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135 lines
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135 lines
5.2 KiB
ReStructuredText
.. 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 simplifying ML compute:
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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 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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Why Ray?
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--------
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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.
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Ray is a unified way to scale Python and AI applications from a laptop to a cluster.
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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.
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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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- `Ray AIR Technical 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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.. _`Ray AIR Technical whitepaper`: https://docs.google.com/document/d/1bYL-638GN6EeJ45dPuLiPImA8msojEDDKiBx3YzB4_s/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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