diff --git a/README.en.md b/README.en.md new file mode 100644 index 0000000..978e713 --- /dev/null +++ b/README.en.md @@ -0,0 +1,140 @@ +.. 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 + +.. image:: https://img.shields.io/badge/Ray-Join%20Slack-blue + :target: https://www.ray.io/join-slack + +.. image:: https://img.shields.io/badge/Discuss-Ask%20Questions-blue + :target: https://discuss.ray.io/ + +.. image:: https://img.shields.io/twitter/follow/raydistributed.svg?style=social&logo=twitter + :target: https://x.com/raydistributed + +.. image:: https://img.shields.io/badge/Get_started_for_free-3C8AE9?logo=data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8%2F9hAAAAAXNSR0IArs4c6QAAAERlWElmTU0AKgAAAAgAAYdpAAQAAAABAAAAGgAAAAAAA6ABAAMAAAABAAEAAKACAAQAAAABAAAAEKADAAQAAAABAAAAEAAAAAA0VXHyAAABKElEQVQ4Ea2TvWoCQRRGnWCVWChIIlikC9hpJdikSbGgaONbpAoY8gKBdAGfwkfwKQypLQ1sEGyMYhN1Pd%2B6A8PqwBZeOHt%2FvsvMnd3ZXBRFPQjBZ9K6OY8ZxF%2B0IYw9PW3qz8aY6lk92bZ%2BVqSI3oC9T7%2FyCVnrF1ngj93us%2B540sf5BrCDfw9b6jJ5lx%2FyjtGKBBXc3cnqx0INN4ImbI%2Bl%2BPnI8zWfFEr4chLLrWHCp9OO9j19Kbc91HX0zzzBO8EbLK2Iv4ZvNO3is3h6jb%2BCwO0iL8AaWqB7ILPTxq3kDypqvBuYuwswqo6wgYJbT8XxBPZ8KS1TepkFdC79TAHHce%2F7LbVioi3wEfTpmeKtPRGEeoldSP%2FOeoEftpP4BRbgXrYZefsAI%2BP9JU7ImyEAAAAASUVORK5CYII%3D + :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/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 +---------------- + +.. 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://x.com/raydistributed +.. _`Slack`: https://www.ray.io/join-slack?utm_source=github&utm_medium=ray_readme&utm_campaign=getting_involved