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/airunway-aks-setup

@d0c21c0 official
by microsoftmicrosoft/github-copilot-for-azure253 stars
206

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".

Ask your Agent

Use this Skill: https://skilld.dev/gh/microsoft/github-copilot-for-azure/airunway-aks-setup

This session only. Nothing lands on disk.

SKILL.md

≈108 tokens always: the name and description. ≈882 when used: this file. ≈5.2k more on demand in 11 files.

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.

Source: SKILL.md on GitHub

Third-party checks

No alerts5mo3 checks · Risk SAFE
  • Gen Agent Trust Hub5mo

    This skill facilitates the setup of AI Runway on Azure Kubernetes Service (AKS) using standard administrative tools. It incorporates security best practices, particularly regarding the handling of sensitive HuggingFace tokens during deployment. No security considerations were identified.

  • Socket5mo

    No alerts

  • Snyk5mo

    Risk: LOW · No issues

Provenance

Signed by skilld at d0c21c0. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 18 hours ago.

Activeupdated 5 months ago

Capability

metadata
{
  "author": "Microsoft",
  "version": "0.0.0-placeholder"
}
argument-hint
[skip-to-step N]

Topics

  • kubernetes
  • aks
  • azure
  • llm
  • model-serving
  • gpu
  • inference
  • kaito
  • vllm
  • mlops

README badge

README badge for microsoft/github-copilot-for-azure/airunway-aks-setup

What it does

Installs and configures AI Runway on an existing AKS cluster, walking through controller setup, GPU assessment, inference provider selection, and model deployment. Targets users deploying large language models or other GPU-accelerated workloads to Kubernetes via KAITO, Dynamo, or KubeRay.

Generated from the current SKILL.md.

Frequently asked

Do I need an existing AKS cluster to use this skill?
Yes. This skill assumes you already have an AKS cluster. If you don't, the skill will hand off to the azure-kubernetes skill to provision one first, then return here.
What inference providers does this skill support?
The skill covers KAITO, Dynamo, and KubeRay as inference provider options. Step 4 recommends and installs the appropriate provider for your setup.
Does this skill work with CPU-only clusters?
Yes, CPU-only inference is acceptable, though the skill includes GPU assessment and provider setup primarily designed for GPU workloads. A bare cluster without GPU resources is still supported.
What happens if I already have part of AI Runway set up?
You can use the `skip-to-step N` argument to resume from a specific phase. The skill will report the current state and skip already-completed steps.
What are the cost implications of using this skill?
GPU node pools incur significant charges — A100-80GB can cost $3–5+/hr. The skill will confirm you understand these costs before provisioning GPU resources.

Generated from the current SKILL.md. These answers refresh after source changes.