Amazon SageMaker HyperPod enhances support for Ray

Amazon SageMaker HyperPod Ray Enhancements

Amazon SageMaker HyperPod now enhances support for Ray with built-in observability, resilient training, accelerated inference, and managed development environments. Ray is a popular open-source framework for scaling AI workloads on a unified compute layer.

HyperPod provides easier development, resilient training, and accelerated inference for Ray. Data scientists can create, edit, monitor, and delete Ray clusters from a web-based interface in Amazon SageMaker Studio, and attach JupyterLab, Code Editor, or a local IDE to a running Ray cluster for interactive iteration.

What to do

• Use the web-based interface in SageMaker Studio to manage Ray clusters.
• Attach JupyterLab, Code Editor, or a local IDE to a running Ray cluster for interactive development.
• Leverage built-in observability with Grafana dashboards and the Ray Dashboard.
• Utilize node auto recovery and hung job detection for resilient training.
• Deploy Amazon SageMaker JumpStart models directly with Ray Serve.

Ray support is available for HyperPod clusters orchestrated by Amazon EKS, in AWS Regions where SageMaker HyperPod is supported.

Source: AWS release notes

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