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https://github.com/ray-project/ray

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 accelerating ML workloads.
https://github.com/ray-project/ray

automl data-science deep-learning deployment distributed hyperparameter-optimization hyperparameter-search java llm-serving machine-learning model-selection optimization parallel python pytorch ray reinforcement-learning rllib serving tensorflow

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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 accelerating ML workloads.

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README 

        
.. 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



.. 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

   :target: https://console.anyscale.com/register/ha?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 `__.

- Debug Ray apps with the `Ray Distributed Debugger `__.



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 `__.



.. _`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