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Hyperpod Version Checker logo

Hyperpod Version Checker

Organization
awslabs
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.

Overview

Publisherawslabs
Repositoryagent-plugins
Skill namehyperpod-version-checker
Stars
917
Forks
162
Bundled files
1
LicenseApache-2.0
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by awslabs on GitHub. Read the source before you install it.

Installation

Install the Hyperpod Version Checker AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/hyperpod-version-checker
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/awslabs/agent-plugins.git /tmp/agent-plugins
mkdir -p .claude/skills
cp -r /tmp/agent-plugins/plugins/sagemaker-ai/skills/hyperpod-version-checker .claude/skills/hyperpod-version-checker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hyperpod Version Checker in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hyperpod Version Checker on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hyperpod Version Checker is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

ComponentDetection MethodApplicable When
NVIDIA Drivernvidia-smiGPU instances (p3/p4/p5/g5)
CUDA Toolkitnvcc, /usr/local/cuda symlinkGPU instances
cuDNNHeader file, packagesGPU instances doing deep learning
NCCLLibrary filename, header, packagesDistributed GPU training
EFA/opt/amazon/efa_installed_packages, fi_infoEFA-capable instances (p4d/p4de/p5/trn1/trn2)
AWS OFI NCCLefa_installed_packages, library searchEFA + NCCL workloads
GDRCopyrpm/dpkg, kernel moduleGPU instances with RDMA (p4d+/p5)
MPImpirun, /opt/amazon/openmpiDistributed training
Neuron SDKneuronx-cc, neuron-ls, packagesTrainium/Inferentia (trn1/trn2/inf1/inf2)
Python/PyTorchpython3, torch importML workloads
Container runtimedocker, containerd, kubectl, nvidia-ctkEKS 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 SeriesSupported CUDA
580+13.x, 12.x, 11.x
570+12.8+ (Blackwell), 12.x, 11.x
545+12.3-12.7, 11.x
525-53512.0-12.2, 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 InstallerAWS OFI NCCL
1.29+v1.7.3+ (recommended)
1.26-1.28v1.7.0-v1.7.2
1.20-1.25v1.6.0+

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Hyperpod Version Checker AI skill do?

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.

Why use Hyperpod Version Checker on TypingMind?

Because you install it once and use it with any model. Hyperpod Version Checker is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hyperpod Version Checker in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/awslabs/agent-plugins/tree/main/plugins/sagemaker-ai/skills/hyperpod-version-checker. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Hyperpod Version Checker?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hyperpod Version Checker?

As many as you like. As long as a model supports skills, you can use Hyperpod Version Checker with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hyperpod Version Checker AI skill free?

Yes. It is published on GitHub by awslabs under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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