From 1eedf07f27880c9f30707f77a2354307e14761db Mon Sep 17 00:00:00 2001
From: Srijan <singhsrijangkp@gmail.com>
Date: Mon, 3 Aug 2026 20:30:33 +0530
Subject: [PATCH 1/2] docs(get-started): simplify quick start and online
 installation guides

Signed-off-by: Srijan <singhsrijangkp@gmail.com>
---
 docs/get-started/deploy-with-helm.md          | 153 ++++------------
 docs/installation/online-installation.md      |  28 +--
 .../current/get-started/deploy-with-helm.md   | 161 +++++------------
 .../installation/online-installation.md       |  28 +--
 .../get-started/deploy-with-helm.md           | 163 ++++++------------
 .../installation/online-installation.md       |  29 ++--
 .../get-started/deploy-with-helm.md           | 157 +++++------------
 .../installation/online-installation.md       |  28 +--
 8 files changed, 236 insertions(+), 511 deletions(-)

diff --git a/docs/get-started/deploy-with-helm.md b/docs/get-started/deploy-with-helm.md
index dc54192c3..4d2f15d6e 100644
--- a/docs/get-started/deploy-with-helm.md
+++ b/docs/get-started/deploy-with-helm.md
@@ -1,110 +1,36 @@
 ---
-title: Deploy HAMi using Helm
+title: Quick Start
+sidebar_label: Get Started
+translated: true
 ---
 
-This guide covers:
-
-- Configuring NVIDIA container runtime on each GPU node
-- Deploying HAMi using Helm
-- Launching a vGPU task
-- Verifying container resource limits
+Get HAMi up and running in minutes by deploying the Helm chart and submitting your first shared GPU workload.
 
 ## Prerequisites {#prerequisites}
 
+Before deploying HAMi, ensure your GPU nodes meet the following prerequisites:
+
 - [Helm](https://helm.sh/docs/) v3+
 - [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/) v1.23+
 - [CUDA](https://developer.nvidia.com/cuda-toolkit) v10.2+
 - [NVIDIA Driver](https://www.nvidia.cn/drivers/unix/) v440+
+- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) (with `nvidia-container-runtime` configured as the container runtime)
 
-## Installation {#installation}
-
-### 1. Configure nvidia-container-toolkit {#configure-nvidia-container-toolkit}
-
-Perform the following steps on all 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.
-
-See [nvidia-container-toolkit installation 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:
-
-#### Install the `nvidia-container-toolkit` {#install-the-nvidia-container-toolkit}
-
-```bash
-curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
-  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
-    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
-    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
-
-sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
-```
-
-#### Configure Docker {#configure-docker}
-
-When running Kubernetes with Docker, edit the configuration file (usually `/etc/docker/daemon.json`) to set `nvidia-container-runtime` as the default runtime:
-
-```json
-{
-  "default-runtime": "nvidia",
-  "runtimes": {
-    "nvidia": {
-      "path": "/usr/bin/nvidia-container-runtime",
-      "runtimeArgs": []
-    }
-  }
-}
-```
-
-Restart Docker:
-
-```bash
-sudo systemctl daemon-reload && sudo systemctl restart docker
-```
-
-#### Configure containerd {#configure-containerd}
-
-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
-[plugins]
-  [plugins."io.containerd.grpc.v1.cri"]
-    [plugins."io.containerd.grpc.v1.cri".containerd]
-      default_runtime_name = "nvidia"
-
-      [plugins."io.containerd.grpc.v1.cri".containerd.runtimes]
-        [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia]
-          privileged_without_host_devices = false
-          runtime_engine = ""
-          runtime_root = ""
-          runtime_type = "io.containerd.runc.v2"
-          [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia.options]
-            BinaryName = "/usr/bin/nvidia-container-runtime"
-```
-
-Restart containerd:
-
-```bash
-sudo systemctl daemon-reload && sudo systemctl restart containerd
-```
+---
 
-### 2. Label your nodes {#label-your-nodes}
+## 1. Label your nodes {#label-your-nodes}
 
-Label your GPU nodes for HAMi scheduling with `gpu=on`. Nodes without this label cannot be managed by the scheduler.
+Label the target GPU nodes with `gpu=on`. Nodes without this label will not be managed by HAMi:
 
 ```bash
 kubectl label nodes <node-name> gpu=on
 ```
 
-### 3. Deploy HAMi using Helm {#deploy-hami-using-helm}
-
-Check your Kubernetes version:
+---
 
-```bash
-kubectl version
-```
+## 2. Deploy HAMi using Helm {#deploy-hami-using-helm}
 
-Add the Helm repository:
+Add the official HAMi Helm repository and deploy the chart:
 
 ```bash
 helm repo add hami-charts https://project-hami.github.io/HAMi/
@@ -112,13 +38,17 @@ helm repo update
 helm install hami hami-charts/hami -n kube-system
 ```
 
-If successful, both `hami-device-plugin` and `hami-scheduler` pods should be in the `Running` state.
+Verify that the `hami-scheduler` and `hami-device-plugin` pods are running:
+
+```bash
+kubectl get pods -n kube-system | grep hami
+```
 
-## Demo {#demo}
+---
 
-### 1. Submit demo task {#submit-demo-task}
+## 3. Submit a vGPU Workload {#submit-a-vgpu-workload}
 
-Containers can now request NVIDIA vGPUs using the `nvidia.com/gpu` resource type.
+Create a Pod requesting 1 vGPU with 10240 MiB of GPU memory limit:
 
 ```yaml
 apiVersion: v1
@@ -132,59 +62,48 @@ spec:
       command: ["bash", "-c", "sleep 86400"]
       resources:
         limits:
-          nvidia.com/gpu: 1 # Request 1 vGPU
-          nvidia.com/gpumem: 10240 # Each vGPU provides 10240 MiB device memory (optional)
+          nvidia.com/gpu: 1
+          nvidia.com/gpumem: 10240
 ```
 
-Wait for the pod to be ready:
+Apply the manifest and wait for the Pod to become ready:
 
 ```bash
+kubectl apply -f gpu-pod.yaml
 kubectl wait --for=condition=Ready pod/gpu-pod --timeout=120s
 ```
 
-### 2. Verify container resource limits {#verify-in-container-resource-control}
+---
+
+## 4. Verify GPU Memory Isolation {#verify-gpu-memory-isolation}
 
-Run the following command:
+Execute `nvidia-smi` inside the running container:
 
 ```bash
 kubectl exec -it gpu-pod -- nvidia-smi
 ```
 
-Expected output:
+Expected output showing HAMi-core hard memory limit (`10240MiB`):
 
 ```text
-[HAMI-core Msg(28:140561996502848:libvgpu.c:836)]: Initializing.....
-Wed Apr 10 09:28:58 2024
+[HAMI-core Msg]: Initializing.....
 +-----------------------------------------------------------------------------------------+
 | NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |
 |-----------------------------------------+------------------------+----------------------+
 | GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
 | Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
-|                                         |                        |               MIG M. |
 |=========================================+========================+======================|
 |   0  Tesla V100-PCIE-32GB           On  |   00000000:3E:00.0 Off |                    0 |
 | N/A   29C    P0             24W /  250W |       0MiB /  10240MiB |      0%      Default |
-|                                         |                        |                  N/A |
-+-----------------------------------------+------------------------+----------------------+
-
-+-----------------------------------------------------------------------------------------+
-| Processes:                                                                              |
-|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
-|        ID   ID                                                               Usage      |
-|=========================================================================================|
-|  No running processes found                                                             |
 +-----------------------------------------------------------------------------------------+
-[HAMI-core Msg(28:140561996502848:multiprocess_memory_limit.c:434)]: Calling exit handler 28
 ```
 
-## Cleanup
+---
+
+## Cleanup {#cleanup}
+
+Delete the test Pod:
 
 ```bash
 kubectl delete pod gpu-pod
 ```
-
-## Next steps
-
-- [Validate HAMi](./verify-hami) - deeper validation including native GPU stack checks
-- [Configure HAMi](../userguide/configure) - resource limits, scheduling policies, and more
-- [Device Sharing](../key-features/device-sharing) - how GPU sharing works under the hood
diff --git a/docs/installation/online-installation.md b/docs/installation/online-installation.md
index a5375b948..0d0a6252b 100644
--- a/docs/installation/online-installation.md
+++ b/docs/installation/online-installation.md
@@ -3,41 +3,41 @@ sidebar_label: Online Installation from Helm
 title: Online Installation from Helm (Recommended)
 ---
 
-The recommended way to deploy HAMi is via Helm.
+The recommended way to deploy HAMi in a Kubernetes cluster is via the official Helm chart.
 
-## Add HAMi repo
+## 1. Add HAMi Helm Repository {#add-hami-repo}
 
-You can add HAMi chart repository using the following command:
+Add the HAMi chart repository and update local repository cache:
 
 ```bash
 helm repo add hami-charts https://project-hami.github.io/HAMi/
 helm repo update
 ```
 
-## Get your Kubernetes version
+## 2. Deploy HAMi {#deploy-hami}
 
-A Kubernetes version is required for proper installation. You can retrieve your Kubernetes server version with:
+Deploy HAMi into the `kube-system` namespace using standard Helm installation:
 
 ```bash
-kubectl version
+helm install hami hami-charts/hami -n kube-system
 ```
 
-## Installation
+### Customizing Helm Configurations
 
-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:
+You can customize your deployment by passing parameters with `--set` or providing a custom `values.yaml` file:
 
 ```bash
-helm install hami hami-charts/hami --set scheduler.kubeScheduler.image.tag=v1.29.0 -n kube-system
+helm install hami hami-charts/hami -n kube-system -f custom-values.yaml
 ```
 
-Customize your installation by editing the [configurations](../userguide/configure.md).
+For a detailed breakdown of available chart options and configuration keys, see the [Configuration Guide](../userguide/configure.md).
 
-## Verify your installation
+## 3. Verify Installation {#verify-installation}
 
-You can verify your installation using the following command:
+Verify that the HAMi components (`hami-device-plugin` and `hami-scheduler`) are running correctly:
 
 ```bash
-kubectl get pods -n kube-system
+kubectl get pods -n kube-system | grep hami
 ```
 
-If both hami-device-plugin and hami-scheduler pods are in the Running state, your installation is successful.
+If both `hami-device-plugin` and `hami-scheduler` pods are in the `Running` state, your installation is successful.
diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/get-started/deploy-with-helm.md b/i18n/zh/docusaurus-plugin-content-docs/current/get-started/deploy-with-helm.md
index ae700991a..b073f0783 100644
--- a/i18n/zh/docusaurus-plugin-content-docs/current/get-started/deploy-with-helm.md
+++ b/i18n/zh/docusaurus-plugin-content-docs/current/get-started/deploy-with-helm.md
@@ -1,130 +1,54 @@
 ---
-title: 使用 Helm 部署 HAMi
+title: 快速开始
+sidebar_label: 快速开始
+translated: true
 ---
 
-本指南将涵盖：
-
-- 为每个 GPU 节点配置 NVIDIA 容器运行时
-- 使用 Helm 部署 HAMi
-- 启动 vGPU 任务
-- 验证容器内设备资源是否受限
+只需几分钟，即可通过部署 Helm Chart 并提交您的第一个 GPU 共享工作负载快速上手 HAMi。
 
 ## 先决条件 {#prerequisites}
 
+在部署 HAMi 之前，请确保您的 GPU 节点满足以下前置条件：
+
 - [Helm](https://helm.sh/zh/docs/) v3+
 - [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/) v1.23+
 - [CUDA](https://developer.nvidia.com/cuda-toolkit) v10.2+
 - [NVIDIA 驱动](https://www.nvidia.cn/drivers/unix/) v440+
+- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) (需预先配置并将 `nvidia-container-runtime` 设为默认容器运行时)
 
-## 安装步骤 {#installation}
-
-### 1. 配置 nvidia-container-toolkit {#configure-nvidia-container-toolkit}
-
-在所有 GPU 节点执行此操作。
-
-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)
-
-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}
-
-```bash
-curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
-  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
-    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
-    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
-
-sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
-```
-
-#### 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 `nvidia-container-runtime` 设为默认底层运行时：
-
-```json
-{
-  "default-runtime": "nvidia",
-  "runtimes": {
-    "nvidia": {
-      "path": "/usr/bin/nvidia-container-runtime",
-      "runtimeArgs": []
-    }
-  }
-}
-```
-
-然后重启 Docker：
-
-```bash
-sudo systemctl daemon-reload && sudo systemctl restart docker
-```
-
-#### 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 `nvidia-container-runtime` 设为默认底层运行时：
-
-```toml
-version = 2
-[plugins]
-  [plugins."io.containerd.grpc.v1.cri"]
-    [plugins."io.containerd.grpc.v1.cri".containerd]
-      default_runtime_name = "nvidia"
-
-      [plugins."io.containerd.grpc.v1.cri".containerd.runtimes]
-        [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia]
-          privileged_without_host_devices = false
-          runtime_engine = ""
-          runtime_root = ""
-          runtime_type = "io.containerd.runc.v2"
-          [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia.options]
-            BinaryName = "/usr/bin/nvidia-container-runtime"
-```
-
-然后重启 containerd：
-
-```bash
-sudo systemctl daemon-reload && sudo systemctl restart containerd
-```
+---
 
-### 2. 标记节点 {#label-your-nodes}
+## 1. 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。
+使用 `gpu=on` 标签标记目标 GPU 节点。未标记的节点将不会由 HAMi 管理：
 
 ```bash
 kubectl label nodes <node-name> gpu=on
 ```
 
-### 3. 使用 Helm 部署 HAMi {#deploy-hami-using-helm}
-
-首先通过以下命令确认 Kubernetes 版本：
+---
 
-```bash
-kubectl version
-```
+## 2. 使用 Helm 部署 HAMi {#deploy-hami-using-helm}
 
-然后添加 Helm 仓库：
+添加官方 HAMi Helm 仓库并进行部署：
 
 ```bash
 helm repo add hami-charts https://project-hami.github.io/HAMi/
+helm repo update
+helm install hami hami-charts/hami -n kube-system
 ```
 
-安装时需设置 Kubernetes 调度器镜像版本与集群版本匹配。例如集群版本为 1.16.8 时，使用以下命令部署：
+确认 `hami-scheduler` 和 `hami-device-plugin` 的 Pod 处于 `Running` 状态：
 
 ```bash
-helm install hami hami-charts/hami \
-  --set scheduler.kubeScheduler.image.tag=v1.16.8 \
-  -n kube-system
+kubectl get pods -n kube-system | grep 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}
+---
 
-### 1. 提交演示任务 {#submit-demo-task}
+## 3. 提交 vGPU 工作负载 {#submit-a-vgpu-workload}
 
-容器现在可通过 `nvidia.com/gpu` 资源类型申请 NVIDIA vGPU：
+创建一个申请 1 个 vGPU 及 10240 MiB 显存限制的 Pod：
 
 ```yaml
 apiVersion: v1
@@ -134,45 +58,52 @@ metadata:
 spec:
   containers:
     - name: ubuntu-container
-      image: ubuntu:18.04
+      image: ubuntu:22.04
       command: ["bash", "-c", "sleep 86400"]
       resources:
         limits:
-          nvidia.com/gpu: 1 # 申请 1 个 vGPU
-          nvidia.com/gpumem: 10240 # 每个 vGPU 包含 10240m 设备显存（可选，整型）
+          nvidia.com/gpu: 1
+          nvidia.com/gpumem: 10240
+```
+
+提交 YAML 并等待 Pod 启动完毕：
+
+```bash
+kubectl apply -f gpu-pod.yaml
+kubectl wait --for=condition=Ready pod/gpu-pod --timeout=120s
 ```
 
-### 2. 验证容器内资源限制 {#verify-in-container-resource-control}
+---
+
+## 4. 验证 GPU 显存隔离 {#verify-gpu-memory-isolation}
 
-执行查询命令：
+在运行的容器中执行 `nvidia-smi`：
 
 ```bash
 kubectl exec -it gpu-pod -- nvidia-smi
 ```
 
-预期输出：
+预期输出显示 HAMi-core 的显存硬限制 (`10240MiB`)：
 
 ```text
-[HAMI-core Msg(28:140561996502848:libvgpu.c:836)]: Initializing.....
-Wed Apr 10 09:28:58 2024
+[HAMI-core Msg]: Initializing.....
 +-----------------------------------------------------------------------------------------+
 | NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |
 |-----------------------------------------+------------------------+----------------------+
 | GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
 | Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
-|                                         |                        |               MIG M. |
 |=========================================+========================+======================|
 |   0  Tesla V100-PCIE-32GB           On  |   00000000:3E:00.0 Off |                    0 |
 | N/A   29C    P0             24W /  250W |       0MiB /  10240MiB |      0%      Default |
-|                                         |                        |                  N/A |
-+-----------------------------------------+------------------------+----------------------+
-
-+-----------------------------------------------------------------------------------------+
-| Processes:                                                                              |
-|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
-|        ID   ID                                                               Usage      |
-|=========================================================================================|
-|  No running processes found                                                             |
 +-----------------------------------------------------------------------------------------+
-[HAMI-core Msg(28:140561996502848:multiprocess_memory_limit.c:434)]: Calling exit handler 28
+```
+
+---
+
+## 清理资源 {#cleanup}
+
+删除测试 Pod：
+
+```bash
+kubectl delete pod gpu-pod
 ```
diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/installation/online-installation.md b/i18n/zh/docusaurus-plugin-content-docs/current/installation/online-installation.md
index 290de6f06..ea4b4c176 100644
--- a/i18n/zh/docusaurus-plugin-content-docs/current/installation/online-installation.md
+++ b/i18n/zh/docusaurus-plugin-content-docs/current/installation/online-installation.md
@@ -4,41 +4,41 @@ title: 通过 Helm 在线安装（推荐）
 translated: true
 ---
 
-推荐使用 Helm 部署 HAMi。
+在 Kubernetes 集群中部署 HAMi 的推荐方式是使用官方 Helm Chart。
 
-## 添加 HAMi 仓库
+## 1. 添加 HAMi Helm 仓库 {#add-hami-repo}
 
-你可以使用以下命令添加 HAMi 图表仓库：
+添加 HAMi Chart 仓库并更新本地仓库缓存：
 
 ```bash
 helm repo add hami-charts https://project-hami.github.io/HAMi/
 helm repo update
 ```
 
-## 获取你的 Kubernetes 版本
+## 2. 部署 HAMi {#deploy-hami}
 
-安装时需要 Kubernetes 版本。你可以使用以下命令获取此信息：
+使用标准 Helm 命令将 HAMi 部署至 `kube-system` 命名空间：
 
 ```bash
-kubectl version
+helm install hami hami-charts/hami -n kube-system
 ```
 
-## 安装
+### 自定义 Helm 配置
 
-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：
+您可以通过 `--set` 参数或指定自定义 `values.yaml` 文件来自定义部署配置：
 
 ```bash
-helm install hami hami-charts/hami --set scheduler.kubeScheduler.image.tag=v1.29.0 -n kube-system
+helm install hami hami-charts/hami -n kube-system -f custom-values.yaml
 ```
 
-你可以通过编辑[配置](../userguide/configure.md)来自定义安装。
+详细的配置项说明请参阅 [配置指南](../userguide/configure.md)。
 
-## 验证你的安装
+## 3. 验证安装 {#verify-installation}
 
-你可以使用以下命令验证你的安装：
+验证 HAMi 组件（`hami-device-plugin` 与 `hami-scheduler`）是否正常运行：
 
 ```bash
-kubectl get pods -n kube-system
+kubectl get pods -n kube-system | grep hami
 ```
 
-如果 hami-device-plugin 和 hami-scheduler 这两个 Pod 都处于 Running 状态，则说明你的安装成功。
+若 `hami-device-plugin` 和 `hami-scheduler` 的 Pod 均处于 `Running` 状态，则安装完成。
diff --git a/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/get-started/deploy-with-helm.md b/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/get-started/deploy-with-helm.md
index b6089dfaf..b073f0783 100644
--- a/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/get-started/deploy-with-helm.md
+++ b/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/get-started/deploy-with-helm.md
@@ -1,110 +1,36 @@
 ---
-title: 使用 Helm 部署 HAMi
+title: 快速开始
+sidebar_label: 快速开始
+translated: true
 ---
 
-本指南将涵盖：
-
-- 为每个 GPU 节点配置 NVIDIA 容器运行时
-- 使用 Helm 部署 HAMi
-- 启动 vGPU 任务
-- 验证容器内设备资源是否受限
+只需几分钟，即可通过部署 Helm Chart 并提交您的第一个 GPU 共享工作负载快速上手 HAMi。
 
 ## 先决条件 {#prerequisites}
 
+在部署 HAMi 之前，请确保您的 GPU 节点满足以下前置条件：
+
 - [Helm](https://helm.sh/zh/docs/) v3+
-- [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/) v1.16+
+- [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/) v1.23+
 - [CUDA](https://developer.nvidia.com/cuda-toolkit) v10.2+
 - [NVIDIA 驱动](https://www.nvidia.cn/drivers/unix/) v440+
+- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) (需预先配置并将 `nvidia-container-runtime` 设为默认容器运行时)
 
-## 安装步骤 {#installation}
-
-### 1. 配置 nvidia-container-toolkit {#configure-nvidia-container-toolkit}
-
-在所有 GPU 节点执行此操作。
-
-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)
-
-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}
-
-```bash
-curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
-  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
-    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
-    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
-
-sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
-```
-
-#### 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 `nvidia-container-runtime` 设为默认底层运行时：
-
-```json
-{
-  "default-runtime": "nvidia",
-  "runtimes": {
-    "nvidia": {
-      "path": "/usr/bin/nvidia-container-runtime",
-      "runtimeArgs": []
-    }
-  }
-}
-```
-
-然后重启 Docker：
-
-```bash
-sudo systemctl daemon-reload && sudo systemctl restart docker
-```
-
-#### 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 `nvidia-container-runtime` 设为默认底层运行时：
-
-```toml
-version = 2
-[plugins]
-  [plugins."io.containerd.grpc.v1.cri"]
-    [plugins."io.containerd.grpc.v1.cri".containerd]
-      default_runtime_name = "nvidia"
-
-      [plugins."io.containerd.grpc.v1.cri".containerd.runtimes]
-        [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia]
-          privileged_without_host_devices = false
-          runtime_engine = ""
-          runtime_root = ""
-          runtime_type = "io.containerd.runc.v2"
-          [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia.options]
-            BinaryName = "/usr/bin/nvidia-container-runtime"
-```
-
-然后重启 containerd：
-
-```bash
-sudo systemctl daemon-reload && sudo systemctl restart containerd
-```
+---
 
-### 2. 标记节点 {#label-your-nodes}
+## 1. 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。
+使用 `gpu=on` 标签标记目标 GPU 节点。未标记的节点将不会由 HAMi 管理：
 
 ```bash
-kubectl label nodes {节点ID} gpu=on
+kubectl label nodes <node-name> gpu=on
 ```
 
-### 3. 使用 Helm 部署 HAMi {#deploy-hami-using-helm}
+---
 
-首先通过以下命令确认 Kubernetes 版本：
+## 2. 使用 Helm 部署 HAMi {#deploy-hami-using-helm}
 
-```bash
-kubectl version
-```
-
-然后添加 Helm 仓库：
+添加官方 HAMi Helm 仓库并进行部署：
 
 ```bash
 helm repo add hami-charts https://project-hami.github.io/HAMi/
@@ -112,13 +38,17 @@ helm repo update
 helm install hami hami-charts/hami -n kube-system
 ```
 
-若一切正常，可见 vgpu-device-plugin 和 vgpu-scheduler 的 Pod 均处于 Running 状态。
+确认 `hami-scheduler` 和 `hami-device-plugin` 的 Pod 处于 `Running` 状态：
 
-## 演示 {#demo}
+```bash
+kubectl get pods -n kube-system | grep hami
+```
+
+---
 
-### 1. 提交演示任务 {#submit-demo-task}
+## 3. 提交 vGPU 工作负载 {#submit-a-vgpu-workload}
 
-容器现在可通过 `nvidia.com/gpu` 资源类型申请 NVIDIA vGPU：
+创建一个申请 1 个 vGPU 及 10240 MiB 显存限制的 Pod：
 
 ```yaml
 apiVersion: v1
@@ -128,45 +58,52 @@ metadata:
 spec:
   containers:
     - name: ubuntu-container
-      image: ubuntu:18.04
+      image: ubuntu:22.04
       command: ["bash", "-c", "sleep 86400"]
       resources:
         limits:
-          nvidia.com/gpu: 1 # 申请 1 个 vGPU
-          nvidia.com/gpumem: 10240 # 每个 vGPU 包含 10240m 设备显存（可选，整型）
+          nvidia.com/gpu: 1
+          nvidia.com/gpumem: 10240
 ```
 
-### 2. 验证容器内资源限制 {#verify-in-container-resource-control}
+提交 YAML 并等待 Pod 启动完毕：
 
-执行查询命令：
+```bash
+kubectl apply -f gpu-pod.yaml
+kubectl wait --for=condition=Ready pod/gpu-pod --timeout=120s
+```
+
+---
+
+## 4. 验证 GPU 显存隔离 {#verify-gpu-memory-isolation}
+
+在运行的容器中执行 `nvidia-smi`：
 
 ```bash
-kubectl exec -it gpu-pod nvidia-smi
+kubectl exec -it gpu-pod -- nvidia-smi
 ```
 
-预期输出：
+预期输出显示 HAMi-core 的显存硬限制 (`10240MiB`)：
 
 ```text
-[HAMI-core Msg(28:140561996502848:libvgpu.c:836)]: Initializing.....
-Wed Apr 10 09:28:58 2024
+[HAMI-core Msg]: Initializing.....
 +-----------------------------------------------------------------------------------------+
 | NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |
 |-----------------------------------------+------------------------+----------------------+
 | GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
 | Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
-|                                         |                        |               MIG M. |
 |=========================================+========================+======================|
 |   0  Tesla V100-PCIE-32GB           On  |   00000000:3E:00.0 Off |                    0 |
 | N/A   29C    P0             24W /  250W |       0MiB /  10240MiB |      0%      Default |
-|                                         |                        |                  N/A |
-+-----------------------------------------+------------------------+----------------------+
-
-+-----------------------------------------------------------------------------------------+
-| Processes:                                                                              |
-|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
-|        ID   ID                                                               Usage      |
-|=========================================================================================|
-|  No running processes found                                                             |
 +-----------------------------------------------------------------------------------------+
-[HAMI-core Msg(28:140561996502848:multiprocess_memory_limit.c:434)]: Calling exit handler 28
+```
+
+---
+
+## 清理资源 {#cleanup}
+
+删除测试 Pod：
+
+```bash
+kubectl delete pod gpu-pod
 ```
diff --git a/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/installation/online-installation.md b/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/installation/online-installation.md
index 53dfbcfb4..ea4b4c176 100644
--- a/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/installation/online-installation.md
+++ b/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/installation/online-installation.md
@@ -4,40 +4,41 @@ title: 通过 Helm 在线安装（推荐）
 translated: true
 ---
 
-推荐使用 Helm 部署 HAMi。
+在 Kubernetes 集群中部署 HAMi 的推荐方式是使用官方 Helm Chart。
 
-## 添加 HAMi 仓库
+## 1. 添加 HAMi Helm 仓库 {#add-hami-repo}
 
-你可以使用以下命令添加 HAMi 图表仓库：
+添加 HAMi Chart 仓库并更新本地仓库缓存：
 
 ```bash
 helm repo add hami-charts https://project-hami.github.io/HAMi/
+helm repo update
 ```
 
-## 获取你的 Kubernetes 版本
+## 2. 部署 HAMi {#deploy-hami}
 
-安装时需要 Kubernetes 版本。你可以使用以下命令获取此信息：
+使用标准 Helm 命令将 HAMi 部署至 `kube-system` 命名空间：
 
 ```bash
-kubectl version --short
+helm install hami hami-charts/hami -n kube-system
 ```
 
-## 安装
+### 自定义 Helm 配置
 
-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 v1.16.8，请使用以下命令进行部署：
+您可以通过 `--set` 参数或指定自定义 `values.yaml` 文件来自定义部署配置：
 
 ```bash
-helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag=v1.16.8 -n kube-system
+helm install hami hami-charts/hami -n kube-system -f custom-values.yaml
 ```
 
-你可以通过编辑[配置](../userguide/configure.md)来自定义安装。
+详细的配置项说明请参阅 [配置指南](../userguide/configure.md)。
 
-## 验证你的安装
+## 3. 验证安装 {#verify-installation}
 
-你可以使用以下命令验证你的安装：
+验证 HAMi 组件（`hami-device-plugin` 与 `hami-scheduler`）是否正常运行：
 
 ```bash
-kubectl get pods -n kube-system
+kubectl get pods -n kube-system | grep hami
 ```
 
-如果 hami-device-plugin 和 hami-scheduler 这两个 Pod 都处于 Running 状态，则说明你的安装成功。
+若 `hami-device-plugin` 和 `hami-scheduler` 的 Pod 均处于 `Running` 状态，则安装完成。
diff --git a/versioned_docs/version-v2.9.0/get-started/deploy-with-helm.md b/versioned_docs/version-v2.9.0/get-started/deploy-with-helm.md
index 2f878dcef..4d2f15d6e 100644
--- a/versioned_docs/version-v2.9.0/get-started/deploy-with-helm.md
+++ b/versioned_docs/version-v2.9.0/get-started/deploy-with-helm.md
@@ -1,110 +1,36 @@
 ---
-title: Deploy HAMi using Helm
+title: Quick Start
+sidebar_label: Get Started
+translated: true
 ---
 
-This guide covers:
-
-- Configuring NVIDIA container runtime on each GPU node
-- Deploying HAMi using Helm
-- Launching a vGPU task
-- Verifying container resource limits
+Get HAMi up and running in minutes by deploying the Helm chart and submitting your first shared GPU workload.
 
 ## Prerequisites {#prerequisites}
 
+Before deploying HAMi, ensure your GPU nodes meet the following prerequisites:
+
 - [Helm](https://helm.sh/docs/) v3+
-- [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/) v1.16+
+- [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/) v1.23+
 - [CUDA](https://developer.nvidia.com/cuda-toolkit) v10.2+
 - [NVIDIA Driver](https://www.nvidia.cn/drivers/unix/) v440+
+- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) (with `nvidia-container-runtime` configured as the container runtime)
 
-## Installation {#installation}
-
-### 1. Configure nvidia-container-toolkit {#configure-nvidia-container-toolkit}
-
-Perform the following steps on all 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.
-
-See [nvidia-container-toolkit installation 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:
-
-#### Install the `nvidia-container-toolkit` {#install-the-nvidia-container-toolkit}
-
-```bash
-curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
-  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
-    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
-    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
-
-sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
-```
-
-#### Configure Docker {#configure-docker}
-
-When running Kubernetes with Docker, edit the configuration file (usually `/etc/docker/daemon.json`) to set `nvidia-container-runtime` as the default runtime:
-
-```json
-{
-  "default-runtime": "nvidia",
-  "runtimes": {
-    "nvidia": {
-      "path": "/usr/bin/nvidia-container-runtime",
-      "runtimeArgs": []
-    }
-  }
-}
-```
-
-Restart Docker:
-
-```bash
-sudo systemctl daemon-reload && sudo systemctl restart docker
-```
-
-#### Configure containerd {#configure-containerd}
-
-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
-[plugins]
-  [plugins."io.containerd.grpc.v1.cri"]
-    [plugins."io.containerd.grpc.v1.cri".containerd]
-      default_runtime_name = "nvidia"
-
-      [plugins."io.containerd.grpc.v1.cri".containerd.runtimes]
-        [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia]
-          privileged_without_host_devices = false
-          runtime_engine = ""
-          runtime_root = ""
-          runtime_type = "io.containerd.runc.v2"
-          [plugins."io.containerd.grpc.v1.cri".containerd.runtimes.nvidia.options]
-            BinaryName = "/usr/bin/nvidia-container-runtime"
-```
-
-Restart containerd:
-
-```bash
-sudo systemctl daemon-reload && sudo systemctl restart containerd
-```
+---
 
-### 2. Label your nodes {#label-your-nodes}
+## 1. Label your nodes {#label-your-nodes}
 
-Label your GPU nodes for HAMi scheduling with `gpu=on`. Nodes without this label cannot be managed by the scheduler.
+Label the target GPU nodes with `gpu=on`. Nodes without this label will not be managed by HAMi:
 
 ```bash
 kubectl label nodes <node-name> gpu=on
 ```
 
-### 3. Deploy HAMi using Helm {#deploy-hami-using-helm}
+---
 
-Check your Kubernetes version:
+## 2. Deploy HAMi using Helm {#deploy-hami-using-helm}
 
-```bash
-kubectl version
-```
-
-Add the Helm repository:
+Add the official HAMi Helm repository and deploy the chart:
 
 ```bash
 helm repo add hami-charts https://project-hami.github.io/HAMi/
@@ -112,13 +38,17 @@ helm repo update
 helm install hami hami-charts/hami -n kube-system
 ```
 
-If successful, both `hami-device-plugin` and `hami-scheduler` pods should be in the `Running` state.
+Verify that the `hami-scheduler` and `hami-device-plugin` pods are running:
 
-## Demo {#demo}
+```bash
+kubectl get pods -n kube-system | grep hami
+```
+
+---
 
-### 1. Submit demo task {#submit-demo-task}
+## 3. Submit a vGPU Workload {#submit-a-vgpu-workload}
 
-Containers can now request NVIDIA vGPUs using the `nvidia.com/gpu` resource type.
+Create a Pod requesting 1 vGPU with 10240 MiB of GPU memory limit:
 
 ```yaml
 apiVersion: v1
@@ -132,41 +62,48 @@ spec:
       command: ["bash", "-c", "sleep 86400"]
       resources:
         limits:
-          nvidia.com/gpu: 1 # Request 1 vGPU
-          nvidia.com/gpumem: 10240 # Each vGPU provides 10240 MiB device memory (optional)
+          nvidia.com/gpu: 1
+          nvidia.com/gpumem: 10240
 ```
 
-### 2. Verify container resource limits {#verify-in-container-resource-control}
+Apply the manifest and wait for the Pod to become ready:
 
-Run the following command:
+```bash
+kubectl apply -f gpu-pod.yaml
+kubectl wait --for=condition=Ready pod/gpu-pod --timeout=120s
+```
+
+---
+
+## 4. Verify GPU Memory Isolation {#verify-gpu-memory-isolation}
+
+Execute `nvidia-smi` inside the running container:
 
 ```bash
 kubectl exec -it gpu-pod -- nvidia-smi
 ```
 
-Expected output:
+Expected output showing HAMi-core hard memory limit (`10240MiB`):
 
 ```text
-[HAMI-core Msg(28:140561996502848:libvgpu.c:836)]: Initializing.....
-Wed Apr 10 09:28:58 2024
+[HAMI-core Msg]: Initializing.....
 +-----------------------------------------------------------------------------------------+
 | NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |
 |-----------------------------------------+------------------------+----------------------+
 | GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
 | Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
-|                                         |                        |               MIG M. |
 |=========================================+========================+======================|
 |   0  Tesla V100-PCIE-32GB           On  |   00000000:3E:00.0 Off |                    0 |
 | N/A   29C    P0             24W /  250W |       0MiB /  10240MiB |      0%      Default |
-|                                         |                        |                  N/A |
-+-----------------------------------------+------------------------+----------------------+
-
-+-----------------------------------------------------------------------------------------+
-| Processes:                                                                              |
-|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
-|        ID   ID                                                               Usage      |
-|=========================================================================================|
-|  No running processes found                                                             |
 +-----------------------------------------------------------------------------------------+
-[HAMI-core Msg(28:140561996502848:multiprocess_memory_limit.c:434)]: Calling exit handler 28
+```
+
+---
+
+## Cleanup {#cleanup}
+
+Delete the test Pod:
+
+```bash
+kubectl delete pod gpu-pod
 ```
diff --git a/versioned_docs/version-v2.9.0/installation/online-installation.md b/versioned_docs/version-v2.9.0/installation/online-installation.md
index 3b2f5eded..0d0a6252b 100644
--- a/versioned_docs/version-v2.9.0/installation/online-installation.md
+++ b/versioned_docs/version-v2.9.0/installation/online-installation.md
@@ -3,41 +3,41 @@ sidebar_label: Online Installation from Helm
 title: Online Installation from Helm (Recommended)
 ---
 
-The recommended way to deploy HAMi is via Helm.
+The recommended way to deploy HAMi in a Kubernetes cluster is via the official Helm chart.
 
-## Add HAMi repo
+## 1. Add HAMi Helm Repository {#add-hami-repo}
 
-You can add HAMi chart repository using the following command:
+Add the HAMi chart repository and update local repository cache:
 
 ```bash
 helm repo add hami-charts https://project-hami.github.io/HAMi/
 helm repo update
 ```
 
-## Get your Kubernetes version
+## 2. Deploy HAMi {#deploy-hami}
 
-A Kubernetes version is required for proper installation. You can retrieve your Kubernetes server version with:
+Deploy HAMi into the `kube-system` namespace using standard Helm installation:
 
 ```bash
-kubectl version
+helm install hami hami-charts/hami -n kube-system
 ```
 
-## Installation
+### Customizing Helm Configurations
 
-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:
+You can customize your deployment by passing parameters with `--set` or providing a custom `values.yaml` file:
 
 ```bash
-helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag=v1.29.0 -n kube-system
+helm install hami hami-charts/hami -n kube-system -f custom-values.yaml
 ```
 
-Customize your installation by editing the [configurations](../userguide/configure.md).
+For a detailed breakdown of available chart options and configuration keys, see the [Configuration Guide](../userguide/configure.md).
 
-## Verify your installation
+## 3. Verify Installation {#verify-installation}
 
-You can verify your installation using the following command:
+Verify that the HAMi components (`hami-device-plugin` and `hami-scheduler`) are running correctly:
 
 ```bash
-kubectl get pods -n kube-system
+kubectl get pods -n kube-system | grep hami
 ```
 
-If both hami-device-plugin and hami-scheduler pods are in the Running state, your installation is successful.
+If both `hami-device-plugin` and `hami-scheduler` pods are in the `Running` state, your installation is successful.

From 40d660616e3b7d58c258d86decbdd5b9ce7b5124 Mon Sep 17 00:00:00 2001
From: Srijan <singhsrijangkp@gmail.com>
Date: Tue, 4 Aug 2026 18:35:41 +0530
Subject: [PATCH 2/2] docs(get-started): address maintainer review feedback

Part of #689

Signed-off-by: Srijan <singhsrijangkp@gmail.com>
---
 docs/get-started/deploy-with-helm.md                  | 11 +++++++++--
 docs/installation/online-installation.md              |  6 +++---
 .../current/get-started/deploy-with-helm.md           | 11 +++++++++--
 .../current/installation/online-installation.md       |  7 +++----
 .../version-v2.9.0/get-started/deploy-with-helm.md    | 11 +++++++++--
 .../installation/online-installation.md               |  7 +++----
 .../version-v2.9.0/get-started/deploy-with-helm.md    | 11 +++++++++--
 .../installation/online-installation.md               |  6 +++---
 8 files changed, 48 insertions(+), 22 deletions(-)

diff --git a/docs/get-started/deploy-with-helm.md b/docs/get-started/deploy-with-helm.md
index 4d2f15d6e..e9025324b 100644
--- a/docs/get-started/deploy-with-helm.md
+++ b/docs/get-started/deploy-with-helm.md
@@ -1,7 +1,6 @@
 ---
 title: Quick Start
 sidebar_label: Get Started
-translated: true
 ---
 
 Get HAMi up and running in minutes by deploying the Helm chart and submitting your first shared GPU workload.
@@ -14,7 +13,7 @@ Before deploying HAMi, ensure your GPU nodes meet the following prerequisites:
 - [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/) v1.23+
 - [CUDA](https://developer.nvidia.com/cuda-toolkit) v10.2+
 - [NVIDIA Driver](https://www.nvidia.cn/drivers/unix/) v440+
-- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) (with `nvidia-container-runtime` configured as the container runtime)
+- [NVIDIA Container Toolkit](../installation/prerequisites) (with `nvidia-container-runtime` set as default runtime)
 
 ---
 
@@ -107,3 +106,11 @@ Delete the test Pod:
 ```bash
 kubectl delete pod gpu-pod
 ```
+
+---
+
+## Next steps {#next-steps}
+
+- Verify your setup in detail with [Verify HAMi Installation](./verify-hami.md).
+- Learn how to customize your deployment parameters in the [Configuration Guide](../userguide/configure.md).
+
diff --git a/docs/installation/online-installation.md b/docs/installation/online-installation.md
index 0d0a6252b..98c709bf8 100644
--- a/docs/installation/online-installation.md
+++ b/docs/installation/online-installation.md
@@ -16,10 +16,10 @@ helm repo update
 
 ## 2. Deploy HAMi {#deploy-hami}
 
-Deploy HAMi into the `kube-system` namespace using standard Helm installation:
+Ensure `scheduler.kubeScheduler.imageTag` matches your Kubernetes server version (e.g. `v1.29.0`). Deploy HAMi into the `kube-system` namespace using Helm:
 
 ```bash
-helm install hami hami-charts/hami -n kube-system
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag=v1.29.0 -n kube-system
 ```
 
 ### Customizing Helm Configurations
@@ -27,7 +27,7 @@ helm install hami hami-charts/hami -n kube-system
 You can customize your deployment by passing parameters with `--set` or providing a custom `values.yaml` file:
 
 ```bash
-helm install hami hami-charts/hami -n kube-system -f custom-values.yaml
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag=v1.29.0 -n kube-system -f custom-values.yaml
 ```
 
 For a detailed breakdown of available chart options and configuration keys, see the [Configuration Guide](../userguide/configure.md).
diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/get-started/deploy-with-helm.md b/i18n/zh/docusaurus-plugin-content-docs/current/get-started/deploy-with-helm.md
index b073f0783..236737f3a 100644
--- a/i18n/zh/docusaurus-plugin-content-docs/current/get-started/deploy-with-helm.md
+++ b/i18n/zh/docusaurus-plugin-content-docs/current/get-started/deploy-with-helm.md
@@ -1,7 +1,6 @@
 ---
 title: 快速开始
 sidebar_label: 快速开始
-translated: true
 ---
 
 只需几分钟，即可通过部署 Helm Chart 并提交您的第一个 GPU 共享工作负载快速上手 HAMi。
@@ -14,7 +13,7 @@ translated: true
 - [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/) v1.23+
 - [CUDA](https://developer.nvidia.com/cuda-toolkit) v10.2+
 - [NVIDIA 驱动](https://www.nvidia.cn/drivers/unix/) v440+
-- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) (需预先配置并将 `nvidia-container-runtime` 设为默认容器运行时)
+- [NVIDIA Container Toolkit](../installation/prerequisites) (需预先配置并将 `nvidia-container-runtime` 设为默认容器运行时)
 
 ---
 
@@ -107,3 +106,11 @@ kubectl exec -it gpu-pod -- nvidia-smi
 ```bash
 kubectl delete pod gpu-pod
 ```
+
+---
+
+## 下一步 {#next-steps}
+
+- 参阅 [验证 HAMi 安装](./verify-hami.md) 查看详细验证步骤。
+- 参阅 [配置指南](../userguide/configure.md) 了解如何自定义部署参数与设备配置。
+
diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/installation/online-installation.md b/i18n/zh/docusaurus-plugin-content-docs/current/installation/online-installation.md
index ea4b4c176..064e5e2bf 100644
--- a/i18n/zh/docusaurus-plugin-content-docs/current/installation/online-installation.md
+++ b/i18n/zh/docusaurus-plugin-content-docs/current/installation/online-installation.md
@@ -1,7 +1,6 @@
 ---
 sidebar_label: 通过 Helm 在线安装
 title: 通过 Helm 在线安装（推荐）
-translated: true
 ---
 
 在 Kubernetes 集群中部署 HAMi 的推荐方式是使用官方 Helm Chart。
@@ -17,10 +16,10 @@ helm repo update
 
 ## 2. 部署 HAMi {#deploy-hami}
 
-使用标准 Helm 命令将 HAMi 部署至 `kube-system` 命名空间：
+请确保 `scheduler.kubeScheduler.imageTag` 与您的 Kubernetes 服务端版本匹配（例如 `v1.29.0`）。将 HAMi 部署至 `kube-system` 命名空间：
 
 ```bash
-helm install hami hami-charts/hami -n kube-system
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag=v1.29.0 -n kube-system
 ```
 
 ### 自定义 Helm 配置
@@ -28,7 +27,7 @@ helm install hami hami-charts/hami -n kube-system
 您可以通过 `--set` 参数或指定自定义 `values.yaml` 文件来自定义部署配置：
 
 ```bash
-helm install hami hami-charts/hami -n kube-system -f custom-values.yaml
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag=v1.29.0 -n kube-system -f custom-values.yaml
 ```
 
 详细的配置项说明请参阅 [配置指南](../userguide/configure.md)。
diff --git a/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/get-started/deploy-with-helm.md b/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/get-started/deploy-with-helm.md
index b073f0783..236737f3a 100644
--- a/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/get-started/deploy-with-helm.md
+++ b/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/get-started/deploy-with-helm.md
@@ -1,7 +1,6 @@
 ---
 title: 快速开始
 sidebar_label: 快速开始
-translated: true
 ---
 
 只需几分钟，即可通过部署 Helm Chart 并提交您的第一个 GPU 共享工作负载快速上手 HAMi。
@@ -14,7 +13,7 @@ translated: true
 - [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/) v1.23+
 - [CUDA](https://developer.nvidia.com/cuda-toolkit) v10.2+
 - [NVIDIA 驱动](https://www.nvidia.cn/drivers/unix/) v440+
-- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) (需预先配置并将 `nvidia-container-runtime` 设为默认容器运行时)
+- [NVIDIA Container Toolkit](../installation/prerequisites) (需预先配置并将 `nvidia-container-runtime` 设为默认容器运行时)
 
 ---
 
@@ -107,3 +106,11 @@ kubectl exec -it gpu-pod -- nvidia-smi
 ```bash
 kubectl delete pod gpu-pod
 ```
+
+---
+
+## 下一步 {#next-steps}
+
+- 参阅 [验证 HAMi 安装](./verify-hami.md) 查看详细验证步骤。
+- 参阅 [配置指南](../userguide/configure.md) 了解如何自定义部署参数与设备配置。
+
diff --git a/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/installation/online-installation.md b/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/installation/online-installation.md
index ea4b4c176..064e5e2bf 100644
--- a/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/installation/online-installation.md
+++ b/i18n/zh/docusaurus-plugin-content-docs/version-v2.9.0/installation/online-installation.md
@@ -1,7 +1,6 @@
 ---
 sidebar_label: 通过 Helm 在线安装
 title: 通过 Helm 在线安装（推荐）
-translated: true
 ---
 
 在 Kubernetes 集群中部署 HAMi 的推荐方式是使用官方 Helm Chart。
@@ -17,10 +16,10 @@ helm repo update
 
 ## 2. 部署 HAMi {#deploy-hami}
 
-使用标准 Helm 命令将 HAMi 部署至 `kube-system` 命名空间：
+请确保 `scheduler.kubeScheduler.imageTag` 与您的 Kubernetes 服务端版本匹配（例如 `v1.29.0`）。将 HAMi 部署至 `kube-system` 命名空间：
 
 ```bash
-helm install hami hami-charts/hami -n kube-system
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag=v1.29.0 -n kube-system
 ```
 
 ### 自定义 Helm 配置
@@ -28,7 +27,7 @@ helm install hami hami-charts/hami -n kube-system
 您可以通过 `--set` 参数或指定自定义 `values.yaml` 文件来自定义部署配置：
 
 ```bash
-helm install hami hami-charts/hami -n kube-system -f custom-values.yaml
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag=v1.29.0 -n kube-system -f custom-values.yaml
 ```
 
 详细的配置项说明请参阅 [配置指南](../userguide/configure.md)。
diff --git a/versioned_docs/version-v2.9.0/get-started/deploy-with-helm.md b/versioned_docs/version-v2.9.0/get-started/deploy-with-helm.md
index 4d2f15d6e..e9025324b 100644
--- a/versioned_docs/version-v2.9.0/get-started/deploy-with-helm.md
+++ b/versioned_docs/version-v2.9.0/get-started/deploy-with-helm.md
@@ -1,7 +1,6 @@
 ---
 title: Quick Start
 sidebar_label: Get Started
-translated: true
 ---
 
 Get HAMi up and running in minutes by deploying the Helm chart and submitting your first shared GPU workload.
@@ -14,7 +13,7 @@ Before deploying HAMi, ensure your GPU nodes meet the following prerequisites:
 - [kubectl](https://kubernetes.io/docs/tasks/tools/install-kubectl/) v1.23+
 - [CUDA](https://developer.nvidia.com/cuda-toolkit) v10.2+
 - [NVIDIA Driver](https://www.nvidia.cn/drivers/unix/) v440+
-- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) (with `nvidia-container-runtime` configured as the container runtime)
+- [NVIDIA Container Toolkit](../installation/prerequisites) (with `nvidia-container-runtime` set as default runtime)
 
 ---
 
@@ -107,3 +106,11 @@ Delete the test Pod:
 ```bash
 kubectl delete pod gpu-pod
 ```
+
+---
+
+## Next steps {#next-steps}
+
+- Verify your setup in detail with [Verify HAMi Installation](./verify-hami.md).
+- Learn how to customize your deployment parameters in the [Configuration Guide](../userguide/configure.md).
+
diff --git a/versioned_docs/version-v2.9.0/installation/online-installation.md b/versioned_docs/version-v2.9.0/installation/online-installation.md
index 0d0a6252b..98c709bf8 100644
--- a/versioned_docs/version-v2.9.0/installation/online-installation.md
+++ b/versioned_docs/version-v2.9.0/installation/online-installation.md
@@ -16,10 +16,10 @@ helm repo update
 
 ## 2. Deploy HAMi {#deploy-hami}
 
-Deploy HAMi into the `kube-system` namespace using standard Helm installation:
+Ensure `scheduler.kubeScheduler.imageTag` matches your Kubernetes server version (e.g. `v1.29.0`). Deploy HAMi into the `kube-system` namespace using Helm:
 
 ```bash
-helm install hami hami-charts/hami -n kube-system
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag=v1.29.0 -n kube-system
 ```
 
 ### Customizing Helm Configurations
@@ -27,7 +27,7 @@ helm install hami hami-charts/hami -n kube-system
 You can customize your deployment by passing parameters with `--set` or providing a custom `values.yaml` file:
 
 ```bash
-helm install hami hami-charts/hami -n kube-system -f custom-values.yaml
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag=v1.29.0 -n kube-system -f custom-values.yaml
 ```
 
 For a detailed breakdown of available chart options and configuration keys, see the [Configuration Guide](../userguide/configure.md).