skill

hyperpod-version-checker

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

awslabs/agent-plugins/plugins/sagemaker-ai/skills/hyperpod-version-checkerofficiallistedunreviewednot running here yet

Use skill

Set up

Skills are installed by placing their folder where your agent loads skills.

Part of the collection awslabs/agent-plugins.

npx skills add awslabs/agent-plugins --skill hyperpod-version-checker

Or by hand: Copy the folder plugins/sagemaker-ai/skills/hyperpod-version-checker from github.com/awslabs/agent-plugins into ~/.claude/skills/hyperpod-version-checker/ (Claude Code) or your agent's skills folder. Check the collection's own README for other agents.

README

synced Oct 14, 2026

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.

HyperPod 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+