Ray is the AI Compute Engine
Ray manages, executes, and optimizes compute needs across AI workloads. It unifies infrastructure via a single, flexible framework—enabling any AI workload from data processing to model training to model serving and beyond.
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10,000+ organizations build with Ray
Key Features
Seamless Scale
There's a reason Ray is the world’s leading AI compute engine. Simple primitives and one Python decorator make scaling from your laptop to the cloud a breeze.
Python Native with Ecosystem Integrations
Ray's Python-first API is extensible and open. Not to mention, it natively integrates with your entire ML Ops ecosystem, including:
- ML frameworks like Pytorch and Tensorflow
- Specialized libraries like vLLM and TRT-LLM
- ML Ops tools like W&B and MLFlow
Unmatched Precision
Coordinate heterogeneous resources with ease. Run your AI workloads on CPUs, GPUs, TPUs, and more with the ability to partition for fine grained optimization of utilization for every AI workload.
Libraries for Developers
We've built ML libraries so you don't have to. Accelerate development efforts with Ray Data, Ray Train, Ray Tune, Ray Serve, and RLLIb. Powered by Ray’s AI compute engine, the libraries offer easy and familiar APIs for the most common AI workloads.
Ray Libraries
The AI Compute Engine for Every Workload
Ray scalably and performantly executes AI workloads so you can focus on what matters. Spend less time on the complexity of modern machine infrastructure and more time running end-to-end machine learning workflows.
Data Preprocessing
Ray Data is the best-in-class choice for unstructured data processing, with support for any data modality and streaming capabilities to speed up training for traditional deep learning and Generative AI workloads.
Legacy You Can Lean On
