GKE TPU Metrics Monitoring Guide

This skill enables the agent to monitor GKE TPU workloads, nodes, and node pools using GKE system metrics. It helps diagnose if workload interruptions or performance issues are caused by underlying infrastructure.

Step 0: Mandatory Context

To run these diagnostics, the following context is required:

• <project_id>: The GCP Project ID.
• <cluster_name>: The GKE Cluster Name.
• <location>: The GKE Cluster Location (region or zone).
• <node_name>: (Optional) The name of the specific GKE node.
• <node_pool_name>: (Optional) The name of the GKE node pool.

Diagnostic Steps

Step 1: Verify TPU Runtime Metrics Configuration Low Risk Auto

Before analyzing runtime metrics, verify that the workload is configured to export them.

• Action: Verify that the Pod specification for the TPU workload includes:
  • containerPort: 8431
  • JAX version 0.4.14 or later (if using JAX).
  • GKE version is 1.27.4-gke.900 or later.
  • GKE System Metrics are enabled on the cluster.

Step 2: Monitor TPU Runtime Metrics Low Risk Auto

If configured correctly, the following metrics are available in Cloud Monitoring (monitored resources k8s_node and k8s_container):

• Container Metrics:
  • kubernetes.io/container/accelerator/duty_cycle: Percentage of time over the past sampling period (60 seconds) during which the TensorCores were actively processing on a TPU chip.
  • kubernetes.io/container/accelerator/memory_used: Amount of accelerator memory allocated in bytes.
  • kubernetes.io/container/accelerator/memory_total: Total accelerator memory in bytes.
• Node Metrics:
  • kubernetes.io/node/accelerator/duty_cycle
  • kubernetes.io/node/accelerator/memory_used
  • kubernetes.io/node/accelerator/memory_total

Step 3: Check Node Status Condition Low Risk Auto

Query the status condition of GKE nodes (GKE version 1.32.1-gke.1357001 or later).

• PromQL Query (Check if a specific node is Ready):

  kubernetes_io:node_status_condition{monitored_resource="k8s_node", cluster_name="<cluster_name>", node_name="<node_name>", condition="Ready", status="True"}
  

• PromQL Query (List nodes with non-Ready conditions that are True):

  kubernetes_io:node_status_condition{monitored_resource="k8s_node", cluster_name="<cluster_name>", condition!="Ready", status="True"}
  

• PromQL Query (List nodes that are NOT Ready):

  kubernetes_io:node_status_condition{monitored_resource="k8s_node", cluster_name="<cluster_name>", condition="Ready", status="False"}
  

• PromQL Query (Fleet-wide node status):

  avg by (condition,status)(avg_over_time(kubernetes_io:node_status_condition{monitored_resource="k8s_node"}[5m]))
  

Step 4: Check Node Pool Status Low Risk Auto

Query the status of multi-host TPU node pools.

• PromQL Query (Verify if a specific node pool is Running):

  kubernetes_io:node_pool_status{monitored_resource="k8s_node_pool", cluster_name="<cluster_name>", node_pool_name="<node_pool_name>", status="Running"}
  

• PromQL Query (Monitor node pools grouped by status):

  count by (status)(count_over_time(kubernetes_io:node_pool_status{monitored_resource="k8s_node_pool"}[5m]))
  

  Possible statuses: Provisioning, Running, Error, Reconciling, Stopping.

Step 5: Check Node Pool Availability Low Risk Auto

Query if all nodes in a multi-host TPU node pool are available.

• PromQL Query (Check availability over time):

  avg by (node_pool_name)(avg_over_time(kubernetes_io:node_pool_multi_host_available{monitored_resource="k8s_node_pool", cluster_name="<cluster_name>"}[5m]))
  

  Value: 1 (True, all nodes available) or 0 (False, some nodes unavailable).

Step 6: Analyze Node Interruptions Low Risk Auto

Query the count of interruptions for GKE nodes.

• PromQL Query (Breakdown of interruptions and causes):

  sum by (interruption_type,interruption_reason)(sum_over_time(kubernetes_io:node_interruption_count{monitored_resource="k8s_node"}[5m]))
  

  Interruption Types: TerminationEvent, MaintenanceEvent, PreemptionEvent. Interruption Reasons: HostError, Eviction, AutoRepair.
• PromQL Query (Filter for Host Maintenance events):

  sum by (interruption_type,interruption_reason)(sum_over_time(kubernetes_io:node_interruption_count{monitored_resource="k8s_node", interruption_reason="HW/SW Maintenance"}[5m]))
  

• PromQL Query (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="<node_pool_name>"}[5m]))
  

Step 7: Calculate Recovery and Interruption Metrics Low Risk Auto

Calculate Mean Time to Recovery (MTTR) and Mean Time Between Interruptions (MTBI) over the last 7 days.

• PromQL Query (MTTR - Mean Time to Recovery):

  sum(sum_over_time(kubernetes_io:node_pool_accelerator_times_to_recover_sum{monitored_resource="k8s_node_pool", cluster_name="<cluster_name>"}[7d])) / sum(sum_over_time(kubernetes_io:node_pool_accelerator_times_to_recover_count{monitored_resource="k8s_node_pool",cluster_name="<cluster_name>"}[7d]))
  

• PromQL Query (MTBI - Mean Time Between Interruptions):

  sum(count_over_time(kubernetes_io:node_memory_total_bytes{monitored_resource="k8s_node", node_name=~"gke-tpu.*|gk3-tpu.*", cluster_name="<cluster_name>"}[7d])) / sum(sum_over_time(kubernetes_io:node_interruption_count{monitored_resource="k8s_node", node_name=~"gke-tpu.*|gk3-tpu.*", cluster_name="<cluster_name>"}[7d]))
  

Step 8: Monitor TPU Host Metrics Low Risk Auto

For GKE version 1.28.1-gke.1066000 or later, monitor TPU host performance.

• Container Metrics:
  • kubernetes.io/container/accelerator/tensorcore_utilization: Current percentage of the TensorCore that is utilized.
  • kubernetes.io/container/accelerator/memory_bandwidth_utilization: Current percentage of the accelerator memory bandwidth that is being used.
• Node Metrics:
  • kubernetes.io/node/accelerator/tensorcore_utilization
  • kubernetes.io/node/accelerator/memory_bandwidth_utilization