digitalocean/ray

digitalocean/ray

Description: Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

License: Apache-2.0

Stars: 1

Forks: 0

Open issues: 40

Created: 2025-06-11T18:52:51Z

Pushed: 2026-02-13T18:39:57Z

Default branch: master

Fork: yes

Parent repository: ray-project/ray

Archived: no

README: .. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png

.. image:: https://readthedocs.org/projects/ray/badge/?version=master :target: http://docs.ray.io/en/master/?badge=master

:target: https://www.ray.io/join-slack

:target: https://discuss.ray.io/

:target: https://twitter.com/raydistributed

:target: https://www.anyscale.com/ray-on-anyscale?utm_source=github&utm_medium=ray_readme&utm_campaign=get_started_badge

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:

.. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/what-is-ray-padded.svg

.. 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.

Learn more about Monitoring and Debugging:

• Monitor Ray apps and clusters with the Ray Dashboard __.
• Debug Ray apps with the Ray Distributed Debugger __.

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 `__.

.. _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 ----------------

.. list-table:: :widths: 25 50 25 25 :header-rows: 1

• - 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://www.ray.io/join-slack?utm_source=github&utm_medium=ray_readme&utm_campaign=getting_involved