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](references/steps/step-1-verify.md) | | 2 | Controller Installation — CRD + controller deployment | [step-2-controller.md](references/steps/step-2-controller.md) | | 3 | GPU Assessment — detect GPU models, flag dtype/attention constraints | [step-3-gpu.md](references/steps/step-3-gpu.md) | | 4 | Provider Setup — recommend and install inference provider | [step-4-provider.md](references/steps/step-4-provider.md) | | 5 | First Deployment — pick a model, deploy, verify Ready | [step-5-deploy.md](references/steps/step-5-deploy.md) | | 6 | Summary — recap, smoke test, next steps | [step-6-summary.md](references/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](references/troubleshooting.md).