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microsoft/azure-skills · Official

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\".

All-time #174 Trending #193 First seen Apr 21, 2026
8-week activity · all time api

Installation

$ npx skills add microsoft/azure-skills --skill airunway-aks-setup
View on skills.sh ↗ GitHub repository ↗

Summary

  • End-to-end AI Runway setup on AKS from bare cluster to running model deployment.
  • Walks through six sequential steps: cluster verification, controller installation, GPU assessment, inference provider setup, first model deployment, and summary Includes cost awareness warnings for GPU node pools and error handling for common deployment failures Supports resuming from any step via skip-to-step N argument if setup is partially complete Uses only kubectl and make CLI tools; no MCP tools required

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Security audits

Partner security reviews for this skill.

agent-trust-hub SAFE

Analyzed Apr 23, 2026

This skill provides a structured workflow for setting up AI Runway on Azure Kubernetes Service (AKS). It includes cluster verification, controller installation, and model deployment. The skill follows security best practices, particularly in its handling of sensitive credentials like HuggingFace tokens. It uses standard Kubernetes and automation tools such as kubectl and make for its operations.

snyk LOW

Analyzed Apr 23, 2026

No issues detected.

socket Score 0.9000 · 0 alerts

Analyzed Apr 23, 2026

  • license 1
  • maintenance 1
  • quality 0.9
  • supply chain 1
  • vulnerability 1

0 alerts

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 1.5K
License MIT
Default branch main
Open issues 9
Last push Sep 10, 2026
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Version1.1.1
LicenseMIT
More metadata
author
Microsoft
version
1.1.1

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 4,107 B
  • docs SUMMARY.md 458 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 316,000 installs

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

  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).