An overview of some of the best reinforcement learning talks presented at the second Ray Summit

An overview of some of the best machine learning talks presented at Ray Summit 2021.

Learn how Ray can be paired with Apache Kafka to power streaming applications.

Ray is a general-purpose distributed system. One of Ray's goals is to seamlessly integrate data processing libraries (e.g., Dask, Spark) into distributed applications. As part of this goal, Ray provides a robust distributed memory manager. The goal...

XGBoost-Ray is a novel backend for distributed XGBoost training. It features multi node and multi GPU training, distributed data loading, advanced fault tolerance such as elastic training, and a seamless integration with hyperparameter optimization f...

Ray has many library integrations, from machine learning libraries such as Horovod and Hugging Face to data processing frameworks such as Spark, Modin, and Dask. But what does it mean to be "integrated with Ray"? And what benefits does it provide to...

In Ray 1.2.0, we’ve added a library for “collective communication primitives” to Ray. These primitives can be used in your Ray program to simplify the exchange of information across many distributed processes at the same time, speeding up certain dis...

This post explores how The Ray Cluster works. To demonstrate how to speed up compute by using Ray Clusters, this post will perform a compute-intensive image processing task (palette extraction) on a large dataset (thousands of movie posters).

Calling all Ray users! Take a few minutes to complete the Ray Community Pulse survey to let us know how your use Ray and help guide our roadmap.

A bold vision — to make distributed computing simple and accessible from anywhere. Open source model, with a developer-first mindset. A vibrant and rapidly growing community. And a stellar team, with a track record of building great technology to sol...