Hyperpod Version Checker Agent Skill
Check and compare software component versions on SageMaker HyperPod cluster nodes - NVIDIA drivers, CUDA toolkit, cuDNN, NCCL, EFA, AWS OFI NCCL, GDRCopy, MPI, Neuron SDK (Trainium/Inferentia), Python, and PyTorch. Use when checking component versions, verifying CUDA/driver compatibility, detecting version mismatches across nodes, planning upgrades, documenting cluster configuration, or troubleshooting version-related issues on HyperPod. Triggers on requests about versions, compatibility, component checks, or upgrade planning for HyperPod clusters.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/awslabs/agent-plugins --skill hyperpod-version-checkerThe instruction itself
5 sections, as written by the authorHyperPod Version Checker
Upload to cluster nodes via hyperpod-ssm skill, then execute.
Usage
# Text report to console + file
bash hyperpod_check_versions.sh
# JSON only to stdout (text report still saved to file) — best for piping/parsing
bash hyperpod_check_versions.sh --json
# Custom output file
bash hyperpod_check_versions.sh --output /tmp/versions.txt
# No color (for logging)
bash hyperpod_check_versions.sh --no-color
Output file: component_versions_<hostname>_<timestamp>.txt (default)
What It Checks
| Component | Detection Method | Applicable When |
| ----------------- | ----------------------------------------------- | --------------------------------------------- |
| NVIDIA Driver | nvidia-smi | GPU instances (p3/p4/p5/g5) |
| CUDA Toolkit | nvcc, /usr/local/cuda symlink | GPU instances |
| cuDNN | Header file, packages | GPU instances doing deep learning |
| NCCL | Library filename, header, packages | Distributed GPU training |
| EFA | /opt/amazon/efa_installed_packages, fi_info | EFA-capable instances (p4d/p4de/p5/trn1/trn2) |
| AWS OFI NCCL | efa_installed_packages, library search | EFA + NCCL workloads |
| GDRCopy | rpm/dpkg, kernel module | GPU instances with RDMA (p4d+/p5) |
| MPI | mpirun, /opt/amazon/openmpi | Distributed training |
| Neuron SDK | neuronx-cc, neuron-ls, packages | Trainium/Inferentia (trn1/trn2/inf1/inf2) |
| Python/PyTorch | python3, torch import | ML workloads |
| Container runtime | docker, containerd, kubectl, nvidia-ctk | EKS clusters |
Multi-Node Comparison
Run on each node individually via the hyperpod-ssm skill. With --json, stdout is clean JSON for easy diffing.
Compatibility Reference
The script automatically analyzes CUDA/driver compatibility. For reference:
| Driver Series | Supported CUDA |
| ------------- | ----------------------------- |
| 580+ | 13.x, 12.x, 11.x |
| 570+ | 12.8+ (Blackwell), 12.x, 11.x |
| 545+ | 12.3-12.7, 11.x |
| 525-535 | 12.0-12.2, 11.x |
| 450+ | 11.x only |
NCCL: Use 2.18+ for CUDA 12.x, 2.12+ for CUDA 11.x. Must be consistent across all nodes.
| EFA Installer | AWS OFI NCCL |
| ------------- | --------------------- |
| 1.29+ | v1.7.3+ (recommended) |
| 1.26-1.28 | v1.7.0-v1.7.2 |
| 1.20-1.25 | v1.6.0+ |
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How to use it
Take awslabs/hyperpod-version-checker from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.