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Set up AI Runway on AKS and deploy your first model.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "airunway-aks-setup" skill from askskill: 1. Download https://raw.githubusercontent.com/microsoft/GitHub-Copilot-for-Azure/main/plugin/skills/airunway-aks-setup/SKILL.md 2. Save it as ~/.claude/skills/airunway-aks-setup/SKILL.md 3. Reload skills and tell me it's ready
Provide the full steps to install AI Runway on an AKS cluster, including cluster verification, controller installation, prerequisite checks, and common requirements.
A step-by-step AI Runway installation guide covering verification, setup, and prerequisites.
Assess whether my AKS cluster is ready for GPU inference, including GPU node availability, driver/plugin status, resource quotas, and likely risk areas.
A GPU readiness assessment highlighting gaps, risks, and recommended next steps.
Guide me to deploy my first model service with AI Runway on an already prepared AKS environment, including provider configuration, deployment commands, and validation steps.
An actionable first-model deployment flow with sample configuration, commands, and a validation checklist.
This skill walks users from a bare Kubernetes cluster to a running AI model deployment. Follow each step in sequence unless the user provides skip-to-step N to resume from a specific phase.
Cost awareness: GPU node pools incur significant compute charges (A100-80GB can cost $3–5+/hr). Confirm the user understands cost implications before provisioning GPU resources.
This skill assumes an AKS cluster already exists. If the user does not have a cluster, hand off to the azure-kubernetes skill first to provision one (with a GPU node pool unless CPU-only inference is acceptable), then return here.
| Property | Value |
|---|---|
| Best for | End-to-end AI Runway onboarding on AKS |
| CLI tools | kubectl, make, curl |
| MCP tools | None |
| Related skills | azure-kubernetes (cluster setup), azure-diagnostics (troubleshooting) |
Use this skill when the user wants to:
This skill uses no MCP tools. All cluster operations are performed directly via kubectl and make.
skip-to-step N, start at step N; assume prior steps are complete| # | Step | Reference |
|---|---|---|
| 1 | Cluster Verification — context check, node inventory, GPU detection | step-1-verify.md |
| 2 | Controller Installation — CRD + controller deployment | step-2-controller.md |
| 3 | GPU Assessment — detect GPU models, flag dtype/attention constraints | step-3-gpu.md |
| 4 | Provider Setup — recommend and install inference provider | step-4-provider.md |
| 5 | First Deployment — pick a model, deploy, verify Ready | step-5-deploy.md |
| 6 | Summary — recap, smoke test, next steps | step-6-summary.md |
| Error / Symptom | Likely Cause | Remediation |
|---|---|---|
| No kubeconfig context | Not connected to a cluster | Run az aks get-credentials or equivalent |
| Controller in CrashLoopBackOff | Config or RBAC issue | kubectl logs -n airunway-system -l control-plane=controller-manager --previous |
| Provider not ready | Image pull or RBAC issue | kubectl logs <pod-name> -n <namespace> for the provider pod |
| ModelDeployment stuck in Pending | GPU scheduling failure or provider not ready | kubectl describe modeldeployment <name> -n <namespace> events |
bfloat16 errors at inference | T4 or V100 lacks bfloat16 support | Add --dtype float16 to serving args |
For full error handling and rollback procedures, see troubleshooting.md.
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