Skill

hyperpod-issue-report

Generates issue reports for HyperPod (EKS/Slurm) clusters by collecting node diagnostics via SSM, storing in S3 for troubleshooting and AWS Support.

AWS

Python

npx claudepluginhub awslabs/agent-plugins --plugin sagemaker-ai

Tool Access

This skill uses the workspace's default tool permissions.

Preview

Collect diagnostic logs from HyperPod cluster nodes via SSM, store results in S3. Supports both EKS and Slurm clusters with auto-detection. Uses the bundled `scripts/hyperpod_issue_report.py` for reliable parallel collection.

Supporting Assets

references/collection-details.mdreferences/troubleshooting.mdscripts/hyperpod_issue_report.py

SKILL.md

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Parent Repo Stars461

Parent Repo Forks57

Last CommitApr 1, 2026

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HyperPod Issue Report

Collect diagnostic logs from HyperPod cluster nodes via SSM, store results in S3. Supports both EKS and Slurm clusters with auto-detection. Uses the bundled scripts/hyperpod_issue_report.py for reliable parallel collection.

Prerequisites

AWS CLI configured with permissions: sagemaker:DescribeCluster, sagemaker:ListClusterNodes, ssm:StartSession, s3:PutObject, s3:GetObject, eks:DescribeCluster
Python 3.8+ and uv (see uv installation docs for install options)
SSM Agent running on target nodes; node IAM roles need s3:GetObject/s3:PutObject on the report bucket
For EKS clusters: kubectl installed and configured (see Workflow step 2)

Workflow

1. Gather Information

Collect from the user:

Cluster identifier (required): accepts cluster name or full cluster ARN (e.g., arn:aws:sagemaker:us-west-2:123456789012:cluster/abc123)
AWS region (required unless extractable from ARN)
S3 path for report storage (required, e.g. s3://bucket/prefix). If the user doesn't have a bucket, create one (e.g., s3://hyperpod-diagnostics-<account-id>-<region>)
Issue description (optional)
Target scope: all nodes, specific instance groups, or specific node IDs (optional)
Additional commands to run on nodes (optional)

2. Verify Environment

aws sts get-caller-identity
aws sagemaker describe-cluster --cluster-name <name-or-arn> --region <region>

If the S3 bucket doesn't exist, create it:

aws s3 mb s3://<bucket-name> --region <region>

For EKS clusters (check Orchestrator.Eks in describe-cluster output):

Ensure kubectl is installed (which kubectl). If missing, install it for the current platform.
Configure kubeconfig using the EKS cluster name from the describe-cluster response:
```
aws eks update-kubeconfig --name <eks-cluster-name> --region <region>
```

3. Run the Collection Script

uv run scripts/hyperpod_issue_report.py \
  --cluster <cluster-name-or-arn> \
  --region <region> \
  --s3-path s3://<bucket>[/prefix]

Use --help for all options including --instance-groups, --nodes, --command, --max-workers, and --debug. Note: --instance-groups and --nodes are mutually exclusive. Node identifiers accept instance IDs (i-*), EKS names (hyperpod-i-*), or Slurm names (ip-*).

4. Present Results

After collection, the script shows statistics and offers interactive download. Report the S3 location and offer to:

Download the report locally
Help analyze collected diagnostics (see references/collection-details.md for what's in each file)
Prepare a summary for AWS Support

Troubleshooting

See references/troubleshooting.md for error handling, large cluster tuning, and known limitations.