deploy: add K3s + NVIDIA GPU setup script for Lambda Cloud

One-shot script to turn a fresh Lambda Cloud instance into a single-node
K3s cluster that can schedule GPU pods: installs K3s, relies on Lambda
Stack's preinstalled NVIDIA driver and Container Toolkit, restarts K3s so
its containerd detects the NVIDIA runtime, registers the nvidia RuntimeClass
the operator requests, deploys the NVIDIA device plugin, and verifies the
node advertises nvidia.com/gpu. Not testable on CPU-only CI; validated on
a real instance.

Author: MicheleCampi

deploy/lambda/setup-k3s-gpu.sh

@@ -0,0 +1,56 @@
+#!/usr/bin/env bash
+# Set up single-node K3s with NVIDIA GPU scheduling on a Lambda Cloud instance.
+# Lambda Stack preinstalls the NVIDIA driver and Container Toolkit, so this
+# script only installs K3s, points containerd at the NVIDIA runtime, registers
+# the RuntimeClass, and deploys the device plugin. Run once on a fresh instance.
+set -euo pipefail
+
+echo "==> [1/4] Installing K3s (single node)"
+curl -sfL https://get.k3s.io | sh -
+# Make kubectl usable without sudo for the rest of the script.
+export KUBECONFIG=/etc/rancher/k3s/k3s.yaml
+sudo chmod 644 /etc/rancher/k3s/k3s.yaml
+kubectl wait --for=condition=Ready node --all --timeout=120s
+echo "    K3s ready."
+
+echo "==> [2/4] Configuring K3s containerd for the NVIDIA runtime"
+# K3s ships its own containerd. Recent K3s autodetects the NVIDIA runtime when
+# the Container Toolkit is present, generating an "nvidia" runtime in its
+# containerd config. Restart K3s so detection runs now that the toolkit is here.
+sudo systemctl restart k3s
+sleep 10
+kubectl wait --for=condition=Ready node --all --timeout=120s
+# Verify the nvidia runtime was registered in K3s containerd config.
+if sudo grep -q 'nvidia' /var/lib/rancher/k3s/agent/etc/containerd/config.toml*; then
+    echo "    NVIDIA runtime detected in K3s containerd config."
+else
+    echo "    WARNING: nvidia runtime not found in containerd config; GPU pods may fail."
+fi
+
+echo "==> [3/4] Registering the nvidia RuntimeClass"
+kubectl apply -f - <<'RTC'
+apiVersion: node.k8s.io/v1
+kind: RuntimeClass
+metadata:
+  name: nvidia
+handler: nvidia
+RTC
+echo "    RuntimeClass 'nvidia' applied."
+
+echo "==> [4/4] Deploying the NVIDIA device plugin"
+kubectl apply -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.17.1/deployments/static/nvidia-device-plugin.yml
+kubectl -n kube-system rollout status daemonset/nvidia-device-plugin-daemonset --timeout=120s
+
+echo "==> Verifying GPU is advertised to the scheduler"
+for i in $(seq 1 12); do
+    gpu=$(kubectl get nodes -o jsonpath='{.items[0].status.allocatable.nvidia\.com/gpu}' 2>/dev/null || true)
+    if [ -n "${gpu:-}" ] && [ "$gpu" != "0" ]; then
+        echo "    Node advertises nvidia.com/gpu=${gpu}. Cluster is GPU-ready."
+        exit 0
+    fi
+    echo "    [$i] waiting for GPU to be advertised..."
+    sleep 5
+done
+echo "    ERROR: no GPU advertised after 60s. Check device plugin logs:"
+echo "      kubectl -n kube-system logs daemonset/nvidia-device-plugin-daemonset"
+exit 1