger.html\u003e`__.\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://docs.ray.io/en/latest/ray-overview/installation.html\u003e`__.\n\n.. _`Serve`: https://docs.ray.io/en/latest/serve/index.html\n.. _`Data`: https://docs.ray.io/en/latest/data/dataset.html\n.. _`Workflow`: https://docs.ray.io/en/latest/workflows/concepts.html\n.. _`Train`: https://docs.ray.io/en/latest/train/train.html\n.. _`Tune`: https://docs.ray.io/en/latest/tune/index.html\n.. _`RLlib`: https://docs.ray.io/en/latest/rllib/index.html\n.. _`ecosystem of community integrations`: https://docs.ray.io/en/latest/ray-overview/ray-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://docs.ray.io/en/latest/ray-air/getting-started.html\n.. _`Ray Core`: https://docs.ray.io/en/latest/ray-core/walkthrough.html\n.. _`Tasks`: https://docs.ray.io/en/latest/ray-core/tasks.html\n.. _`Actors`: https://docs.ray.io/en/latest/ray-core/actors.html\n.. _`Objects`: https://docs.ray.io/en/latest/ray-core/objects.html\n.. _`Documentation`: http://docs.ray.io/en/latest/index.html\n.. _`Ray Architecture v1 whitepaper`: https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/preview\n.. _`Ray Architecture whitepaper`: https://docs.google.com/document/d/1tBw9A4j62ruI5omIJbMxly-la5w4q_TjyJgJL_jN2fI/preview\n.. _`Exoshuffle: large-scale data shuffle in Ray`: https://arxiv.org/abs/2203.05072\n.. _`Ownership: a distributed futures system for fine-grained tasks`: https://www.usenix.org/system/files/nsdi21-wang.pdf\n.. _`Ray paper`: https://arxiv.org/abs/1712.05889\n.. _`Ray HotOS paper`: https://arxiv.org/abs/1703.03924\n.. _`RLlib paper`: https://arxiv.org/abs/1712.09381\n.. _`Tune paper`: https://arxiv.org/abs/1807.05118\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://discuss.ray.io/\n.. _`GitHub Issues`: https://github.com/ray-project/ray/issues\n.. _`StackOverflow`: https://stackoverflow.com/questions/tagged/ray\n.. _`Meetup Group`: https://www.meetup.com/Bay-Area-Ray-Meetup/\n.. _`Twitter`: https://twitter.com/raydistributed\n.. _`Slack`: https://www.ray.io/join-slack?utm_source=github\u0026utm_medium=ray_readme\u0026utm_campaign=getting_involved\n","funding_links":[],"categories":["Models and Tools","Python","HarmonyOS","Graph Computation","🛠️ General ML Testing Frameworks","🚀 Deployment \u0026 Serving","Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL)","Libraries","🎯 Tool Categories","Sensor Processing","Python / C++","参数优化","Python (144)","Pytorch \u0026 related libraries｜Pytorch \u0026 相关库","Supplementary tools","\u003cspan id=\"head41\"\u003e3.5. 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