Deploy K8s Gpu Cluster Agent Skill
Deploy a Kubernetes GPU cluster with DeepOps (Kubespray + GPU Operator) and prove it schedules GPU pods. Use when asked to deploy or rebuild Kubernetes on GPU servers with this repository.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/NVIDIA/deepops --skill deploy-k8s-gpu-clusterThe instruction itself
4 sections, as written by the authorDeploy a Kubernetes GPU cluster
Preconditions
- Ubuntu 22.04/24.04 or RHEL/Rocky 8/9 hosts you may fully manage (driver
installs may reboot them; no active users or workloads).
- SSH access from the provisioning machine to every host as a sudo-capable
user.
submodules/kubesprayinitialized — Kubernetes playbooks fail on missing
kubespray_defaults role imports without it.
- Run everything from the repository root.
Procedure
- Prepare the environment and verify it:
git submodule update --init --recursive
./scripts/setup.sh
cp -r config.example config
- Edit
config/inventory: control plane nodes under
[kube_control_plane] and [etcd], workers under [kube_node] (a
single machine can hold all three roles).
- Preflight — must pass before deploying:
python3 scripts/validation/deepops_doctor.py --remote --json
- Deploy:
ansible-playbook -l k8s_cluster playbooks/k8s-cluster.yml
This runs Kubespray and installs the NVIDIA GPU Operator. Expect
roughly 45–90 minutes on a first run.
- Validate — the success signal is this, not the play recap:
python3 scripts/validation/validate_k8s.py --json --cuda-smoke
Require "ok": true with nodes_ready == nodes_total,
gpus_allocatable > 0, and cuda_smoke_ok: true.
Failure branches
- Playbook fails on a transient error: rerun the same playbook;
Kubespray is rerun-safe. A converged rerun reports changed=0.
- Syntax/import error mentioning
kubespray_defaults: the submodule is
not initialized; run git submodule update --init --recursive.
gpus_allocatable: 0: the GPU Operator stack is not ready. Check
kubectl get pods -A | grep -i nvidia — the driver DaemonSet can take
10+ minutes on first deploy; if pods are crash-looping, follow
skills/diagnose-driver-install/.
- CUDA smoke pod stuck
Pending: `kubectl -n deepops-validate
describe pod deepops-validate-cuda` — usually no allocatable GPU
(see above) or an image pull problem on airgapped networks (use
--cuda-image to point at a mirrored image).
- Single-node clusters: control plane taints are handled by the
playbook for the single-node case; if pods stay Pending on a multi-role
node, check taints with kubectl describe node <name> | grep -i taint.
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How to use it
Take nvidia/deploy-k8s-gpu-cluster from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.