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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".

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

This session only. Nothing lands on disk.

referencesstepsstep-3-gpu.md

≈209 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Step 3 — GPU Assessment

Goal: Match detected hardware to known profiles and surface compatibility constraints.

Load and consult gpu-profiles.md.

For each GPU type detected in Step 1:

  1. Look up the model in gpu-profiles.md
  2. Report: VRAM per card, total cluster VRAM, supported dtypes, recommended attention backend
  3. Surface compatibility warnings:
GPU Warning
T4 Does not support bfloat16. Set --dtype float16 in serving args.
V100 Does not support bfloat16; limited flash attention. Use xformers backend.

CPU-only path: Note CPU-only inference is available via KAITO + llama.cpp. Recommend google/gemma-3-1b-it-qat-q8_0-gguf.

Use the Model Sizing Guide in model-sizing.md to calculate maximum model size for the cluster.

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 19 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.