GPU Operators

Deploy NVIDIA operator

The NVIDIA operator allows administrators of Kubernetes clusters to manage GPUs just like CPUs. It includes everything needed for pods to operate GPUs.

Host OS requirements

To expose the GPU to the pod correctly, the NVIDIA kernel drivers and the libnvidia-ml library must be correctly installed in the host OS. The NVIDIA Operator can automatically install drivers and libraries on some operating systems. Refer to the NVIDIA documentation for information on supported operating system releases. The installation of the NVIDIA components on your host OS is out of the scope of this documentation, refer to the NVIDIA documentation for instructions.

The following three commands should return a correct output if the kernel driver was correctly installed:

lsmod | grep nvidia returns a list of nvidia kernel modules. For example:

nvidia_uvm           2129920  0
nvidia_drm            131072  0
nvidia_modeset       1572864  1 nvidia_drm
video                  77824  1 nvidia_modeset
nvidia               9965568  2 nvidia_uvm,nvidia_modeset
ecc                    45056  1 nvidia

cat /proc/driver/nvidia/version returns the NVRM and GCC version of the driver. For example:

NVRM version: NVIDIA UNIX Open Kernel Module for x86_64  555.42.06  Release Build  (abuild@host)  Thu Jul 11 12:00:00 UTC 2024
GCC version:  gcc version 7.5.0 (SUSE Linux)

find /usr/ -iname libnvidia-ml.so returns a path to the libnvidia-ml.so library. For example:
```
/usr/lib64/libnvidia-ml.so
```
This library is used by Kubernetes components to interact with the kernel driver.

Operator installation

Once the OS is ready and RKE2 is running, install the GPU Operator with the following yaml manifest:

The NVIDIA operator restarts containerd with a hangup call which restarts RKE2.

The envvars ACCEPT_NVIDIA_VISIBLE_DEVICES_ENVVAR_WHEN_UNPRIVILEGED, ACCEPT_NVIDIA_VISIBLE_DEVICES_AS_VOLUME_MOUNTS and DEVICE_LIST_STRATEGY are required to properly isolate GPU resources as explained in Preventing unprivileged access to GPUs in Kubernetes.

apiVersion: helm.cattle.io/v1
kind: HelmChart
metadata:
  name: gpu-operator
  namespace: kube-system
spec:
  repo: https://helm.ngc.nvidia.com/nvidia
  chart: gpu-operator
  version: v25.3.4
  targetNamespace: gpu-operator
  createNamespace: true
  valuesContent: |-
    toolkit:
      env:
      - name: CONTAINERD_SOCKET
        value: /run/k3s/containerd/containerd.sock
      - name: ACCEPT_NVIDIA_VISIBLE_DEVICES_ENVVAR_WHEN_UNPRIVILEGED
        value: "false"
      - name: ACCEPT_NVIDIA_VISIBLE_DEVICES_AS_VOLUME_MOUNTS
        value: "true"
    devicePlugin:
      env:
      - name: DEVICE_LIST_STRATEGY
        value: volume-mounts

After one minute approximately, you can perform the following checks to verify that everything is working as expected:

Assuming the drivers and libnvidia-ml.so library were previously installed, check if the operator detects them correctly:
```
kubectl get node $NODENAME -o jsonpath='{.metadata.labels}' | grep "nvidia.com/gpu.deploy.driver"
```
You should see the value pre-installed. If you see true, the drivers were not correctly installed. If the pre-requirements were correct, it is possible that you forgot to reboot the node after installing all packages.

You can also check other driver labels with:
```
kubectl get node $NODENAME -o jsonpath='{.metadata.labels}' |  grep "nvidia.com"
```
You should see labels specifying driver and GPU (e.g. nvidia.com/gpu.machine or nvidia.com/cuda.driver.major).
Check if the GPU was added by nvidia-device-plugin-daemonset as an allocatable resource in the node:
```
kubectl get node $NODENAME -o jsonpath='{.status.allocatable}'
```
You should see "nvidia.com/gpu": followed by the number of GPUs in the node.
Check that the container runtime binary was installed by the operator (in particular by the nvidia-container-toolkit-daemonset):
```
ls /usr/local/nvidia/toolkit/nvidia-container-runtime
```
Verify if containerd config was updated to include the NVIDIA container runtime:
```
grep nvidia /var/lib/rancher/rke2/agent/etc/containerd/config.toml
```

Run a pod to verify that the GPU resource can successfully be scheduled on a pod and the pod can detect it

apiVersion: v1
kind: Pod
metadata:
  name: nbody-gpu-benchmark
  namespace: default
spec:
  restartPolicy: OnFailure
  runtimeClassName: nvidia
  containers:
** name: cuda-container
image: nvcr.io/nvidia/k8s/cuda-sample:nbody
args: ["nbody", "-gpu", "-benchmark"]
resources:
limits:
  nvidia.com/gpu: 1

Version Gate

Available as of October 2024 releases: v1.28.15+rke2r1, v1.29.10+rke2r1, v1.30.6+rke2r1, v1.31.2+rke2r1.

RKE2 will now use PATH to find alternative container runtimes, in addition to checking the default paths used by the container runtime packages. In order to use this feature, you must modify the RKE2 service’s PATH environment variable to add the directories containing the container runtime binaries.

It’s recommended that you modify one of this two environment files:

/etc/default/rke2-server # or rke2-agent
/etc/sysconfig/rke2-server # or rke2-agent

This example will add the PATH in /etc/default/rke2-server:

PATH changes should be done with care to avoid placing untrusted binaries in the path of services that run as root.

echo PATH=$PATH >> /etc/default/rke2-server