airunway-aks-setup

Name: airunway-aks-setup
Author: microsoft

Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first…

INSTALLATION

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

Run in your project or agent environment. Adjust flags if your CLI version differs.

SKILL.md

$27

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

Cluster Verification — context check, node inventory, GPU detection

step-1-verify.md

Controller Installation — CRD + controller deployment

step-2-controller.md

GPU Assessment — detect GPU models, flag dtype/attention constraints

step-3-gpu.md

Provider Setup — recommend and install inference provider

step-4-provider.md

First Deployment — pick a model, deploy, verify Ready

step-5-deploy.md

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.