Readme
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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 MLTrain_: Distributed TrainingTune_: Scalable Hyperparameter TuningRLlib_: Scalable Reinforcement LearningServe_: Scalable and Programmable ServingOr 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
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.
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
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Discourse Forum_GitHub Issues_Slack_StackOverflow_Meetup Group_Twitter_.. _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
FAQs
Ray provides a simple, universal API for building distributed applications.
We found that ray demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 26 open source maintainers collaborating on the project.
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