Ray is an open-source framework for spreading Python and AI work across many machines, so the same code can grow from one laptop to a big cluster.
In plain words
Some jobs, such as training a big model or processing millions of files, are too large for one computer. Ray gives Python programs simple building blocks to run work in parallel, and it handles the hard parts of a distributed system, such as orchestrating all the parts. On top of that core it ships libraries for data processing, training, hyperparameter tuning, reinforcement learning and serving models. It can run on Kubernetes and the major clouds.
A quick example
Imagine a script that resizes a million images one at a time on a laptop. With Ray you mark the resize function so it can run as many parallel tasks. The same script can then run on your laptop's cores today and on a cluster of cloud machines tomorrow, with little change to the code.
Where you'll meet it
- Distributed model training and hyperparameter tuning.
- Large data processing jobs for machine learning.
- Serving models at scale on Kubernetes or cloud clusters.
Related
Sources
- DocsOverview, Ray documentation · read 29 Sept 2026
- Docsray-project/ray, Ray project (GitHub) · read 29 Sept 2026
Checked by WhatIsAI against the sources above · 29 Sept 2026. Spotted an error? Report it.