fix: correct broken relative links in faq and troubleshooting docs

docs/faq/faq.md

@@ -178,7 +178,7 @@ If the official Device Plugin cannot provide the required information, HAMi deve
 
 ## How does HAMi enforce GPU memory and compute limits?
 
-HAMi injects `libvgpu.so` into containers via `/etc/ld.so.preload`. The library intercepts CUDA memory allocation calls and returns OOM when the `nvidia.com/gpumem` limit is exceeded; compute limits use a token-bucket throttle on kernel launch calls. Applications that bypass the CUDA library (Docker-in-Docker, direct driver API) are not covered. For the full interception flow, see [GPU Virtualization](./core-concepts/gpu-virtualization).
+HAMi injects `libvgpu.so` into containers via `/etc/ld.so.preload`. The library intercepts CUDA memory allocation calls and returns OOM when the `nvidia.com/gpumem` limit is exceeded; compute limits use a token-bucket throttle on kernel launch calls. Applications that bypass the CUDA library (Docker-in-Docker, direct driver API) are not covered. For the full interception flow, see [GPU Virtualization](../core-concepts/gpu-virtualization).
 
 ## How does HAMi vGPU differ from NVIDIA MIG? When should I use each?
 
@@ -194,19 +194,19 @@ HAMi vGPU is software-only with no hardware requirements. NVIDIA MIG is hardware
 | Dynamic reconfiguration | Yes, no node drain needed | Requires MIG profile reconfiguration |
 | Multi-tenant noise isolation | Best-effort | Strong |
 
-Use HAMi vGPU when the GPU does not support MIG, workloads need flexible memory sizes, or dynamic repacking without node drains is needed. Use MIG when hard hardware isolation is a compliance or SLA requirement. HAMi also supports dynamic MIG via `mig-parted`; see [Dynamic MIG Support](./userguide/nvidia-device/dynamic-mig-support).
+Use HAMi vGPU when the GPU does not support MIG, workloads need flexible memory sizes, or dynamic repacking without node drains is needed. Use MIG when hard hardware isolation is a compliance or SLA requirement. HAMi also supports dynamic MIG via `mig-parted`; see [Dynamic MIG Support](../userguide/nvidia-device/dynamic-mig-support).
 
 ## Why does nvidia-smi inside my container show less memory than on the host?
 
-`libvgpu.so` intercepts `nvmlDeviceGetMemoryInfo` and related calls, returning the `nvidia.com/gpumem` limit instead of physical VRAM. This is intentional: workloads that size their allocations based on reported memory (such as vLLM) will use only their budget. The host’s `nvidia-smi` always shows physical memory. See [GPU Virtualization](./core-concepts/gpu-virtualization).
+`libvgpu.so` intercepts `nvmlDeviceGetMemoryInfo` and related calls, returning the `nvidia.com/gpumem` limit instead of physical VRAM. This is intentional: workloads that size their allocations based on reported memory (such as vLLM) will use only their budget. The host’s `nvidia-smi` always shows physical memory. See [GPU Virtualization](../core-concepts/gpu-virtualization).
 
 ## Why is my nvidia.com/gpumem limit not enforced? {#why-is-my-nvidiagpumem-limit-not-enforced}
 
-The four most common causes: `CUDA_DISABLE_CONTROL=true` is set, the workload runs inside Docker-in-Docker, the application calls the GPU driver directly (bypassing `libvgpu.so`), or `nvidia-container-runtime` is not the default runtime on the node. See [Troubleshooting](./troubleshooting) for resolution steps.
+The four most common causes: `CUDA_DISABLE_CONTROL=true` is set, the workload runs inside Docker-in-Docker, the application calls the GPU driver directly (bypassing `libvgpu.so`), or `nvidia-container-runtime` is not the default runtime on the node. See [Troubleshooting](../troubleshooting/troubleshooting.md) for resolution steps.
 
 ## Does HAMi replace kube-scheduler or run alongside it?
 
-HAMi runs alongside kube-scheduler as a [scheduler extender](https://github.com/kubernetes/design-proposals-archive/blob/main/scheduling/scheduler_extender.md) - it does not replace it. The MutatingWebhook sets `schedulerName: hami-scheduler` only on pods requesting HAMi resources; all other pods follow the default scheduler path unchanged. See [Architecture](./core-concepts/architecture).
+HAMi runs alongside kube-scheduler as a [scheduler extender](https://github.com/kubernetes/design-proposals-archive/blob/main/scheduling/scheduler_extender.md) - it does not replace it. The MutatingWebhook sets `schedulerName: hami-scheduler` only on pods requesting HAMi resources; all other pods follow the default scheduler path unchanged. See [Architecture](../core-concepts/architecture).
 
 ## Does HAMi work with vLLM, and what are the known limitations for multi-GPU tensor parallelism?
 
@@ -221,8 +221,8 @@ devicePlugin:
   enabled: false
 ```
 
-DCGM Exporter is not affected and continues to report physical-level counters normally. HAMi’s per-container virtual metrics are separate; see [GPU Utilization Metrics](./developers/gpu-utilization-metrics).
+DCGM Exporter is not affected and continues to report physical-level counters normally. HAMi’s per-container virtual metrics are separate; see [GPU Utilization Metrics](../developers/gpu-utilization-metrics).
 
 ## 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.monitorPort`). See [Grafana Dashboard](../userguide/monitoring/grafana-dashboard) for the full setup including Prometheus scrape config and dashboard import.

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

@@ -175,7 +175,7 @@ HAMi 的调度器需要从节点获取足够的 GPU 信息来完成资源调度
 
 ## HAMi 如何强制执行 GPU 显存和算力限制？
 
-HAMi 通过 `/etc/ld.so.preload` 将 `libvgpu.so` 注入容器。该库拦截 CUDA 显存分配调用，当超过 `nvidia.com/gpumem` 限制时返回 OOM；算力限制通过令牌桶算法对 kernel launch 调用进行节流。绕过 CUDA 库的应用（如 Docker-in-Docker、直接调用驱动 API）不受管控。完整的拦截流程参见 [GPU 虚拟化](./core-concepts/gpu-virtualization)。
+HAMi 通过 `/etc/ld.so.preload` 将 `libvgpu.so` 注入容器。该库拦截 CUDA 显存分配调用，当超过 `nvidia.com/gpumem` 限制时返回 OOM；算力限制通过令牌桶算法对 kernel launch 调用进行节流。绕过 CUDA 库的应用（如 Docker-in-Docker、直接调用驱动 API）不受管控。完整的拦截流程参见 [GPU 虚拟化](../core-concepts/gpu-virtualization)。
 
 ## HAMi vGPU 与 NVIDIA MIG 有何区别？各适用于什么场景？
 
@@ -191,19 +191,19 @@ HAMi vGPU 是纯软件方案，无硬件要求。NVIDIA MIG 是硬件分区，
 | 动态重配置     | 支持，无需排空节点            | 需要重新配置 MIG 配置文件                  |
 | 多租户噪声隔离 | 尽力而为                      | 强隔离                                     |
 
-当 GPU 不支持 MIG、工作负载需要灵活的显存大小、或需要无需排空节点的动态重打包时，使用 HAMi vGPU。当硬隔离是合规或 SLA 要求时，使用 MIG。HAMi 也通过 `mig-parted` 支持动态 MIG；参见[动态 MIG 支持](./userguide/nvidia-device/dynamic-mig-support)。
+当 GPU 不支持 MIG、工作负载需要灵活的显存大小、或需要无需排空节点的动态重打包时，使用 HAMi vGPU。当硬隔离是合规或 SLA 要求时，使用 MIG。HAMi 也通过 `mig-parted` 支持动态 MIG；参见[动态 MIG 支持](../userguide/nvidia-device/dynamic-mig-support)。
 
 ## 为什么容器内 nvidia-smi 显示的显存比宿主机少？
 
-`libvgpu.so` 拦截了 `nvmlDeviceGetMemoryInfo` 及相关调用，返回 `nvidia.com/gpumem` 限制值而非物理显存。这是预期行为：根据上报显存大小进行分配的工作负载（如 vLLM）将只使用其配额。宿主机的 `nvidia-smi` 始终显示物理显存。参见 [GPU 虚拟化](./core-concepts/gpu-virtualization)。
+`libvgpu.so` 拦截了 `nvmlDeviceGetMemoryInfo` 及相关调用，返回 `nvidia.com/gpumem` 限制值而非物理显存。这是预期行为：根据上报显存大小进行分配的工作负载（如 vLLM）将只使用其配额。宿主机的 `nvidia-smi` 始终显示物理显存。参见 [GPU 虚拟化](../core-concepts/gpu-virtualization)。
 
 ## 为什么 nvidia.com/gpumem 限制未生效？ {#why-is-my-nvidiagpumem-limit-not-enforced}
 
-最常见的四种原因：设置了 `CUDA_DISABLE_CONTROL=true`、工作负载运行在 Docker-in-Docker 中、应用直接调用 GPU 驱动（绕过了 `libvgpu.so`）、或节点上 `nvidia-container-runtime` 未设为默认运行时。解决步骤参见[排障手册](./troubleshooting)。
+最常见的四种原因：设置了 `CUDA_DISABLE_CONTROL=true`、工作负载运行在 Docker-in-Docker 中、应用直接调用 GPU 驱动（绕过了 `libvgpu.so`）、或节点上 `nvidia-container-runtime` 未设为默认运行时。解决步骤参见[排障手册](../troubleshooting/troubleshooting.md)。
 
 ## HAMi 是替换 kube-scheduler 还是与其并行运行？
 
-HAMi 作为 [scheduler extender](https://github.com/kubernetes/design-proposals-archive/blob/main/scheduling/scheduler_extender.md) 与 kube-scheduler 并行运行，不会替换它。MutatingWebhook 仅在请求 HAMi 资源的 Pod 上设置 `schedulerName: hami-scheduler`；所有其他 Pod 仍走默认调度器路径。参见[架构](./core-concepts/architecture)。
+HAMi 作为 [scheduler extender](https://github.com/kubernetes/design-proposals-archive/blob/main/scheduling/scheduler_extender.md) 与 kube-scheduler 并行运行，不会替换它。MutatingWebhook 仅在请求 HAMi 资源的 Pod 上设置 `schedulerName: hami-scheduler`；所有其他 Pod 仍走默认调度器路径。参见[架构](../core-concepts/architecture)。
 
 ## HAMi 是否支持 vLLM？多 GPU 张量并行有哪些已知限制？
 
@@ -218,8 +218,8 @@ devicePlugin:
   enabled: false
 ```
 
-DCGM Exporter 不受影响，继续正常上报物理级计数器。HAMi 的每容器虚拟指标是独立的；参见 [GPU 利用率指标](./developers/gpu-utilization-metrics)。
+DCGM Exporter 不受影响，继续正常上报物理级计数器。HAMi 的每容器虚拟指标是独立的；参见 [GPU 利用率指标](../developers/gpu-utilization-metrics)。
 
 ## 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.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)。

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

@@ -16,7 +16,7 @@ translated: true
   containerd config dump | grep default_runtime_name
   ```
 
-  输出必须显示 `nvidia`。如未显示，请按照[前置条件](./installation/online-installation)指南操作。
+  输出必须显示 `nvidia`。如未显示，请按照[前置条件](../installation/online-installation)指南操作。
 
 - 如果在使用 NVIDIA 镜像的设备插件时不请求 vGPU，机器上的所有 GPU 可能会在容器内暴露。
 - 目前，A100 MIG 仅支持 "none" 和 "mixed" 模式。