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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.

referencestroubleshooting.md

≈392 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Troubleshooting & Rollback

Error Handling

Error / Symptom Likely Cause Remediation
No kubeconfig context Not connected to a cluster Run az aks get-credentials or equivalent
make: *** No rule to make target Not in AI Runway repo root cd to repo root and retry
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 describe pod for the provider pod
ModelDeployment stuck in Pending GPU scheduling failure or provider not ready kubectl describe modeldeployment events
Pod shows ImagePullBackOff Wrong image reference or missing pull secret kubectl describe pod for the model pod
401 from HuggingFace at model load Gated model, token secret not wired into CR Ensure huggingFaceTokenSecretRef is set in the CR
bfloat16 errors at inference T4 or V100 lacks bfloat16 support Add --dtype float16 to serving args

Rollback

If a step fails and you need to undo a partial setup, work backwards through the steps:

What to undo Command
Model deployment kubectl delete modeldeployment <model-name> -n <namespace>
HuggingFace token secret kubectl delete secret hf-token -n <namespace>
Provider cd providers/<provider> && make undeploy (from repo root)
Controller make controller-undeploy && make controller-uninstall (from repo root)

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 17 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.