Amazon SageMaker HyperPod now offers troubleshooting skills for AI coding assistants
Amazon SageMaker HyperPod now provides troubleshooting skills that bring expert-level AI/ML cluster diagnostics directly into AI coding assistants such as Claude Code, Cursor, and Kiro. SageMaker HyperPod is a purpose-built infrastructure for developing, training, and deploying foundation models at scale. It provides a resilient and performant environment with built-in fault tolerance, and automated cluster recovery, reducing the undifferentiated heavy lifting of managing large-scale AI/ML infrastructure. HyperPod skills enable you to diagnose and resolve cluster issues through natural language, reducing the time and expertise required to troubleshoot distributed training and inference infrastructure.
Debugging GPU hardware faults, diagnosing NCCL communication failures, and identifying performance bottlenecks across large distributed clusters remains complex and time-consuming. Operators often need to manually SSM into nodes, parse logs across dozens of instances, and cross-reference documentation. The new HyperPod troubleshooting skills help with faster time to resolution with capabilities spanning cluster health validation, hardware and communication diagnostics, software version drifts, and automated diagnostic reporting. Each skill encodes AWS best practices into structured diagnostic workflows that systematically guides AI agents to collect evidence from your cluster nodes via AWS Systems Manager, analyze patterns, and provide actionable recommendations. The skills work with your existing HyperPod infrastructure — no modifications are required.
The HyperPod troubleshooting skills are open source and available today for both Slurm and Amazon EKS orchestrated HyperPod clusters via the SageMaker AI skills plugin. To get started, visit the AWSLabs github repository to install the sagemaker-ai plugin in your preferred coding assistant.