--- title: "ray" type: "tool" slug: "ray-project-ray" canonical_url: "https://www.graphcanon.com/tools/ray-project-ray" github_url: "https://github.com/ray-project/ray" homepage_url: "https://ray.io" stars: 43153 forks: 7772 primary_language: "Python" license: "Apache-2.0" categories: ["model-training", "inference-serving", "developer-tools"] tags: ["data-science", "deep-learning", "hyperparameter-search", "distributed", "deployment", "llm", "large-language-models", "hyperparameter-optimization"] updated_at: "2026-07-07T18:18:38.961393+00:00" --- # ray > Unified framework for scaling AI and Python applications Ray is an open-source project that provides a core distributed runtime along with AI libraries to simplify machine learning compute tasks. It supports scalable datasets, distributed training, hyperparameter tuning, reinforcement learning, and model serving. ## Facts - Repository: https://github.com/ray-project/ray - Homepage: https://ray.io - Stars: 43,153 · Forks: 7,772 · Open issues: 3,465 · Watchers: 482 - Primary language: Python - License: Apache-2.0 - Last pushed: 2026-07-07T17:30:12+00:00 ## Categories - [Model Training](/categories/model-training.md) - [Inference & Serving](/categories/inference-serving.md) - [Developer Tools](/categories/developer-tools.md) ## Tags data-science, deep-learning, hyperparameter-search, distributed, deployment, llm, large language models, hyperparameter-optimization ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,962) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,417) - [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,019) - [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,347) - [JavaGuide](/tools/snailclimb-javaguide.md) - Snailclimb/JavaGuide: 面试 & 后端通用面试指南，覆盖计算机基础、数据库、分布式、高并发、系统设计与 AI 应用开发 (★ 156,863) - [open-webui](/tools/open-webui-open-webui.md) - User-friendly AI Interface (Supports Ollama, OpenAI API, ...) 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(★ 103,315) - [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) - Implement a ChatGPT-like LLM in PyTorch from scratch (★ 98,711) ## README (excerpt) ```text .. 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` ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/ray-project-ray`](/api/graphcanon/tools/ray-project-ray) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_