NVIDIA GPU (OSS drivers)

In this guide we’ll follow the procedure to support NVIDIA GPU using OSS drivers on Talos.

Enabling NVIDIA GPU support on Talos is bound by NVIDIA EULA. The Talos published NVIDIA OSS drivers are bound to a specific Talos release. The extensions versions also needs to be updated when upgrading Talos.

We will be using the following NVIDIA OSS system extensions:

  • nvidia-open-gpu-kernel-modules
  • nvidia-container-toolkit

Create the boot assets which includes the system extensions mentioned above (or create a custom installer and perform a machine upgrade if Talos is already installed).

Make sure the driver version matches for both the nvidia-open-gpu-kernel-modules and nvidia-container-toolkit extensions. The nvidia-open-gpu-kernel-modules extension is versioned as <nvidia-driver-version>-<talos-release-version> and the nvidia-container-toolkit extension is versioned as <nvidia-driver-version>-<nvidia-container-toolkit-version>.

Enabling the NVIDIA OSS modules

Patch Talos machine configuration using the patch gpu-worker-patch.yaml:

      - name: nvidia
      - name: nvidia_uvm
      - name: nvidia_drm
      - name: nvidia_modeset
    net.core.bpf_jit_harden: 1

Now apply the patch to all Talos nodes in the cluster having NVIDIA GPU’s installed:

talosctl patch mc --patch @gpu-worker-patch.yaml

The NVIDIA modules should be loaded and the system extension should be installed.

This can be confirmed by running:

talosctl read /proc/modules

which should produce an output similar to below:

nvidia_uvm 1146880 - - Live 0xffffffffc2733000 (PO)
nvidia_drm 69632 - - Live 0xffffffffc2721000 (PO)
nvidia_modeset 1142784 - - Live 0xffffffffc25ea000 (PO)
nvidia 39047168 - - Live 0xffffffffc00ac000 (PO)
talosctl get extensions

which should produce an output similar to below:

NODE           NAMESPACE   TYPE              ID                                                                           VERSION   NAME                             VERSION   runtime     ExtensionStatus   000.ghcr.io-siderolabs-nvidia-container-toolkit-515.65.01-v1.10.0            1         nvidia-container-toolkit         515.65.01-v1.10.0   runtime     ExtensionStatus   000.ghcr.io-siderolabs-nvidia-open-gpu-kernel-modules-515.65.01-v1.2.0       1         nvidia-open-gpu-kernel-modules   515.65.01-v1.2.0
talosctl read /proc/driver/nvidia/version

which should produce an output similar to below:

NVRM version: NVIDIA UNIX x86_64 Kernel Module  515.65.01  Wed Mar 16 11:24:05 UTC 2022
GCC version:  gcc version 12.2.0 (GCC)

Deploying NVIDIA device plugin

First we need to create the RuntimeClass

Apply the following manifest to create a runtime class that uses the extension:

apiVersion: node.k8s.io/v1
kind: RuntimeClass
  name: nvidia
handler: nvidia

Install the NVIDIA device plugin:

helm repo add nvdp https://nvidia.github.io/k8s-device-plugin
helm repo update
helm install nvidia-device-plugin nvdp/nvidia-device-plugin --version=0.13.0 --set=runtimeClassName=nvidia

(Optional) Setting the default runtime class as nvidia

Do note that this will set the default runtime class to nvidia for all pods scheduled on the node.

Create a patch yaml nvidia-default-runtimeclass.yaml to update the machine config similar to below:

- op: add
  path: /machine/files
    - content: |
              default_runtime_name = "nvidia"        
      path: /etc/cri/conf.d/20-customization.part
      op: create

Now apply the patch to all Talos nodes in the cluster having NVIDIA GPU’s installed:

talosctl patch mc --patch @nvidia-default-runtimeclass.yaml

Testing the runtime class

Note the spec.runtimeClassName being explicitly set to nvidia in the pod spec.

Run the following command to test the runtime class:

kubectl run \
  nvidia-test \
  --restart=Never \
  -ti --rm \
  --image nvcr.io/nvidia/cuda:12.1.0-base-ubuntu22.04 \
  --overrides '{"spec": {"runtimeClassName": "nvidia"}}' \