Simplest path to
scaling Python
Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud — with no changes.
Webinar | On demand
Learn how to get started with RLlib for scalable reinforcement learning, from training to serving.
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Watch the tutorialParallelize Python, with minimal code changes
Simple, flexible Python primitives
Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes
Distributed libraries
Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries.
Integrations
Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations.
Building on top of Ray has allowed us to deliver a state-of-the-art low-code deep learning platform that lets our users focus on obtaining best-in-class machine learning models for their data, not distributed systems and infrastructure.
Travis Addair, CTO, Predibase and Maintainer, Horovod / Ludwig AI
Scalable machine learning libraries
Native Ray libraries — such as Ray Tune and Ray Serve — lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code.
Choose a workload to scale with Ray
Build and run distributed apps
(no distributed systems expertise needed)
Creating distributed apps is hard. Ray handles all aspects of distributed execution so that developers can create performant distributed apps without building and managing infrastructure or becoming distributed systems experts.
Flawless distributed operations
Ray handles all aspects of distributed execution from scheduling and sequencing to scaling and fault tolerance.
Autoscaling
Ray dynamically provisions new nodes (or removes them) to handle variable workload needs.
Fault-tolerant
Ray gracefully handles machine failures to deliver uninterrupted execution.
My team at Afresh trains large time-series forecasters with a massive hyperparameter space. We googled Pytorch hyperparameter tuning, and found Ray Lightning. It took me 20 minutes to integrate into my code, and it worked beautifully. I was honestly shocked.
Philip Cerles, Senior Machine Learning Engineer
Scale on any cloud or infrastructure
Public cloud, private data centers, bare metal, Kubernetes cluster — Ray runs anywhere. Or choose Anyscale, and leave the infrastructure to us.
Get started with Ray
From a dedicated Slack channels to in-person meetups, we have the resources you need to get started and be successful with Ray.
Documentation
Reference guides, tutorials, and examples to help you get started on and advance your Ray journey.
Discussion Forum
Join the forum to get technical help and share best practices and tips with the Ray community.
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