---
title: MicroK8s on NVIDIA DGX
description: Canonical makes open source secure, reliable and easy to use, providing
  support for Ubuntu and a portfolio of enterprise-grade technologies. Founded in
  2004, Canonical operates globally with team members in over 80 countries.
url: https://canonical.com/microk8s/docs/nvidia-dgx?format=md
---

*Submit*

# MicroK8s on NVIDIA DGX

On DGX systems you will need to enable GPU support right after you install MicroK8s:

```
sudo snap install microk8s --classic
sudo microk8s enable gpu
```

MicroK8s installs the NVIDIA operator which allows you to take advantage of the GPU hardware available.

### [Verify the installation](https://canonical.com/microk8s/docs/nvidia-dgx?format=md#p-31265-verify-the-installation)

To verify the installation works as expected you can try to perform a CUDA vector addition by applying the following manifest (save this file and use [kubectl apply](https://kubectl.docs.kubernetes.io/references/kubectl/apply/)):

```
apiVersion: v1
kind: Pod
metadata:
  name: cuda-vector-add
spec:
  restartPolicy: OnFailure
  containers:
    - name: cuda-vector-add
      image: "k8s.gcr.io/cuda-vector-add:v0.1"
      resources:
        limits:
          nvidia.com/gpu: 1
```

After the addition completes, check the logs of the `cuda-vector-add` pod to see if it succeeded.

### [Multi-Instance GPU (MIG)](https://canonical.com/microk8s/docs/nvidia-dgx?format=md#p-31265-multi-instance-gpu-mig)

Multi-Instance GPU (MIG) allows GPU partitioning so they can be safely used by CUDA applications. Starting from MicroK8s v1.23 MIG can be configured via configMaps, please see the [NVIDIA operator docs](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/gpu-operator-mig.html) on this topic.

Last updated 2 years ago. [Help improve this document in the forum](https://discuss.kubernetes.io/t/microk8s-on-nvidia-dgx/18225).