/airunway-aks-setup

Step 6 — Summary & Smoke Test

Goal: Confirm everything is working and guide the user on next steps.

Report:

• Cluster context and node inventory (Step 1)
• Controller version and namespace (Step 2)
• GPU types and constraints in effect (Step 3)
• Provider installed and registration status (Step 4)
• Model deployed and endpoint URL (Step 5)

Smoke Test

Retrieve the endpoint and test it (only after STATUS = Ready):

Replace <namespace> with the namespace used during deployment (default: default).

ENDPOINT=$(kubectl get modeldeployment <model-name> -n <namespace> -o jsonpath='{.status.endpoint}')

if [ -z "$ENDPOINT" ]; then
  echo "Endpoint not yet available — model may still be starting"
else
  curl -X POST "${ENDPOINT}/v1/chat/completions" \
    -H "Content-Type: application/json" \
    -d '{"model": "<model-id>", "messages": [{"role": "user", "content": "Hello!"}]}'
fi

If the endpoint is a cluster-internal URL (e.g., http://svc-name.namespace:port), set up port-forwarding first:

# Discover the service port (vLLM: 8000, llama.cpp: 8080)
SERVICE_PORT=$(kubectl get svc <service-name> -n <namespace> -o jsonpath='{.spec.ports[0].port}')

kubectl port-forward svc/<service-name> 8080:${SERVICE_PORT} -n <namespace> &
# Then use http://localhost:8080 as the endpoint

See powershell-notes.md for the PowerShell equivalent.

Expected output: A JSON response with a choices array containing the model's reply.

Suggest next steps:

• Open the AI Runway Web UI to browse and deploy additional models
• Configure an ingress gateway for external access
• Review controller/config/samples/ for advanced ModelDeployment options