Created
April 17, 2023 20:31
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RKE2/K3s Nvidia GPU-Operator installation
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prep: | |
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia \ | |
&& helm repo update | |
install: | |
helm install --wait nvidiagpu \ | |
-n gpu-operator --create-namespace \ | |
--set toolkit.env[0].name=CONTAINERD_CONFIG \ | |
--set toolkit.env[0].value=/var/lib/rancher/k3s/agent/etc/containerd/config.toml \ | |
--set toolkit.env[1].name=CONTAINERD_SOCKET \ | |
--set toolkit.env[1].value=/run/k3s/containerd/containerd.sock \ | |
--set toolkit.env[2].name=CONTAINERD_RUNTIME_CLASS \ | |
--set toolkit.env[2].value=nvidia \ | |
--set toolkit.env[3].name=CONTAINERD_SET_AS_DEFAULT \ | |
--set-string toolkit.env[3].value=true \ | |
nvidia/gpu-operator | |
delete: | |
helm uninstall -n gpu-operator nvidiagpu | |
cluster-info: | |
kubectl get nodes -o wide |
probably you could try nvcr.io/nvidia/l4t-cuda:12.2.2-devel-arm64-ubuntu22.04
or images from https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-cuda/tags
note: I am particularly sure and didn't tried it out.
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I want to run it on Jetson platform (arm), there is no
nvidia-smi
, as I know. But anyway - thanks for the precise command.