Getting Started Guide#

This tutorial will give you a quick tour of Ray’s features. To get started, we’ll start by installing Ray. Most of the examples in this guide are based on Python, but we’ll also show you how to use Ray Core in Java.


To use Ray in Python, install it with

pip install ray


To use Ray in Java, first add the ray-api and ray-runtime dependencies in your project.

Want to build Ray from source or with docker? Need more details? Check out our detailed installation guide.

Starting your first local Ray cluster#

pip install ray Successfully installed ray python import ray; ray.init() ... INFO -- Started a local Ray instance. View the dashboard at ...

Ray AI Runtime Quick Start#

Ray AI Runtime (AIR) is an open-source, Python-based, domain-specific library that equips ML engineers, data scientists, and researchers with a scalable and unified toolkit for ML applications. To use Ray’s AI Runtime install Ray with the optional extra air packages:

pip install "ray[air]"

Learn more about Ray AIR

Ray Libraries Quick Start#

Ray has a rich ecosystem of libraries and frameworks built on top of it. Simply click on the dropdowns below to see examples of our most popular libraries.

Ray Core Quick Start#

Ray Core provides simple primitives for building and running distributed applications. Below you find examples that show you how to turn your functions and classes easily into Ray tasks and actors, for both Python and Java.

Ray Cluster Quick Start#

You can deploy your applications on Ray clusters, often with minimal code changes to your existing code. See an example of this below.

Learn More#

Here are some talks, papers, and press coverage involving Ray and its libraries. Please raise an issue if any of the below links are broken, or if you’d like to add your own talk!

Blog and Press#

Talks (Videos)#