diff --git a/install/mindspore_gpu_install.md b/install/mindspore_gpu_install.md
index 7bdf1afc63243be6f107e7fb7e0115e9f02070a4..e0996c1b3a0ef75db4ba99ec5fee334edf632d29 100644
--- a/install/mindspore_gpu_install.md
+++ b/install/mindspore_gpu_install.md
@@ -13,6 +13,7 @@
         - [通过可执行文件安装](#通过可执行文件安装)
         - [从源码编译安装](#从源码编译安装)
     - [安装验证](#安装验证)
+- [安装MindInsight](#安装mindinsight)
 - [安装MindArmour](#安装mindarmour)
 
 <!-- /TOC -->
@@ -112,6 +113,76 @@
       [ 2.  2.  2.  2.]]]
     ```
 
+# 安装MindInsight
+
+当您需要查看训练过程中的标量、图像、计算图以及模型超参等信息时，可以选装MindInsight。
+
+## 环境要求
+
+### 系统要求和软件依赖
+
+| 版本号 | 操作系统 | 可执行文件安装依赖 | 源码编译安装依赖 |
+| ---- | :--- | :--- | :--- |
+| MindInsight master | - Ubuntu 16.04（及以上） x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore master <br> - 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindinsight/blob/master/requirements.txt) | **编译依赖：**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [node.js](https://nodejs.org/en/download/) >= 10.19.0 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [pybind11](https://pypi.org/project/pybind11/) >= 2.4.3 <br> **安装依赖：**<br> 与可执行文件安装依赖相同 |
+
+- 在联网状态下，安装whl包时会自动下载requirements.txt中的依赖项，其余情况需自行安装。
+
+## 安装指南
+
+### 通过可执行文件安装
+
+1. 从[MindSpore网站下载地址](https://www.mindspore.cn/versions)下载whl包，建议先进行SHA-256完整性校验，执行如下命令安装MindInsight。
+
+    ```bash
+    pip install mindinsight-{version}-cp37-cp37m-linux_{arch}.whl
+    ```
+
+2. 执行如下命令，如果提示`web address: http://127.0.0.1:8080`，则说明安装成功。
+
+    ```bash
+    mindinsight start
+    ```
+
+### 从源码编译安装
+
+1. 从代码仓下载源码。
+
+    ```bash
+    git clone https://gitee.com/mindspore/mindinsight.git
+    ```
+
+2. 可选择以下任意一种安装方式：
+
+   (1) 进入源码的根目录，执行安装命令。
+
+      ```bash
+      cd mindinsight
+      pip install -r requirements.txt
+      python setup.py install
+      ```
+
+   (2) 构建whl包进行安装。
+
+      进入源码的build目录，执行MindInsight编译脚本。
+
+      ```bash
+      cd mindinsight/build
+      bash build.sh
+      ```
+
+      进入源码的output目录，即可查看生成的MindInsight安装包，执行安装命令。
+
+      ```bash
+      cd mindinsight/output
+      pip install mindinsight-{version}-cp37-cp37m-linux_{arch}.whl
+      ```
+
+3. 执行如下命令，如果提示`web address: http://127.0.0.1:8080`，则说明安装成功。
+
+    ```bash
+    mindinsight start
+    ```
+
 # 安装MindArmour
 
 当您进行AI模型安全研究或想要增强AI应用模型的防护能力时，可以选装MindArmour。
diff --git a/install/mindspore_gpu_install_en.md b/install/mindspore_gpu_install_en.md
index 8bee4177c55fead9eb9f9169e946a1f10da83d1b..f2e111607f2957b7a6925189d9e1d3177ee9e96f 100644
--- a/install/mindspore_gpu_install_en.md
+++ b/install/mindspore_gpu_install_en.md
@@ -13,6 +13,7 @@ This document describes how to quickly install MindSpore on a NVIDIA GPU environ
         - [Installing Using Executable Files](#installing-using-executable-files)
         - [Installing Using the Source Code](#installing-using-the-source-code)
     - [Installation Verification](#installation-verification)
+- [Installing MindInsight](#installing-mindinsight)
 - [Installing MindArmour](#installing-mindarmour)
 
 <!-- /TOC -->
@@ -112,6 +113,76 @@ This document describes how to quickly install MindSpore on a NVIDIA GPU environ
       [ 2.  2.  2.  2.]]]
     ```
 
+# Installing MindInsight
+
+If you need to analyze information such as model scalars, graphs, and model traceback, you can install MindInsight.
+
+## Environment Requirements
+
+### System Requirements and Software Dependencies
+
+| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
+| ---- | :--- | :--- | :--- |
+| MindInsight master | - Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore master <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindinsight/blob/master/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [node.js](https://nodejs.org/en/download/) >= 10.19.0 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [pybind11](https://pypi.org/project/pybind11/) >= 2.4.3 <br> **Installation dependencies:**<br> same as the executable file installation dependencies. |
+
+- When the network is connected, dependency items in the requirements.txt file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items.
+
+## Installation Guide
+
+### Installing Using Executable Files
+
+1. Download the .whl package from the [MindSpore website](https://www.mindspore.cn/versions/en). It is recommended to perform SHA-256 integrity verification first  and run the following command to install MindInsight:
+
+    ```bash
+    pip install mindinsight-{version}-cp37-cp37m-linux_{arch}.whl
+    ```
+
+2. Run the following command. If `web address: http://127.0.0.1:8080` is displayed, the installation is successful.
+
+    ```bash
+    mindinsight start
+    ```
+
+### Installing Using the Source Code
+
+1. Download the source code from the code repository.
+
+    ```bash
+    git clone https://gitee.com/mindspore/mindinsight.git
+    ```
+
+2. Install MindInsight by using either of the following installation methods:
+
+   (1) Access the root directory of the source code and run the following installation command:
+
+      ```bash
+      cd mindinsight
+      pip install -r requirements.txt
+      python setup.py install
+      ```
+
+   (2) Create a .whl package to install MindInsight.
+
+      Access the build directory of the source code and run the MindInsight compilation script.
+
+      ```bash
+      cd mindinsight/build
+      bash build.sh
+      ```
+
+      Access the output directory of the source code, where the generated MindInsight installation package is stored, and run the installation command.
+
+      ```bash
+      cd mindinsight/output
+      pip install mindinsight-{version}-cp37-cp37m-linux_{arch}.whl
+      ```
+
+3. Run the following command. If `web address: http://127.0.0.1:8080` is displayed, the installation is successful.
+
+    ```bash
+    mindinsight start
+    ```
+
 # Installing MindArmour
 
 If you need to conduct AI model security research or enhance the security of the model in you applications, you can install MindArmour.
diff --git a/tutorials/source_en/advanced_use/visualization_tutorials.md b/tutorials/source_en/advanced_use/visualization_tutorials.md
index 8547409b5f628c748cd65cebe63cc82e36409672..31048b04c3e211fc7b63b12b63774ff0315d366e 100644
--- a/tutorials/source_en/advanced_use/visualization_tutorials.md
+++ b/tutorials/source_en/advanced_use/visualization_tutorials.md
@@ -6,8 +6,6 @@
     - [Overview](#overview)
     - [Operation Process](#operation-process)
     - [Preparing the Training Script](#preparing-the-training-script)
-        - [Basic Script Editing](#basic-script-editing)
-        - [Recording the Computational Graph After Operator Fusion](#recording-the-computational-graph-after-operator-fusion)
     - [MindInsight Commands](#mindinsight-commands)
     - [Visualization Components](#visualization-components)
         - [Computational Graph Visualization](#computational-graph-visualization)
@@ -35,9 +33,6 @@ Currently, MindSpore uses the `Callback` mechanism to save scalars, images, comp
 Scalar and image data is recorded by using the `Summary` operator. A computational graph is saved to the summary log file by using `SummaryRecord` after network compilation is complete.
 Model parameters are saved to the summary log file by using `TrainLineage` or `EvalLineage`.
 
-
-### Basic Script Editing
-
 Step 1: Call the `Summary` operator in the `construct` function of the derived class that inherits `nn.Cell` to collect image or scalar data.
 
 For example, when a network is defined, image data is recorded in `construct` of the network. When the loss function is defined, the loss value is recorded in `construct` of the loss function.
@@ -160,11 +155,11 @@ def test_summary():
     summary_writer.close()
 ```
 
-### Recording the Computational Graph After Operator Fusion
-
-After completing the script by referring to "Basic Writing", use the `save_graphs` option of `context` to record the computational graph after operator fusion.
+After completing the script, use the `save_graphs` option of `context` to record the computational graph after operator fusion.
 `ms_output_after_hwopt.pb` is the computational graph after operator fusion.
 
+> Currently MindSpore supports recording computational graph after operator fusion for Ascend 910 AI processor only.
+
 ## MindInsight Commands
 
 ### View the command help information.
@@ -232,7 +227,7 @@ gunicorn  <PID>  <USER>  <FD>  <TYPE>  <DEVICE>  <SIZE/OFF>  <NODE>  <WORKSPACE>
 ## Visualization Components
 ### Computational Graph Visualization
 
-Computational graph visualization is used to display the graph structure, data flow direction, and control flow direction of a computational graph.
+Computational graph visualization is used to display the graph structure, data flow direction, and control flow direction of a computational graph. It supports visualization of summary log files and pb files generated by `save_graphs` configuration in `context`.
 
 ![graph.png](./images/graph.png)
 
diff --git a/tutorials/source_zh_cn/advanced_use/visualization_tutorials.md b/tutorials/source_zh_cn/advanced_use/visualization_tutorials.md
index 70b54d2b84b8be099403deb84e09a57142aec426..621abd6bd666ba013df21e9a9a88e1f378e85047 100644
--- a/tutorials/source_zh_cn/advanced_use/visualization_tutorials.md
+++ b/tutorials/source_zh_cn/advanced_use/visualization_tutorials.md
@@ -6,8 +6,6 @@
     - [概述](#概述)
     - [操作流程](#操作流程)
     - [准备训练脚本](#准备训练脚本)
-        - [基础写法](#基础写法)
-        - [记录算子融合后的计算图](#记录算子融合后的计算图)
     - [MindInsight相关命令](#mindinsight相关命令)
         - [查看命令帮助信息](#查看命令帮助信息)
         - [查看版本信息](#查看版本信息)
@@ -40,9 +38,6 @@
 其中标量、图像是通过Summary算子实现记录数据，计算图是在网络编译完成后，通过 `SummaryRecord` 将其保存到summary日志文件中，
 模型参数是通过 `TrainLineage` 或 `EvalLineage` 保存到summary日志文件中。
 
-
-### 基础写法
-
 步骤一：在继承 `nn.Cell` 的衍生类的 `construct` 函数中调用Summary算子来采集图像或标量数据。
 
 比如，在定义网络时，在网络的 `construct` 中记录图像数据；在定义损失函数时，在损失函数的 `construct`中记录损失值。
@@ -165,11 +160,11 @@ def test_summary():
     summary_writer.close()
 ```
 
-### 记录算子融合后的计算图
-
-参照“基础写法”完成脚本后，可以通过`context`的`save_graphs`选项配置记录算子融合后的计算图。
+完成脚本后，可以通过`context`的`save_graphs`选项配置记录算子融合后的计算图。
 其中`ms_output_after_hwopt.pb`为算子融合后的计算图。
 
+> 目前MindSpore仅支持在Ascend 910 AI处理器上导出算子融合后的计算图。
+
 ## MindInsight相关命令
 
 ### 查看命令帮助信息
@@ -237,7 +232,7 @@ gunicorn  <PID>  <USER>  <FD>  <TYPE>  <DEVICE>  <SIZE/OFF>  <NODE>  <WORKSPACE>
 ## 可视化组件
 ### 计算图可视化
 
-计算图可视化用于展示计算图的图结构，数据流以及控制流的走向。
+计算图可视化用于展示计算图的图结构，数据流以及控制流的走向，支持展示summary日志文件与通过`context`的`save_graphs`参数导出的`pb`文件。
 
 ![graph.png](./images/graph.png)