{"data":{"slug":"ray-project-ray","name":"ray","tagline":"Unified framework for scaling AI and Python applications","github_url":"https://github.com/ray-project/ray","owner":"ray-project","repo":"ray","owner_avatar_url":"https://avatars.githubusercontent.com/u/22125274?v=4","primary_language":"Python","stars":43863,"forks":8052,"topics":["data-science","deep-learning","deployment","distributed","hyperparameter-optimization","hyperparameter-search","large-language-models","llm","llm-inference","llm-serving","machine-learning","optimization","parallel","python","pytorch","ray","reinforcement-learning","rllib","serving","tensorflow"],"archived":false,"github_pushed_at":"2026-09-18T06:15:07+00:00","maintenance_label":"Very active","stars_delta_30d":337,"url":"https://www.graphcanon.com/tools/ray-project-ray","markdown_url":"https://www.graphcanon.com/tools/ray-project-ray.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/ray-project-ray","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=ray-project-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.","homepage_url":"https://ray.io","license":"Apache-2.0","open_issues":3590,"watchers":486,"ai_summary":"Ray is a distributed runtime and a set of AI libraries for accelerating ML workloads, including distributed training, hyperparameter tuning, reinforcement learning, and scalable serving.","readme_excerpt":".. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png\n\n.. image:: https://readthedocs.org/projects/ray/badge/?version=master\n    :target: http://docs.ray.io/en/master/?badge=master\n\n.. image:: https://img.shields.io/badge/Ray-Join%20Slack-blue\n    :target: https://www.ray.io/join-slack\n\n.. image:: https://img.shields.io/badge/Discuss-Ask%20Questions-blue\n    :target: https://discuss.ray.io/\n\n.. image:: https://img.shields.io/twitter/follow/raydistributed.svg?style=social&logo=twitter\n    :target: https://x.com/raydistributed\n\n.. 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\n   :target: https://www.anyscale.com/ray-on-anyscale?utm_source=github&utm_medium=ray_readme&utm_campaign=get_started_badge\n\nRay 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:\n\n.. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/what-is-ray-padded.svg\n\n..\n  https://docs.google.com/drawings/d/1Pl8aCYOsZCo61cmp57c7Sja6HhIygGCvSZLi_AuBuqo/edit\n\nLearn more about `Ray AI Libraries`_:\n\n- `Data`_: Scalable Datasets for ML\n- `Train`_: Distributed Training\n- `Tune`_: Scalable Hyperparameter Tuning\n- `RLlib`_: Scalable Reinforcement Learning\n- `Serve`_: Scalable and Programmable Serving\n\nOr more about `Ray Core`_ and its key abstractions:\n\n- `Tasks`_: Stateless functions executed in the cluster.\n- `Actors`_: Stateful worker processes created in the cluster.\n- `Objects`_: Immutable values accessible across the cluster.\n\nLearn more about Monitoring and Debugging:\n\n- Monitor Ray apps and clusters with the `Ray Dashboard <https://docs.ray.io/en/latest/ray-core/ray-dashboard.html>`__.\n- Debug Ray apps with the `Ray Distributed Debugger <https://docs.ray.io/en/latest/ray-observability/ray-distributed-debugger.html>`__.\n\nRay runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing\n`ecosystem of community integrations`_.\n\nInstall Ray with: ``pip install ray``. For nightly wheels, see the\n`Installation page <https://docs.ray.io/en/latest/ray-overview/installation.html>`__.\n\n.. _`Serve`: https://docs.ray.io/en/latest/serve/index.html\n.. _`Data`: https://docs.ray.io/en/latest/data/data.html\n.. _`Workflow`: https://docs.ray.io/en/latest/workflows/\n.. _`Train`: https://docs.ray.io/en/latest/train/train.html\n.. _`Tune`: https://docs.ray.io/en/latest/tune/index.html\n.. _`RLlib`: https://docs.ray.io/en/latest/rllib/index.html\n.. _`ecosystem of community integrations`: https://docs.ray.io/en/latest/ray-overview/ray-libraries.html\n\n\nWhy Ray?\n--------\n\nToday'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.\n\nRay is a unified way to scale Python and AI applications from a laptop to a cluster.\n\nWith 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.\n\nMore Information\n----------------\n\n- `Documentation`_\n- `Ray Architecture whitepaper`_\n- `Exoshuffle: large-scale data shuffle in Ray`","github_created_at":"2016-10-25T19:38:30+00:00","created_at":"2026-07-07T17:31:29.637319+00:00","updated_at":"2026-09-20T04:32:25.885406+00:00","categories":[{"slug":"developer-tools","name":"Developer Tools","url":"https://www.graphcanon.com/categories/developer-tools","markdown_url":"https://www.graphcanon.com/categories/developer-tools.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/developer-tools"},{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"},{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"data-science","name":"data-science"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"deployment","name":"deployment"},{"slug":"distributed","name":"distributed"},{"slug":"hyperparameter-optimization","name":"hyperparameter-optimization"},{"slug":"large-language-models","name":"large-language-models"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"optimization","name":"optimization"}],"trust":{"provenance":{"is_fork":false,"github_id":71932349,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-18T08:39:30.599Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":4,"days_since_push":0,"last_release_at":"2026-08-23T05:42:08Z","stars_delta_30d":337,"open_issues_delta_30d":107},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-09-18T08:39:32.896Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-18T08:39:29.480Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-09-18T08:39:29.480Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-18T08:39:29.480Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need to perform distributed training, as Ray's Train library simplifies the setup and management of distributed training jobs.","For scalable hyperparameter tuning, Ray's Tune library provides a powerful interface for optimizing hyperparameters across multiple nodes.","When working with reinforcement learning, Ray's RLlib offers a unified API for implementing and scaling RL algorithms.","If you require scalable serving for machine learning models, Ray's Serve library allows for deploying and managing scalable model serving applications."],"when_not_to_use":["If your project is limited to single-node development and does not require scaling across multiple machines or clusters.","When the specific requirements of your project are better met by specialized tools that offer more tailored functionality for a particular task, such as TensorFlow for deep learning or PyTorch for GPU","If you are working in a non-Python environment, as Ray is primarily designed for Python applications."],"source":"enrich:decision_facts","observed_at":"2026-09-18T11:22:16.060Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Ray is a unified framework for scaling AI and Python applications, offering a core distributed runtime and AI libraries for accelerating ML workloads."}]}}