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-# 功能调试
-
-[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/mindspore/source_zh_cn/orange_pi/debug.md)
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+# 环境搭建指南
+
+[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/mindspore/source_zh_cn/orange_pi/environment_setup.md)
+
+本章节将介绍如何在OrangePi AIpro上烧录镜像，自定义安装CANN和MindSpore，并配置运行环境。
+
+## 1. 镜像烧录（以Windows系统为例）
+ 
+镜像烧录可以在任何操作系统内执行，这里将以在Windows系统为例 ，演示使用相应版本的balenaEtcher工具，快速烧录镜像至您的Micro SD卡。
+
+### 1.1 制卡前准备
+
+步骤1 将Micro SD卡插入读卡器，并将读卡器插入PC
+
+![environment-setup-1-1](./images/environment_setup_1-1.jpg)
+
+###	1.2 下载Ubuntu镜像
+
+步骤1 点击[此链接](http://www.orangepi.cn/html/hardWare/computerAndMicrocontrollers/service-and-support/Orange-Pi-AIpro.html)进入镜像下载页面
+
+> 注意：此处演示为8-12T算力开发板镜像下载地址，20T算力开发板镜像下载请点击[此链接](http://www.orangepi.cn/html/hardWare/computerAndMicrocontrollers/details/Orange-Pi-AIpro(20T).html)，请注意区分两种不同算力开发板的镜像。
+
+步骤2 点击图片中箭头图标跳转百度网盘下载页面
+
+![environment-setup-1-2](./images/environment_setup_1-2.png)
+
+步骤3 选择桌面版本下载，建议下载0318版本环境
+
+![environment-setup-1-3](./images/environment_setup_1-3.png)
+
+步骤4 备选下载方式
+
+如果百度网盘下载过慢，可以使用[此链接](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/OrangePi/20240318/opiaipro_ubuntu22.04_desktop_aarch64_20240318.img.xz )直接下载
+
+### 1.3	下载制卡工具
+
+有两种制卡工具balenaEtcher、rufus，可根据自己电脑情况任选一种工具进行烧录。
+
+- balenaEtcher制卡工具：
+
+步骤1 下载balenaEtcher
+
+点击[此链接](https://etcher.balena.io/)可跳转到软件官网，点击绿色的下载按钮会跳到软件下载的地方。
+
+![environment-setup-1-4](./images/environment_setup_1-4.png)
+
+步骤2 选择下载 Portable版本
+
+Portable版本无需安装，双击打开即可使用。
+
+![environment-setup-1-5](./images/environment_setup_1-5.png)
+
+步骤3 备选下载方式
+
+如果官方网站下载过慢，可以使用以[此链接](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/OrangePi/balenaEtcher/balenaEtcher-Setup-1.18.4.exe )直接下载balenaEtcher-Setup-1.18.4软件
+
+步骤4 打开balenaEtcher
+
+![environment-setup-1-6](./images/environment_setup_1-6.png)
+
+![environment-setup-1-7](./images/environment_setup_1-7.png)
+
+- Rufus制卡工具：
+
+步骤1 Rufus下载
+
+点击[此链接](https://etcher.balena.io/)，进行下载、安装。
+
+### 1.4 选择和烧录镜像
+
+这里介绍balenaEtcher、rufus两种制卡工具烧录镜像，您可按对应工具进行烧录。
+
+- balenaEtcher烧录镜像：
+
+步骤1 选择镜像、TF卡，启动烧录
+
+1.选择要烧录的镜像文件（1.2下载Ubuntu镜像的保存路径）
+
+2.选择TF卡的盘符
+
+3.点击开始烧录，如下图：
+
+![environment-setup-1-8](./images/environment_setup_1-8.png)
+
+烧录和验证大概需要20分钟左右，请耐心等待：
+
+![environment-setup-1-9](./images/environment_setup_1-9.png)
+
+![environment-setup-1-10](./images/environment_setup_1-10.png)
+
+步骤2 烧录完成
+
+烧录完成后，balenaEtcher的显示界面如下图所示，如果显示绿色的指示图标说明镜像烧录成功，此时就可以退出balenaEtcher，拔出TF卡，插入到开发板的TF卡槽中使用：
+
+![environment-setup-1-11](./images/environment_setup_1-11.png)
+
+- Rufus烧录镜像：
+
+步骤1 选择镜像、TF卡，烧录镜像
+
+sd卡插入读卡器，读卡器插入电脑、选择镜像与sd卡，点击“开始”
+
+![environment-setup-1-12](./images/environment_setup_1-12.png)
+
+步骤2 烧录完成
+
+等待结束后直接拔出读卡器
+
+![environment-setup-1-13](./images/environment_setup_1-13.png)
+
+## 2. CANN升级
+
+### 2.1 Toolkit升级
+
+步骤1 打开终端，切换root用户
+
+使用`CTRL+ALT+T`快捷键或点击页面下方带有`$_`的图标打开终端
+
+![environment-setup-1-14](./images/environment_setup_1-14.png)
+
+切换root用户，root用户密码：Mind@123
+
+```bash
+
+# 打开开发板的一个终端，运行如下命令
+
+(base) HwHiAiUser@orangepiaipro:~$ su – root 
+ Password:
+(base) root@orangepiaipro: ~#
+
+```
+步骤2 删除镜像中已安装CANN包释放磁盘空间，防止安装新的CANN包时报错磁盘空间不足
+
+```bash
+
+(base) root@orangepiaipro: ~# cd /usr/local/Ascend/ascend-toolkit
+(base) root@orangepiaipro: /usr/local/Ascend/ascend-toolkit # rm -rf *
+
+```
+步骤3 打开昇腾CANN官网访问社区版资源[下载地址](https://www.hiascend.com/developer/download/community/result?module=cann)，下载所需版本的toolkit包，该处以8.0.RC2.alpha003版本为例，如下图：
+
+![environment-setup-1-14](./images/environment_setup_1-15.png)
+
+> 注意：执行如下命令，根据环境实际输出信息选择aarch64或是x86_64对应的软件包
+> 
+   ```bash
+  uname -a 
+   ```
+步骤4 进入Toolkit包下载目录
+
+   ```bash
+(base) root@orangepiaipro: /usr/local/Ascend/ascend-toolkit# cd /home/HwHiAiUser/Downloads
+   ```
+>注意：Orange Pi AI Pro浏览器文件默认下载目录：/home/HwHiAiUser/Downloads，用户在更换保存路径时请同步修改上述命令中的路径
+
+步骤5 给CANN包添加执行权限
+
+   ```bash
+(base) root@orangepiaipro: /home/HwHiAiUser/Downloads# chmod +x ./Ascend-cann-toolkit_8.0.RC2.alpha003_linux-aarch64.run
+   ```
+步骤6 执行以下命令升级软件
+
+   ```bash
+(base) root@orangepiaipro: /home/HwHiAiUser/Downloads#./Ascend-cann-toolkit_8.0.RC2.alpha003_linux-aarch64.run --install
+   ```
+![environment-setup-1-15](./images/environment_setup_1-16.png)
+
+安装时弹出此提示后输入Y，然后按回车键继续安装。
+
+![environment-setup-1-16](./images/environment_setup_1-17.png)
+
+步骤7 配置并加载环境变量
+
+   ```bash
+
+(base) root@orangepiaipro: /home/HwHiAiUser/Downloads # echo “source /usr/local/Ascend/ascend-toolkit/set_env.sh” >> ~/.bashrc
+(base) root@orangepiaipro: /home/HwHiAiUser/Downloads # source ~/.bashrc
+
+   ```
+### 2.2 Kernels升级
+
+>二进制算子包Kernels依赖CANN软件包Toolkit，执行升级时，当前环境需已安装配套版本的Toolkit，并使用同一用户安装。
+
+步骤1 打开终端，并切root用户
+
+root用户密码：Mind@123
+
+```bash
+
+# 打开开发板的一个终端，运行如下命令
+
+(base) HwHiAiUser@orangepiaipro:~$ su – root 
+ Password:
+(base) root@orangepiaipro: ~#
+
+```
+步骤2 执行如下命令，获取开发板NPU型号
+
+```bash
+npu-smi info
+```
+步骤3 打开昇腾CANN官网访问社区版资源[下载地址](https://www.hiascend.com/developer/download/community/result?module=cann)，下载与CANN包版本一致，并且匹配NPU型号的kernel包，如下图：
+
+![environment-setup-1-17](./images/environment_setup_1-18.png)
+
+步骤4 进入Kernels包下载目录
+
+```bash
+(base) root@orangepiaipro: /usr/local/Ascend/ascend-toolkit# cd /home/HwHiAiUser/Downloads
+```
+>注意：Orange Pi AI Pro浏览器文件默认下载目录：/home/HwHiAiUser/Downloads
+
+步骤5 给kernels包添加执行权限
+
+```bash
+(base) root@orangepiaipro: /home/HwHiAiUser/Downloads# chmod +x ./Ascend-cann-kernels-310b_8.0.RC2.alpha003_linux.run
+```
+步骤6 执行以下命令升级软件
+
+```bash
+(base) root@orangepiaipro: /home/HwHiAiUser/Downloads#./Ascend-cann-kernels-310b_8.0.RC2.alpha003_linux.run --install
+```
+升级完成后，若显示如下信息，则说明软件升级成功：
+
+```bash
+xxx install success
+```
+* xxx表示升级的实际软件包名。
+
+* 安装升级后的路径（以root用户默认安装升级路径为例）：“/usr/local/Ascend/ ascend-toolkit/latest/opp/built-in/op_impl/ai_core/tbe/kernel”。
+
+## 3. MindSpore升级
+
+### 3.1 安装官网正式版（以MindSpore2.3.1为例）
+
+方法一：以HwHiAiUser用户打开终端，在终端直接运行pip install命令
+
+```bash
+pip install mindspore==2.3.1
+```
+方法二：参考[昇思MindSpore官网安装教程](https://www.mindspore.cn/install) 安装
+
+```bash
+
+pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.3.1/MindSpore/unified/aarch64/mindspore-2.3.1-cp39-cp39-linux_aarch64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+# 注意确认操作系统和编程语言，香橙派开发板默认环境下是linux-aarch64和python3.9
+
+```
+
+### 3.2 安装MindSpore daily包（以9月11日daily包为例）
+
+香橙派开发板支持自定义安装MindSpore daily包，可从[此链接](https://repo.mindspore.cn/mindspore/mindspore/version/)获取到对应日期的软件包。
+
+* 目标 daily whl包具体查找过程如下：
+
+1. 进入以master为前缀的目录。若是出现多个目录前缀是master时，推荐进入日期更靠后的目录
+
+2. 进入unified目录
+
+3. 根据实际操作系统信息，进入对应目录。由于香橙派开发板默认操作系统为linux-aarch64，所以进入aarch64目录
+
+4. 根据实际python版本信息，找到对应daily whl包。由于香橙派开发板默认为python3.9，所以目标daily包为mindspore-2.4.0-cp39-cp39-linux_aarch64.whl
+
+
+![environment-setup-1-19](./images/environment_setup_1-19.png)
+
+
+> 注意：本教程旨在让开发者体验到最新的版本特定，但由于daily包并不是正式发布版本，在运行中可能回出现一些问题，开发者可通过[社区](https://gitee.com/mindspore/mindspore)提交issue，或可自行修改并提交PR。
+
+
+* 下载whl包进行安装，终端运行如下命令
+
+```bash
+
+# wget下载whl包
+wget https://repo.mindspore.cn/mindspore/mindspore/version/202409/20240911/master_20240911160029_917adc670d5f93049d35d6c3ab4ac6aa2339a74b_newest/unified/aarch64/mindspore-2.4.0-cp39-cp39-linux_aarch64.whl
+
+# 在终端进入到whl包所在路径，再运行pip install命令进行安装
+pip install mindspore-2.4.0-cp39-cp39-linux_aarch64.whl
+
+```
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diff --git a/docs/mindspore/source_zh_cn/orange_pi/index.rst b/docs/mindspore/source_zh_cn/orange_pi/index.rst
index 56f2629997f652271d72498764f59f486130e9f9..bffcc1d31fdc75c71e6bae507177c642cb946a82 100644
--- a/docs/mindspore/source_zh_cn/orange_pi/index.rst
+++ b/docs/mindspore/source_zh_cn/orange_pi/index.rst
@@ -6,6 +6,6 @@
    :maxdepth: 1
 
    overview
-   quick_start
-   debug
+   environment_setup
    model_infer
+   quick_start
\ No newline at end of file
diff --git a/docs/mindspore/source_zh_cn/orange_pi/model_infer.md b/docs/mindspore/source_zh_cn/orange_pi/model_infer.md
index a61812a2d3d86e8df135e74d9d2786267cd74762..0aa8508be760e001ebfb1557697efc838552a46c 100644
--- a/docs/mindspore/source_zh_cn/orange_pi/model_infer.md
+++ b/docs/mindspore/source_zh_cn/orange_pi/model_infer.md
@@ -1,7 +1,71 @@
-# 模型推理
+# 模型在线推理
 
 [![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/mindspore/source_zh_cn/orange_pi/model_infer.md)
 
-## 在线推理
+本章节将介绍如何在OrangePi AIpro（下称：香橙派开发板）下载昇思MindSpore在线推理案例，并启动Jupyter Lab界面执行推理。
 
-## 离线推理
+## 1. 下载案例
+
+步骤1 下载案例代码
+
+```bash
+# 打开开发板的一个终端，运行如下命令
+cd samples/notebooks/
+git clone https://github.com/mindspore-courses/orange-pi-mindspore.git
+```
+
+步骤2 进入案例目录
+
+下载的代码包在香橙派开发板的如下目录中：/home/HwHiAiUser/samples/notebooks
+
+项目目录如下：
+
+```bash
+/home/HwHiAiUser/samples/notebooks/orange-pi-mindspore/tutorial/
+01-	quick start
+02-	ResNet50
+03-	ViT
+04-	FCN
+05-	Shufflenet
+06-	SSD
+07-	RNN
+08-	LSTM+CRF
+09-	GAN
+10-	DCGAN
+11-	Pix2Pix
+12-	Diffusion
+13- ResNet50_transfer
+```
+
+## 2. 推理执行
+
+步骤1 启动Jupyter Lab界面
+
+```bash
+cd /home/HwHiAiUser/samples/notebooks/
+./start_notebook.sh	
+```
+
+在执行该脚本后，终端会出现如下打印信息，在打印信息中会有登录Jupyter Lab的网址链接。
+
+![model-infer1](./images/model_infer1.png)
+
+然后打开火狐浏览器
+
+![model-infer2](./images/model_infer2.png)
+
+再在浏览器中输入上面看到的网址链接，就可以登录Jupyter Lab软件了。
+
+![model-infer3](./images/model_infer3.png)
+
+步骤2 在Jupyter Lab界面双击下图所示的案例目录，此处以“04-FCN”为例，即可进入到该案例的目录中。
+
+![model-infer4](./images/model_infer4.png)
+
+步骤3 在该目录下有运行该示例的所有资源，其中mindspore_fcn8s.ipynb是在Jupyter Lab中运行该样例的文件，双击打开mindspore_fcn8s.ipynb，在右侧窗口中会显示mindspore_fcn8s.ipynb文件中的内容，如下图所示：
+
+![model-infer5](./images/model_infer5.png)
+
+步骤4 单击⏩按钮运行样例，在弹出的对话框中单击“Restart”按钮，此时该样例开始运行。
+
+![model-infer6](./images/model_infer6.png)
\ No newline at end of file
diff --git a/docs/mindspore/source_zh_cn/orange_pi/overview.md b/docs/mindspore/source_zh_cn/orange_pi/overview.md
index ad19dbe45ff41db7217ad40dfa628b97b51437cb..f3933c9cbaf257b083cddc6e71849a6c3134396b 100644
--- a/docs/mindspore/source_zh_cn/orange_pi/overview.md
+++ b/docs/mindspore/source_zh_cn/orange_pi/overview.md
@@ -1,3 +1,11 @@
 # 了解香橙派
 
 [![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/mindspore/source_zh_cn/orange_pi/overview.md)
+
+OrangePi AIpro（香橙派 AIpro）采用昇腾AI技术路线，具体为4核64位处理器+AI处理器，集成图形处理器，目前支持8-12TOPS和20TOPS AI算力，其中8-12TOPS算力开发板拥有12GB/24GB LPDDR4X，20TOPS算力开发板拥8GB/16GB LPDDR4X。两种算力的开发板均可以外接32GB/64GB/256GB eMMC模块，支持双4K高清输出。 
+
+日前，昇思MindSpore开源社区与香橙派通过紧密合作攻关，实现OrangePi AIpro开发板的系统镜像预置昇思MindSpore AI框架，并在后续版本迭代中持续演进，当前已支持MindSpore官网教程涵盖的全部网络模型。OrangePi AIpro开发板向开发者提供的官方系统镜像有openEuler版本预ubuntu版本，两个镜像版本均已预置昇思MindSpore，便于用户体验软硬协同优化后带来的高效开发体验。同时，欢迎开发者自定义配置MindSpore和CANN运行环境。 
+
+接下来的教程将演示如何基于OrangePi AIpro进行自定义环境搭建，如何在OrangePi AIpro启动Jupyter Lab，并以手写数字识别为例，介绍OrangePi AIpro上基于MindSpore进行在线推理需要完成的操作。
+
+> 注意：以下操作基于OrangePi AIpro 8-12TOPS 16G开发板，20TOPS开发板操作方式相同。
\ No newline at end of file
diff --git a/docs/mindspore/source_zh_cn/orange_pi/quick_start.ipynb b/docs/mindspore/source_zh_cn/orange_pi/quick_start.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0d41e961b06acaa51fcfa3df14273a0702461757
--- /dev/null
+++ b/docs/mindspore/source_zh_cn/orange_pi/quick_start.ipynb
@@ -0,0 +1,582 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 快速入门\n",
+    "\n",
+    "因开发者可能会在OrangePi AIpro（下称：香橙派开发板）进行自定义模型和案例开发，本章节通过基于MindSpore的手写数字识别案例，说明香橙派开发板中的开发注意事项。\n",
+    "\n",
+    "> 由于Mindspore2.3及之前的版本暂未支持内存动态按需申请，以及CANN缺少对应的动态算子，所以执行案例之前需要通过set_context设置运行环境。后续会随着版本不断更新解决以上问题，从而在不需要进行环境配置的前提下可直接执行案例。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    }
+   ],
+   "source": [
+    "from mindspore import nn\n",
+    "from mindspore.dataset import vision, transforms\n",
+    "from mindspore.dataset import MnistDataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 设置运行环境\n",
+    "\n",
+    " max_device_memory=\"2GB\" : 设置设备可用的最大内存为2GB。\n",
+    "\n",
+    " mode=mindspore.GRAPH_MODE : 表示在GRAPH_MODE模式中运行。\n",
+    "\n",
+    " device_target=\"Ascend\" : 表示待运行的目标设备为Ascend。\n",
+    "\n",
+    " jit_config={\"jit_level\":\"O2\"} : 编译优化级别开启极致性能优化，使用下沉的执行方式。\n",
+    "\n",
+    " scend_config={\"precision_mode\":\"allow_mix_precision\"} : 自动混合精度，自动将部分算子的精度降低到float16或bfloat16。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mindspore\n",
+    "mindspore.set_context(max_device_memory=\"2GB\", mode=mindspore.GRAPH_MODE, device_target=\"Ascend\",  jit_config={\"jit_level\":\"O2\"}, ascend_config={\"precision_mode\":\"allow_mix_precision\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 处理数据集\n",
+    "\n",
+    "MindSpore提供基于Pipeline的[数据引擎](https://www.mindspore.cn/docs/zh-CN/r2.3.0/design/data_engine.html)，通过[数据集（Dataset）](https://www.mindspore.cn/tutorials/zh-CN/r2.3.0/beginner/dataset.html)和[数据变换（Transforms）](https://www.mindspore.cn/tutorials/zh-CN/r2.3.0/beginner/transforms.html)实现高效的数据预处理。在本案例中，我们使用Mnist数据集，自动下载完成后，使用`mindspore.dataset`提供的数据变换进行预处理。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip (10.3 MB)\n",
+      "\n",
+      "file_sizes: 100%|██████████████████████████| 10.8M/10.8M [00:01<00:00, 7.63MB/s]\n",
+      "Extracting zip file...\n",
+      "Successfully downloaded / unzipped to ./\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download data from open datasets\n",
+    "from download import download\n",
+    "\n",
+    "url = \"https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/\" \\\n",
+    "      \"notebook/datasets/MNIST_Data.zip\"\n",
+    "path = download(url, \"./\", kind=\"zip\", replace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "MNIST数据集目录结构如下：\n",
+    "\n",
+    "```text\n",
+    "MNIST_Data\n",
+    "└── train\n",
+    "    ├── train-images-idx3-ubyte (60000个训练图片)\n",
+    "    ├── train-labels-idx1-ubyte (60000个训练标签)\n",
+    "└── test\n",
+    "    ├── t10k-images-idx3-ubyte (10000个测试图片)\n",
+    "    ├── t10k-labels-idx1-ubyte (10000个测试标签)\n",
+    "\n",
+    "```\n",
+    "\n",
+    "数据下载完成后，获得数据集对象。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dataset = MnistDataset('MNIST_Data/train')\n",
+    "test_dataset = MnistDataset('MNIST_Data/test')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "打印数据集中包含的数据列名，用于dataset的预处理。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['image', 'label']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(train_dataset.get_col_names())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "MindSpore的dataset使用数据处理流水线（Data Processing Pipeline），需指定map、batch、shuffle等操作。这里我们使用map对图像数据及标签进行变换处理，将输入的图像缩放为1/255，根据均值0.1307和标准差值0.3081进行归一化处理，然后将处理好的数据集打包为大小为64的batch。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def datapipe(dataset, batch_size):\n",
+    "    image_transforms = [\n",
+    "        vision.Rescale(1.0 / 255.0, 0),\n",
+    "        vision.Normalize(mean=(0.1307,), std=(0.3081,)),\n",
+    "        vision.HWC2CHW()\n",
+    "    ]\n",
+    "    label_transform = transforms.TypeCast(mindspore.int32)\n",
+    "\n",
+    "    dataset = dataset.map(image_transforms, 'image')\n",
+    "    dataset = dataset.map(label_transform, 'label')\n",
+    "    dataset = dataset.batch(batch_size)\n",
+    "    return dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Map vision transforms and batch dataset\n",
+    "train_dataset = datapipe(train_dataset, 64)\n",
+    "test_dataset = datapipe(test_dataset, 64)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "可使用[create_tuple_iterator](https://www.mindspore.cn/docs/zh-CN/r2.3.0/api_python/dataset/dataset_method/iterator/mindspore.dataset.Dataset.create_tuple_iterator.html) 或[create_dict_iterator](https://www.mindspore.cn/docs/zh-CN/r2.3.0/api_python/dataset/dataset_method/iterator/mindspore.dataset.Dataset.create_dict_iterator.html)对数据集进行迭代访问，查看数据和标签的shape和datatype。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of image [N, C, H, W]: (64, 1, 28, 28) Float32\n",
+      "Shape of label: (64,) Int32\n"
+     ]
+    }
+   ],
+   "source": [
+    "for image, label in test_dataset.create_tuple_iterator():\n",
+    "    print(f\"Shape of image [N, C, H, W]: {image.shape} {image.dtype}\")\n",
+    "    print(f\"Shape of label: {label.shape} {label.dtype}\")\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of image [N, C, H, W]: (64, 1, 28, 28) Float32\n",
+      "Shape of label: (64,) Int32\n"
+     ]
+    }
+   ],
+   "source": [
+    "for data in test_dataset.create_dict_iterator():\n",
+    "    print(f\"Shape of image [N, C, H, W]: {data['image'].shape} {data['image'].dtype}\")\n",
+    "    print(f\"Shape of label: {data['label'].shape} {data['label'].dtype}\")\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Network<\n",
+      "  (flatten): Flatten<>\n",
+      "  (dense_relu_sequential): SequentialCell<\n",
+      "    (0): Dense<input_channels=784, output_channels=512, has_bias=True>\n",
+      "    (1): ReLU<>\n",
+      "    (2): Dense<input_channels=512, output_channels=512, has_bias=True>\n",
+      "    (3): ReLU<>\n",
+      "    (4): Dense<input_channels=512, output_channels=10, has_bias=True>\n",
+      "    >\n",
+      "  >\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define model\n",
+    "class Network(nn.Cell):\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.flatten = nn.Flatten()\n",
+    "        self.dense_relu_sequential = nn.SequentialCell(\n",
+    "            nn.Dense(28*28, 512),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Dense(512, 512),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Dense(512, 10)\n",
+    "        )\n",
+    "\n",
+    "    def construct(self, x):\n",
+    "        x = self.flatten(x)\n",
+    "        logits = self.dense_relu_sequential(x)\n",
+    "        return logits\n",
+    "\n",
+    "model = Network()\n",
+    "print(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 模型训练"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在模型训练中，一个完整的训练过程（step）需要实现以下三步：\n",
+    "\n",
+    "1. **正向计算**：模型预测结果（logits），并与正确标签（label）求预测损失（loss）。\n",
+    "2. **反向传播**：利用自动微分机制，自动求模型参数（parameters）对于loss的梯度（gradients）。\n",
+    "3. **参数优化**：将梯度更新到参数上。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "MindSpore使用函数式自动微分机制，因此针对上述步骤需要实现：\n",
+    "\n",
+    "1. 定义正向计算函数。\n",
+    "2. 使用[value_and_grad](https://www.mindspore.cn/docs/zh-CN/r2.3.0/api_python/mindspore/mindspore.value_and_grad.html)通过函数变换获得梯度计算函数。\n",
+    "3. 定义训练函数，使用[set_train](https://www.mindspore.cn/docs/zh-CN/r2.3.0/api_python/nn/mindspore.nn.Cell.html#mindspore.nn.Cell.set_train)设置为训练模式，执行正向计算、反向传播和参数优化。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Instantiate loss function and optimizer\n",
+    "loss_fn = nn.CrossEntropyLoss()\n",
+    "optimizer = nn.SGD(model.trainable_params(), 1e-2)\n",
+    "\n",
+    "# 1. Define forward function\n",
+    "def forward_fn(data, label):\n",
+    "    logits = model(data)\n",
+    "    loss = loss_fn(logits, label)\n",
+    "    return loss, logits\n",
+    "\n",
+    "# 2. Get gradient function\n",
+    "grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)\n",
+    "\n",
+    "# 3. Define function of one-step training\n",
+    "def train_step(data, label):\n",
+    "    (loss, _), grads = grad_fn(data, label)\n",
+    "    optimizer(grads)\n",
+    "    return loss\n",
+    "\n",
+    "def train(model, dataset):\n",
+    "    size = dataset.get_dataset_size()\n",
+    "    model.set_train()\n",
+    "    for batch, (data, label) in enumerate(dataset.create_tuple_iterator()):\n",
+    "        loss = train_step(data, label)\n",
+    "\n",
+    "        if batch % 100 == 0:\n",
+    "            loss, current = loss.asnumpy(), batch\n",
+    "            print(f\"loss: {loss:>7f}  [{current:>3d}/{size:>3d}]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "除训练外，我们定义测试函数，用来评估模型的性能。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test(model, dataset, loss_fn):\n",
+    "    num_batches = dataset.get_dataset_size()\n",
+    "    model.set_train(False)\n",
+    "    total, test_loss, correct = 0, 0, 0\n",
+    "    for data, label in dataset.create_tuple_iterator():\n",
+    "        pred = model(data)\n",
+    "        total += len(data)\n",
+    "        test_loss += loss_fn(pred, label).asnumpy()\n",
+    "        correct += (pred.argmax(1) == label).asnumpy().sum()\n",
+    "    test_loss /= num_batches\n",
+    "    correct /= total\n",
+    "    print(f\"Test: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "训练过程需多次迭代数据集，一次完整的迭代称为一轮（epoch）。在每一轮，遍历训练集进行训练，结束后使用测试集进行预测。打印每一轮的loss值和预测准确率（Accuracy），可以看到loss在不断下降，Accuracy在不断提高。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1\n",
+      "-------------------------------\n",
+      "loss: 2.302898  [  0/938]\n",
+      "loss: 1.729961  [100/938]\n",
+      "loss: 0.865714  [200/938]\n",
+      "loss: 0.782822  [300/938]\n",
+      "loss: 0.389282  [400/938]\n",
+      "loss: 0.293149  [500/938]\n",
+      "loss: 0.474819  [600/938]\n",
+      "loss: 0.242542  [700/938]\n",
+      "loss: 0.542277  [800/938]\n",
+      "loss: 0.342929  [900/938]\n",
+      "Test: \n",
+      " Accuracy: 90.7%, Avg loss: 0.321954 \n",
+      "\n",
+      "Epoch 2\n",
+      "-------------------------------\n",
+      "loss: 0.249492  [  0/938]\n",
+      "loss: 0.347967  [100/938]\n",
+      "loss: 0.220382  [200/938]\n",
+      "loss: 0.308149  [300/938]\n",
+      "loss: 0.353044  [400/938]\n",
+      "loss: 0.392116  [500/938]\n",
+      "loss: 0.396438  [600/938]\n",
+      "loss: 0.231412  [700/938]\n",
+      "loss: 0.194819  [800/938]\n",
+      "loss: 0.228290  [900/938]\n",
+      "Test: \n",
+      " Accuracy: 93.0%, Avg loss: 0.249993 \n",
+      "\n",
+      "Epoch 3\n",
+      "-------------------------------\n",
+      "loss: 0.343888  [  0/938]\n",
+      "loss: 0.307786  [100/938]\n",
+      "loss: 0.153425  [200/938]\n",
+      "loss: 0.254917  [300/938]\n",
+      "loss: 0.198072  [400/938]\n",
+      "loss: 0.108963  [500/938]\n",
+      "loss: 0.202033  [600/938]\n",
+      "loss: 0.340418  [700/938]\n",
+      "loss: 0.144911  [800/938]\n",
+      "loss: 0.175447  [900/938]\n",
+      "Test: \n",
+      " Accuracy: 93.7%, Avg loss: 0.212180 \n",
+      "\n",
+      "Done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "epochs = 3\n",
+    "for t in range(epochs):\n",
+    "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
+    "    train(model, train_dataset)\n",
+    "    test(model, test_dataset, loss_fn)\n",
+    "print(\"Done!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 保存模型\n",
+    "\n",
+    "模型训练完成后，需要将其参数进行保存。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved Model to model.ckpt\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save checkpoint\n",
+    "mindspore.save_checkpoint(model, \"model.ckpt\")\n",
+    "print(\"Saved Model to model.ckpt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 加载模型"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "加载保存的权重分为两步：\n",
+    "\n",
+    "1. 重新实例化模型对象，构造模型。\n",
+    "2. 加载模型参数，并将其加载至模型上。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Instantiate a random initialized model\n",
+    "model = Network()\n",
+    "# Load checkpoint and load parameter to model\n",
+    "param_dict = mindspore.load_checkpoint(\"model.ckpt\")\n",
+    "param_not_load, _ = mindspore.load_param_into_net(model, param_dict)\n",
+    "print(param_not_load)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> `param_not_load`是未被加载的参数列表，为空时代表所有参数均加载成功。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "加载后的模型可以直接用于预测推理。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted: \"[1 2 0 4 6 4 9 0 2 2]\", Actual: \"[1 2 0 4 6 9 9 0 2 2]\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.set_train(False)\n",
+    "for data, label in test_dataset:\n",
+    "    pred = model(data)\n",
+    "    predicted = pred.argmax(1)\n",
+    "    print(f'Predicted: \"{predicted[:10]}\", Actual: \"{label[:10]}\"')\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 更多基于昇思MindSpore的香橙派开发板案例详见：[GitHub链接](https://github.com/mindspore-courses/orange-pi-mindspore/tree/master/tutorial)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "MindSpore",
+   "language": "python",
+   "name": "mindspore"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8c9da313289c39257cb28b126d2dadd33153d4da4d524f730c81a4aaccbd2ca7"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/mindspore/source_zh_cn/orange_pi/quick_start.md b/docs/mindspore/source_zh_cn/orange_pi/quick_start.md
deleted file mode 100644
index a5a172d95a26a69e228960758f7cda68f3f9b32e..0000000000000000000000000000000000000000
--- a/docs/mindspore/source_zh_cn/orange_pi/quick_start.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# 快速入门
-
-[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/mindspore/source_zh_cn/orange_pi/quick_start.md)
-
-## 安装
-
-## 启动
-
-## 运行