airunway-aks-setup

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.

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

1. Execute steps in sequence — load the reference for each step as you reach it
2. Report cluster state at each step: ✓ healthy, ✗ missing/failed
3. Ask for user confirmation before any install or deployment action
4. If a step is already complete, report status and skip to the next step
5. 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.