Running CUDA workloads

If you want to run CUDA workloads on the K3s container you need to customize the container.
CUDA workloads require the NVIDIA Container Runtime, so containerd needs to be configured to use this runtime.
The K3s container itself also needs to run with this runtime.
If you are using Docker you can install the NVIDIA Container Toolkit.

Building a customized K3s image

To get the NVIDIA container runtime in the K3s image you need to build your own K3s image.
The native K3s image is based on Alpine but the NVIDIA container runtime is not supported on Alpine yet.
To get around this we need to build the image with a supported base image.

Dockerfile

Dockerfile:

{%
  include-markdown "./cuda/Dockerfile"
  comments=false
%}

This Dockerfile is based on the K3s Dockerfile The following changes are applied:

Change the base images to nvidia/cuda:12.4.1-base-ubuntu22.04 so the NVIDIA Container Toolkit can be installed. The version of cuda:xx.x.x must match the one you're planning to use.
Add a manifest for the NVIDIA driver plugin for Kubernetes with an added RuntimeClass definition. See k3s documentation.

The NVIDIA device plugin

To enable NVIDIA GPU support on Kubernetes you also need to install the NVIDIA device plugin. The device plugin is a daemonset and allows you to automatically:

Expose the number of GPUs on each nodes of your cluster
Keep track of the health of your GPUs
Run GPU enabled containers in your Kubernetes cluster.

{%
  include-markdown "./cuda/device-plugin-daemonset.yaml"
  comments=false
%}

Two modifications have been made to the original NVIDIA daemonset:

Added RuntimeClass definition to the YAML frontmatter.

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

Added runtimeClassName: nvidia to the Pod spec.

Note: you must explicitly add runtimeClassName: nvidia to all your Pod specs to use the GPU. See k3s documentation.

Build the K3s image

To build the custom image we need to build K3s because we need the generated output.

Put the following files in a directory:

The build.sh script is configured using exports & defaults to v1.28.8+k3s1. Please set at least the IMAGE_REGISTRY variable! The script performs the following steps builds the custom K3s image including the nvidia drivers.

build.sh:

{%
  include-markdown "./cuda/build.sh"
  comments=false
%}

Run and test the custom image with k3d

You can use the image with k3d:

k3d cluster create gputest --image=$IMAGE --gpus=1

Deploy a test pod:

kubectl apply -f cuda-vector-add.yaml
kubectl logs cuda-vector-add

This should output something like the following:

$ kubectl logs cuda-vector-add

[Vector addition of 50000 elements]
Copy input data from the host memory to the CUDA device
CUDA kernel launch with 196 blocks of 256 threads
Copy output data from the CUDA device to the host memory
Test PASSED
Done

If the cuda-vector-add pod is stuck in Pending state, probably the device-driver daemonset didn't get deployed correctly from the auto-deploy manifests. In that case, you can apply it manually via #!bash kubectl apply -f device-plugin-daemonset.yaml.

Acknowledgements

Most of the information in this article was obtained from various sources:

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