Ray compatibility

2025-09-25

4 min read time

Applies to Linux

Ray is a unified framework for scaling AI and Python applications from your laptop to a full cluster, without changing your code. Ray consists of a core distributed runtime and a set of AI libraries for simplifying machine learning computations.

Ray is a general-purpose framework that runs many types of workloads efficiently. Any Python application can be scaled with Ray, without extra infrastructure.

ROCm support for Ray is upstreamed, and you can build the official source code with ROCm support:

• ROCm support for Ray is hosted in the official ROCm/ray repository.

• Due to independent compatibility considerations, this location differs from the ray-project/ray upstream repository.

• To install Ray, use the prebuilt Docker image which includes ROCm, Ray, and all required dependencies.

  • See the ROCm Ray installation guide for instructions to get started.

  • See the Installation section in the upstream Ray documentation.

  • The Docker image provided is based on the upstream Ray Daily Release (Nightly) wheels corresponding to commit 005c372.

Note

Ray is supported on ROCm 6.4.1.

Supported devices

Officially Supported: AMD Instinct™ MI300X, MI210

Use cases and recommendations

• The Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm Integration blog provides an overview of Volcano Engine Reinforcement Learning (verl) for large language models (LLMs) and discusses its benefits in large-scale reinforcement learning from human feedback (RLHF). It uses Ray as part of a hybrid orchestration engine to schedule and coordinate training and inference tasks in parallel, enabling optimized resource utilization and potential overlap between these phases. This dynamic resource allocation strategy significantly improves overall system efficiency. The blog presents verl’s performance results, focusing on throughput and convergence accuracy achieved on AMD Instinct™ MI300X GPUs. Follow this guide to get started with verl on AMD Instinct GPUs and accelerate your RLHF training with ROCm-optimized performance.

• The Exploring Use Cases for Scalable AI: Implementing Ray with ROCm Support for Efficient ML Workflows blog post describes key use cases such as training and inference for large language models (LLMs), model serving, hyperparameter tuning, reinforcement learning, and the orchestration of large-scale workloads using Ray in the ROCm environment.

For more use cases and recommendations, see the AMD GPU tabs in the Accelerator Support topic of the Ray core documentation and refer to the AMD ROCm blog, where you can search for Ray examples and best practices to optimize your workloads on AMD GPUs.

Docker image compatibility

AMD validates and publishes ready-made ROCm Ray Docker images with ROCm backends on Docker Hub. The following Docker image tags and associated inventories represent the latest Ray version from the official Docker Hub and are validated for ROCm 6.4.1. Click the icon to view the image on Docker Hub.

Docker image	Ray	Pytorch	Ubuntu	Python
rocm/ray	2.48.0.post0	2.6.0+git684f6f2	24.04	3.12.10