Ray Clusters Deploy a Ray cluster on AWS, GCP, Azure, or Kubernetes to seamlessly scale workloads for production. Learn more

Overview # Ray is an open-source unified framework for scaling AI and Python applications like machine learning. It provides the compute layer for parallel processing so that you don’t need to be a …

Installing Ray # Ray currently officially supports x86_64, aarch64 (ARM) for Linux, and Apple silicon (M1) hardware. Ray on Windows is currently in beta.

User Guides # This section explains how to use Ray’s key concepts to build distributed applications. If you’re brand new to Ray, we recommend starting with the walkthrough.

Ray and its AI libraries provide a unified compute runtime for teams looking to simplify their ML platform. Ray’s libraries such as Ray Train, Ray Data, and Ray Serve can be used to compose end-to-end ML …

Getting Started # Ray is an open source unified framework for scaling AI and Python applications. It provides a simple, universal API for building distributed applications that can scale from a laptop to a …

Agentic-Ray Integration Apache Airflow® is an open-source platform that enables users to programmatically author, schedule, and monitor workflows using directed acyclic graphs (DAGs). …

To run this example, install the following: pip install "ray[tune]". In this quick-start example you minimize a simple function of the form f(x) = a**2 + b, our objective function. The closer a is to zero and the …

Algorithms # The following table is an overview of all available algorithms in RLlib. Note that all algorithms support multi-GPU training on a single (GPU) node in Ray (open-source) () as well as …

Configuring Ray # Note For running Java applications, see Java Applications. This page discusses the various ways to configure Ray, both from the Python API and from the command line. Take a look at …