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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