Développement
rocketride-server
A high-performance AI pipeline engine - build LLM workflows as portable .pipe files
★ 17,8 k étoilesMIT
Voir le guide →ray est un projet de développement open source, créé par ray-project (ray-project/ray sur GitHub). Il compte 43 994 étoiles sur GitHub et 8 123 forks, il est publié sous la licence Apache-2.0 et a été mis à jour pour la dernière fois sur GitHub le 8 octobre 2026.
An AI compute engine: a distributed runtime plus libraries for scaling ML and LLM workloads
Decide what you want from ray and check the README and requirements to confirm it fits your setup and Python environment.
Clone the repository from GitHub (github.com/ray-project/ray) or use the documented install channel if you do not need the source.
Follow the README quickstart to install dependencies and set any required configuration or credentials.
Work through the README's first example end to end, then adapt it to your own project or data.
A core distributed runtime for scaling Python workloads across machines.
Libraries for training, tuning, serving and reinforcement learning (RLlib).
Supports LLM inference and serving workloads at scale.
Ray Tune provides hyperparameter optimization for ML experiments.
Scales data processing for ML pipelines alongside training.
Works with PyTorch, TensorFlow and other frameworks.
Start by getting the source and entering the project directory.
git clone https://github.com/ray-project/ray.git
cd rayInstall the Python package from PyPI.
pip install rayImport ray, initialize it, and run a remote function as in the documentation examples.
python -c "import ray; ray.init(); print(ray.cluster_resources())" # see docs for full examplesIt is maintained by ray-project and contributors on GitHub, where the repository github.com/ray-project/ray is the canonical home for code, issues and releases.
Yes — it is open source under the Apache-2.0 license. Read the license in the repository for what is permitted, including any conditions on redistribution or commercial use.
No. Ray is infrastructure software that runs on your own machines or cloud account.
Compared with plain multiprocessing or Dask: Ray is purpose-built as an AI compute engine with ML libraries for training, tuning and serving, not just task parallelism.
Clone github.com/ray-project/ray, follow the README quickstart to install it, and run the documented first example before adapting it to your own work.
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.