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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 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

Package profile

ray-cpp

Releases

Release table

PyPI Downloads

Dependencies

Details

Project URLs

Dependencies

Package description