What is validate-gpu-cluster?

A health-check workflow for GPU clusters running Slurm or Kubernetes, systems that schedule computing workloads across multiple machines. It produces a machine-readable JSON result and passes only when all checks succeed.

Use it to check nodes, GPU availability, cluster subsystems, and optional CUDA or Slurm test jobs, then report the recorded failures or health counts.

It gives a consistent verdict after deployment or hardware and driver changes, while allowing read-only checks when running test jobs would be disruptive.

validate-gpu-cluster — Skill by NVIDIA

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/nvidia/deepops/validate-gpu-cluster

Any agent

npx skills add NVIDIA/deepops --skill validate-gpu-cluster

Clone the repo

git clone --depth 1 https://github.com/NVIDIA/deepops

Made for: Claude Code, Codex.

Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 681 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00054	$0.00681
Opus 5	$0.00027	$0.00341
Sonnet 5	$0.00011	$0.00136
Haiku 4.5	$0.00005	$0.00068

Measured 2d ago against content hash 838e129bb798, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

validate-gpu-cluster scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/validate-gpu-cluster/SKILL.md · 55 lines

How it starts

The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Validate a GPU cluster

Which validator

Slurm cluster → scripts/validation/validate_slurm.py (run on a cluster node)
Kubernetes cluster → scripts/validation/validate_k8s.py (run wherever kubectl reaches the cluster)
Not deployed yet / checking the provisioning environment → scripts/validation/deepops_doctor.py (run from the repository root)

All tools support --json and exit 0 only when every check passes. Full contract: docs/deepops/validation.md.

Procedure

Run the matching validator with --json.
```
python3 scripts/validation/validate_slurm.py --json
python3 scripts/validation/validate_k8s.py --json --cuda-smoke
```
Use --skip-gpu-job (Slurm) or omit --cuda-smoke (Kubernetes) when a strictly read-only check is required — for example on a busy production cluster where a test job would queue behind real work.
Read the verdict from the JSON, not from ad-hoc commands:
- ok: true — report healthy, include the key counts (nodes_total, gpus_configured/gpus_allocatable).
- ok: false — report each entry in failures verbatim; they name the failing subsystem and the next diagnostic step.
- Use the name-sorted nodes list to identify individual failures. Slurm entries report name, normalized state, and gpus_configured; Kubernetes entries report name, ready, and gpus_allocatable.
When a GPU check fails, do not conclude "driver broken" from a bare nvidia-smi over SSH — on Slurm nodes GPUs are hidden outside jobs. Follow skills/diagnose-driver-install/ instead.

Interpreting common results

Signal	Meaning
Slurm `nodes_unavailable > 0`	Node down/drained — `scontrol show node <name>` for the reason.
Slurm `gpus_configured: 0`	GRES not configured — check `config/group_vars/slurm-cluster.yml` GPU settings and rerun the Slurm playbook.
K8s `gpus_allocatable: 0`	GPU Operator stack not ready — `kubectl get pods -A \| grep -i nvidia`; first-deploy driver builds can take 10+ minutes.
K8s smoke pod `Pending`	No schedulable GPU or image pull failure — `kubectl -n deepops-validate describe pod`.
Direct `nvidia-smi` over SSH says "No devices were found" on a Slurm node	Expected GPU-hide behavior, not a failure.

Read the full file on GitHub · 55 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2d ago First seen · 55 lines · 54 tokens per session scan A 838e129bb798

Subscribe to this mod's changes

validate-gpu-cluster is a skill published in the GitHub repository NVIDIA/deepops (1,469 stars, last pushed 5d ago), licensed BSD-3-Clause. It adds 54 tokens to every session and 681 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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