SkillLow riskUnclaimed

AKS GPU Inference Day-2

Diagnose Day-2 AKS GPU and KAITO incidents using profile-aware, read-only evidence. WHEN: 'Insufficient nvidia.com/gpu', GPU pod Pending, model-load OOM, DCGM/VRAM, KAITO Workspace not ready, or GPU autoscaling. DO NOT USE FOR: setup (airunway-aks-setup), non-GPU incidents (aks-troubleshooting), standalone VM quota (azure-quotas), or generic cost (cost-analysis or cost-optimization from the optional azure-cost plugin).

microsoftmicrosoft/aks-gpu-inference★ 3.1kUpdated Oct 9, 2026

Description

Quick Reference

Property Value
Best for AKS GPU/KAITO failures
Evidence Bound target, events/status, gpuProfile, DCGM

When to Use This Skill

Use for scheduling, VRAM/OOM, KAITO readiness, and workload scaling. Route exclusions as described above.

MCP Tools

Use a fitting host-advertised Azure read or the references' read-only queries.

Host Capability Gate

Before executing any referenced pipeline, verify that the host authorizes the required shell and kubectl/az commands against the bound target. File-backed collection also requires approved artifact storage and access to any bundled files it uses. A governed Azure CLI tool alone does not establish these capabilities. Equivalent host reads may replace commands only where their advertised schemas provide the required evidence.

If execution is unavailable or prohibited, say so and analyze supplied or redacted events, status, logs, and metrics, or give the operator a scoped collection plan. State which reads did not run and leave conclusions requiring missing evidence unconfirmed. Never route kubectl through Azure MCP or bypass host policy. The authorization requirements below still apply.

Workflow

  1. Bind subscription, cluster, kube context, pool, and affected resource.
  2. Capture exact events/status and observed gpuProfile; state which reads ran.
  3. Route to scheduling, observability, KAITO, or scaling.
  4. Separate container/host OOM from device-allocation failures. For OOMKilled, correlate container memory limits/usage and node conditions. For Managed+Install device-memory evidence, use the exporter on port 19400 and incident-window FB_USED/FB_FREE; sizing tables do not establish a cause.
  5. For KAITO not-ready, warn that Workspace deletion leaves its GPU pools; cleanup is separate and needs explicit authorization.
  6. Report evidence, confidence, missing evidence, and owner handoff.

Require explicit authorization before scaling, cordon/drain, deletion, add-on enablement, or monitoring mutation.

Error Handling

Condition Response
Missing/contradictory evidence Mark unconfirmed; request the owner/read
Unknown profile Preserve evidence; do not prescribe stack repair
Mutation required Propose separately and wait for authorization

Permissions

DeclaredDetected
Runs code—None
Installs—None
Runs install scripts—None
Network—docs.pytorch.orglearn.microsoft.com
Needs credentials—None
Outside the workspace—None
Agent tools—None

Checks

Low risk · Nothing worth a warning was found.

Not reviewed by a person · Checked by rules; the model review is not switched on yet.

Files5 files · 12.2 KB

  • SKILL.md3.0 KB
references/4
  • gpu-cost-and-scaling.md1.6 KB
  • gpu-observability.md2.7 KB
  • gpu-scheduling.md3.2 KB
  • kaito-workspaces.md1.8 KB

Versions

  1. #11.1.1latestOct 10, 2026