ray-project / ray

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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:

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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/ .. _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

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• • 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