此注册表固定版本所收录的最新上游作者日期。

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

已同步 v1.0.0 许可证: Apache-2.0 来源: google/skills/skills/cloud/gke-ai-troubleshooting-tpu-dynamic-slices-monitoring 提交: 99c871e 收录的最新变更:

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
协助解读 TPU 切片状态、找出布建问题、验证工作负载设置,并引导安全的清理或控制器变更。
使用时机
当切片回报状态需要厘清、需要检查工作负载配置要求,或删除及控制器变更涉及作业风险时使用。
输出结果
提供依状态条件整理的诊断、节点、拓扑、预留资源与工作负载设置的针对性检查,以及高风险清理的明确风险与确认指引。
上游变更(3 条,最新收录 2026-09-21)
becc4b8 Enforce semantic versioning (MAJOR.MINOR.PATCH) for skills. 在此技能的 metadata 中添加值为 1.0.0 的 version 字段。
a59e4d9 Rename GKE TPU monitoring skills to gke-ai-troubleshooting prefix 将技能名称改为 gke-ai-troubleshooting 前缀,并重新排版生命周期状态表格,但未改变表格中的指引。
426c077 Migrate and validate gke-tpu-dynamic-slices-monitoring skill 添加 GKE TPU Dynamic Slices 监控与管理技能,涵盖切片诊断、工作负载验证,以及问题处理与清理流程。
npx skills add "lettucebo/Skills#v2.2.0@gke-ai-troubleshooting-tpu-dynamic-slices-monitoring" --full-depth

⚠ v2.2.0 发布后即可使用

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.

历史

v1.0.0 mapping-added 99c871e