diff --git a/install/mindspore_cpu_install.md b/install/mindspore_cpu_install.md
index 7cacc407ef36cf42dae674d1450f2c851fa3fc5c..cb00d08becb3640762af13fa0de40a9b4748bd50 100644
--- a/install/mindspore_cpu_install.md
+++ b/install/mindspore_cpu_install.md
@@ -21,7 +21,7 @@
| 版本号 | 操作系统 | 可执行文件安装依赖 | 源码编译安装依赖 |
| ---- | :--- | :--- | :--- |
-| MindSpore master | Ubuntu 16.04（及以上） x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
**安装依赖：**
与可执行文件安装依赖相同 |
+| MindSpore master | Ubuntu 16.04（及以上） x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.2/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
**安装依赖：**
与可执行文件安装依赖相同 |
- Ubuntu版本为18.04时，GCC 7.3.0可以直接通过apt命令安装。
- 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。
@@ -62,7 +62,7 @@
1. 从代码仓下载源码。
```bash
- git clone https://gitee.com/mindspore/mindspore.git
+ git clone https://gitee.com/mindspore/mindspore.git -b r0.2
```
2. 在源码根目录下执行如下命令编译MindSpore。
@@ -122,7 +122,7 @@
1. 从代码仓下载源码。
```bash
- git clone https://gitee.com/mindspore/mindarmour.git
+ git clone https://gitee.com/mindspore/mindarmour.git -b r0.2
```
2. 在源码根目录下，执行如下命令编译并安装MindArmour。
diff --git a/install/mindspore_cpu_install_en.md b/install/mindspore_cpu_install_en.md
index f8a3c44643e67466ce5f394a34848ac7968dca4f..63bc58cee989d5e2d82c4efc9c65bdd52abeb0f7 100644
--- a/install/mindspore_cpu_install_en.md
+++ b/install/mindspore_cpu_install_en.md
@@ -21,7 +21,7 @@ This document describes how to quickly install MindSpore on a Ubuntu system with
| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
| ---- | :--- | :--- | :--- |
-| MindSpore master | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
same as the executable file installation dependencies. |
+| MindSpore master | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.2/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
same as the executable file installation dependencies. |
- When Ubuntu version is 18.04, GCC 7.3.0 can be installed by using apt command.
- 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.
@@ -62,7 +62,7 @@ This document describes how to quickly install MindSpore on a Ubuntu system with
1. Download the source code from the code repository.
```bash
- git clone https://gitee.com/mindspore/mindspore.git
+ git clone https://gitee.com/mindspore/mindspore.git -b r0.2
```
2. Run the following command in the root directory of the source code to compile MindSpore:
@@ -122,7 +122,7 @@ If you need to conduct AI model security research or enhance the security of the
1. Download the source code from the code repository.
```bash
- git clone https://gitee.com/mindspore/mindarmour.git
+ git clone https://gitee.com/mindspore/mindarmour.git -b r0.2
```
2. Run the following command in the root directory of the source code to compile and install MindArmour:
diff --git a/install/mindspore_cpu_win_install.md b/install/mindspore_cpu_win_install.md
index 4f58546b958d8c452d7f161cf0d945f3e8098ab0..7c6a9ea17ee5c64302ad0879d020808013c13101 100644
--- a/install/mindspore_cpu_win_install.md
+++ b/install/mindspore_cpu_win_install.md
@@ -20,7 +20,7 @@
| 版本号 | 操作系统 | 可执行文件安装依赖 | 源码编译安装依赖 |
| ---- | :--- | :--- | :--- |
-| MindSpore master | Windows 10 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- [MinGW-W64 GCC-7.3.0](https://sourceforge.net/projects/mingw-w64/files/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/7.3.0/threads-posix/seh/x86_64-7.3.0-release-posix-seh-rt_v5-rev0.7z) x86_64-posix-seh
- [ActivePerl](http://downloads.activestate.com/ActivePerl/releases/5.24.3.2404/ActivePerl-5.24.3.2404-MSWin32-x64-404865.exe) 5.24.3.2404
- [CMake](https://cmake.org/download/) 3.14.1
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
**安装依赖：**
与可执行文件安装依赖相同 |
+| MindSpore master | Windows 10 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.2/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- [MinGW-W64 GCC-7.3.0](https://sourceforge.net/projects/mingw-w64/files/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/7.3.0/threads-posix/seh/x86_64-7.3.0-release-posix-seh-rt_v5-rev0.7z) x86_64-posix-seh
- [ActivePerl](http://downloads.activestate.com/ActivePerl/releases/5.24.3.2404/ActivePerl-5.24.3.2404-MSWin32-x64-404865.exe) 5.24.3.2404
- [CMake](https://cmake.org/download/) 3.14.1
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
**安装依赖：**
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。
@@ -62,7 +62,7 @@
1. 从代码仓下载源码。
```bash
- git clone https://gitee.com/mindspore/mindspore.git
+ git clone https://gitee.com/mindspore/mindspore.git -b r0.2
```
2. 在源码根目录下执行如下命令编译MindSpore。
diff --git a/install/mindspore_cpu_win_install_en.md b/install/mindspore_cpu_win_install_en.md
index 81aef71b70a9a04ff7793ec405c9c23d80446581..183d9c6719d3f059219f010568b43dbd0d3cfaa3 100644
--- a/install/mindspore_cpu_win_install_en.md
+++ b/install/mindspore_cpu_win_install_en.md
@@ -20,7 +20,7 @@ This document describes how to quickly install MindSpore on a Windows system wit
| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
| ---- | :--- | :--- | :--- |
-| MindSpore master | Windows 10 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [MinGW-W64 GCC-7.3.0](https://sourceforge.net/projects/mingw-w64/files/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/7.3.0/threads-posix/seh/x86_64-7.3.0-release-posix-seh-rt_v5-rev0.7z) x86_64-posix-seh
- [ActivePerl](http://downloads.activestate.com/ActivePerl/releases/5.24.3.2404/ActivePerl-5.24.3.2404-MSWin32-x64-404865.exe) 5.24.3.2404
- [CMake](https://cmake.org/download/) 3.14.1
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
**Installation dependencies:**
same as the executable file installation dependencies. |
+| MindSpore master | Windows 10 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.2/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [MinGW-W64 GCC-7.3.0](https://sourceforge.net/projects/mingw-w64/files/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/7.3.0/threads-posix/seh/x86_64-7.3.0-release-posix-seh-rt_v5-rev0.7z) x86_64-posix-seh
- [ActivePerl](http://downloads.activestate.com/ActivePerl/releases/5.24.3.2404/ActivePerl-5.24.3.2404-MSWin32-x64-404865.exe) 5.24.3.2404
- [CMake](https://cmake.org/download/) 3.14.1
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
**Installation dependencies:**
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.
@@ -62,7 +62,7 @@ This document describes how to quickly install MindSpore on a Windows system wit
1. Download the source code from the code repository.
```bash
- git clone https://gitee.com/mindspore/mindspore.git
+ git clone https://gitee.com/mindspore/mindspore.git -b r0.2
```
2. Run the following command in the root directory of the source code to compile MindSpore:
diff --git a/install/mindspore_d_install.md b/install/mindspore_d_install.md
index eaccf67a89635f74274888b55bce95c8f7c78ea1..ba8ca88e8ab09e41718a279e9289ca4244a56fe7 100644
--- a/install/mindspore_d_install.md
+++ b/install/mindspore_d_install.md
@@ -33,7 +33,7 @@
| 版本号 | 操作系统 | 可执行文件安装依赖 | 源码编译安装依赖 |
| ---- | :--- | :--- | :--- |
-| MindSpore master | - Ubuntu 16.04（及以上） x86_64
- EulerOS 2.8 arrch64
- EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI处理器配套软件包（对应版本Atlas T 1.1.T107）
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI处理器配套软件包（对应版本Atlas T 1.1.T107）
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
**安装依赖：**
与可执行文件安装依赖相同 |
+| MindSpore master | - Ubuntu 16.04（及以上） x86_64
- EulerOS 2.8 arrch64
- EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI处理器配套软件包（对应版本Atlas T 1.1.T107）
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.2/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI处理器配套软件包（对应版本Atlas T 1.1.T107）
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
**安装依赖：**
与可执行文件安装依赖相同 |
- 确认当前用户有权限访问Ascend 910 AI处理器配套软件包（对应版本Atlas T 1.1.T107）的安装路径`/usr/local/Ascend`，若无权限，需要root用户将当前用户添加到`/usr/local/Ascend`所在的用户组，具体配置请详见配套软件包的说明文档。
- Ubuntu版本为18.04时，GCC 7.3.0可以直接通过apt命令安装。
@@ -82,7 +82,7 @@
1. 从代码仓下载源码。
```bash
- git clone https://gitee.com/mindspore/mindspore.git
+ git clone https://gitee.com/mindspore/mindspore.git -b r0.2
```
2. 在源码根目录下，执行如下命令编译MindSpore。
@@ -185,7 +185,7 @@
1. 从代码仓下载源码。
```bash
- git clone https://gitee.com/mindspore/mindinsight.git
+ git clone https://gitee.com/mindspore/mindinsight.git -b r0.2
```
> **不能**直接在仓库主页下载zip包获取源码。
@@ -250,7 +250,7 @@
1. 从代码仓下载源码。
```bash
- git clone https://gitee.com/mindspore/mindarmour.git
+ git clone https://gitee.com/mindspore/mindarmour.git -b r0.2
```
2. 在源码根目录下，执行如下命令编译并安装MindArmour。
diff --git a/install/mindspore_d_install_en.md b/install/mindspore_d_install_en.md
index 90bc7a9dc0045762687cd2f00fe9ac61e6a5c779..e519b7b78a34cd1330c6d5b2b246deb92b49524d 100644
--- a/install/mindspore_d_install_en.md
+++ b/install/mindspore_d_install_en.md
@@ -32,7 +32,7 @@ This document describes how to quickly install MindSpore on an Ascend AI process
| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
| ---- | :--- | :--- | :--- |
-| MindSpore master | - Ubuntu 16.04 or later x86_64
- EulerOS 2.8 arrch64
- EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI processor software package(Version:Atlas T 1.1.T107)
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI processor software package(Version:Atlas T 1.1.T107)
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
**Installation dependencies:**
same as the executable file installation dependencies. |
+| MindSpore master | - Ubuntu 16.04 or later x86_64
- EulerOS 2.8 arrch64
- EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI processor software package(Version:Atlas T 1.1.T107)
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.2/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI processor software package(Version:Atlas T 1.1.T107)
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
**Installation dependencies:**
same as the executable file installation dependencies. |
- Confirm that the current user has the right to access the installation path `/usr/local/Ascend `of Ascend 910 AI processor software package(Version:Atlas T 1.1.T107). If not, the root user needs to add the current user to the user group where `/usr/local/Ascend` is located. For the specific configuration, please refer to the software package instruction document.
- When Ubuntu version is 18.04, GCC 7.3.0 can be installed by using apt command.
@@ -81,7 +81,7 @@ The compilation and installation must be performed on the Ascend 910 AI processo
1. Download the source code from the code repository.
```bash
- git clone https://gitee.com/mindspore/mindspore.git
+ git clone https://gitee.com/mindspore/mindspore.git -b r0.2
```
2. Run the following command in the root directory of the source code to compile MindSpore:
@@ -184,7 +184,7 @@ If you need to analyze information such as model scalars, graphs, and model trac
1. Download the source code from the code repository.
```bash
- git clone https://gitee.com/mindspore/mindinsight.git
+ git clone https://gitee.com/mindspore/mindinsight.git -b r0.2
```
> You are **not** supposed to obtain the source code from the zip package downloaded from the repository homepage.
@@ -251,7 +251,7 @@ If you need to conduct AI model security research or enhance the security of the
1. Download the source code from the code repository.
```bash
- git clone https://gitee.com/mindspore/mindarmour.git
+ git clone https://gitee.com/mindspore/mindarmour.git -b r0.2
```
2. Run the following command in the root directory of the source code to compile and install MindArmour:
diff --git a/install/mindspore_gpu_install.md b/install/mindspore_gpu_install.md
index 537b32933aced82e98e0debc2a5239e3a2922af0..21528ed8fc5201b5b5a25d29a205ae28a50a7990 100644
--- a/install/mindspore_gpu_install.md
+++ b/install/mindspore_gpu_install.md
@@ -28,7 +28,7 @@
| 版本号 | 操作系统 | 可执行文件安装依赖 | 源码编译安装依赖 |
| ---- | :--- | :--- | :--- |
-| MindSpore master | Ubuntu 16.04（及以上） x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
- [OpenMPI](https://www.open-mpi.org/faq/?category=building#easy-build) 3.1.5 （可选，单机多卡/多机多卡训练需要）
- [NCCL](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) 2.4.8-1 （可选，单机多卡/多机多卡训练需要）
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
- [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69
- [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30
- [Automake](https://www.gnu.org/software/automake) >= 1.15.1
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
**安装依赖：**
与可执行文件安装依赖相同 |
+| MindSpore master | Ubuntu 16.04（及以上） x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
- [OpenMPI](https://www.open-mpi.org/faq/?category=building#easy-build) 3.1.5 （可选，单机多卡/多机多卡训练需要）
- [NCCL](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) 2.4.8-1 （可选，单机多卡/多机多卡训练需要）
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.2/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
- [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69
- [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30
- [Automake](https://www.gnu.org/software/automake) >= 1.15.1
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
**安装依赖：**
与可执行文件安装依赖相同 |
- Ubuntu版本为18.04时，GCC 7.3.0可以直接通过apt命令安装。
- 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。
@@ -64,7 +64,7 @@
1. 从代码仓下载源码。
```bash
- git clone https://gitee.com/mindspore/mindspore.git
+ git clone https://gitee.com/mindspore/mindspore.git -b r0.2
```
2. 在源码根目录下执行如下命令编译MindSpore。
@@ -149,7 +149,7 @@
1. 从代码仓下载源码。
```bash
- git clone https://gitee.com/mindspore/mindinsight.git
+ git clone https://gitee.com/mindspore/mindinsight.git -b r0.2
```
> **不能**直接在仓库主页下载zip包获取源码。
@@ -214,7 +214,7 @@
1. 从代码仓下载源码。
```bash
- git clone https://gitee.com/mindspore/mindarmour.git
+ git clone https://gitee.com/mindspore/mindarmour.git -b r0.2
```
2. 在源码根目录下，执行如下命令编译并安装MindArmour。
diff --git a/install/mindspore_gpu_install_en.md b/install/mindspore_gpu_install_en.md
index 4c41f09d87c8ad1f8cc16651314a7fbf4bb3dd4a..6b7cb85a27b232cd30974ee98c955ff0dd6cd5bc 100644
--- a/install/mindspore_gpu_install_en.md
+++ b/install/mindspore_gpu_install_en.md
@@ -28,7 +28,7 @@ This document describes how to quickly install MindSpore on a NVIDIA GPU environ
| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
| ---- | :--- | :--- | :--- |
-| MindSpore master | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
- [OpenMPI](https://www.open-mpi.org/faq/?category=building#easy-build) 3.1.5 (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
- [NCCL](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) 2.4.8-1 (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
- [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69
- [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30
- [Automake](https://www.gnu.org/software/automake) >= 1.15.1
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
**Installation dependencies:**
same as the executable file installation dependencies. |
+| MindSpore master | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
- [OpenMPI](https://www.open-mpi.org/faq/?category=building#easy-build) 3.1.5 (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
- [NCCL](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) 2.4.8-1 (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.2/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
- [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69
- [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30
- [Automake](https://www.gnu.org/software/automake) >= 1.15.1
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
**Installation dependencies:**
same as the executable file installation dependencies. |
- When Ubuntu version is 18.04, GCC 7.3.0 can be installed by using apt command.
- 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.
@@ -64,7 +64,7 @@ This document describes how to quickly install MindSpore on a NVIDIA GPU environ
1. Download the source code from the code repository.
```bash
- git clone https://gitee.com/mindspore/mindspore.git
+ git clone https://gitee.com/mindspore/mindspore.git -b r0.2
```
2. Run the following command in the root directory of the source code to compile MindSpore:
@@ -149,7 +149,7 @@ If you need to analyze information such as model scalars, graphs, and model trac
1. Download the source code from the code repository.
```bash
- git clone https://gitee.com/mindspore/mindinsight.git
+ git clone https://gitee.com/mindspore/mindinsight.git -b r0.2
```
> You are **not** supposed to obtain the source code from the zip package downloaded from the repository homepage.
@@ -216,7 +216,7 @@ If you need to conduct AI model security research or enhance the security of the
1. Download the source code from the code repository.
```bash
- git clone https://gitee.com/mindspore/mindarmour.git
+ git clone https://gitee.com/mindspore/mindarmour.git -b r0.2
```
2. Run the following command in the root directory of the source code to compile and install MindArmour:
diff --git a/tutorials/source_en/advanced_use/computer_vision_application.md b/tutorials/source_en/advanced_use/computer_vision_application.md
index 388e9816c43842d474f2816a6ddd057e9600e4ed..8ea13b1a715ff078c7fa300375c0fd057191b503 100644
--- a/tutorials/source_en/advanced_use/computer_vision_application.md
+++ b/tutorials/source_en/advanced_use/computer_vision_application.md
@@ -62,7 +62,7 @@ Next, let's use MindSpore to solve the image classification task. The overall pr
5. Call the high-level `Model` API to train and save the model file.
6. Load the saved model for inference.
-> This example is for the hardware platform of the Ascend 910 AI processor. You can find the complete executable sample code at： .
+> This example is for the hardware platform of the Ascend 910 AI processor. You can find the complete executable sample code at： .
The key parts of the task process code are explained below.
diff --git a/tutorials/source_en/advanced_use/distributed_training.md b/tutorials/source_en/advanced_use/distributed_training.md
index 2882b4703b5adfa97743aef8b5c4dae37b2230ab..1f8127f63c43abf8397625df878ef4ceb257665c 100644
--- a/tutorials/source_en/advanced_use/distributed_training.md
+++ b/tutorials/source_en/advanced_use/distributed_training.md
@@ -30,7 +30,7 @@ Among them:
In this tutorial, we will learn how to train the ResNet-50 network in `DATA_PARALLEL` or `AUTO_PARALLEL` mode on MindSpore.
> The current sample is for the Ascend 910 AI processor. CPU and GPU processors are not supported for now.
-> You can find the complete executable sample code at：.
+> You can find the complete executable sample code at：.
## Preparations
@@ -160,7 +160,7 @@ def create_dataset(repeat_num=1, batch_size=32, rank_id=0, rank_size=1):
In `DATA_PARALLEL` and `AUTO_PARALLEL` modes, the network definition mode is the same as that of a single-node system. For sample code, see at
- .
+ .
## Defining the Loss Function and Optimizer
diff --git a/tutorials/source_en/advanced_use/model_security.md b/tutorials/source_en/advanced_use/model_security.md
index 1ecc9389c4b74c11fb3d70674acfd2303c3df75f..46d12d1b1a5b72ac3d73212de32dc714010d458c 100644
--- a/tutorials/source_en/advanced_use/model_security.md
+++ b/tutorials/source_en/advanced_use/model_security.md
@@ -27,7 +27,7 @@ At the beginning of AI algorithm design, related security threats are sometimes
This section describes how to use MindArmour in adversarial attack and defense by taking the Fast Gradient Sign Method (FGSM) attack algorithm and Natural Adversarial Defense (NAD) algorithm as examples.
-> The current sample is for CPU, GPU and Ascend 910 AI processor. You can find the complete executable sample code at：
+> The current sample is for CPU, GPU and Ascend 910 AI processor. You can find the complete executable sample code at：
> - mnist_attack_fgsm.py: contains attack code.
> - mnist_defense_nad.py: contains defense code.
diff --git a/tutorials/source_en/advanced_use/on_device_inference.md b/tutorials/source_en/advanced_use/on_device_inference.md
index 7a93e30dafc1cb77eab4f7fdcc21a85c009844d0..ecdedf6beb3f2d0aad3cc061f8c54605a87ca50c 100644
--- a/tutorials/source_en/advanced_use/on_device_inference.md
+++ b/tutorials/source_en/advanced_use/on_device_inference.md
@@ -58,7 +58,7 @@ The compilation procedure is as follows:
2. Download source code from the code repository.
```bash
- git clone https://gitee.com/mindspore/mindspore.git
+ git clone https://gitee.com/mindspore/mindspore.git -b r0.2
```
3. Run the following command in the root directory of the source code to compile MindSpore Predict: -I indicates options for compiling MindSpore Predict and the parameter is the target platform architecture. Currently, only the Android arm64 platform is supported.
diff --git a/tutorials/source_en/quick_start/quick_start.md b/tutorials/source_en/quick_start/quick_start.md
index c14baceec3229dbd99a86e518a8e38b85ae83964..170f91426ccbb22dc99cdc660bfb2bf47ec78d54 100644
--- a/tutorials/source_en/quick_start/quick_start.md
+++ b/tutorials/source_en/quick_start/quick_start.md
@@ -35,7 +35,7 @@ During the practice, a simple image classification function is implemented. The
5. Load the saved model for inference.
6. Validate the model, load the test dataset and trained model, and validate the result accuracy.
-> You can find the complete executable sample code at .
+> You can find the complete executable sample code at .
This is a simple and basic application process. For other advanced and complex applications, extend this basic process as needed.
diff --git a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md
index 0135c636c84649fa8aa449126fe3abb40be2b3c0..e72afe86c840104b9a6721efff6328bf6d6e519b 100644
--- a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md
+++ b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md
@@ -63,7 +63,7 @@ MindSpore当前支持的图像分类网络包括：典型网络LeNet、AlexNet
6. 加载保存的模型进行推理
-> 本例面向Ascend 910 AI处理器硬件平台，你可以在这里下载完整的样例代码：
+> 本例面向Ascend 910 AI处理器硬件平台，你可以在这里下载完整的样例代码：
下面对任务流程中各个环节及代码关键片段进行解释说明。
diff --git a/tutorials/source_zh_cn/advanced_use/distributed_training.md b/tutorials/source_zh_cn/advanced_use/distributed_training.md
index 890dab6d0c6fa226b917dc2ec859d6668e29a6a9..11f44d1285b3cca6a4bf6664efead0f5d6d58f53 100644
--- a/tutorials/source_zh_cn/advanced_use/distributed_training.md
+++ b/tutorials/source_zh_cn/advanced_use/distributed_training.md
@@ -30,7 +30,7 @@
本篇教程我们主要讲解如何在MindSpore上通过数据并行及自动并行模式训练ResNet-50网络。
> 本例面向Ascend 910 AI处理器硬件平台，暂不支持CPU和GPU场景。
-> 你可以在这里下载完整的样例代码：
+> 你可以在这里下载完整的样例代码：
## 准备环节
@@ -164,7 +164,7 @@ def create_dataset(repeat_num=1, batch_size=32, rank_id=0, rank_size=1):
## 定义网络
-数据并行及自动并行模式下，网络定义方式与单机一致。代码请参考：
+数据并行及自动并行模式下，网络定义方式与单机一致。代码请参考：
## 定义损失函数及优化器
diff --git a/tutorials/source_zh_cn/advanced_use/model_security.md b/tutorials/source_zh_cn/advanced_use/model_security.md
index 88ed988d64685fbe5a20ec75d18a3d6edc4809fe..499564b8d3ed56a365c76ca76071d32922a40813 100644
--- a/tutorials/source_zh_cn/advanced_use/model_security.md
+++ b/tutorials/source_zh_cn/advanced_use/model_security.md
@@ -26,7 +26,7 @@ AI算法设计之初普遍未考虑相关的安全威胁，使得AI算法的判
这里通过图像分类任务上的对抗性攻防，以攻击算法FGSM和防御算法NAD为例，介绍MindArmour在对抗攻防上的使用方法。
-> 本例面向CPU、GPU、Ascend 910 AI处理器，你可以在这里下载完整的样例代码：
+> 本例面向CPU、GPU、Ascend 910 AI处理器，你可以在这里下载完整的样例代码：
> - mnist_attack_fgsm.py：包含攻击代码。
> - mnist_defense_nad.py：包含防御代码。
diff --git a/tutorials/source_zh_cn/advanced_use/on_device_inference.md b/tutorials/source_zh_cn/advanced_use/on_device_inference.md
index 5830298f8b606a206ef1f1840d00366af559e679..e64ad20e4981b427d0471b43e0778a2e8b37364d 100644
--- a/tutorials/source_zh_cn/advanced_use/on_device_inference.md
+++ b/tutorials/source_zh_cn/advanced_use/on_device_inference.md
@@ -57,7 +57,7 @@ MindSpore Predict是一个轻量级的深度神经网络推理引擎，提供了
2. 从代码仓下载源码。
```bash
- git clone https://gitee.com/mindspore/mindspore.git
+ git clone https://gitee.com/mindspore/mindspore.git -b r0.2
```
3. 在源码根目录下，执行如下命令编译MindSpore Predict。-I为编译MindSpore Predict的编译参数，-I的参数为目标端侧平台，目前仅支持安卓arm64平台。
diff --git a/tutorials/source_zh_cn/quick_start/quick_start.md b/tutorials/source_zh_cn/quick_start/quick_start.md
index 605238850a856f7ddb8b052e48369e87ce0717ef..66ce68dd97375074a965f0aa977248ffa57e6e96 100644
--- a/tutorials/source_zh_cn/quick_start/quick_start.md
+++ b/tutorials/source_zh_cn/quick_start/quick_start.md
@@ -36,7 +36,7 @@
5. 加载保存的模型，进行推理。
6. 验证模型，加载测试数据集和训练后的模型，验证结果精度。
-> 你可以在这里找到完整可运行的样例代码： 。
+> 你可以在这里找到完整可运行的样例代码： 。
这是简单、基础的应用流程，其他高级、复杂的应用可以基于这个基本流程进行扩展。