Ray provides a simple, universal API for building distributed applications.
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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