Introducing new Ray capabilities on SageMaker HyperPod | Amazon Web Services
Today, we are announcing new Ray capabilities on Amazon SageMaker HyperPod that integrate Ray with the HyperPod purpose-built infrastructure for foundation model training and serving.
Ray is an open-source framework that data scientists use to scale distributed Python workloads across clusters of GPUs, from distributed training with Ray Train to model serving with Ray Serve. On Kubernetes, Ray clusters are managed by KubeRay, an open-source operator that handles cluster lifecycle through custom resources (RayCluster, RayJob, RayService). SageMaker HyperPod provides purpose-built infrastructure for large-scale machine learning (ML) on Amazon Elastic Kubernetes Service (Amazon EKS) with built-in node health monitoring and automatic recovery. Until now, running Ray on Kubernetes required data scientists to write YAML manifests, manage Docker image rebuilds for every dependency change, set up kubectl port-forward to access their Ray Dashboard, and configure Prometheus and Grafana manually for observability.
With this launch, data scientists can create Ray clusters, open...
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