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Introducing auto scaling on Amazon SageMaker HyperPod

Machine Learning Blog



AWS has announced auto scaling for Amazon SageMaker HyperPod using Karpenter, an open-source Kubernetes node lifecycle manager. This feature provides managed node automatic scaling for machine learning workloads.

  • Enables just-in-time provisioning of compute resources
  • Supports scaling to zero nodes without maintaining dedicated infrastructure
  • Provides workload-aware node selection and automatic node consolidation
  • Integrates with SageMaker HyperPod's resilience and continuous provisioning capabilities
  • Allows customers to dynamically scale GPU nodes based on real-time demand

The solution can be further enhanced by integrating Kubernetes Event-driven Autoscaling (KEDA) to scale pods based on various metrics, creating a comprehensive auto scaling architecture for machine learning workloads.



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