Accelerating AI Research in Universities with Amazon SageMaker HyperPod

This post was written with Mohamed Hossam of Brightskies.

Research universities engaged in large-scale AI and high-performance computing (HPC) often face significant infrastructure challenges that impede innovation and delay research outcomes. Traditional on-premises HPC clusters come with long GPU procurement cycles, rigid scaling limits, and complex maintenance requirements. These obstacles restrict researchers’ ability to iterate quickly on AI workloads such as natural language processing (NLP), computer vision, and foundation model (FM) training. Amazon SageMaker HyperPod alleviates the undifferentiated heavy lifting involved in building AI models. It helps quickly scale model development tasks such as training, fine-tuning, or inference across a cluster of hundreds or thousands of AI accelerators (NVIDIA GPUs H100, A100, and others) integrated with preconfigured HPC tools and automated scaling.

In this post, we demonstrate how a research university implemented SageMaker HyperPod to accelerate AI research by using dynamic SLURM partitions, fine-grained GPU resource management, budget-aware compute cost tracking, and multi-login node load balancing—all integrated seamlessly into the SageMaker HyperPod environment.

Accelerating AI Research with Amazon SageMaker HyperPod: A Transformative Case Study

This post was written in collaboration with Mohamed Hossam of Brightskies.

Research universities engaged in large-scale AI and high-performance computing (HPC) often confront significant infrastructure challenges that can hinder innovation and delay research outcomes. Traditional on-premises HPC clusters present a number of obstacles, including long GPU procurement cycles, rigid scaling limits, and complex maintenance requirements. These challenges restrict researchers’ ability to effectively iterate on AI workloads such as natural language processing (NLP), computer vision, and foundation model training.

Amazon SageMaker HyperPod alleviates much of the undifferentiated heavy lifting involved in building AI models, allowing researchers to quickly scale model development tasks—be it training, fine-tuning, or inference—across a cluster of hundreds or thousands of AI accelerators (including NVIDIA GPUs like H100 and A100). With preconfigured HPC tools and automated scaling, SageMaker HyperPod transforms the landscape for researchers.

In this post, we’ll explore how a research university successfully implemented SageMaker HyperPod to accelerate AI research through dynamic SLURM partitions, fine-grained GPU resource management, budget-aware compute cost tracking, and multi-login node load balancing.

Solution Overview

Amazon SageMaker HyperPod is designed to support large-scale machine learning operations specifically for researchers and ML scientists. This service is fully managed by AWS, removing operational overhead while ensuring enterprise-grade security and performance.

Architecture Insights

Users can access SageMaker HyperPod using secure AWS connections (like AWS Site-to-Site VPN or AWS Direct Connect). Traffic is efficiently distributed across login nodes through a Network Load Balancer, which serves as the primary entry point for job submission and cluster interaction. The architecture centers around SageMaker HyperPod compute:

• Controller Node: Orchestrates cluster operations.
• Compute Nodes: Arranged in a grid configuration to support efficient distributed training workloads.

In addition, the storage infrastructure integrates Amazon FSx for Lustre for high-performance file systems and Amazon S3 for secure dataset storage, ensuring both fast data access and persistent training artifacts.

Step-by-Step Deployment

Here’s a summary of how to deploy and configure SageMaker HyperPod.

Prerequisites

Before diving in, ensure the following prerequisites are ready:

• AWS Configuration: Set up the AWS Command Line Interface (AWS CLI) with the necessary permissions.
• Cluster Configuration Files: Prepare cluster-config.json and provisioning-parameters.json.
• Network Setup: Ensure an AWS Identity and Management (IAM) role is configured.

Launch the CloudFormation Stack

Utilizing an AWS CloudFormation stack provisions the necessary infrastructure components:

$ aws cloudformation create-stack --stack-name <stack-name> --template-body file://<template-file>

Customize SLURM Cluster Configuration

Create SLURM partitions to align compute resources with departmental research needs (e.g., NLP, computer vision). Utilize SLURM’s configuration to define custom partitions and enable accounting.

Provision and Validate the Cluster

Validating your configuration files and creating the cluster can be done via CLI:

$ aws sagemaker create-cluster --cli-input-json file://cluster-config.json

Implement Cost Tracking and Budget Enforcement

Each SageMaker HyperPod resource was tagged to track monthly spending, allowing efficient usage and predictable research budgets. Configure alerts to notify researchers when approaching budget thresholds.

Enable Load Balancing for Login Nodes

As concurrent users increased, a multi-login node architecture was adopted, using EC2 Auto Scaling groups for load balancing and session management.

Configure Federated Access and User Mapping

Integrate AWS IAM Identity Center with on-premises Active Directory (AD) to manage user identities across SageMaker HyperPod, aligning access with institutional policies.

Post-Deployment Optimizations

To prevent idle sessions from consuming compute resources, SLURM was configured with Pluggable Authentication Modules (PAM) for automatic logouts, along with QoS policies to control resource consumption.

Clean Up

To avoid ongoing charges, delete the SageMaker HyperPod cluster and CloudFormation stack using:

$ aws sagemaker delete-cluster --cluster-name <cluster-name>
$ aws cloudformation delete-stack --stack-name <stack-name>

Conclusion

Amazon SageMaker HyperPod enables research universities to access a powerful, fully-managed HPC solution tailored for AI workloads. By automating infrastructure provisioning, scaling, and resource optimization, institutes can accelerate innovation while maintaining budget control and operational efficiency. Through customized SLURM configurations, GPU sharing, federated access, and robust load balancing, SageMaker HyperPod showcases its potential to revolutionize research computing, allowing researchers to focus on science rather than infrastructure.

For more details on maximizing SageMaker HyperPod, check out the SageMaker HyperPod workshop and explore related blog posts.

About the Authors

Tasneem Fathima is a Senior Solutions Architect at AWS, supporting Higher Education and Research customers in the UAE to leverage cloud technologies and improve their time to science.

Mohamed Hossam is a Senior HPC Cloud Solutions Architect at Brightskies. He specializes in HPC and AI infrastructure on AWS, assisting universities and research institutions in accelerating AI adoption and migrating HPC workloads to the AWS Cloud. In his spare time, Mohamed enjoys video gaming.