What Is Ray?

Concepts

Ray is an open-source framework for building and running distributed applications. It was developed by the University of California, Berkeley, RISELab. It aims to simplify distributed computing of machine learning (ML) workloads and supports complex task graphs. This enables developers to write parallel or distributed programs that can efficiently use all available resources in the cluster.

Ray provides an easy-to-use API to implement parallel and distributed execution of tasks. It allows users to convert Python functions into remote procedure calls (RPCs) so that they can be concurrently executed on multiple nodes. In addition, Ray introduces the concept of dynamic task graph, which enables it to handle workloads that require flexible scheduling, such as reinforcement learning, hyperparameter tuning, and other iterative algorithms.

By providing support for distributed computing, Ray promotes faster model training and more efficient resource usage, and is a powerful tool for researchers and engineers who want to expand their applications on multiple machines. Additionally, the Ray ecosystem includes some advanced libraries, such as Ray Tune (for hyperparameter adjustment), RLlib (for reinforcement learning), and Ray Serve (for model services), to meet requirements in different scenarios.

Use Cases

As a distributed machine learning computing framework, Ray is typically used in scenarios such as model training and model fine-tuning. The parallel computing capability provided by Ray can significantly improve the computing efficiency.