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airunway-aks-setup

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".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

microsoft/azure-skills
연동 도구Kubernetes
설치 방법 보기

포함된 파일(12)

  • SKILL.md4.4 KB
  • references/gpu-profiles.md2.5 KB
  • references/model-sizing.md1.8 KB
  • references/optional-gpu-day2.md1.1 KB
  • references/powershell-notes.md3.1 KB
  • references/steps/step-1-verify.md1.9 KB
  • references/steps/step-2-controller.md1.0 KB
  • references/steps/step-3-gpu.md834 B
  • references/steps/step-4-provider.md2.0 KB
  • references/steps/step-5-deploy.md3.6 KB
  • references/steps/step-6-summary.md1.7 KB
  • references/troubleshooting.md1.5 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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

PropertyValue
Best forEnd-to-end AI Runway onboarding on AKS
CLI toolskubectl, make, curl
MCP toolsNone
Related skillsazure-kubernetes (cluster setup), azure-diagnostics (baseline troubleshooting), optional aks-gpu-inference (existing GPU/inference Day-2 incidents only)

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

GPU setup versus Day-2 incidents

Keep initial GPU node-pool, provider, controller, and model setup in this skill. For an incident affecting an existing GPU or inference deployment, load optional-gpu-day2.md before deciding whether to offer the focused optional add-on.

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

#StepReference
1Cluster Verification — context check, node inventory, GPU detectionstep-1-verify.md
2Controller Installation — CRD + controller deploymentstep-2-controller.md
3GPU Assessment — detect GPU models, flag dtype/attention constraintsstep-3-gpu.md
4Provider Setup — recommend and install inference providerstep-4-provider.md
5First Deployment — pick a model, deploy, verify Readystep-5-deploy.md
6Summary — recap, smoke test, next stepsstep-6-summary.md

Error Handling

Error / SymptomLikely CauseRemediation
No kubeconfig contextNot connected to a clusterRun az aks get-credentials or equivalent
Controller in CrashLoopBackOffConfig or RBAC issuekubectl logs -n airunway-system -l control-plane=controller-manager --previous
Provider not readyImage pull or RBAC issuekubectl logs <pod-name> -n <namespace> for the provider pod
ModelDeployment stuck in PendingGPU scheduling failure or provider not readykubectl describe modeldeployment <name> -n <namespace> events
bfloat16 errors at inferenceT4 or V100 lacks bfloat16 supportAdd --dtype float16 to serving args

For full error handling and rollback procedures, see troubleshooting.md.

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Diagnose Day-2 AKS GPU and KAITO incidents using profile-aware, read-only evidence. WHEN: 'Insufficient nvidia.com/gpu', GPU pod Pending, model-load OOM, DCGM/VRAM, KAITO Workspace not ready, or GPU autoscaling. DO NOT USE FOR: setup (airunway-aks-setup), non-GPU incidents (aks-troubleshooting), standalone VM quota (azure-quotas), or generic cost (cost-analysis or cost-optimization from the optional azure-cost plugin).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

microsoft/azure-skills1.6천2026년 10월 22일 업데이트

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

microsoft/azure-skills1.6천2026년 10월 22일 업데이트

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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Microsoft 공식무료

Debug live Azure Kubernetes Service (AKS) incidents with a read-only, evidence-first investigation. WHEN: pod crashes or Pending, CrashLoopBackOff, OOMKilled, ImagePullBackOff, node NotReady, DNS or ingress failure, connectivity timeout, network policy, SNAT exhaustion, node-pool scaling blocked by QuotaExceeded or InsufficientVCPUQuota, upgrade stuck, spot or zone disruption, a bare VMExtensionProvisioningError or AllocationFailed wrapper, an uncataloged capacity symptom, or 'investigate my AKS cluster'. DO NOT USE FOR: packet capture (use aks-network-capture); GPU or model-serving issues (use aks-gpu-inference); cluster creation or provisioning (use azure-kubernetes); cost (use cost-analysis from the optional azure-cost plugin); pod rightsizing (use azure-kubernetes); a fully qualified documented AKS signature with every required nested qualifier (use aks-known-issues); standalone failures on non-AKS Azure resources (use azure-diagnostics). Unqualified errors and open-ended incidents stay here only for AKS.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

microsoft/azure-skills1.6천2026년 10월 22일 업데이트

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