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gke-ai-troubleshooting-tpu-dynamic-slices-monitoring

已同步 v1.0.0 授權: Apache-2.0 來源: google/skills/skills/cloud/gke-ai-troubleshooting-tpu-dynamic-slices-monitoring 提交: 99c871e 收錄的最新變更:

Monitors, troubleshoots, and manages GKE TPU Dynamic Slices custom resources. Use when checking TPU slice lifecycle states, troubleshooting slice provisioning failures, validating single-slice or multi-slice (JobSet) workload manifests, or safely patching stuck finalizers and disabling the slice controller. Don't use for generic GKE cluster node pool creation or standard non-TPU workload management (use gke-basics or gke-cluster-creation instead).

摘要

用途
協助解讀 TPU 切片狀態、找出佈建問題、驗證工作負載設定,並引導安全的清理或控制器變更。
使用時機
當切片回報狀態需要釐清、需要檢查工作負載配置要求,或刪除及控制器變更涉及作業風險時使用。
輸出結果
提供依狀態條件整理的診斷、節點、拓撲、預留資源與工作負載設定的針對性檢查,以及高風險清理的明確風險與確認指引。
上游變更(3 筆,最新收錄 2026-09-21)
becc4b8 Enforce semantic versioning (MAJOR.MINOR.PATCH) for skills. 在此技能的 metadata 中新增值為 1.0.0 的 version 欄位。
a59e4d9 Rename GKE TPU monitoring skills to gke-ai-troubleshooting prefix 將技能名稱改為 gke-ai-troubleshooting 前綴,並重新排版生命週期狀態表格,但未改變表格中的指引。
426c077 Migrate and validate gke-tpu-dynamic-slices-monitoring skill 新增 GKE TPU Dynamic Slices 監控與管理技能,涵蓋切片診斷、工作負載驗證,以及問題處理與清理流程。
npx skills add "lettucebo/Skills#v2.2.0@gke-ai-troubleshooting-tpu-dynamic-slices-monitoring" --full-depth

⚠ v2.2.0 發布後即可使用

GKE TPU Dynamic Slices Monitoring & Management

Monitors the status of TPU Slice custom resources, troubleshoots provisioning failures, validates workload manifests on dynamic slices, and performs cleanups.

Prerequisites

  • Cloud Logging enabled for the project.
  • kubectl and gcloud CLIs configured to access the GKE cluster.

Diagnostic Workflow

Step 0: Context Acquisition & Time Window Definition

Gather project, cluster, and slice context using cluster tools or the following parameters:

  • Project ID: {project_id} (e.g., my-gcp-project)
  • Cluster Name: {cluster_name} (e.g., tpu-cluster)
  • Region/Zone: {location} (e.g., us-central1-a)
  • Slice Name: {slice_name} (e.g., test-slice)
  • Issue Time: {timestamp} (Optional; default to the last 30 minutes window [T - 30m] to [T + 30m])

Step 1: Describe the Slice Custom Resource [Low Risk]

When asked to inspect, troubleshoot, or check a slice status, immediately execute kubectl describe slice {slice_name} using available cluster tools to perform the inspection. Parse the resulting Status.Conditions output against the condition table below to diagnose the exact state and provide concrete recommendations.

  • Command:

    kubectl describe slice {slice_name}
    

State & Reason Analysis

Analyze the Status.Conditions (especially Type: Ready and its Reason and Status):

Lifecycle State / Reason Meaning Recommended Action
SliceNotCreated GKE Slice Controller is initializing the slice and performing resource checks. Wait a few minutes and re-check slice status.
SliceCreationFailed Prerequisites validation failed (e.g., selected nodes don't exist, nodes are already used by another slice, or the topology doesn't match the number of partitions). Verify selected nodes exist, are unallocated, and topology matches partition count.
ACTIVATING GKE is actively forming and provisioning the TPU slice. Monitor node provisioning.
ACTIVE The TPU slice is successfully formed and ready to host workloads. Proceed to deploy or check workloads.
ACTIVE_DEGRADED The slice is usable, but one or more sub-blocks are degraded. Monitor workload logs for interconnect or device errors. Check faulty node VMs.
FAILED GKE failed to form the TPU slice (e.g., selected nodes are not part of the same reservation block). Ensure all selected nodes belong to the same reservation block.
DEACTIVATING The slice is dismantling (triggered by user deletion or a critical systemic failure). Wait for dismantling to finish, or patch finalizers if stuck.
INCOMPLETE The terminal phase before the Slice CR is deleted from the cluster. No action required; the resource will be removed shortly.

Provisioning Failure Troubleshooting Checklist

When investigating slice creation or provisioning failures (SliceCreationFailed or FAILED), perform the following verification steps:

  1. Node Existence & Allocation Check: Verify that the selected TPU nodes exist in the cluster and are not already allocated to another slice (kubectl get nodes -l cloud.google.com/gke-tpu-slice, kubectl get slice -A).
  2. Topology Alignment: Confirm that the partition count matches the requested topology dimensions (e.g. topology 2x2 requires 4 nodes).
  3. Reservation Block Alignment Check: Confirm that all selected TPU nodes belong to the same reservation and reservation block.

Step 2: Verify Workload Specification [Low Risk]

Ensure workload manifests are configured correctly to target the dynamic slice.

1. Single-Slice Workload Requirements

Check that the Pod template contains the following annotations and selectors:

  • Annotations:
    • cloud.google.com/gke-tpu-slice-topology: "{topology}" (e.g., "4x4x4")
  • NodeSelector:
    • cloud.google.com/gke-tpu-topology: "{topology}" (e.g., "4x4x4")
    • cloud.google.com/gke-tpu-accelerator: "{accelerator_type}" (e.g., "tpu7x")
    • cloud.google.com/gke-tpu-slice: "{slice_name}" (e.g., "test-slice")

2. Multi-Slice (JobSet) Workload Requirements

If deploying a multi-slice JobSet, verify:

  • JobSet Annotation:
    • alpha.jobset.sigs.k8s.io/exclusive-topology: cloud.google.com/gke-tpu-slice
  • Pod Template Annotations:
    • cloud.google.com/gke-tpu-slice-topology: "{topology}"
  • Pod Template NodeSelector:
    • cloud.google.com/gke-tpu-topology: "{topology}"
    • cloud.google.com/gke-tpu-accelerator: "{accelerator_type}"
    • Note: Do NOT manually specify cloud.google.com/gke-tpu-slice in the nodeSelector; JobSet handles slice assignment automatically.

Resolution & Management Workflow

Resolution 1: Force Delete a Stuck Slice [High Risk]

If a slice is stuck in DEACTIVATING or deletion hangs indefinitely due to stuck finalizers:

  1. Identify Cause: Explain that finalizers on the slice resource (metadata.finalizers) are preventing Kubernetes from completing resource deletion.

  2. Propose Resolution: Propose removing finalizers from the metadata path (/metadata/finalizers) using a JSON patch operation:

    kubectl patch slice {slice_name} --type json -p='[{"op": "remove", "path": "/metadata/finalizers"}]'
    
  3. Provide Warning: Explicitly warn the user that removing finalizers bypasses standard controller dismantling and may leave underlying VM, network, or accelerator resources uncleaned or orphaned.

  4. CRITICAL SAFETY MANDATE: The response MUST explicitly ask the user for confirmation (e.g. "Removing finalizers on /metadata/finalizers via JSON patch is a high-risk operation that may leave orphaned resources. Do you confirm you want to apply this patch to slice {slice_name}?") and pause for user confirmation before applying or executing the patch.


Resolution 2: Disable and Clean Up Slice Controller [High Risk]

If dynamic slicing needs to be disabled:

  1. Check for existing Slices:

    kubectl get slice -A
    

    Ensure all slices are deleted before disabling the controller.

  2. Disable Slice Controller via gcloud:

    gcloud container clusters update {cluster_name} \
        --location={location} \
        --no-enable-slice-controller
    
  3. Delete the Slice CRD:

    kubectl delete crd slices.accelerator.gke.io
    
  4. Clean up Node Labels: Remove GKE TPU Slice labels from all nodes in the cluster:

    kubectl label nodes --all cloud.google.com/gke-tpu-slice- cloud.google.com/gke-tpu-slice-topology-
    
  • Safety Rule: Propose the exact commands and confirm before executing disabling or destructive cleanup steps.

歷程

v1.0.0 mapping-added 99c871e