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".
适合你，如果需要在 Azure Kubernetes 上快速搭建 AI 推理环境
npx oh-my-skill add microsoft/azure-skills/airunway-aks-setupcurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- microsoft/azure-skills/airunway-aks-setupnpx oh-my-skill verify microsoft/azure-skills/airunway-aks-setup怎么用
商店整理自技能原文 · 版本 1940385 · 表述以原文为准Claude 会引导你逐步在 Azure Kubernetes Service (AKS) 集群上安装 AI Runway，包括检查集群、安装控制器、评估 GPU、选择推理提供商并部署第一个模型。
当你提到“设置 AI Runway”、“安装 AI Runway”、“部署模型到 AKS”等关键词，或要求进行 GPU 推理、KAITO 设置、运行大语言模型时触发。
技能原文 SKILL.md
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
- 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](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).