docs(i18n): localize zh sidebar labels and Neuron docs

docs/userguide/Kueue/how-to-use-kueue.md → docs/userguide/kueue/how-to-use-kueue.md

@@ -271,4 +271,4 @@ For example:
 
 ## Example
 
-See the [example file](./examples/defalt_use.md) for a complete working example.
+See the [example file](./examples/defalt-use.md) for a complete working example.

i18n/zh/docusaurus-plugin-content-docs/current.json

@@ -83,6 +83,10 @@
     "message": "管理 AWS Neuron 设备",
     "description": "The label for category 'Managing AWS Neuron devices' in sidebar 'docs'"
   },
+  "sidebar.docs.category.Managing Vastai devices": {
+    "message": "管理 Vastai 设备",
+    "description": "The label for category 'Managing Vastai devices' in sidebar 'docs'"
+  },
   "sidebar.docs.category.Optimize Kunlunxin devices scheduling": {
     "message": "优化昆仑芯设备调度",
     "description": "The label for category 'Optimize Kunlunxin devices scheduling' in sidebar 'docs'"
@@ -119,6 +123,10 @@
     "message": "示例",
     "description": "The label for category 'Examples' in sidebar 'docs'"
   },
+  "sidebar.docs.category.vastai-examples": {
+    "message": "示例",
+    "description": "The label for category 'Examples' in sidebar 'docs'"
+  },
   "sidebar.docs.category.metax-sgpu-examples": {
     "message": "示例",
     "description": "The label for category 'Examples' in sidebar 'docs'"

i18n/zh/docusaurus-plugin-content-docs/version-v2.8.0/userguide/AWSNeuron-device/enable-awsneuron-managing.md → i18n/zh/docusaurus-plugin-content-docs/current/userguide/awsneuron-device/enable-awsneuron-managing.md

@@ -3,8 +3,6 @@ title: 启用 AWS-Neuron 设备共享
 linktitle: AWS-Neuron 共享
 ---
 
-## 启用 AWS-Neuron 设备共享
-
 AWS Neuron 设备是 AWS 专为机器学习工作负载设计的硬件加速器，特别针对深度学习推理和训练场景进行了优化。这些设备属于 AWS Inferentia 和 Trainium 产品家族，可在 AWS 云上为 AI 应用提供高性能、高性价比且可扩展的解决方案。
 
 HAMi 现已集成[my-scheduler](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/containers/kubernetes-getting-started.html#deploy-neuron-scheduler-extension)，提供以下核心功能：

i18n/zh/docusaurus-plugin-content-docs/version-v2.8.0/userguide/Cambricon-device/enable-cambricon-mlu-sharing.md → i18n/zh/docusaurus-plugin-content-docs/current/userguide/cambricon-device/enable-cambricon-mlu-sharing.md

@@ -23,7 +23,7 @@ translated: true
 
 ## 开启 MLU 复用
 
-* 通过 helm 部署本组件，参照[主文档中的开启 vgpu 支持章节](https://github.com/Project-HAMi/HAMi/blob/master/README_cn.md#kubernetes开启vgpu支持)
+* 通过 helm 部署本组件，参照[主文档中的开启 vgpu 支持章节](https://github.com/Project-HAMi/HAMi/blob/master/readme_cn.md#kubernetes开启vgpu支持)
 
 * 使用以下指令，为 MLU 节点打上 label
 

i18n/zh/docusaurus-plugin-content-docs/current/userguide/Kueue/how-to-use-kueue.md → i18n/zh/docusaurus-plugin-content-docs/current/userguide/kueue/how-to-use-kueue.md

@@ -138,8 +138,8 @@ spec:
 
 该 ClusterQueue 跟踪以下资源：
 
-* `nvidia.com/total-gpucores`：所有 vGPU 的 GPU 核心总量（每个单位表示 1% 的 GPU 核心）
-* `nvidia.com/total-gpumem`：所有 vGPU 的 GPU 显存总量（单位为 MiB）
+- `nvidia.com/total-gpucores`：所有 vGPU 的 GPU 核心总量（每个单位表示 1% 的 GPU 核心）
+- `nvidia.com/total-gpumem`：所有 vGPU 的 GPU 显存总量（单位为 MiB）
 
 ### 创建 LocalQueue
 
@@ -195,10 +195,10 @@ spec:
 
 在该示例中：
 
-* `kueue.x-k8s.io/queue-name` 标签将 Deployment 关联到 `hami-local-queue`
-* `nvidia.com/gpu: 1` 请求 1 个 vGPU
-* `nvidia.com/gpucores: 50` 为每个 vGPU 请求 50% 的 GPU 核心
-* `nvidia.com/gpumem: 1024` 为每个 vGPU 请求 1024 MiB 的 GPU 显存
+- `kueue.x-k8s.io/queue-name` 标签将 Deployment 关联到 `hami-local-queue`
+- `nvidia.com/gpu: 1` 请求 1 个 vGPU
+- `nvidia.com/gpucores: 50` 为每个 vGPU 请求 50% 的 GPU 核心
+- `nvidia.com/gpumem: 1024` 为每个 vGPU 请求 1024 MiB 的 GPU 显存
 
 ### 请求多个 vGPU 的 Deployment
 
@@ -260,14 +260,14 @@ status:
 
 Kueue 的 ResourceTransformation 会自动转换 HAMi vGPU 的资源请求：
 
-* `nvidia.com/gpu` × `nvidia.com/gpucores` → `nvidia.com/total-gpucores`
-* `nvidia.com/gpu` × `nvidia.com/gpumem` → `nvidia.com/total-gpumem`
+- `nvidia.com/gpu` × `nvidia.com/gpucores` → `nvidia.com/total-gpucores`
+- `nvidia.com/gpu` × `nvidia.com/gpumem` → `nvidia.com/total-gpumem`
 
 例如：
 
-* 一个 Deployment 有 2 个副本，每个副本请求 `nvidia.com/gpu: 1`、`nvidia.com/gpucores: 50` 和 `nvidia.com/gpumem: 1024`
-* 将消耗：`nvidia.com/total-gpucores: 100`（2 个副本 × 1 GPU × 50 核心）以及 `nvidia.com/total-gpumem: 2048`（2 个副本 × 1 GPU × 1024 MiB）
+- 一个 Deployment 有 2 个副本，每个副本请求 `nvidia.com/gpu: 1`、`nvidia.com/gpucores: 50` 和 `nvidia.com/gpumem: 1024`
+- 将消耗：`nvidia.com/total-gpucores: 100`（2 个副本 × 1 GPU × 50 核心）以及 `nvidia.com/total-gpumem: 2048`（2 个副本 × 1 GPU × 1024 MiB）
 
 ## 示例
 
-完整可运行示例请参见 [示例文件](./examples/defalt_use.md)。
+完整可运行示例请参见 [示例文件](./examples/defalt-use.md)。

i18n/zh/docusaurus-plugin-content-docs/version-v2.8.0/userguide/Metax-device/Metax-sGPU/enable-metax-gpu-sharing.md → i18n/zh/docusaurus-plugin-content-docs/current/userguide/metax-device/metax-sgpu/enable-metax-gpu-sharing.md

@@ -16,14 +16,14 @@ translated: true
 
 ### 需求
 
-* Metax Driver >= 2.32.0
-* Metax GPU Operator >= 0.10.2
-* Kubernetes >= 1.23
+- Metax Driver >= 2.32.0
+- Metax GPU Operator >= 0.10.2
+- Kubernetes >= 1.23
 
 ### 开启复用沐曦设备
 
-* 部署 Metax GPU Operator (请联系您的设备提供方获取)
-* 根据 readme.md 部署 HAMi
+- 部署 Metax GPU Operator (请联系您的设备提供方获取)
+- 根据 README.md 部署 HAMi
 
 ### 运行沐曦任务
 

i18n/zh/docusaurus-plugin-content-docs/version-v1.3.0/installation/prerequisites.md

@@ -13,7 +13,7 @@ title: Prerequisites
 
 Execute the following steps on all your GPU nodes.
 
-This README assumes pre-installation of NVIDIA drivers and the `nvidia-container-toolkit`. Additionally, it assumes configuration of the `nvidia-container-runtime` as the default low-level runtime.
+This readme assumes pre-installation of NVIDIA drivers and the `nvidia-container-toolkit`. Additionally, it assumes configuration of the `nvidia-container-runtime` as the default low-level runtime.
 
 Please see: [https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
 
@@ -47,17 +47,16 @@ When running `Kubernetes` with `Docker`, edit the configuration file, typically
 
 And then restart `Docker`:
 
-```
+```bash
 sudo systemctl daemon-reload && systemctl restart docker
 ```
 
-
 #### Configure `containerd`
 
 When running `Kubernetes` with `containerd`, modify the configuration file typically located at `/etc/containerd/config.toml`, to set up
 `nvidia-container-runtime` as the default low-level runtime:
 
-```
+```yaml
 version = 2
 [plugins]
   [plugins."io.containerd.grpc.v1.cri"]
@@ -76,14 +75,14 @@ version = 2
 
 And then restart `containerd`:
 
-```
+```bash
 sudo systemctl daemon-reload && systemctl restart containerd
 ```
 
 ### Label your nodes
 
 Label your GPU nodes for scheduling with HAMi by adding the label "gpu=on". Without this label, the nodes cannot be managed by our scheduler.
 
-```
+```bash
 kubectl label nodes {nodeid} gpu=on
 ```

i18n/zh/docusaurus-plugin-content-docs/version-v1.3.0/installation/webui-installation.md

@@ -6,7 +6,7 @@ title: WebUI Installation
 
 This topic includes instructions for installing and running HAMi-WebUI on Kubernetes using Helm Charts.
 
-The WebUI can only be accessed by your localhost, so you need to connect your localhost to the cluster by configuring `~/.kube/config` 
+The WebUI can only be accessed by your localhost, so you need to connect your localhost to the cluster by configuring `~/.kube/config`
 
 [Helm](https://helm.sh/) is an open-source command line tool used for managing Kubernetes applications. It is a graduate project in the [CNCF Landscape](https://www.cncf.io/projects/helm/).
 
@@ -58,7 +58,6 @@ To set up the HAMi-WebUI Helm repository so that you download the correct HAMi-W
 
 1. Configure ~/.kube/config in your localhost to be able to connect your cluster.
 
-
 2. Run the following command to do a port-forwarding of the HAMi-WebUI service on port `3000` in your localhost.
 
    ```bash
@@ -88,7 +87,6 @@ kubectl logs --namespace=hami deploy/my-hami-webui -c hami-webui-be-oss
 
 For more information about accessing Kubernetes application logs, refer to [Pods](https://kubernetes.io/docs/reference/kubectl/cheatsheet/#interacting-with-running-pods) and [Deployments](https://kubernetes.io/docs/reference/kubectl/cheatsheet/#interacting-with-deployments-and-services).
 
-
 ## Uninstall the HAMi-WebUI deployment
 
 To uninstall the HAMi-WebUI deployment, run the command:
@@ -105,4 +103,4 @@ If you want to delete the namespace `hami`, then run the command:
 
 ```bash
 kubectl delete namespace hami
-```
+```

i18n/zh/docusaurus-plugin-content-docs/version-v2.6.0/userguide/Metax-device/Metax-sGPU/enable-metax-gpu-sharing.md → i18n/zh/docusaurus-plugin-content-docs/version-v2.6.0/userguide/metax-device/metax-sgpu/enable-metax-gpu-sharing.md

@@ -5,13 +5,13 @@ translated: true
 
 ## 介绍
 
-**我们目前支持复用沐曦GPU设备，提供与vGPU类似的复用功能**，包括：
+**我们目前支持复用沐曦 GPU 设备，提供与 vGPU 类似的复用功能**，包括：
 
 ***GPU 共享***: 每个任务可以只占用一部分显卡，多个任务可以共享一张显卡
 
-***可限制分配的显存大小***: 你现在可以用显存值（例如4G）来分配GPU，本组件会确保任务使用的显存不会超过分配数值
+***可限制分配的显存大小***: 你现在可以用显存值（例如 4G）来分配 GPU，本组件会确保任务使用的显存不会超过分配数值
 
-***可限制计算单元数量***: 你现在可以指定任务使用的算力比例（例如60即代表使用60%算力）来分配GPU，本组件会确保任务使用的算力不会超过分配数值
+***可限制计算单元数量***: 你现在可以指定任务使用的算力比例（例如 60 即代表使用 60% 算力）来分配 GPU，本组件会确保任务使用的算力不会超过分配数值
 
 ### 需求
 
@@ -21,8 +21,8 @@ translated: true
 
 ### 开启复用沐曦设备
 
-* 部署Metax GPU Operator (请联系您的设备提供方获取)
-* 根据readme.md部署HAMi
+* 部署 Metax GPU Operator (请联系您的设备提供方获取)
+* 根据 README.md 部署 HAMi
 
 ### 运行沐曦任务