Guidance for Training Protein Language Models (ESM-2) with Amazon SageMaker HyperPod
Overview
This Guidance demonstrates how to streamline and accelerate the training of complex protein folding AI models using AWS SageMaker HyperPod's managed platform. By leveraging NVIDIA GPUs and automated cluster provisioning, researchers can significantly simplify the distributed training process for generative AI models like ESM-2. The solution addresses key challenges in high-performance computing for life sciences, enabling efficient model customization and deployment at scale. This approach helps research teams reduce operational complexity while maximizing computational resources, ultimately accelerating breakthrough discoveries in protein research and drug development.
Benefits
Accelerate ML model training deployment
Streamline ESM-2 model training with pre-configured HyperPod clusters that automatically handle distributed computing requirements. Reduce time-to-market while maintaining operational excellence through automated infrastructure deployment.
Optimize ML infrastructure costs
Reserve compute capacity through Flexible Training Plans and On-Demand Capacity Reservations for predictable pricing. Scale ML training resources efficiently while maintaining cost optimization through managed infrastructure.
Enhance ML operations visibility
Monitor training progress through comprehensive observability tools that provide real-time metrics. Track cluster health and performance indicators while maintaining operational excellence through unified dashboards.
How it works
Deploy SageMaker HyperPod cluster with SLURM orchestrator
This reference architecture demonstrates how to deploy Amazon SageMaker AI HyperPod clusters based on HPC (SLURM) orchestrator.
Run protein language model distributed training workloads on HyperPod-SLURM clusters
This reference architecture demonstrates how to run distributed ESM-2 model training jobs on a SLURM based HyperPod cluster.
Deploy SageMaker HyperPod cluster with Amazon EKS (Kubernetes) orchestrator
This reference architecture demonstrates how to deploy SageMaker HyperPod clusters based on Amazon EKS orchestrator.
Run protein language model distributed training workloads on HyperPod-EKS clusters
This reference architecture demonstrates how to run distributed ESM-2 training jobs on an Amazon EKS based HyperPod cluster.
Deploy with confidence
We'll walk you through it
Dive deep into the implementation guide for additional customization options and service configurations to tailor to your specific needs.
Let's make it happen
Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs.
Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
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