DeepOps is a collection of automation tools for deploying and managing compute clusters that use NVIDIA GPUs with Kubernetes or Slurm. It supports environments ranging from full data-center clusters to individual machines needing GPU drivers, Docker, and NVIDIA container support.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/NVIDIA/deepopsnpx agentmods add skills/nvidia/deepops/validate-gpu-clusterWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia/deepops/validate-gpu-cluster)<a href="https://agentmods.dev/skills/nvidia/deepops/validate-gpu-cluster"><img src="https://agentmods.dev/badge/skills/nvidia/deepops/validate-gpu-cluster/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia/deepops/validate-gpu-cluster"><img src="https://agentmods.dev/badge/skills/nvidia/deepops/validate-gpu-cluster.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00054 | $0.00681 |
| Opus 5.5 | $0.00022 | $0.00272 |
| Sonnet 5.5 | $0.00011 | $0.00136 |
| Haiku 4.5 | $0.00005 | $0.00068 |
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 1mo 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.
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 whereverkubectlreaches 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-smokeUse
--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 infailuresverbatim; they name the failing subsystem and the next diagnostic step.- Use the name-sorted
nodeslist to identify individual failures. Slurm entries reportname, normalizedstate, andgpus_configured; Kubernetes entries reportname,ready, andgpus_allocatable.
-
When a GPU check fails, do not conclude "driver broken" from a bare
nvidia-smiover SSH — on Slurm nodes GPUs are hidden outside jobs. Followskills/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. |
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.
- 1mo ago First seen · 55 lines · 54 tokens per session scan A 838e129bb798
validate-gpu-cluster is a skill published in the GitHub repository NVIDIA/deepops (1,479 stars, last pushed today), licensed BSD-3-Clause. It adds 54 tokens to every session and 681 once invoked, about $0.0002 per session on Opus 5.5. 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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