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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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.. 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.

Monitor and debug Ray applications and clusters using the Ray dashboard <https://docs.ray.io/en/latest/ray-core/ray-dashboard.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/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

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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://twitter.com/raydistributed .. Slack: https://forms.gle/9TSdDYUgxYs8SA9e8