Update heading levels in get-started/deploy-with-helm.md

docs/get-started/deploy-with-helm.md

@@ -2,37 +2,33 @@
 title: Deploy HAMi using Helm
 ---
 
-This guide will cover:
+This guide covers:
 
-- Configure nvidia container runtime in each GPU nodes
-- Install HAMi using helm
-- Launch a vGPU task
-- Check if the corresponding device resources are limited inside container
+- Configuring NVIDIA container runtime on each GPU node
+- Deploying HAMi using Helm
+- Launching a vGPU task
+- Verifying container resource limits
 
 ## Prerequisites {#prerequisites}
 
-- [Helm](https://helm.sh/zh/docs/) version v3+
-- [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/) version v1.16+
-- [CUDA](https://developer.nvidia.com/cuda-toolkit) version v10.2+
-- [NvidiaDriver](https://www.nvidia.cn/drivers/unix/) v440+
+- [Helm](https://helm.sh/zh/docs/) v3+
+- [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/) v1.16+
+- [CUDA](https://developer.nvidia.com/cuda-toolkit) v10.2+
+- [NVIDIA Driver](https://www.nvidia.cn/drivers/unix/) v440+
 
 ## Installation {#installation}
 
-### Configure nvidia-container-toolkit {#configure-nvidia-container-toolkit}
+### 1. Configure nvidia-container-toolkit {#configure-nvidia-container-toolkit}
 
-<summary> Configure nvidia-container-toolkit </summary>
+Perform the following steps on all GPU nodes.
 
-Execute the following steps on all your GPU nodes.
+This guide assumes that NVIDIA drivers and the `nvidia-container-toolkit` are already installed, and that `nvidia-container-runtime` is set 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.
+See [nvidia-container-toolkit installation guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html).
 
-Please see: [nvidia-container-toolkit install-guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
+The following example applies to Debian-based systems using Docker or containerd:
 
-#### Example for debian-based systems with `Docker` and `containerd` {#example-for-debian-based-systems-with-docker-and-containerd}
-
-##### Install the `nvidia-container-toolkit` {#install-the-nvidia-container-toolkit}
+#### Install the `nvidia-container-toolkit` {#install-the-nvidia-container-toolkit}
 
 ```bash
 distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
@@ -43,11 +39,9 @@ curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-
 sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
 ```
 
-##### Configure `Docker` {#configure-docker}
+#### Configure Docker {#configure-docker}
 
-When running `Kubernetes` with `Docker`, edit the configuration file,
-typically located at `/etc/docker/daemon.json`, to set up
-`nvidia-container-runtime` as the default low-level runtime:
+When running Kubernetes with Docker, edit the configuration file (usually `/etc/docker/daemon.json`) to set `nvidia-container-runtime` as the default runtime:
 
 ```json
 {
@@ -61,17 +55,15 @@ typically located at `/etc/docker/daemon.json`, to set up
 }
 ```
 
-And then restart `Docker`:
+Restart Docker:
 
 ```bash
 sudo systemctl daemon-reload && systemctl restart docker
 ```
 
-##### Configure `containerd` {#configure-containerd}
+#### Configure containerd {#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:
+When using Kubernetes with containerd, modify the configuration file (usually `/etc/containerd/config.toml`) to set `nvidia-container-runtime` as the default runtime:
 
 ```toml
 version = 2
@@ -90,53 +82,49 @@ version = 2
             BinaryName = "/usr/bin/nvidia-container-runtime"
 ```
 
-And then restart `containerd`:
+Restart containerd:
 
 ```bash
 sudo systemctl daemon-reload && systemctl restart containerd
 ```
 
-#### 2. Label your nodes {#label-your-nodes}
+### 2. Label your nodes {#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.
+Label your GPU nodes for HAMi scheduling with `gpu=on`. Nodes without this label cannot be managed by the scheduler.
 
 ```bash
 kubectl label nodes {nodeid} gpu=on
 ```
 
-#### 3. Deploy HAMi using Helm {#deploy-hami-using-helm}
+### 3. Deploy HAMi using Helm {#deploy-hami-using-helm}
 
-First, you need to check your Kubernetes version by using the following command:
+Check your Kubernetes version:
 
 ```bash
 kubectl version
 ```
 
-Then, add our repo in helm
+Add the Helm repository:
 
 ```bash
 helm repo add hami-charts https://project-hami.github.io/HAMi/
 ```
 
-During installation, set the Kubernetes scheduler image version to match your
-Kubernetes server version. For instance, if your cluster server version is
-1.16.8, use the following command for deployment:
+During installation, set the Kubernetes scheduler image to match your cluster version. For example, if your cluster version is 1.16.8:
 
 ```bash
 helm install hami hami-charts/hami \
   --set scheduler.kubeScheduler.imageTag=v1.16.8 \
   -n kube-system
 ```
 
-If everything goes well, you will see both vgpu-device-plugin and vgpu-scheduler pods are in the Running state
+If successful, both `vgpu-device-plugin` and `vgpu-scheduler` pods should be in the `Running` state.
 
-### Demo {#demo}
+## Demo {#demo}
 
-#### 1. Submit demo task {#submit-demo-task}
+### 1. Submit demo task {#submit-demo-task}
 
-Containers can now request NVIDIA vGPUs using the `nvidia.com/gpu` resource
-type.
+Containers can now request NVIDIA vGPUs using the `nvidia.com/gpu` resource type.
 
 ```yaml
 apiVersion: v1
@@ -150,19 +138,19 @@ spec:
       command: ["bash", "-c", "sleep 86400"]
       resources:
         limits:
-          nvidia.com/gpu: 1 # requesting 1 vGPUs
-          nvidia.com/gpumem: 10240 # Each vGPU contains 10240m device memory (Optional,Integer)
+          nvidia.com/gpu: 1 # Request 1 vGPU
+          nvidia.com/gpumem: 10240 # Each vGPU provides 10240 MiB device memory (optional)
 ```
 
-#### 2. Verify in container resource control {#verify-in-container-resource-control}
+### 2. Verify container resource limits {#verify-in-container-resource-control}
 
-Execute the following query command:
+Run the following command:
 
 ```bash
 kubectl exec -it gpu-pod nvidia-smi
 ```
 
-The result should be:
+Expected output:
 
 ```text
 [HAMI-core Msg(28:140561996502848:libvgpu.c:836)]: Initializing.....

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

@@ -5,7 +5,7 @@ title: 使用 Helm 部署 HAMi
 本指南将涵盖：
 
 - 为每个 GPU 节点配置 NVIDIA 容器运行时
-- 使用 Helm 安装 HAMi
+- 使用 Helm 部署 HAMi
 - 启动 vGPU 任务
 - 验证容器内设备资源是否受限
 
@@ -16,21 +16,19 @@ title: 使用 Helm 部署 HAMi
 - [CUDA](https://developer.nvidia.com/cuda-toolkit) v10.2+
 - [NVIDIA 驱动](https://www.nvidia.cn/drivers/unix/) v440+
 
-## 安装步骤 {#installation}
+## 安装步骤  {#installation}
 
-### 配置 nvidia-container-toolkit {#configure-nvidia-container-toolkit}
+### 1. 配置 nvidia-container-toolkit {#configure-nvidia-container-toolkit}
 
-<summary> 配置 nvidia-container-toolkit </summary>
+在所有 GPU 节点执行此操作。
 
-在所有 GPU 节点执行以下操作。
-
-本文档假设已预装 NVIDIA 驱动和 `nvidia-container-toolkit`，并已将 `nvidia-container-runtime` 配置为默认底层运行时。
+本文假设已预装 NVIDIA 驱动和 `nvidia-container-toolkit`，并已将 `nvidia-container-runtime` 配置为默认底层运行时。
 
 参考：[nvidia-container-toolkit 安装指南](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
 
-#### 基于 Debian 系统（使用 `Docker` 和 `containerd`）示例 {#example-for-debian-based-systems-with-docker-and-containerd}
+以下是基于 Debian 系统（使用 Docker 和 containerd）的示例：
 
-##### 安装 `nvidia-container-toolkit` {#install-the-nvidia-container-toolkit}
+#### 安装 `nvidia-container-toolkit` {#install-the-nvidia-container-toolkit}
 
 ```bash
 distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
@@ -41,9 +39,9 @@ curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-
 sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
 ```
 
-##### 配置 `Docker` {#configure-docker}
+#### 配置 Docker {#configure-docker}
 
-当使用 `Docker` 运行 `Kubernetes` 时，编辑配置文件（通常位于 `/etc/docker/daemon.json`），将
+当使用 Docker 运行 Kubernetes 时，编辑配置文件（通常位于 `/etc/docker/daemon.json`），将
 `nvidia-container-runtime` 设为默认底层运行时：
 
 ```json
@@ -58,15 +56,15 @@ sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
 }
 ```
 
-然后重启 `Docker`：
+然后重启 Docker：
 
 ```bash
 sudo systemctl daemon-reload && systemctl restart docker
 ```
 
-##### 配置 `containerd` {#configure-containerd}
+#### 配置 containerd {#configure-containerd}
 
-当使用 `containerd` 运行 `Kubernetes` 时，修改配置文件（通常位于 `/etc/containerd/config.toml`），将
+当使用 containerd 运行 Kubernetes 时，修改配置文件（通常位于 `/etc/containerd/config.toml`），将
 `nvidia-container-runtime` 设为默认底层运行时：
 
 ```toml
@@ -86,21 +84,21 @@ version = 2
             BinaryName = "/usr/bin/nvidia-container-runtime"
 ```
 
-然后重启 `containerd`：
+然后重启 containerd：
 
 ```bash
 sudo systemctl daemon-reload && systemctl restart containerd
 ```
 
-#### 2. 标记节点 {#label-your-nodes}
+### 2. 标记节点 {#label-your-nodes}
 
 通过添加 "gpu=on" 标签将 GPU 节点标记为可调度 HAMi 任务。未标记的节点将无法被调度器管理。
 
 ```bash
 kubectl label nodes {节点ID} gpu=on
 ```
 
-#### 3. 使用 Helm 部署 HAMi {#deploy-hami-using-helm}
+### 3. 使用 Helm 部署 HAMi {#deploy-hami-using-helm}
 
 首先通过以下命令确认 Kubernetes 版本：
 
@@ -124,9 +122,9 @@ helm install hami hami-charts/hami \
 
 若一切正常，可见 vgpu-device-plugin 和 vgpu-scheduler 的 Pod 均处于 Running 状态。
 
-### 演示 {#demo}
+## 演示 {#demo}
 
-#### 1. 提交演示任务 {#submit-demo-task}
+### 1. 提交演示任务 {#submit-demo-task}
 
 容器现在可通过 `nvidia.com/gpu` 资源类型申请 NVIDIA vGPU：
 
@@ -146,7 +144,7 @@ spec:
           nvidia.com/gpumem: 10240 # 每个 vGPU 包含 10240m 设备显存（可选，整型）
 ```
 
-#### 2. 验证容器内资源限制 {#verify-in-container-resource-control}
+### 2. 验证容器内资源限制 {#verify-in-container-resource-control}
 
 执行查询命令：