docs: fix HAMi pod label selectors and Helm value references

docs/developers/dynamic-mig.md

@@ -46,7 +46,7 @@ data:
             - name: 4g.24gb
               memory: 24576
               count: 1
-      - models: [ "A100-SXM4-40GB", "A100-40GB-PCIe", "A100-PCIE-40GB", "A100-SXM4-40GB" ]
+      - models: [ "A100-SXM4-40GB", "A100-40GB-PCIe", "A100-PCIE-40GB" ]
         allowedGeometries:
           -
             - name: 1g.5gb

docs/faq/faq.md

@@ -225,4 +225,4 @@ DCGM Exporter is not affected and continues to report physical-level counters no
 
 ## How do I set up Prometheus and Grafana monitoring for HAMi vGPU metrics?
 
-The `hami-device-plugin` pod on each node exposes per-container vGPU metrics on port `31992` (configurable via `devicePlugin.monitorPort`). See [Grafana Dashboard](../userguide/monitoring/grafana-dashboard) for the full setup including Prometheus scrape config and dashboard import.
+The `hami-device-plugin` pod on each node exposes per-container vGPU metrics on port `31992` (configurable via `devicePlugin.service.httpPort`). See [Grafana Dashboard](../userguide/monitoring/grafana-dashboard) for the full setup including Prometheus scrape config and dashboard import.

docs/get-started/verify-hami.md

@@ -155,7 +155,7 @@ If you encounter issues, follow this sequence:
 2. Container Runtime: Ensure `sudo ctr run` or `docker run` works outside K8s.
 3. Stale Plugins: Remove conflicting plugins: `kubectl delete daemonset nvidia-device-plugin-daemonset -n kube-system --ignore-not-found`.
 4. Node Resources: Verify K8s sees the GPU: `kubectl get nodes -o jsonpath='{.items[*].status.allocatable}' | grep -i nvidia`.
-5. Scheduler Layer: Check HAMi logs: `kubectl logs -n kube-system -l app=hami-scheduler`.
+5. Scheduler Layer: Check HAMi logs: `kubectl logs -n kube-system -l app.kubernetes.io/component=hami-scheduler`.
 
 ## Cleanup
 

docs/installation/offline-installation.md

@@ -9,7 +9,7 @@ If your cluster can’t directly access the internet, you can install HAMi offli
 You need to save the following images into a tarball file and copy it into the cluster.
 
 ```yaml
-projecthami/hami:{HAMi version} ghcr.io/kubernetes/ingress-nginx/kube-webhook-certgen:v1.5.2 registry.cn-hangzhou.aliyuncs.com/google_containers/kube-scheduler:{your kubernetes version}
+projecthami/hami:{HAMi version} docker.io/jettech/kube-webhook-certgen:v1.5.2 liangjw/kube-webhook-certgen:v1.1.1 registry.cn-hangzhou.aliyuncs.com/google_containers/kube-scheduler:{your kubernetes version}
 ```
 
 Load the images, tag them with your internal registry, and push them to your registry.
@@ -18,8 +18,9 @@ Load the images, tag them with your internal registry, and push them to your reg
 docker load -i {HAMi_image}.tar
 docker tag projecthami/hami:{HAMi version} {your_inner_registry}/hami:{HAMi version}
 docker push {your_inner_registry}/hami:{HAMi version}
-docker tag ghcr.io/kubernetes/ingress-nginx/kube-webhook-certgen:v1.5.2 {your_inner_registry}/kube-webhook-certgen:v1.5.2
+docker tag docker.io/jettech/kube-webhook-certgen:v1.5.2 {your_inner_registry}/kube-webhook-certgen:v1.5.2
 docker push {your_inner_registry}/kube-webhook-certgen:v1.5.2
+docker tag liangjw/kube-webhook-certgen:v1.1.1 {your_inner_registry}/kube-webhook-certgen:v1.1.1
 docker tag registry.cn-hangzhou.aliyuncs.com/google_containers/kube-scheduler:{your kubernetes version} {your_inner_registry}/kube-scheduler:{your kubernetes version}
 docker push {your_inner_registry}/kube-scheduler:{your kubernetes version}
 ```
@@ -45,7 +46,7 @@ scheduler:
 Run the following command in your $\{CHART_PATH\} folder:
 
 ```bash
-helm install hami hami --set scheduler.kubeScheduler.imageTag={your k8s server version} -n kube-system
+helm install hami hami --set scheduler.kubeScheduler.image.tag={your k8s server version} -n kube-system
 ```
 
 ## Verify your installation

docs/installation/online-installation.md

@@ -24,10 +24,10 @@ kubectl version
 
 ## Installation
 
-Ensure the `scheduler.kubeScheduler.imageTag` matches your Kubernetes server version. For instance, if your cluster server is v1.29.0, use the following command to deploy:
+Ensure the `scheduler.kubeScheduler.image.tag` matches your Kubernetes server version. For instance, if your cluster server is v1.29.0, use the following command to deploy:
 
 ```bash
-helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag=v1.29.0 -n kube-system
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.image.tag=v1.29.0 -n kube-system
 ```
 
 Customize your installation by editing the [configurations](../userguide/configure.md).

docs/installation/uninstall.md

@@ -98,7 +98,7 @@ To reinstall HAMi, follow the [installation guide](./online-installation.md).
 If HAMi pods are stuck in the "Terminating" state, you can force delete them:
 
 ```bash
-kubectl delete pods -n kube-system -l app=hami --grace-period=0 --force
+kubectl delete pods -n kube-system -l app.kubernetes.io/instance=hami --grace-period=0 --force
 ```
 
 Then try the uninstall command again.

docs/installation/upgrade.md

@@ -35,7 +35,7 @@ helm get values hami -n kube-system > hami-backup-values.yaml
 kubectl get configmap hami-scheduler-device -n kube-system -o yaml > hami-configmap-backup.yaml
 
 # Check current state
-kubectl get all -n kube-system -l app=hami -o yaml > hami-state-backup.yaml
+kubectl get all -n kube-system -l app.kubernetes.io/instance=hami -o yaml > hami-state-backup.yaml
 ```
 
 ### 3. Clear Running Workloads
@@ -65,11 +65,11 @@ Before proceeding, ensure all HAMi components are healthy:
 
 ```bash
 # Check pod status
-kubectl get pods -n kube-system -l app=hami
+kubectl get pods -n kube-system -l app.kubernetes.io/instance=hami
 
 # Check for errors
-kubectl logs -n kube-system -l app=hami-scheduler --tail=50
-kubectl logs -n kube-system -l app=hami-device-plugin --tail=50
+kubectl logs -n kube-system -l app.kubernetes.io/component=hami-scheduler --tail=50
+kubectl logs -n kube-system -l app.kubernetes.io/component=hami-device-plugin --tail=50
 ```
 
 ## Upgrade Process
@@ -123,7 +123,7 @@ After the upgrade completes, verify that HAMi is functioning correctly:
 ### 1. Check Pod Status
 
 ```bash
-kubectl get pods -n kube-system -l app=hami
+kubectl get pods -n kube-system -l app.kubernetes.io/instance=hami
 ```
 
 All pods should be in `Running` state.
@@ -132,10 +132,10 @@ All pods should be in `Running` state.
 
 ```bash
 # Check scheduler logs for errors
-kubectl logs -n kube-system -l app=hami-scheduler | grep -i "error\|warning" | head -20
+kubectl logs -n kube-system -l app.kubernetes.io/component=hami-scheduler | grep -i "error\|warning" | head -20
 
 # Check device plugin logs
-kubectl logs -n kube-system -l app=hami-device-plugin | grep -i "error" | head -20
+kubectl logs -n kube-system -l app.kubernetes.io/component=hami-device-plugin | grep -i "error" | head -20
 ```
 
 ### 3. Test GPU Allocation
@@ -188,7 +188,7 @@ If pods remain in pending state after upgrade:
 kubectl describe pod <pod-name>
 
 # Check scheduler logs
-kubectl logs -n kube-system -l app=hami-scheduler | grep -i "pending\|error"
+kubectl logs -n kube-system -l app.kubernetes.io/component=hami-scheduler | grep -i "pending\|error"
 
 # Verify GPU availability
 kubectl describe nodes | grep -i "gpu"
@@ -214,7 +214,7 @@ nvidia-smi
 exit
 
 # Restart device plugin on affected node
-kubectl delete pods -n kube-system -l app=hami-device-plugin --field-selector spec.nodeName=<node-name>
+kubectl delete pods -n kube-system -l app.kubernetes.io/component=hami-device-plugin --field-selector spec.nodeName=<node-name>
 ```
 
 ### Segmentation Fault During Upgrade

docs/userguide/iluvatar-device/enable-iluvatar-gpu-sharing.md

@@ -34,7 +34,7 @@ Install only gpu-manager. Do not install the gpu-admission package.
 - Set `devices.iluvatar.enabled=true` when installing HAMi
 
 ```bash
-helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag={your kubernetes version} --set devices.iluvatar.enabled=true -n kube-system
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.image.tag={your kubernetes version} --set devices.iluvatar.enabled=true -n kube-system
 ```
 
 The currently supported GPU models and resource names are defined in [device-configmap.yaml](https://github.com/Project-HAMi/HAMi/blob/master/charts/hami/templates/scheduler/device-configmap.yaml):

docs/userguide/monitoring/grafana-dashboard.md

@@ -25,7 +25,7 @@ The dashboard includes panels for:
 
 ## Prometheus Scrape Config
 
-The `hami-device-plugin` pod on each node exposes metrics on port `31992` (configurable via `devicePlugin.monitorPort`). Add a scrape job:
+The `hami-device-plugin` pod on each node exposes metrics on port `31992` (configurable via `devicePlugin.service.httpPort`). Add a scrape job:
 
 ```yaml
 scrape_configs:

docs/userguide/monitoring/real-time-usage.md

@@ -77,7 +77,7 @@ Verify the HAMi device plugin is running and reporting resources:
 
 ```bash
 kubectl get pods -n kube-system | grep hami
-kubectl logs -n kube-system -l app=hami-scheduler -f
+kubectl logs -n kube-system -l app.kubernetes.io/component=hami-scheduler -f
 ```
 
 ## Resource Annotation Tracking

docs/userguide/mthreads-device/enable-mthreads-gpu-sharing.md

@@ -40,7 +40,7 @@ You can remove `mt-mutating-webhook` and `mt-gpu-scheduler` after installation (
 - set the 'devices.mthreads.enabled = true' when installing hami
 
 ```bash
-helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag={your kubernetes version} --set devices.mthreads.enabled=true -n kube-system
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.image.tag={your kubernetes version} --set devices.mthreads.enabled=true -n kube-system
 ```
 
 ## Running Mthreads jobs

docs/userguide/nvidia-device/dynamic-mig-support.md

@@ -85,7 +85,7 @@ nvidia:
           memory: 24576
           count: 1
 
-    - models: ["A100-SXM4-40GB", "A100-40GB-PCIe", "A100-PCIE-40GB", "A100-SXM4-40GB"]
+    - models: ["A100-SXM4-40GB", "A100-40GB-PCIe", "A100-PCIE-40GB"]
       allowedGeometries:
         - name: 1g.5gb
           memory: 5120

i18n/zh/docusaurus-plugin-content-docs/current/developers/dynamic-mig.md

@@ -46,7 +46,7 @@ data:
             - name: 4g.24gb
               memory: 24576
               count: 1
-      - models: [ "A100-SXM4-40GB", "A100-40GB-PCIe", "A100-PCIE-40GB", "A100-SXM4-40GB" ]
+      - models: [ "A100-SXM4-40GB", "A100-40GB-PCIe", "A100-PCIE-40GB" ]
         allowedGeometries:
           -
             - name: 1g.5gb

i18n/zh/docusaurus-plugin-content-docs/current/faq/faq.md

@@ -222,4 +222,4 @@ DCGM Exporter 不受影响，继续正常上报物理级计数器。HAMi 的每
 
 ## 如何为 HAMi vGPU 指标设置 Prometheus 和 Grafana 监控？
 
-每个节点上的 `hami-device-plugin` Pod 在端口 `31992`（可通过 `devicePlugin.monitorPort` 配置）上暴露每容器 vGPU 指标。完整的设置步骤（包括 Prometheus 采集配置和 Dashboard 导入）参见 [Grafana Dashboard](../userguide/monitoring/grafana-dashboard)。
+每个节点上的 `hami-device-plugin` Pod 在端口 `31992`（可通过 `devicePlugin.service.httpPort` 配置）上暴露每容器 vGPU 指标。完整的设置步骤（包括 Prometheus 采集配置和 Dashboard 导入）参见 [Grafana Dashboard](../userguide/monitoring/grafana-dashboard)。

i18n/zh/docusaurus-plugin-content-docs/current/get-started/deploy-with-helm.md

@@ -114,7 +114,7 @@ helm repo add hami-charts https://project-hami.github.io/HAMi/
 
 ```bash
 helm install hami hami-charts/hami \
-  --set scheduler.kubeScheduler.imageTag=v1.16.8 \
+  --set scheduler.kubeScheduler.image.tag=v1.16.8 \
   -n kube-system
 ```
 

i18n/zh/docusaurus-plugin-content-docs/current/get-started/verify-hami.md

@@ -151,7 +151,7 @@ kubectl exec -it gpu-pod -- nvidia-smi
 2. 容器运行时：确保在 K8s 之外 `sudo ctr run` 或 `docker run` 可以正常工作。
 3. 残留插件：移除冲突的插件：`kubectl delete daemonset nvidia-device-plugin-daemonset -n kube-system --ignore-not-found`。
 4. 节点资源：验证 K8s 是否识别到 GPU：`kubectl get nodes -o jsonpath='{.items[*].status.allocatable}' | grep -i nvidia`。
-5. 调度器层：检查 HAMi 日志：`kubectl logs -n kube-system -l app=hami-scheduler`。
+5. 调度器层：检查 HAMi 日志：`kubectl logs -n kube-system -l app.kubernetes.io/component=hami-scheduler`。
 
 ## 清理
 

i18n/zh/docusaurus-plugin-content-docs/current/installation/offline-installation.md

@@ -46,7 +46,7 @@ scheduler:
 在 `${CHART_PATH}` 目录下执行以下命令：
 
 ```bash
-helm install hami hami --set scheduler.kubeScheduler.imageTag={你的 k8s 服务器版本} -n kube-system
+helm install hami hami --set scheduler.kubeScheduler.image.tag={你的 k8s 服务器版本} -n kube-system
 ```
 
 ## 验证安装

i18n/zh/docusaurus-plugin-content-docs/current/installation/online-installation.md

@@ -25,10 +25,10 @@ kubectl version
 
 ## 安装
 
-确保 `scheduler.kubeScheduler.imageTag` 与你的 Kubernetes 服务器版本匹配。例如，如果你的集群服务器版本是 v1.29.0，请使用以下命令进行部署：
+确保 `scheduler.kubeScheduler.image.tag` 与你的 Kubernetes 服务器版本匹配。例如，如果你的集群服务器版本是 v1.29.0，请使用以下命令进行部署：
 
 ```bash
-helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag=v1.29.0 -n kube-system
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.image.tag=v1.29.0 -n kube-system
 ```
 
 你可以通过编辑[配置](../userguide/configure.md)来自定义安装。

i18n/zh/docusaurus-plugin-content-docs/current/installation/uninstall.md

@@ -92,7 +92,7 @@ kubectl get all -n kube-system -o wide | grep -i hami
 如果 HAMi Pod 一直处于“Terminating”状态，可以强制删除：
 
 ```bash
-kubectl delete pods -n kube-system -l app=hami --grace-period=0 --force
+kubectl delete pods -n kube-system -l app.kubernetes.io/instance=hami --grace-period=0 --force
 ```
 
 然后重新执行卸载命令。

i18n/zh/docusaurus-plugin-content-docs/current/installation/upgrade.md

@@ -35,7 +35,7 @@ helm get values hami -n kube-system > hami-backup-values.yaml
 kubectl get configmap hami-scheduler-device -n kube-system -o yaml > hami-configmap-backup.yaml
 
 # 备份当前状态
-kubectl get all -n kube-system -l app=hami -o yaml > hami-state-backup.yaml
+kubectl get all -n kube-system -l app.kubernetes.io/instance=hami -o yaml > hami-state-backup.yaml
 ```
 
 ### 3. 清理运行中的工作负载
@@ -65,11 +65,11 @@ kubectl patch deployment <deployment-name> -n <namespace> -p '{"spec":{"template
 
 ```bash
 # 查看 Pod 状态
-kubectl get pods -n kube-system -l app=hami
+kubectl get pods -n kube-system -l app.kubernetes.io/instance=hami
 
 # 查看错误日志
-kubectl logs -n kube-system -l app=hami-scheduler --tail=50
-kubectl logs -n kube-system -l app=hami-device-plugin --tail=50
+kubectl logs -n kube-system -l app.kubernetes.io/component=hami-scheduler --tail=50
+kubectl logs -n kube-system -l app.kubernetes.io/component=hami-device-plugin --tail=50
 ```
 
 ## 升级流程
@@ -123,7 +123,7 @@ helm install hami hami-charts/hami -n kube-system
 ### 1. 检查 Pod 状态
 
 ```bash
-kubectl get pods -n kube-system -l app=hami
+kubectl get pods -n kube-system -l app.kubernetes.io/instance=hami
 ```
 
 所有 Pod 应处于 `Running` 状态。
@@ -132,10 +132,10 @@ kubectl get pods -n kube-system -l app=hami
 
 ```bash
 # 检查 scheduler 日志错误
-kubectl logs -n kube-system -l app=hami-scheduler | grep -i "error\|warning" | head -20
+kubectl logs -n kube-system -l app.kubernetes.io/component=hami-scheduler | grep -i "error\|warning" | head -20
 
 # 检查 device plugin 日志
-kubectl logs -n kube-system -l app=hami-device-plugin | grep -i "error" | head -20
+kubectl logs -n kube-system -l app.kubernetes.io/component=hami-device-plugin | grep -i "error" | head -20
 ```
 
 ### 3. 测试 GPU 分配
@@ -188,7 +188,7 @@ kubectl get nodes -o yaml | grep -A 10 "hami.io"
 kubectl describe pod <pod-name>
 
 # 查看 scheduler 日志
-kubectl logs -n kube-system -l app=hami-scheduler | grep -i "pending\|error"
+kubectl logs -n kube-system -l app.kubernetes.io/component=hami-scheduler | grep -i "pending\|error"
 
 # 检查 GPU 资源
 kubectl describe nodes | grep -i "gpu"
@@ -214,7 +214,7 @@ nvidia-smi
 exit
 
 # 重启对应节点的 device plugin
-kubectl delete pods -n kube-system -l app=hami-device-plugin --field-selector spec.nodeName=<node-name>
+kubectl delete pods -n kube-system -l app.kubernetes.io/component=hami-device-plugin --field-selector spec.nodeName=<node-name>
 ```
 
 ### 升级过程中出现 Segmentation Fault

i18n/zh/docusaurus-plugin-content-docs/current/userguide/iluvatar-device/enable-iluvatar-gpu-sharing.md

@@ -30,7 +30,7 @@ title: 启用天数智芯 GPU 共享
 - 安装 HAMi 时设置 `devices.iluvatar.enabled=true`
 
 ```bash
-helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag={your kubernetes version} --set devices.iluvatar.enabled=true -n kube-system
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.image.tag={your kubernetes version} --set devices.iluvatar.enabled=true -n kube-system
 ```
 
 **说明：** 当前支持的 GPU 型号及资源名称定义如下（位于 [https://github.com/Project-HAMi/HAMi/blob/master/charts/hami/templates/scheduler/device-configmap.yaml](https://github.com/Project-HAMi/HAMi/blob/master/charts/hami/templates/scheduler/device-configmap.yaml)）：

i18n/zh/docusaurus-plugin-content-docs/current/userguide/monitoring/grafana-dashboard.md

@@ -24,7 +24,7 @@ Dashboard 包含以下面板：
 
 ## Prometheus 采集配置
 
-每个节点上的 `hami-device-plugin` Pod 在端口 `31992`（可通过 `devicePlugin.monitorPort` 配置）上暴露指标。添加采集任务：
+每个节点上的 `hami-device-plugin` Pod 在端口 `31992`（可通过 `devicePlugin.service.httpPort` 配置）上暴露指标。添加采集任务：
 
 ```yaml
 scrape_configs:

i18n/zh/docusaurus-plugin-content-docs/current/userguide/monitoring/real-time-usage.md

@@ -77,7 +77,7 @@ watch -n 1 nvidia-smi
 
 ```bash
 kubectl get pods -n kube-system | grep hami
-kubectl logs -n kube-system -l app=hami-scheduler -f
+kubectl logs -n kube-system -l app.kubernetes.io/component=hami-scheduler -f
 ```
 
 ## 资源注解追踪

i18n/zh/docusaurus-plugin-content-docs/current/userguide/mthreads-device/enable-mthreads-gpu-sharing.md

@@ -42,7 +42,7 @@ translated: true
 - 在安装 HAMi 时配置'devices.mthreads.enabled = true'参数
 
 ```bash
-helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag={your kubernetes version} --set devices.mthreads.enabled=true -n kube-system
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.image.tag={your kubernetes version} --set devices.mthreads.enabled=true -n kube-system
 ```
 
 ## 运行 GPU 任务

i18n/zh/docusaurus-plugin-content-docs/current/userguide/nvidia-device/dynamic-mig-support.md

@@ -88,7 +88,7 @@ nvidia:
           memory: 24576
           count: 1
 
-    - models: ["A100-SXM4-40GB", "A100-40GB-PCIe", "A100-PCIE-40GB", "A100-SXM4-40GB"]
+    - models: ["A100-SXM4-40GB", "A100-40GB-PCIe", "A100-PCIE-40GB"]
       allowedGeometries:
         - name: 1g.5gb
           memory: 5120