In this Part 1 video, we explore the key challenges of training and deploying large-scale generative AI models — from hardware failures and underutilization to scalability and observability — and introduce Amazon SageMaker HyperPod as the purpose-built solution. We walk through HyperPod’s architecture, its EKS-based orchestration layer, the full ML software stack, and key HyperPod features that help team improve ML performance and resource utilization.
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/4aARwdd
Learn more:
Amazon SageMaker HyperPod Documentation: https://go.aws/4qWpii8
GitHub code of presentation demo: https://go.aws/4s8z9lN
ML Framework’s team repository: https://go.aws/4rHd3Hi
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