AWS Machine Learning Blog· Giuseppe Angelo Porcelli·· 8 d ago
Share GPU clusters across teams with isolation and fairness using Amazon SageMaker HyperPod
Share GPU clusters across teams with isolation and fairness using Amazon SageMaker HyperPod
AI summary
A reference architecture for securely sharing one Amazon SageMaker HyperPod EKS cluster across multiple teams, using AWS IAM Identity Center for authentication, per-team SageMaker Domains and Kubernetes namespaces for isolation, HyperPod Task Governance for fairness, and namespace-level cost allocation for chargeback.
Selection record
Threshold 60Official, first-handFirst 22Second 31
Not admittedSum of both 53 < twice the threshold 120
- Source tier
- Official, first-hand; this tier's threshold is 60
- Pre-filter
- passed：AWS博客详述SageMaker HyperPod多租户GPU集群AI训练架构
A model scores each item twice, independently, against one written standard, out of 100. An item is admitted only when the two scores add up to twice the threshold. The threshold is set per source tier.
Source: AWS Machine Learning Blog · aws.amazon.com