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- .. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png
- .. image:: https://readthedocs.org/projects/ray/badge/?version=master
- :target: http://docs.ray.io/en/master/?badge=master
- .. image:: https://img.shields.io/badge/Ray-Join%20Slack-blue
- :target: https://forms.gle/9TSdDYUgxYs8SA9e8
- .. image:: https://img.shields.io/badge/Discuss-Ask%20Questions-blue
- :target: https://discuss.ray.io/
- .. image:: https://img.shields.io/twitter/follow/raydistributed.svg?style=social&logo=twitter
- :target: https://twitter.com/raydistributed
- |
- Ray 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:
- .. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/what-is-ray-padded.svg
- ..
- https://docs.google.com/drawings/d/1Pl8aCYOsZCo61cmp57c7Sja6HhIygGCvSZLi_AuBuqo/edit
- Learn more about `Ray AI Libraries`_:
- - `Data`_: Scalable Datasets for ML
- - `Train`_: Distributed Training
- - `Tune`_: Scalable Hyperparameter Tuning
- - `RLlib`_: Scalable Reinforcement Learning
- - `Serve`_: Scalable and Programmable Serving
- Or more about `Ray Core`_ and its key abstractions:
- - `Tasks`_: Stateless functions executed in the cluster.
- - `Actors`_: Stateful worker processes created in the cluster.
- - `Objects`_: Immutable values accessible across the cluster.
- Monitor and debug Ray applications and clusters using the `Ray dashboard <https://docs.ray.io/en/latest/ray-core/ray-dashboard.html>`__.
- Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing
- `ecosystem of community integrations`_.
- Install Ray with: ``pip install ray``. For nightly wheels, see the
- `Installation page <https://docs.ray.io/en/latest/installation.html>`__.
- .. _`Serve`: https://docs.ray.io/en/latest/serve/index.html
- .. _`Data`: https://docs.ray.io/en/latest/data/dataset.html
- .. _`Workflow`: https://docs.ray.io/en/latest/workflows/concepts.html
- .. _`Train`: https://docs.ray.io/en/latest/train/train.html
- .. _`Tune`: https://docs.ray.io/en/latest/tune/index.html
- .. _`RLlib`: https://docs.ray.io/en/latest/rllib/index.html
- .. _`ecosystem of community integrations`: https://docs.ray.io/en/latest/ray-overview/ray-libraries.html
- Why Ray?
- --------
- Today'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.
- Ray is a unified way to scale Python and AI applications from a laptop to a cluster.
- With 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.
- More Information
- ----------------
- - `Documentation`_
- - `Ray Architecture whitepaper`_
- - `Exoshuffle: large-scale data shuffle in Ray`_
- - `Ownership: a distributed futures system for fine-grained tasks`_
- - `RLlib paper`_
- - `Tune paper`_
- *Older documents:*
- - `Ray paper`_
- - `Ray HotOS paper`_
- - `Ray Architecture v1 whitepaper`_
- .. _`Ray AI Libraries`: https://docs.ray.io/en/latest/ray-air/getting-started.html
- .. _`Ray Core`: https://docs.ray.io/en/latest/ray-core/walkthrough.html
- .. _`Tasks`: https://docs.ray.io/en/latest/ray-core/tasks.html
- .. _`Actors`: https://docs.ray.io/en/latest/ray-core/actors.html
- .. _`Objects`: https://docs.ray.io/en/latest/ray-core/objects.html
- .. _`Documentation`: http://docs.ray.io/en/latest/index.html
- .. _`Ray Architecture v1 whitepaper`: https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/preview
- .. _`Ray Architecture whitepaper`: https://docs.google.com/document/d/1tBw9A4j62ruI5omIJbMxly-la5w4q_TjyJgJL_jN2fI/preview
- .. _`Exoshuffle: large-scale data shuffle in Ray`: https://arxiv.org/abs/2203.05072
- .. _`Ownership: a distributed futures system for fine-grained tasks`: https://www.usenix.org/system/files/nsdi21-wang.pdf
- .. _`Ray paper`: https://arxiv.org/abs/1712.05889
- .. _`Ray HotOS paper`: https://arxiv.org/abs/1703.03924
- .. _`RLlib paper`: https://arxiv.org/abs/1712.09381
- .. _`Tune paper`: https://arxiv.org/abs/1807.05118
- Getting Involved
- ----------------
- .. list-table::
- :widths: 25 50 25 25
- :header-rows: 1
- * - Platform
- - Purpose
- - Estimated Response Time
- - Support Level
- * - `Discourse Forum`_
- - For discussions about development and questions about usage.
- - < 1 day
- - Community
- * - `GitHub Issues`_
- - For reporting bugs and filing feature requests.
- - < 2 days
- - Ray OSS Team
- * - `Slack`_
- - For collaborating with other Ray users.
- - < 2 days
- - Community
- * - `StackOverflow`_
- - For asking questions about how to use Ray.
- - 3-5 days
- - Community
- * - `Meetup Group`_
- - For learning about Ray projects and best practices.
- - Monthly
- - Ray DevRel
- * - `Twitter`_
- - For staying up-to-date on new features.
- - Daily
- - Ray DevRel
- .. _`Discourse Forum`: https://discuss.ray.io/
- .. _`GitHub Issues`: https://github.com/ray-project/ray/issues
- .. _`StackOverflow`: https://stackoverflow.com/questions/tagged/ray
- .. _`Meetup Group`: https://www.meetup.com/Bay-Area-Ray-Meetup/
- .. _`Twitter`: https://twitter.com/raydistributed
- .. _`Slack`: https://forms.gle/9TSdDYUgxYs8SA9e8
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