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CRI documentation improvements #4573

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Jun 25, 2024
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CRI documentation improvements #4573

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770caca
CRI documentation improvements
nekwar Jun 7, 2024
43b480f
Correct dynamic CRI configuration doc details
nekwar Jun 10, 2024
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52 changes: 22 additions & 30 deletions docs/runtime.md
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Original file line number Diff line number Diff line change
Expand Up @@ -66,6 +66,12 @@ decision] to replace rather than merge individual plugin configuration sections
from imported configuration files. However, this behavior [may][containerd#7347]
[change][containerd#9982] in future releases of containerd.

Please note, that in order for drop-ins in `/etc/k0s/containerd.d` to take effect on running configuration, `/etc/k0s/containerd.toml` needs to be k0s managed.

If you change `/etc/k0s/containerd.toml` (by accident or on purpose), it automatically becomes "not k0s managed". To make it "k0s managed" again, remove it and restart k0s service on the node, it'll be recreated by k0s.
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To confirm that drop-ins are applied to running configuration, check the content of `/run/k0s/containerd-cri.toml`, drop-in specific configuration should be present in this file.

[merge patch]: https://datatracker.ietf.org/doc/html/rfc7396
[containerd's decision]: https://github.com/containerd/containerd/pull/3574/commits/24b9e2c1a0a72a7ad302cdce7da3abbc4e6295cb
[containerd#7347]: https://github.com/containerd/containerd/pull/7347
Expand Down Expand Up @@ -148,42 +154,28 @@ Following chapters provide some examples how to configure different runtimes for
[ 0.000000] Starting gVisor...
```

#### Using `nvidia-container-runtime`
#### Using nvidia-container-runtime

First, install the NVIDIA runtime components:
First, deploy the NVIDIA GPU operator Helm chart with the following commands on top of your k0s cluster:

```shell
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install -y nvidia-container-runtime
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update
helm install nvidia-gpu-operator -n nvidia-gpu-operator \
--create-namespace \
--set operator.defaultRuntime=containerd \
--set toolkit.env[0].name=CONTAINERD_CONFIG \
--set toolkit.env[0].value=/etc/k0s/containerd.d/nvidia.toml \
--set toolkit.env[1].name=CONTAINERD_SOCKET \
--set toolkit.env[1].value=/run/k0s/containerd.sock \
--set toolkit.env[2].name=CONTAINERD_RUNTIME_CLASS \
--set toolkit.env[2].value=nvidia \
nvidia/gpu-operator
```

Next, drop in the NVIDIA runtime's configuration into into `/etc/k0s/containerd.d/nvidia.toml`:

```toml
[plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia]
privileged_without_host_devices = false
runtime_engine = ""
runtime_root = ""
runtime_type = "io.containerd.runc.v1"
[plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia.options]
BinaryName = "/usr/bin/nvidia-container-runtime"
```

Create the needed `RuntimeClass`:

```shell
cat <<EOF | kubectl apply -f -
apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
name: nvidia
handler: nvidia
EOF
```
With this Helm chart values, NVIDIA GPU operator will deploy both driver and toolkit to the GPU nodes and additionally will configure containerd with NVIDIA specific runtime.

**Note** Detailed instruction on how to run `nvidia-container-runtime` on your node is available [here](https://docs.nvidia.com/datacenter/cloud-native/kubernetes/install-k8s.html#install-nvidia-container-toolkit-nvidia-docker2).
**Note** Detailed instruction on how to deploy NVIDIA GPU operator on your k0s cluster is available [here](https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/getting-started.html).

## Using custom CRI runtimes

Expand Down