Generated banner for Gke AI Troubleshooting Jobset Interruption
Generated logo for Gke AI Troubleshooting Jobset Interruption

Gke AI Troubleshooting Jobset Interruption

GoogleOfficialPublished by Google

  • Skill
  • Review and audit
  • Cloud and DevOps
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

Diagnoses GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads autonomously. Use when troubleshooting JobSet restart loops, spot VM preemptions, node readiness failures, host VM issues, or coordinator worker crashes. Don't use for general GKE cluster creation, basic workload deployment, or non-JobSet application issues.
— the description by Google

AutoGineer summary

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

This skill provides a systematic, autonomous diagnostic workflow to identify the root causes of JobSet interruptions in GKE clusters. It is designed for AI/ML training workloads that frequently encounter restart loops, spot VM preemptions, or hardware-related failures. The skill guides users through a multi-step process: gathering cluster context, analyzing restart metrics, correlating nodepool interruptions with physical host events, and inspecting pod lifecycle phases and worker logs. It includes specific resolution strategies for optimizing scheduling policies or quarantining faulty physical host hardware.

What it does

  • Identifies JobSet restart frequency and patterns using MQL and PromQL queries.
  • Detects nodepool-level interruptions such as spot preemptions or scheduled maintenance.
  • Correlates node readiness failures with underlying physical host VM topology.
  • Analyzes pod lifecycle phases and worker container logs for application-level crashes.
  • Provides actionable recommendations for mitigating preemptions and handling faulty hardware.
  • Includes a structured checklist for consistent diagnostic execution.

When to use it

  • When AI/ML training jobs managed by JobSet are experiencing unexpected restarts or failure loops.
  • When investigating if spot VM preemptions are causing training interruptions.
  • When suspecting physical host hardware failures are impacting node stability.
  • When diagnosing coordinator worker crashes or network communication timeouts in distributed training.

Advantages

  • Reduces manual investigation time by automating complex log and metric correlation.
  • Provides clear diagnostic paths for distinguishing between infrastructure and application issues.
  • Offers specific, high-impact remediation steps for GKE-based AI workloads.
  • Ensures consistent troubleshooting practices across different cluster environments.

Usage examples

“My JobSet 'training-run-1' in cluster 'ai-cluster' is stuck in a restart loop. Can you help diagnose why?”
The agent will initiate the diagnostic workflow, querying restart metrics, checking for nodepool interruptions, and inspecting pod logs to identify the cause of the restart loop.
“Investigate why my distributed training job in the 'ml-namespace' keeps failing every 30 minutes.”
The agent will define a time window around the reported failures, analyze node health and host topology, and report if the failures correlate with specific physical host issues or spot preemptions.

Install

  1. Download the skill as a ZIP:

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

    mkdir -p .agents/skills && unzip gke-ai-troubleshooting-jobset-interruption.zip -d .agents/skills

Documentation by Google

GKE JobSet Interruption Troubleshooting

Use this skill to systematically diagnose and resolve JobSet interruptions, restarts, and preemptions on GKE clusters hosting large-scale AI/ML workloads.

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-jobset-interruption)" and append agent-skills/1.0 (skill:gke-ai-troubleshooting-jobset-interruption) to User-Agent headers.

⚠️ Prerequisites & Sandbox Rules

  • JobSet metrics package must be enabled in kube-state-metrics for your cluster.

  • Cloud Logging and Cloud Monitoring enabled for the Google Cloud Project.

  • Autonomous & Sandbox Execution Rule: If API queries, tools, or commands encounter 403 Permission Denied, authentication errors, or network isolation, do NOT enter authentication or credential troubleshooting loops. Populate the query templates with the acquired variables ({project_id}, {cluster_name}, {workload_name}, {start_time}, {end_time}), inspect any locally staged telemetry or mock data files if available, and complete the diagnostic workflow and resolution recommendations autonomously.

🔍 Diagnostic Workflow

Step 0: Context Acquisition & Time Window Definition

Independently gather context using tools, workspace files, environment details, or user prompt context:

  • Project ID ({project_id})

  • Cluster Name ({cluster_name})

  • Workload Name (JobSet Name) ({workload_name})

  • Workload Namespace ({namespace})

  • Issue Time ({issue_time})

If specific variables are not explicitly provided by the user, inspect cluster resources or logs to determine them, or use the {variable} placeholders provided.

Time Handling Rules
  1. Autonomous Time Window: If a relative time (e.g., "X minutes ago") or no exact timestamp is provided, calculate the query window based on current time or available log timestamps.

  2. Window Calculation: If a timestamp {issue_time} is available (or calculated as T), set {start_time} = T - 30m and {end_time} = T + 30m.


Step 1: Identify JobSet Restarts and Attempts [Low Risk]

Verify if the JobSet is experiencing restart loops and determine the frequency of restarts.

Visual Chart / MQL Query - restarts
  • MQL Query Specification:

    fetch prometheus_target
    | metric 'prometheus.googleapis.com/kube_jobset_restarts/gauge'
    | filter resource.cluster_name == '{cluster_name}' && metric.jobset_name == '{workload_name}'
    | align next_older(1m)
    | every 1m
    | group_by [metric.jobset_name], [val: max(value)]
PromQL Metric Query - restarts
  • PromQL Query Specification:

    kube_jobset_restarts{jobset_name="{workload_name}", cluster="{cluster_name}"}
  • Diagnostic Logic: A non-zero or increasing value for restarts indicates that the JobSet is being actively restarted by the controller due to worker failure or interruption.

  • Automation: Proceed to Step 2 automatically after reporting findings.


Step 2: Inspect Nodepool Interruptions [Low Risk]

Determine if the JobSet restarts were triggered by physical nodepool-level events (such as spot preemptions, maintenance, or host terminations).

A. Metrics Query (Nodepool Interruption Counts)
Visual Chart / MQL Query - interruptions
  • MQL Query Specification:

    fetch k8s_node_pool
    | metric 'kubernetes.io/node_pool/interruption_count'
    | filter cluster_name == '{cluster_name}'
    | align next_older(10m)
    | every 10m
    | group_by [metric.interruption_type, metric.interruption_reason, metadata.system.node_pool_name], [val: sum(value)]
PromQL Query - interruptions
  • PromQL Query Specification:

    sum by (interruption_type, interruption_reason, node_pool_name, cluster_name) (
      avg_over_time(kubernetes_io:node_pool_interruption_count{cluster_name="{cluster_name}"}[10m])
    )
B. Log Query (Nodepool Life Events)
  • LQL Log Filter Specification:

    resource.type="gke_nodepool"
    AND resource.labels.cluster_name="{cluster_name}"
    AND timestamp >= "{start_time}"
    AND timestamp <= "{end_time}"
  • Diagnostic Logic:

    • PreemptionEvent: Spot VMs were preempted, or node was scale-down.

    • MaintenanceEvent: Node pool updated or Google scheduled maintenance.

    • TerminationEvent: Serious host failures. Check interruption_reason or logs for host issues.

    • See Failure Signatures for examples of node termination logs and preemption events.

  • Automation: Proceed to Step 3 automatically.


Step 3: Inspect Nodes and Underlying Host VMs [Low Risk]

Correlate node readiness failures with physical host VMs to see if a single faulty host repeatedly fails coordinator pods.

A. Metrics Query (Node Ready Status Check)
Visual Chart / MQL Query - node status
  • MQL Query Specification:

    fetch k8s_node
    | metric 'kubernetes.io/node/status_condition'
    | filter cluster_name == '{cluster_name}' && metric.condition == 'Ready' && metric.status == 'False'
    | align next_older(1m)
    | every 1m
    | group_by [node_name, metadata.user.gke_nodepool], [val: max(value)]
PromQL Query - node status
  • PromQL Query Specification:

    sum by (status, condition, node_pool_name) (
      kubernetes_io:node_status_condition{cluster_name="{cluster_name}", condition="Ready", status="False"}
    )
B. Metrics Query (Node-to-Host Metadata Topology Correlation)
  • MQL Query Specification:

    fetch k8s_node
    | metric 'kubernetes.io/node/cpu/total_cores'
    | filter cluster_name == '{cluster_name}'
    | align next_older(1m)
    | every 1m
    | group_by [node_name, metadata.user.gce_topology_host, metadata.user.gke_nodepool], [val: max(value)]
C. Log Query (Node Fault Logs)
  • LQL Log Filter Specification:

    resource.type="k8s_node"
    AND resource.labels.cluster_name="{cluster_name}"
    AND (textPayload:"host error" OR textPayload:"kernel panic" OR textPayload:"hardware failure" OR textPayload:"NodeNotReady")
    AND timestamp >= "{start_time}"
    AND timestamp <= "{end_time}"
  • Diagnostic Logic: Identify if specific nodes are unhealthy (Ready=False or Unknown) and correlate them to their GCE physical host ID via metadata.user.gce_topology_host. Check if the same host is repeatedly failing.

  • Automation: Proceed to Step 4 automatically.


Step 4: Inspect Pod and Worker / Container Failures [Low Risk]

Analyze pod status phases and retrieve coordinator worker logs to identify application-level crashes or network deadlocks.

Required Execution Order: You MUST analyze pod status phases (Section A) and unschedulable pod metrics (Section B) to assess overall workload health before inspecting specific worker container logs (Section C).

A. Metrics Query (Pod Lifecycle Phases)
Visual Chart / MQL Query - pod phase
  • MQL Query Specification:

    fetch k8s_pod
    | metric 'kubernetes.io/pod/status/phase'
    | filter cluster_name == '{cluster_name}' && pod_name ==~ '{workload_name}.*'
    | align next_older(10m)
    | every 10m
    | group_by [metric.phase], [val: count()]
PromQL Query - pod phase
  • PromQL Query Specification:

    sum by (phase) (
      avg_over_time(kube_pod_status_phase{cluster="{cluster_name}", pod=~"{workload_name}.*"}[10m])
    )
B. Metrics Query (Unschedulable Pod Count)
  • MQL Query Specification:

    fetch k8s_pod
    | metric 'kubernetes.io/pod/status/unschedulable'
    | filter cluster_name == '{cluster_name}' && pod_name ==~ '{workload_name}.*'
    | align next_older(10m)
    | every 10m
    | group_by [pod_name], [val: max(value)]
C. Log Query (Worker Container Logs)
  • LQL Log Filter Specification:

    resource.type="k8s_container"
    AND resource.labels.cluster_name="{cluster_name}"
    AND labels."k8s-pod/jobset_sigs_k8s_io/jobset-name"="{workload_name}"
    AND timestamp >= "{start_time}"
    AND timestamp <= "{end_time}"
  • Diagnostic Logic:

    1. Check the pod timeline to spot pending or unschedulable pods.

    2. Use worker container logs to analyze worker 0 in slice 0 (coordinator) for NCCL timeouts, collective communication issues, or MegaScale hangs.

  • Automation: Proceed to Resolution.


🛠️ Resolution Workflow

Resolution 1: Preemption & Autoscaling Optimizations [Low Risk]

If Step 2 showed high preemption counts on Spot VMs:

  • Action: Suggest switching critical long-running training workloads to GKE Reserved/On-Demand VMs or utilizing Compact Placement Policies to minimize defragmentation interruptions.

  • Justification: Eliminates spot-market preemptions and reduces training restarts.

Resolution 2: Quarantine Faulty Host VMs [High Risk]

If Step 3 identified a specific host ID (gce-topology-host) that consistently fails or triggers restarts across multiple attempts:

  • Action: Recommend cordoning/draining the GKE node, deleting the underlying GCE VM instance to trigger instance recreation, and opening a support ticket with Google Cloud Support specifying the physical host ID.

  • Justification: GKE auto-repair will recreate the VM instance on healthy physical hardware, preventing infinite restart loops.


📋 Copypaste Checklist

  • [ ] Gather context and compute {start_time} ({issue_time} - 30m) and {end_time} ({issue_time} + 30m) window.

  • [ ] Query JobSet restart attempts.

  • [ ] Check Nodepool interruptions (spot preemptions vs. hardware terminations).

  • [ ] Query node-to-host mapping and check node logs for physical host errors.

  • [ ] Inspect pod timeline status and coordinator worker container logs.

  • [ ] Recommend appropriate scheduling strategy (On-demand vs Spot) or host VM quarantining.

Files

LICENSE


                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

      "You" (or "Your") shall mean an individual or Legal Entity
      exercising permissions granted by this License.

      "Source" form shall mean the preferred form for making modifications,
      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions
      to that Work or Derivative Works thereof, that is intentionally
      submitted to Licensor for inclusion in the Work by the copyright owner
      or by an individual or Legal Entity authorized to submit on behalf of
      the copyright owner. For the purposes of this definition, "submitted"
      means any form of electronic, verbal, or written communication sent
      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the
      Licensor for the purpose of discussing and improving the Work, but
      excluding communication that is conspicuously marked or otherwise
      designated in writing by the copyright owner as "Not a Contribution."

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      copyright license to reproduce, prepare Derivative Works of,
      publicly display, publicly perform, sublicense, and distribute the
      Work and such Derivative Works in Source or Object form.

   3. Grant of Patent License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their
      Contribution(s) alone or by combination of their Contribution(s)
      with the Work to which such Contribution(s) was submitted. If You
      institute patent litigation against any entity (including a
      cross-claim or counterclaim in a lawsuit) alleging that the Work
      or a Contribution incorporated within the Work constitutes direct
      or contributory patent infringement, then any patent licenses
      granted to You under this License for that Work shall terminate
      as of the date such litigation is filed.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices
          stating that You changed the files; and

      (c) You must retain, in the Source form of any Derivative Works
          that You distribute, all copyright, patent, trademark, and
          attribution notices from the Source form of the Work,
          excluding those notices that do not pertain to any part of
          the Derivative Works; and

      (d) If the Work includes a "NOTICE" text file as part of its
          distribution, then any Derivative Works that You distribute must
          include a readable copy of the attribution notices contained
          within such NOTICE file, excluding those notices that do not
          pertain to any part of the Derivative Works, in at least one
          of the following places: within a NOTICE text file distributed
          as part of the Derivative Works; within the Source form or
          documentation, if provided along with the Derivative Works; or,
          within a display generated by the Derivative Works, if and
          wherever such third-party notices normally appear. The contents
          of the NOTICE file are for informational purposes only and
          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and
      may provide additional or different license terms and conditions
      for use, reproduction, or distribution of Your modifications, or
      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with
      the conditions stated in this License.

   5. Submission of Contributions. Unless You explicitly state otherwise,
      any Contribution intentionally submitted for inclusion in the Work
      by You to the Licensor shall be under the terms and conditions of
      this License, without any additional terms or conditions.
      Notwithstanding the above, nothing herein shall supersede or modify
      the terms of any separate license agreement you may have executed
      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

   7. Disclaimer of Warranty. Unless required by applicable law or
      agreed to in writing, Licensor provides the Work (and each
      Contributor provides its Contributions) on an "AS IS" BASIS,
      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
      implied, including, without limitation, any warranties or conditions
      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
      PARTICULAR PURPOSE. You are solely responsible for determining the
      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly
      negligent acts) or agreed to in writing, shall any Contributor be
      liable to You for damages, including any direct, indirect, special,
      incidental, or consequential damages of any character arising as a
      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

   9. Accepting Warranty or Additional Liability. While redistributing
      the Work or Derivative Works thereof, You may choose to offer,
      and charge a fee for, acceptance of support, warranty, indemnity,
      or other liability obligations and/or rights consistent with this
      License. However, in accepting such obligations, You may act only
      on Your own behalf and on Your sole responsibility, not on behalf
      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "[]"
      replaced with your own identifying information. (Don't include
      the brackets!)  The text should be enclosed in the appropriate
      comment syntax for the file format. We also recommend that a
      file or class name and description of purpose be included on the
      same "printed page" as the copyright notice for easier
      identification within third-party archives.

   Copyright [yyyy] [name of copyright owner]

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

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

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Node.js

Security

What our scanner noticed· Low risk

  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Google
Publisher
Google (Official)
Source
google/skills · skills/cloud/gke-ai-troubleshooting-jobset-interruption · commit 2194d3b
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Google or Google.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download gke-ai-troubleshooting-jobset-interruption.zip