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:target: https://forms.gle/9TSdDYUgxYs8SA9e8
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:target: https://discuss.ray.io/
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:target: https://twitter.com/raydistributed
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:target: https://www.anyscale.com/ray-on-anyscale?utm_source=github&utm_medium=ray_readme&utm_campaign=get_started_badge
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.
Learn more about Monitoring and Debugging:
- Monitor Ray apps and clusters with the `Ray Dashboard <https://docs.ray.io/en/latest/ray-core/ray-dashboard.html>`__.
- Debug Ray apps with the `Ray Distributed Debugger <https://docs.ray.io/en/latest/ray-observability/ray-distributed-debugger.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/ray-overview/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://www.ray.io/join-slack?utm_source=github&utm_medium=ray_readme&utm_campaign=getting_involved
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"description": ".. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png\n\n.. image:: https://readthedocs.org/projects/ray/badge/?version=master\n :target: http://docs.ray.io/en/master/?badge=master\n\n.. image:: https://img.shields.io/badge/Ray-Join%20Slack-blue\n :target: https://forms.gle/9TSdDYUgxYs8SA9e8\n\n.. image:: https://img.shields.io/badge/Discuss-Ask%20Questions-blue\n :target: https://discuss.ray.io/\n\n.. image:: https://img.shields.io/twitter/follow/raydistributed.svg?style=social&logo=twitter\n :target: https://twitter.com/raydistributed\n\n.. image:: https://img.shields.io/badge/Get_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://www.anyscale.com/ray-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://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 <https://docs.ray.io/en/latest/ray-core/ray-dashboard.html>`__.\n- Debug Ray apps with the `Ray Distributed Debugger <https://docs.ray.io/en/latest/ray-observability/ray-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 <https://docs.ray.io/en/latest/ray-overview/installation.html>`__.\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. 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