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](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html).
## 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.
### Adapt the Dockerfile
```Dockerfile
FROM ubuntu:18.04 as base
RUN apt-get update -y && apt-get install -y ca-certificates
RUN curl -s -L https://nvidia.github.io/nvidia-container-runtime/gpgkey | apt-key add -
RUN curl -s -L https://nvidia.github.io/nvidia-container-runtime/ubuntu18.04/nvidia-container-runtime.list | tee /etc/apt/sources.list.d/nvidia-container-runtime.list
This [Dockerfile](cuda/Dockerfile) is based on the [K3S Dockerfile](https://github.com/rancher/k3s/blob/master/package/Dockerfile).
The following changes are applied:
1. Change the base images to Ubuntu 18.04 so the NVIDIA Container Runtime can be installed
2. Add a custom containerd `config.toml` template to add the NVIDIA Container Runtime. This replaces the default `runc` runtime
3. Add a manifest for the NVIDIA driver plugin for Kubernetes
### Configure containerd
We need to configure containerd to use the NVIDIA Container Runtime. We need to customize the config.toml that is used at startup. K3S provides a way to do this using a [config.toml.tmpl](cuda/config.toml.tmpl) file. More information can be found on the [K3S site](https://rancher.com/docs/k3s/latest/en/advanced/#configuring-containerd).
{{ if $v.Auth.Username }}username = "{{ $v.Auth.Username }}"{{end}}
{{ if $v.Auth.Password }}password = "{{ $v.Auth.Password }}"{{end}}
{{ if $v.Auth.Auth }}auth = "{{ $v.Auth.Auth }}"{{end}}
{{ if $v.Auth.IdentityToken }}identitytoken = "{{ $v.Auth.IdentityToken }}"{{end}}
{{end}}
{{ if $v.TLS }}
[plugins.cri.registry.configs."{{$k}}".tls]
{{ if $v.TLS.CAFile }}ca_file = "{{ $v.TLS.CAFile }}"{{end}}
{{ if $v.TLS.CertFile }}cert_file = "{{ $v.TLS.CertFile }}"{{end}}
{{ if $v.TLS.KeyFile }}key_file = "{{ $v.TLS.KeyFile }}"{{end}}
{{end}}
{{end}}
{{end}}
```
### The NVIDIA device plugin
To enable NVIDIA GPU support on Kubernetes you also need to install the [NVIDIA device plugin](https://github.com/NVIDIA/k8s-device-plugin). The device plugin is a deamonset 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.
```yaml
apiVersion: apps/v1
kind: DaemonSet
metadata:
name: nvidia-device-plugin-daemonset
namespace: kube-system
spec:
selector:
matchLabels:
name: nvidia-device-plugin-ds
template:
metadata:
# Mark this pod as a critical add-on; when enabled, the critical add-on scheduler
# reserves resources for critical add-on pods so that they can be rescheduled after
# a failure. This annotation works in tandem with the toleration below.
annotations:
scheduler.alpha.kubernetes.io/critical-pod: ""
labels:
name: nvidia-device-plugin-ds
spec:
tolerations:
# Allow this pod to be rescheduled while the node is in "critical add-ons only" mode.
# This, along with the annotation above marks this pod as a critical add-on.
- key: CriticalAddonsOnly
operator: Exists
containers:
- env:
- name: DP_DISABLE_HEALTHCHECKS
value: xids
image: nvidia/k8s-device-plugin:1.11
name: nvidia-device-plugin-ctr
securityContext:
allowPrivilegeEscalation: true
capabilities:
drop: ["ALL"]
volumeMounts:
- name: device-plugin
mountPath: /var/lib/kubelet/device-plugins
volumes:
- name: device-plugin
hostPath:
path: /var/lib/kubelet/device-plugins
```
### Build the K3S image
To build the custom image we need to build K3S because we need the generated output.
k3d cluster create --no-lb --image k3s-gpu:v1.18.10-k3s1 --gpus all
```
Deploy a [test pod](cuda/cuda-vector-add.yaml):
```
kubectl apply -f cuda-vector-add.yaml
kubectl logs cuda-vector-add
```
## Known issues
* This approach does not work on WSL2 yet. The NVIDIA driver plugin and container runtime rely on the NVIDIA Management Library (NVML) which is not yet supported. See the [CUDA on WSL User Guide](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#known-limitations).
## Acknowledgements:
Most of the information in this article was obtained from various sources:
* [Add NVIDIA GPU support to k3s with containerd](https://dev.to/mweibel/add-nvidia-gpu-support-to-k3s-with-containerd-4j17)
