ray-project/ray
Fork: 5841 Star: 34365 (更新于 2024-12-11 05:41:01)
license: Apache-2.0
Language: Python .
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
最后发布版本: ray-2.37.0 ( 2024-09-25 07:37:52)
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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:
.. 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 <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/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
最近版本更新:(数据更新于 2024-10-02 06:21:42)
2024-09-25 07:37:52 ray-2.37.0
2024-09-24 02:47:25 ray-2.36.1
2024-09-18 02:30:24 ray-2.36.0
2024-08-28 08:11:51 ray-2.35.0
2024-08-01 02:02:13 ray-2.34.0
2024-07-26 04:28:04 ray-2.33.0
2024-07-11 00:40:35 ray-2.32.0
2024-06-27 06:06:37 ray-2.31.0
2024-06-21 07:08:13 ray-2.30.0
2024-06-07 02:16:36 ray-2.24.0
主题(topics):
automl, data-science, deep-learning, deployment, distributed, hyperparameter-optimization, hyperparameter-search, java, llm-serving, machine-learning, model-selection, optimization, parallel, python, pytorch, ray, reinforcement-learning, rllib, serving, tensorflow
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