airunway-aks-setup

✓ Official★ 1,300

by microsoft · part of microsoft/azure-skills

Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".

🔥🔥🔥✓ VerifiedFreeQuick setup

#calendar #ai-video #cloud #devops #git

🧩 One of 7 skills in the microsoft/azure-skills package — works on its own, and pairs well with its siblings.

Inspect the full instructions your agent will receiveExpand

This is the exact playbook injected into your agent when the skill activates — shown here so you can audit it before installing. You don't need to read it to use the skill.

by microsoft

Quick Reference

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)

When to Use This Skill

Use this skill when the user wants to:

Set up AI Runway on an existing AKS cluster from scratch
Install the AI Runway controller and CRDs
Assess GPU hardware compatibility for model deployment
Choose and install an inference provider (KAITO, Dynamo, KubeRay)
Deploy their first AI model to AKS via AI Runway
Resume a partially-complete AI Runway setup from a specific step

MCP Tools

This skill uses no MCP tools. All cluster operations are performed directly via kubectl and make.

Rules

Execute steps in sequence — load the reference for each step as you reach it
Report cluster state at each step: ✓ healthy, ✗ missing/failed
Ask for user confirmation before any install or deployment action
If a step is already complete, report status and skip to the next step
If the user provides skip-to-step N, start at step N; assume prior steps are complete

Steps

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 Handling

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.

Copy & paste — that's it

npx skills add https://github.com/microsoft/azure-skills --skill airunway-aks-setup

Run this in your project — your agent picks the skill up automatically.

AI Runway AKS Setup

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.

Prerequisites

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.