Optimizing GPU Resource Management Across Multiple AWS Accounts with SageMaker HyperPod Task Governance

Introduction to the Importance of GPUs in Generative AI Workloads

The Need for a Centralized GPU Infrastructure

Leveraging Amazon SageMaker HyperPod for Multi-Account Access

Understanding SageMaker HyperPod Task Governance

Solution Overview: Architecture for Multi-Account Resource Sharing

Cross-Account Access for Data Scientists

Setting Up IAM Roles for Secure Resource Allocation

Cross-Account Access to Prepared Data

Utilizing EKS Pod Identity and S3 Access Points

Conclusion: Effective Strategies for Scalable Cloud Deployments

About the Authors

Unlocking the Power of GPUs in Multi-Account Strategies: A Guide to SageMaker HyperPod and EKS

In today’s digital landscape, GPUs (Graphics Processing Units) have emerged as invaluable assets. Their adaptability across various applications—from running simulations and inference to supporting complex generative AI workloads—coupled with their limited supply and high costs, make them a precious resource for organizations. In this blog post, we’ll explore how enterprises can leverage centralized GPU computing infrastructures, like Amazon SageMaker HyperPod orchestrated by Amazon Elastic Kubernetes Service (EKS), to maximize GPU utilization across multiple accounts.

The Value of Centralized GPU Computing

Organizations venturing into generative AI or heavy data science projects often find themselves facing a conundrum: how to optimally share high-performance GPU resources among diverse teams, business units, or AWS accounts.

A centralized GPU infrastructure allows enterprises to:

• Maximize Resource Utilization: Sharing GPUs prevents resource silos, ensuring that expensive computing resources are not left underutilized.
• Enhance Financial Oversight: By employing multiple AWS accounts for different teams, businesses can watch their cloud expenditures more closely and maintain accountability.
• Improve Flexibility and Security: A multi-account strategy allows for granular control and isolation between business units, development environments, or production systems.

Accessing Shared SageMaker HyperPod Clusters

With a well-architected multi-account strategy, organizations can effectively manage workloads for their diverse data science teams. The integration of Amazon SageMaker HyperPod cluster governance facilitates smooth resource allocation, enabling cluster administrators to impose policies that ensure efficient compute usage.

Solution Overview

The following architecture depicts how an organization can split resources across multiple AWS accounts:

• Account A: Hosts the SageMaker HyperPod cluster.
• Account B: Houses data scientists.
• Account C: Manages data preparation and storage for training.

Setting Up Cross-Account Access

1. Cross-Account Access for Data Scientists

When a compute allocation is made using SageMaker HyperPod task governance, unique Kubernetes namespaces are created for each team within the EKS cluster.

• IAM Roles Setup:
  • A dedicated IAM role is assigned to each team as a cluster access role in Account A, granting permissions specific to that team’s namespace.
  • Data scientists in Account B will assume these roles via their own IAM roles.

For example, here’s how to set up the trust policy for the cluster access role in Account A:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "AWS": "arn:aws:iam::XXXXXXXXXXBBB:role/DataScientistRole"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

This ensures that data scientists from Team A cannot access resources allocated to Team B.

2. Cross-Account Access to Prepared Data

In conjunction with pod-level identity management, EKS Pod Identity allows pods in the EKS cluster to access data in Account C.

• Setup S3 Access Points:
  • Create S3 access points in Account C for streamlined data access.
  • Assign permissions for the data access role in Account A, thus enabling data scientists running jobs on the HyperPod cluster to read from the data stored in Account C.

Sample access point policy:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "AWS": "arn:aws:iam:::role/"
      },
      "Action": [
        "s3:ListBucket",
        "s3:GetObject"
      ],
      "Resource": [
        "arn:aws:s3:::accesspoint/",
        "arn:aws:s3:::accesspoint//object/*"
      ]
    }
  ]
}

Testing the Setup

After configuring access, it’s crucial to verify the successful cross-account access to data. You can test this by using the following command to list the accessible S3 buckets:

kubectl exec -it aws-test -n hyperpod-ns-team-a -- aws s3 ls s3://

Conclusion

By adopting a sophisticated multi-account strategy enabled by Amazon SageMaker HyperPod and EKS, organizations can significantly enhance their GPU resource management. With optimized task governance and streamlined cross-account access, enterprises are well-equipped to leverage their GPU computing infrastructure efficiently.

Ready to dive deeper into this multi-account setup? Check out the SageMaker HyperPod task governance documentation and AWS workshops to harness the true potential of your GPU resources!

About the Authors

Nisha Nadkarni – Senior GenAI Specialist Solutions Architect at AWS
Anoop Saha – Sr GTM Specialist at AWS
Kareem Syed-Mohammed – Product Manager at AWS
Rajesh Ramchander – Principal ML Engineer at AWS

This team is dedicated to helping organizations deploy large-scale AI solutions effectively and efficiently, ensuring that the right resources are utilized across the right teams. Join us in exploring the future of generative AI with AWS!