.. 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://www.ray.io/join-slack
.. 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://x.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://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 MLTrain_: Distributed TrainingTune_: Scalable Hyperparameter TuningRLlib_: Scalable Reinforcement LearningServe_: Scalable and Programmable Serving
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 Ray apps and clusters with the
Ray Dashboard__. - Debug Ray apps with the
Ray Distributed Debugger__.
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/data.html
.. _Workflow: https://docs.ray.io/en/latest/workflows/
.. _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
_
- Ray paper
_ - Ray HotOS paper
_ - Ray Architecture v1 whitepaper
_
: 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://x.com/raydistributed
.. _Slack`: https://www.ray.io/join-slack?utm_source=github&utm_medium=ray_readme&utm_campaign=getting_involved