/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
$ npx -y skills add awslabs/agent-plugins --skill hyperpod-version-checker --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/hyperpod-version-checker
Context preview
The summary Claude sees to decide when to auto-load this 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
SKILL.md
hyperpod-version-checker.SKILL.mdname: 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
# 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+ |
Read more
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
# 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+ |
Read this in other languages: 日本語 Generative AI can make mistakes. You should consider reviewing all output and costs generated by your chosen AI model and agentic coding assistant. See AWS Responsible AI Policy.
Other skills on agent-plugins.
- /amazon-location-service
Integrates Amazon Location Service APIs for AWS applications. Use this skill when users want to add maps (interactive MapLibre or static images); geocode addresses to coordinates or reverse geocode coordinates to addresses; calculate routes, travel times, or service areas; find
Open skill - /amplify-workflow
Build and deploy full-stack web and mobile apps with AWS Amplify Gen2
Open skill - /api-gateway
Build, manage, and operate APIs with Amazon API Gateway (REST, HTTP, and WebSocket). Triggers on phrases like: API Gateway, REST API, HTTP API, WebSocket API, custom domain, Lambda authorizer, usage plan, throttling, CORS, VPC link, private API. Also covers troubleshooting API
Open skill - /aws-lambda-durable-functions
Build resilient, long-running, multi-step applications with AWS Lambda durable functions with automatic state persistence, retry logic, and orchestration for long-running executions. Covers the critical replay model, step operations, wait/callback patterns, error handling with
Open skill - /aws-lambda-managed-instances
Evaluate, configure, and migrate workloads to AWS Lambda Managed Instances (LMI). Triggers on: Lambda Managed Instances, LMI, capacity provider, multi-concurrency Lambda, dedicated instance Lambda, EC2-backed Lambda, cold start elimination, Graviton Lambda, instance type for
Open skill - /aws-lambda-microvms
Build, run, debug, and operate applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments that run inside a container with up to 8-hour lifetimes. Triggers on: Lambda MicroVMs, Firecracker isolation, snapshot-resumable compute,
Open skill

