Pricing
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Overview
Ray is an open-source distributed computing framework for scaling Python and AI workloads from a laptop to large clusters. It provides a general execution layer for parallel and distributed tasks together with higher-level libraries for model training, hyperparameter tuning, reinforcement learning, data processing, and serving machine-learning applications. Developers can often scale existing Python code without manually building low-level distributed systems.
Ray is widely used in AI infrastructure because modern training, inference, and agent workloads frequently need to coordinate many CPUs, GPUs, processes, or machines. Components such as Ray Data, Ray Train, Ray Tune, RLlib, and Ray Serve cover different stages of the machine-learning lifecycle. The framework is best suited to engineering and research teams whose workloads have outgrown a single machine but who want a unified Python-native platform for distributed execution.
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 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/data.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
Releases
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