gke-ai-troubleshooting-jobset-interruption

name	gke-ai-troubleshooting-jobset-interruption
metadata	{"category":"Containers"}
description	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.

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.

⚠️ 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.

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📋 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.