Gke AI Troubleshooting Handle Disruption Gpu Tpu Agent Skill

Handle Disruption on GPUs and TPUs Troubleshooting

🔍 Diagnostic Workflow

Step 0: Context Acquisition

• Mandatory: When a user asks to debug or investigate an actual workload

disruption, node crash, or unexpected restart without providing complete

cluster details, you MUST immediately halt and request all missing mandatory

parameters (project_id, location, cluster_name, timestamp) BEFORE

delivering theories or general diagnostic commands. Only skip context

acquisition if the user explicitly requests a generic reusable runbook or

provides a complete static telemetry/log dump for offline analysis.

• Optional: node_name, workload_name, workload_namespace,

nodepool_name.

Step 1: [Low Risk] Check for Upcoming Scheduled Maintenance

• Action: Propose running kubectl to check if nodes have the scheduled

maintenance label indicating an upcoming disruption.

• Example Command:

    kubectl get nodes -l cloud.google.com/scheduled-maintenance-time -L cloud.google.com/scheduled-maintenance-time

• Interpretation: The SCHEDULED-MAINTENANCE-TIME column shows the Unix

epoch time when the VM is scheduled for maintenance. If this label exists, a

disruption is guaranteed to occur.

Step 2: [Low Risk] Investigation via Cloud Monitoring (PromQL)

• Action: Call any available monitoring tool or provide PromQL for manual

verification.

• Mandatory Monitoring Rule: Whenever recommending follow-up monitoring or

interruption tracking over time, you MUST explicitly present a PromQL

query using the metric kubernetes_io:node_interruption_count filtered by

interruption_reason="HW/SW Maintenance". Do not suggest general Cloud

Monitoring dashboards or Metrics Explorer without providing this specific

PromQL metric expression.

• Example Query:

    # Fetch host maintenance events for nodes
    sum by (interruption_type,interruption_reason)( sum_over_time( kubernetes_io:node_interruption_count{monitored_resource="k8s_node", interruption_reason="HW/SW Maintenance"}[${__interval}]))

    # See the interruption count aggregated by node pool
    sum by (node_pool_name,interruption_type,interruption_reason)( sum_over_time( kubernetes_io:node_pool_interruption_count{monitored_resource="k8s_node_pool", interruption_reason="HW/SW Maintenance", node_pool_name="{nodepool_name}" }[${__interval}]))

• Interpretation: If kubernetes_io:node_interruption_count shows

values > 0 for interruption_reason="HW/SW Maintenance", it indicates the

underlying Compute Engine VM was interrupted due to scheduled host

maintenance.

Step 3: [Low Risk] Investigation via Cloud Logging & Node Taints

• Action: Call query_logs or instruct the user to filter their GKE logs

for active host maintenance events, and check node taints.

• Guidance: Look for occurrences in Cloud Logging where

cloud.google.com/active-node-maintenance is set to ONGOING. To check if

GKE has cordoned the terminating node to prevent new workloads from being

scheduled, verify whether the

cloud.google.com/impending-node-termination:NoSchedule taint is present

(either in GKE event logs or directly via kubectl describe node).

• Interpretation:
• cloud.google.com/active-node-maintenance set to ONGOING means

workloads are actively being stopped by GKE due to host maintenance.

• cloud.google.com/impending-node-termination:NoSchedule taint means GKE

has cordoned the node to prevent new Pods from being scheduled on the

terminating node. DO NOT recommend tolerating this taint.

Step 4: Conclusion and Resolution

• Action: Provide a summary of findings to the user and suggest

appropriate mitigation strategies if host maintenance events were confirmed

or scheduled.

• Reporting Rule: Signal Only. Report high-signal information indicating

that the disruption was caused by Compute Engine host maintenance,

specifically affecting the underlying GPU/TPU nodes. DO NOT dump raw logs.

• Negative Findings Rule-Out: If node scheduled-maintenance labels, PromQL

interruption counts, and active maintenance logs all return negative/empty

results, definitively conclude that Compute Engine host maintenance did NOT

cause the disruption. Direct the user to investigate application-level

causes (such as OOMKill events, CUDA runtime errors, or resource limits) and

do not propose host maintenance mitigations as the primary resolution.

• Mandatory Workload Protection Triad: Whenever host maintenance is

identified or anticipated on GPU/TPU nodes, consistently recommend all three

complementary mitigations together:

• Configure Graceful Termination: For workloads that need time to save

state (e.g., ML frameworks checkpointing via Orbax), follow the guide to

Enable disruption handling

and set spec.terminationGracePeriodSeconds (up to 60 minutes) to

handle the SIGTERM signal before node shutdown.

• Enable Opportunistic Maintenance: To automatically trigger

maintenance when GKE detects that GPU/TPU nodes are idle, configure

Opportunistic Maintenance.

• Configure PodDisruptionBudgets (PDBs): Ensure your workload uses a

PodDisruptionBudget to maintain minAvailable replicas during

evictions and disruptions.