---
name: hyperpod-version-checker
description: 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
```bash
# 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+ |