Train AI Models on Amazon SageMaker HyperPod EKS | Amazon Web Services

In this part 2, we go hands-on with distributed model training on SageMaker HyperPod. Learn how the HyperPod Training Operator (HPTO) reduces recovery time from minutes to seconds with process-level fault tolerance and real-time health monitoring. We walk through building a GPU-optimized container image, configuring FSDP (Fully Sharded Data Parallel) to train a 1-billion parameter Llama model across multiple nodes, demonstrate how HyperPod’s self-healing capabilities keep your training jobs running with minimal interruption, and integrate the cluster with Amazon Managed Prometheus and Amazon Managed Grafana for cluster observability.

AI on SageMaker HyperPod – our new website and GitHub repo containing Slurm and EKS reference architectures, training and inference code samples, tips and tricks, and setup guides based on 2+ years and way to many cluster deployments to count. Linked here: https://go.aws/46rtVtb

Learn more:
Amazon SageMaker HyperPod Documentation: https://go.aws/4qS4Qi7
GitHub code of presentation demo: https://go.aws/3ZSa7eK
ML Framework’s team repository: https://go.aws/3OJ9Ic4

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