AI Runway AKS Setup

Airunway aks setup

by microsoft · MIT license · ★ 1,532 on GitHub

Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment.

---
name: airunway-aks-setup
description: "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\"."
license: MIT
metadata:
  author: Microsoft
  version: "1.1.1"
argument-hint: "[skip-to-step N]"
---

# 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

Overview

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.

Manual install (npx, ZIP)

Claude Codenpx skills add microsoft/azure-skills --skill airunway-aks-setup -g -a claude-code

Claude Code (degit)npx degit microsoft/azure-skills/skills/airunway-aks-setup#main ~/.claude/skills/airunway-aks-setup

Claude.ai / desktopZip the folder and upload it under Settings → Capabilities → Skills.

Codex, Cursor, other agentsPoint them at SKILL.md. The other files are plain text.

Connects with Agent Alley MCP · agentalley.io/mcp

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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
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.

1	---
2	name: airunway-aks-setup
3	description: "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\"."
4	license: MIT
5	metadata:
6	author: Microsoft
7	version: "1.1.1"
8	argument-hint: "[skip-to-step N]"
9	---
10
11	# AI Runway AKS Setup
12
13	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.
14
15	> 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.
16
17	## Prerequisites
18
19	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.
20
21	## Quick Reference
22
23	\| Property \| Value \|
24	\|----------\|-------\|
25	\| Best for \| End-to-end AI Runway onboarding on AKS \|
26	\| CLI tools \| `kubectl`, `make`, `curl` \|
27	\| MCP tools \| None \|
28	\| Related skills \| `azure-kubernetes` (cluster setup), `azure-diagnostics` (troubleshooting) \|
29
30	## When to Use This Skill
31
32	Use this skill when the user wants to:
33	- Set up AI Runway on an existing AKS cluster from scratch
34	- Install the AI Runway controller and CRDs
35	- Assess GPU hardware compatibility for model deployment
36	- Choose and install an inference provider (KAITO, Dynamo, KubeRay)
37	- Deploy their first AI model to AKS via AI Runway
38	- Resume a partially-complete AI Runway setup from a specific step
39
40	## MCP Tools
41
42	This skill uses no MCP tools. All cluster operations are performed directly via `kubectl` and `make`.
43
44	## Rules
45
46	1. Execute steps in sequence — load the reference for each step as you reach it
47	2. Report cluster state at each step: ✓ healthy, ✗ missing/failed
48	3. Ask for user confirmation before any install or deployment action
49	4. If a step is already complete, report status and skip to the next step
50	5. If the user provides `skip-to-step N`, start at step N; assume prior steps are complete
51
52	## Steps
53
54	\| # \| Step \| Reference \|
55	\|---\|------\|-----------\|
56	\| 1 \| Cluster Verification — context check, node inventory, GPU detection \| [step-1-verify.md](references/steps/step-1-verify.md) \|
57	\| 2 \| Controller Installation — CRD + controller deployment \| [step-2-controller.md](references/steps/step-2-controller.md) \|
58	\| 3 \| GPU Assessment — detect GPU models, flag dtype/attention constraints \| [step-3-gpu.md](references/steps/step-3-gpu.md) \|
59	\| 4 \| Provider Setup — recommend and install inference provider \| [step-4-provider.md](references/steps/step-4-provider.md) \|
60	\| 5 \| First Deployment — pick a model, deploy, verify Ready \| [step-5-deploy.md](references/steps/step-5-deploy.md) \|
61	\| 6 \| Summary — recap, smoke test, next steps \| [step-6-summary.md](references/steps/step-6-summary.md) \|
62
63	## Error Handling
64
65	\| Error / Symptom \| Likely Cause \| Remediation \|
66	\|-----------------\|--------------\|-------------\|
67	\| No kubeconfig context \| Not connected to a cluster \| Run `az aks get-credentials` or equivalent \|
68	\| Controller in CrashLoopBackOff \| Config or RBAC issue \| `kubectl logs -n airunway-system -l control-plane=controller-manager --previous` \|
69	\| Provider not ready \| Image pull or RBAC issue \| `kubectl logs <pod-name> -n <namespace>` for the provider pod \|
70	\| ModelDeployment stuck in Pending \| GPU scheduling failure or provider not ready \| `kubectl describe modeldeployment <name> -n <namespace>` events \|
71	\| `bfloat16` errors at inference \| T4 or V100 lacks bfloat16 support \| Add `--dtype float16` to serving args \|
72
73	For full error handling and rollback procedures, see [troubleshooting.md](references/troubleshooting.md).
74