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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, 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.

Published by awslabs ↗awslabs/agent-plugins

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Use hyperpod-version-checker to check component versions across [HyperPod cluster nodes] and produce a compatibility and cross-node mismatch report for the applicable GPU, networking, ML-runtime, and container components.

Required inputs

[HyperPod cluster nodes]

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SKILL.md

name: hyperpod-version-checkerdescription: 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.metadata:   version: "1.0.0"

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+