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Gke AI Troubleshooting Tpu Reservation Stockout

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  • Cloud and DevOps
  • Apache-2.0
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From the author

Diagnose and resolve cluster-wide GKE TPU node pool creation or scale-up failures caused by Compute Engine reservation stockouts (GCE_STOCKOUT, ZONE_RESOURCE_POOL_EXHAUSTED) when existing unhealthy or NotReady TPU node pools in the cluster have node auto-repair disabled and hold reserved TPU capacity. Use when TPU node pool creation or scale-up fails with GCE_STOCKOUT, ZONE_RESOURCE_POOL_EXHAUSTED, or ZONE_RESOURCE_POOL_EXHAUSTED_WITH_DETAILS despite having a specific Compute Engine TPU reservation. Don't use for general pod scheduling without a TPU reservation or workload-level container crashes.
— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a diagnostic workflow to troubleshoot GKE TPU node pool creation or scale-up failures triggered by GCE_STOCKOUT or ZONE_RESOURCE_POOL_EXHAUSTED errors. It specifically addresses scenarios where existing, unhealthy TPU node pools with auto-repair disabled continue to consume reserved TPU capacity, preventing new node pools from scaling. The skill guides users through verifying reservation utilization, identifying the specific node pools holding the capacity, and determining the appropriate remediation steps to free up resources.

What it does

  • Confirms GKE TPU reservation stockout errors via audit logs and operation history.
  • Checks Compute Engine reservation utilization to identify consumed capacity.
  • Maps VMs consuming reservation slots to specific GKE clusters and node pools.
  • Verifies the status of node auto-repair on suspected leaking node pools.
  • Provides guidance on safely freeing reservation capacity for new node pool deployments.

When to use it

  • When GKE TPU node pool creation fails with GCE_STOCKOUT or ZONE_RESOURCE_POOL_EXHAUSTED.
  • When you suspect existing, unhealthy TPU node pools are holding onto reserved capacity.
  • When you need to verify if node auto-repair is correctly configured for TPU workloads.
  • When you need to identify which specific node pools are consuming your TPU reservation.

Advantages

  • Systematic approach to identifying resource leaks in GKE TPU reservations.
  • Helps distinguish between genuine resource exhaustion and capacity held by stale nodes.
  • Provides clear, actionable steps for resolving capacity conflicts.
  • Adheres to safe operational practices by prioritizing read-only diagnostics.

Usage examples

“My GKE TPU node pool creation is failing with GCE_STOCKOUT. How do I check if my reservation is full?”
The agent will guide you through checking the reservation utilization and identifying which node pools are consuming the slots.
“How can I tell if an unhealthy node pool is preventing my new TPU node pool from scaling up?”
The agent will help you correlate the failing operation with existing node pool health and reservation consumption metrics.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/google/gke-ai-troubleshooting-tpu-reservation-stockout/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip gke-ai-troubleshooting-tpu-reservation-stockout.zip -d .agents/skills

Documentation by Google

Troubleshoot GKE TPU reservation stockouts caused by leaked node pool capacity

Use this skill to diagnose and resolve GKE TPU node pool creation or scale-up failures that fail with GCE_STOCKOUT, ZONE_RESOURCE_POOL_EXHAUSTED, or ZONE_RESOURCE_POOL_EXHAUSTED_WITH_DETAILS (or queue due to lack of capacity) even though they target a specific Compute Engine TPU reservation.

If node auto-repair is disabled on an existing TPU node pool, GKE doesn't repair its nodes when they become NotReady. While their VMs are still running, they keep consuming slots in the reservation (inUseCount is the number of running instances that consume the reservation). Per Ensure reservation availability, the reservation must have sufficient available TPU chips at the time of node pool creation, and per Multi-host TPU slice node pools, if GKE is unable to deploy one node in a slice, no nodes in the TPU slice node pool are deployed. When the free slots in the reservation (specificReservation.count - specificReservation.inUseCount; see Consumable instances in a reservation) are fewer than the number of nodes that the new TPU node pool needs, creation fails with GCE_STOCKOUT or ZONE_RESOURCE_POOL_EXHAUSTED (or queues due to lack of capacity).

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gke-ai-troubleshooting-tpu-reservation-stockout)" and append agent-skills/1.0 (skill:gke-ai-troubleshooting-tpu-reservation-stockout) to User-Agent headers.

Prerequisites

  • Required IAM Roles:

    • Logs Viewer (roles/logging.viewer): Query Cloud Audit Logs in Cloud Logging.

    • Monitoring Viewer (roles/monitoring.viewer): Query GKE node readiness metrics in Cloud Monitoring.

    • Kubernetes Engine Viewer (roles/container.viewer): Inspect GKE node pools (management.autoRepair), cluster operations, and node conditions.

    • Compute Viewer (roles/compute.viewer): Inspect Compute Engine reservation utilization (specificReservation.inUseCount vs specificReservation.count), consuming instances, and VM labels (including in the reservation's owner project if using a shared reservation).

    • For remediation ([High Risk] steps): Kubernetes Engine Cluster Admin (roles/container.clusterAdmin)

  • API Enablement & Billing:

    • Ensure the target project has an active Google Cloud billing account attached: gcloud billing projects describe {project_id}.

    • Enable required APIs: container.googleapis.com, compute.googleapis.com, logging.googleapis.com, and monitoring.googleapis.com.

  • CLI & Authentication Setup:

  • Cluster mode: This skill applies to Standard clusters. Autopilot clusters always auto-repair nodes, and you can't turn this off (see Settings for Autopilot and Standard).

  • Reference Documentation:

Read-only rule: Run read-only diagnostic commands only. Never drain, delete, or re-create nodes, or run any other command that changes the cluster. Give the user any fix to apply themselves.

When you recommend a fix, link the doc section that describes it.


Diagnostic workflow

Step 0: Collect context and set the investigation window

Identify the following environment parameters from the user prompt or cluster context:

  • {project_id}, {cluster_name}, {location}, {failing_nodepool_name}, {reservation_name}, {reservation_project_id} (the project that owns {reservation_name}, which defaults to {project_id} for a single-project reservation or is the owner project for a shared reservation; see Consuming a specific shared reservation), {zone} (the zone of the reservation), and {issue_time}.

  • Time window: By default, query a 60-minute window [{issue_time} - 30m, {issue_time} + 30m] ({start_time} to {end_time}).


Step 1: Confirm the stockout error and reservation utilization [Low Risk]
  1. Check Cloud Audit Logs and GKE operations for stockout or capacity errors:

  2. Check Compute Engine reservation utilization:

    • Per Limitations, query shared reservations in the owner project where they were created ({reservation_project_id}).

    • Follow the View reservations section to run gcloud compute reservations describe {reservation_name} --zone={zone} --project={reservation_project_id} or gcloud compute reservations list --project={reservation_project_id}, and compare specificReservation.inUseCount against specificReservation.count.

Interpret the results
  • Don't suggest deleting any node pool until you have identified which existing VMs and node pools hold the reserved capacity (Step 2).

  • Stockout confirmed: Per Ensure reservation availability and Multi-host TPU slice node pools, if GKE can't deploy one node in a multi-host slice, no nodes in the slice are deployed. If a stockout or lack-of-capacity message is present and the free slots (specificReservation.count - specificReservation.inUseCount; see Consumable instances in a reservation) are fewer than the number of nodes that {failing_nodepool_name} needs, proceed to Step 2.


Step 2: Find which VMs and node pools hold the reservation [Low Risk]
  1. Check which VMs are consuming {reservation_name}:

  2. Check node readiness in {cluster_name}:

    • Query the Cloud Monitoring metric kubernetes_io:node_status_condition (monitored_resource="k8s_node", cluster_name="{cluster_name}", condition="Ready") described in Monitor health metrics for TPU nodes and node pools. The status label value is True, False, or Unknown (capitalized); use status!="True" to find nodes whose Ready condition is False or Unknown.

    • List TPU nodes and their node pool labels with kubectl get nodes -l cloud.google.com/gke-tpu-accelerator -L cloud.google.com/gke-nodepool.

  3. Exclude the failing node pool: Leave {failing_nodepool_name} out of the unhealthy list. It's the node pool that couldn't get capacity, not the one holding it.

Interpret the results
  • VMs outside {cluster_name}: If some of the VMs consuming {reservation_name} aren't nodes in {cluster_name} (for example, their labels show another GKE cluster or they are Compute Engine VMs without GKE labels), tell the user which VMs or other clusters hold those reservation slots.

  • Fewer listed VMs than specificReservation.inUseCount: Per View a list of instances that are consuming a reservation, the list only includes VMs in one project. If the list has fewer VMs than specificReservation.inUseCount, tell the user that VMs in other projects that share {reservation_name} are consuming the remaining slots.

  • Unhealthy node pool in {cluster_name} ({leaking_nodepool_name}): If the VMs consuming {reservation_name} belong to an existing NotReady node pool in {cluster_name} ({leaking_nodepool_name}), proceed to Step 3.

  • Healthy reservation (rule out a leak): If the reserved slots are held by healthy, in-use node pools, there is no leak. Explain that healthy node pools hold the reserved slots, so too few slots are free for {failing_nodepool_name}. Tell the user not to delete or restart those healthy node pools, and point the user to the consumption options in Insufficient TPU resources to satisfy the TPU request.


Step 3: Verify node auto-repair on the leaking node pool [Low Risk]

Follow Verify node auto-repair is enabled for a Standard node pool for {leaking_nodepool_name}:

  • Auto-repair is disabled (management.autoRepair: false): GKE doesn't repair the NotReady nodes in {leaking_nodepool_name}. Their running VMs keep counting toward specificReservation.inUseCount in {reservation_name}, so {failing_nodepool_name} can't get reserved capacity. Proceed to Step 4.

  • Auto-repair is enabled (management.autoRepair: true), but nodes are still NotReady:


Step 4: Recover or release the reservation capacity [High Risk]

Guardrail: Don't modify GKE-managed instance groups or VMs directly through Compute Engine. Manage node pools through GKE. Never delete healthy node pools that run active jobs.

  1. If the user wants to repair {leaking_nodepool_name} in place, enable node auto-repair (doesn't free slots for {failing_nodepool_name}):

  2. If {leaking_nodepool_name} is stale or the user wants to free its slots for {failing_nodepool_name}, delete it through GKE: Link the user to Delete a node pool to delete {leaking_nodepool_name}, which frees the reserved slots its VMs use.

  3. After the user deletes {leaking_nodepool_name}, verify that enough reservation slots are free: Follow View reservations to run gcloud compute reservations describe {reservation_name} --zone={zone} --project={reservation_project_id} and confirm that specificReservation.inUseCount drops so the free slots (specificReservation.count - specificReservation.inUseCount) are at least the number of nodes that {failing_nodepool_name} needs before retrying {failing_nodepool_name}.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

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Needs: Needs Node.js

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google/skills · skills/cloud/gke-ai-troubleshooting-tpu-reservation-stockout · commit 2194d3b
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