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c7056309c4
This PR revamps and aligns the README and Ray intro doc page: New "What is Ray" diagram that introduces AIR vs Ray core (diagram TBD finalized, this is the working placeholder) Update the description of Ray Link out to the user guides for key libraries and key concepts Remove old / broken links, as well as the inline library descriptions from the README
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4.5 KiB
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123 lines
4.5 KiB
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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://forms.gle/9TSdDYUgxYs8SA9e8
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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://twitter.com/raydistributed
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Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads:
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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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Learn more about `Ray AIR`_ and its libraries:
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- `Datasets`_: Distributed Data Preprocessing
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- `Train`_: Distributed Training
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- `Tune`_: Scalable Hyperparameter Tuning
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- `RLlib`_: Scalable Reinforcement Learning
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- `Serve`_: Scalable and Programmable Serving
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Or more about `Ray Core`_ and its key abstractions:
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- `Tasks`_: Stateless functions executed in the cluster.
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- `Actors`_: Stateful worker processes created in the cluster.
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- `Objects`_: Immutable values accessible across the cluster.
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Ray runs on any machine, cluster, cloud provider, and Kubernetes, and also features a growing
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`ecosystem of community integrations`_.
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Install Ray with: ``pip install ray``. For nightly wheels, see the
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`Installation page <https://docs.ray.io/en/latest/installation.html>`__.
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.. _`Serve`: https://docs.ray.io/en/latest/serve/index.html
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.. _`Datasets`: https://docs.ray.io/en/latest/data/dataset.html
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.. _`Workflow`: https://docs.ray.io/en/latest/workflows/concepts.html
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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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More Information
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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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*Older documents:*
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- `Ray paper`_
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- `Ray HotOS paper`_
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.. _`Ray AIR`: 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 whitepaper`: https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/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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Getting Involved
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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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* - Platform
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- Purpose
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- Estimated Response Time
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- Support Level
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* - `Discourse Forum`_
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- For discussions about development and questions about usage.
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- < 1 day
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- Community
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* - `GitHub Issues`_
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- For reporting bugs and filing feature requests.
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- < 2 days
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- Ray OSS Team
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* - `Slack`_
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- For collaborating with other Ray users.
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- < 2 days
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- Community
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* - `StackOverflow`_
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- For asking questions about how to use Ray.
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- 3-5 days
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- Community
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* - `Meetup Group`_
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- For learning about Ray projects and best practices.
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- Monthly
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- Ray DevRel
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* - `Twitter`_
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- For staying up-to-date on new features.
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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://twitter.com/raydistributed
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.. _`Slack`: https://forms.gle/9TSdDYUgxYs8SA9e8
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