sagemaker-hyperpod

Provision and manage SageMaker HyperPod clusters on EKS and Slurm.

5|1|Updated Feb 10, 2026
One-click install
npx skills add https://github.com/dgallitelli/aws-hyperpod-skill --skill sagemaker-hyperpod
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: sagemaker-hyperpod
Source: https://github.com/dgallitelli/aws-hyperpod-skill/tree/main
Command: npx skills add https://github.com/dgallitelli/aws-hyperpod-skill --skill sagemaker-hyperpod

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jq, awscli, bc, and includes scripts (resource) and references (resource) components.

What problem does it solve?

HyperPod cluster provisioning and management for distributed ML training in SageMaker, enabling teams to deploy scalable GPU and Trainium-based clusters quickly and with governance.

Core Features & Use Cases

  • Unified provisioning across EKS and Slurm for HyperPod clusters
  • Seamless job submission and monitoring for distributed training workloads
  • Prerequisites checks, quota awareness, and add-on compatibility validation
  • Troubleshooting guidance and production-grade best practices

Quick Start

Install the HyperPod CLI, run hyp init to create a cluster stack, validate the configuration with hyp validate, and deploy the cluster with hyp create.

Frequently Asked Questions about sagemaker-hyperpod

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I provision a SageMaker HyperPod cluster for distributed ML training?

To provision a SageMaker HyperPod cluster, use the HyperPod CLI to initialize a stack with hyp init, validate the configuration with hyp validate, and deploy the cluster using hyp create for GPU and Trainium workloads.

Can I use EKS and Slurm to orchestrate SageMaker HyperPod clusters?

Yes, SageMaker HyperPod supports both EKS and Slurm orchestrators for cluster creation, job submission, and monitoring across distributed training workloads on multi-node GPU and Trainium environments.

What prerequisites do I need to check before creating a HyperPod cluster?

Before creating a HyperPod cluster, you must pass prerequisites validation, verify AWS service quotas, integrate lifecycle scripts, and ensure add-on compatibility to maintain reliable and compliant deployments.

How do I submit and monitor distributed training jobs on SageMaker HyperPod?

You can submit and monitor distributed training jobs on SageMaker HyperPod by utilizing the integrated EKS or Slurm orchestrator commands provided by the Skill for multi-node production-grade workloads.

How do I troubleshoot a SageMaker HyperPod cluster deployment?

To troubleshoot a SageMaker HyperPod deployment, use the built-in troubleshooting guidance to validate prerequisites, check service quotas, and verify lifecycle script integration for production-grade workloads.

Does SageMaker HyperPod support Trainium and GPU accelerators for multi-node workloads?

Yes, SageMaker HyperPod provisions and manages distributed ML training clusters equipped with both Trainium and GPU accelerators for scalable production-grade multi-node workloads.