nvidia/diagnose-driver-install
Diagnose NVIDIA driver installation failures on DeepOps-managed nodes — nvidia-smi errors, "No devices were found", DKMS build failures, or GPU pods crash-looping. Use before reinstalling anything.
npx skills add https://github.com/NVIDIA/deepops --skill diagnose-driver-install
Work through these in order; most "driver failures" are one of the first
three and need no reinstall.
On DeepOps Slurm nodes, GPUs are hidden from ordinary SSH sessions by
design. Bare nvidia-smi over SSH reporting No devices were found on an
otherwise healthy node is expected.
srun --gpus=1 nvidia-smi # the authoritative test on Slurm nodes
If the srun job sees the GPU, the driver is fine. Stop here.
lspci | grep -i nvidia
No output → not a driver problem. The GPU is absent, unseated, or bound by
VFIO passthrough or platform firmware; escalate to hardware support before
touching software.
nvidia-smi: No devices were found immediately after a clean install is the
classic symptom of the wrong module flavor for the GPU generation:
supported — DeepOps default nvidia_driver_ubuntu_use_open_kernel_modules: true
is correct.
set nvidia_driver_ubuntu_use_open_kernel_modules: false in
config/group_vars/all.yml and rerun the driver play.
Check what is loaded: modinfo nvidia | grep -i license (open modules say
MIT/GPL, proprietary says NVIDIA).
dkms status # driver module state per kernel
dmesg | grep -iE 'nvidia|nvrm' | tail -20
lsmod | grep nvidia
(linux-headers-$(uname -r)) or a kernel updated after the driver
install. Install headers or reboot into the matching kernel, then rerun
the driver play.
nvidia-smi fails → check dmesg for RmInitAdapter orfallen-off-the-bus errors; these are hardware/firmware territory.
After correcting configuration, converge with the playbook rather than
manual package surgery, then validate:
ansible-playbook -l <host> playbooks/nvidia-software/nvidia-driver.yml
python3 scripts/validation/validate_slurm.py --json # or validate_k8s.py
Reruns are idempotent; a healthy converged rerun reports changed=0.
GPU Operator crash-looping driver pods: `kubectl logs -n <gpu-operator
namespace> <driver-pod>` usually names the same root causes — missing
headers, wrong module flavor, or a node reboot needed. Fix via the operator
values or node state, not by installing drivers by hand on the host (the
operator owns the driver on Kubernetes nodes unless DeepOps was configured
for host drivers).
Take nvidia/diagnose-driver-install 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.