What is Ray?
Ray is an open source framework that provides a simple, flexible, and universal API for building and running distributed applications.
Webinar | On demand
Learn how to get started with RLlib for scalable reinforcement learning, from training to serving.
Watch the videoRay Summit | On Demand
Missed Ray Summit? All the sessions and tutorials are now available on-demand.
Watch nowRay Core Tutorial | On Demand
New to Ray? Jump-start your learning with this free hands-on tutorial on Ray Core.
Watch the tutorial
Parallelize single machine code, with minimal changes
With simple primitives, Ray makes converting your single-machine code to run in distributed mode intuitive, flexible, and easy.
Horizontal scaling, without the hassle
Run the same code — from prototyping on your laptop to running a petabyte-scale system in production.
Ray handles all the tricky details of distributed execution — compute orchestration, scheduling, autoscaling, fault tolerance, and more — letting you enjoy seamless access to infinite compute.
Rich ecosystem of libraries
Ray includes a rich set of data processing and machine learning libraries, as well as hooks into popular ones like Tensorflow, PyTorch, XGBoost and more. Scale your end-to-end machine learning application on a single distributed compute substrate, eliminating architectural complexity and simplifying operations.
Deploy your way
Seamlessly scale workloads on infrastructure of your choosing — public cloud, private data centers, bare metal, Kubernetes cluster, etc.
Or choose Anyscale, and leave the infrastructure to us for bonus operational happiness.
Ready to get started?
Follow along these short tutorials to begin your Ray journey.
Sign up for product updates
By signing up, you agree to receive occasional emails from Anyscale. Your personal data will be processed in accordance with Anyscale’s Privacy Policy.