Gke AI Troubleshooting Jobset Interruption
GoogleOfficialPublished by Google
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- Cloud and DevOps
- Apache-2.0
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- Has scripts
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google/gke-ai-troubleshooting-jobset-interruption/downloadUnzip 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-metricsfor 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
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.
Window Calculation: If a timestamp
{issue_time}is available (or calculated asT), set{start_time}=T - 30mand{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_reasonor 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=FalseorUnknown) and correlate them to their GCE physical host ID viametadata.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:
Check the pod timeline to spot pending or unschedulable pods.
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
- ATTRIBUTION.mdAdded by AutoGineer902 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md11.3 KiB
- references/attribution.md1.9 KiB
- references/failure_signatures.md2.5 KiB
- scripts/validate_queries.shScript3.6 KiB
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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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Attribution and licence
- Published by
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- Google (Official)
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- google/skills · skills/cloud/gke-ai-troubleshooting-jobset-interruption · commit 2194d3b
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- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
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