Amazon SageMaker HyperPod Administration and Best Practices Guide
AWS has shared new guidelines for the management and auditing of Amazon SageMaker HyperPod infrastructure for shared use by machine learning teams.
Amazon Web Services has published best practices for the management and auditing of Amazon SageMaker HyperPod infrastructure shared by multiple machine learning teams.
Shared Infrastructure and Governance Challenges
Machine learning teams gain access to large-scale accelerated computing pools for model training and fine-tuning.
When multiple teams share the same cluster, while the technical setup is straightforward, issues such as management and capacity sharing create challenges.
Four-Tier Control Mechanism
Governance processes are handled through four core control layers: organization, project, cluster, and workload.
Through these layers, regulations are established regarding who will use which capacity and how resources will be shared.
Integration with Unified Studio
With Amazon SageMaker Unified Studio integration, teams can connect their projects to existing clusters.
Users can launch workloads directly from their project workspaces, inspect cluster statuses, and open JupyterLab workflows.
Infrastructure and Operational Separation
Infrastructure teams can continue cluster management through cloud operations processes.
Approved computing resources are securely provided within the project context of machine learning teams.