!188 Update links of 0.3.0-alpha

Merge pull request !188 from TingWang/update-links-for-r0.3

!188 Update links of 0.3.0-alpha
Merge pull request !188 from TingWang/update-links-for-r0.3
cd7e73b9 · mindspore-ci-bot · Gitee · 2ec8b28c · e29cdf68 · cd7e73b9
32 changed file
--- a/api/source_en/conf.py
+++ b/api/source_en/conf.py
@@ -29,7 +29,7 @@ copyright = '2020, MindSpore'
 author = 'MindSpore'
 # The full version, including alpha/beta/rc tags
-release = 'master'
+release = '0.3.0-alpha'
 # -- General configuration ---------------------------------------------------

--- a/api/source_en/index.rst
+++ b/api/source_en/index.rst
@@ -45,4 +45,4 @@ MindSpore API
   :maxdepth: 1
   :caption: C++ API
-   predict <https://www.mindspore.cn/apicc/en/master/predict/namespacemembers.html>
+   predict <https://www.mindspore.cn/apicc/en/0.3.0-alpha/predict/namespacemembers.html>
--- a/api/source_zh_cn/conf.py
+++ b/api/source_zh_cn/conf.py
@@ -29,7 +29,7 @@ copyright = '2020, MindSpore'
 author = 'MindSpore'
 # The full version, including alpha/beta/rc tags
-release = 'master'
+release = '0.3.0-alpha'
 # -- General configuration ---------------------------------------------------

--- a/api/source_zh_cn/index.rst
+++ b/api/source_zh_cn/index.rst
@@ -45,4 +45,4 @@ MindSpore API
   :maxdepth: 1
   :caption: C++ API
-   predict <https://www.mindspore.cn/apicc/zh-CN/master/predict/namespacemembers.html>
+   predict <https://www.mindspore.cn/apicc/zh-CN/0.3.0-alpha/predict/namespacemembers.html>
--- a/docs/source_en/conf.py
+++ b/docs/source_en/conf.py
@@ -20,7 +20,7 @@ copyright = '2020, MindSpore'
 author = 'MindSpore'
 # The full version, including alpha/beta/rc tags
-release = 'master'
+release = '0.3.0-alpha'
 # -- General configuration ---------------------------------------------------

--- a/docs/source_en/constraints_on_network_construction.md
+++ b/docs/source_en/constraints_on_network_construction.md
@@ -148,8 +148,8 @@ Currently, the following syntax is not supported in network constructors:
 ## Network Definition Constraints
 ### Instance Types on the Entire Network
-* Common Python function with the [@ms_function](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.html#mindspore.ms_function) decorator.
+* Common Python function with the [@ms_function](https://www.mindspore.cn/api/en/0.3.0-alpha/api/python/mindspore/mindspore.html#mindspore.ms_function) decorator.
-* Cell subclass inherited from [nn.Cell](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Cell).
+* Cell subclass inherited from [nn.Cell](https://www.mindspore.cn/api/en/0.3.0-alpha/api/python/mindspore/mindspore.nn.html#mindspore.nn.Cell).
 ### Network Input Type
 * The training data input parameters of the entire network must be of the Tensor type.
@@ -162,13 +162,13 @@ Currently, the following syntax is not supported in network constructors:
 | Category                 | Content
 | :-----------         |:--------
-| `Cell` instance           |[mindspore/nn/*](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html), and custom [Cell](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Cell).
+| `Cell` instance           |[mindspore/nn/*](https://www.mindspore.cn/api/en/0.3.0-alpha/api/python/mindspore/mindspore.nn.html), and custom [Cell](https://www.mindspore.cn/api/en/0.3.0-alpha/api/python/mindspore/mindspore.nn.html#mindspore.nn.Cell).
 | Member function of a `Cell` instance | Member functions of other classes in the construct function of Cell can be called.
 | Function                 | Custom Python functions and system functions listed in the preceding content.
 | Dataclass instance        | Class decorated with @dataclass.
-| Primitive operator        |[mindspore/ops/operations/*](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.operations.html).
+| Primitive operator        |[mindspore/ops/operations/*](https://www.mindspore.cn/api/en/0.3.0-alpha/api/python/mindspore/mindspore.ops.operations.html).
-| Composite operator        |[mindspore/ops/composite/*](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.composite.html).
+| Composite operator        |[mindspore/ops/composite/*](https://www.mindspore.cn/api/en/0.3.0-alpha/api/python/mindspore/mindspore.ops.composite.html).
-| Operator generated by constexpr     |Uses the value generated by [@constexpr](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.ops.html#mindspore.ops.constexpr) to calculate operators.
+| Operator generated by constexpr     |Uses the value generated by [@constexpr](https://www.mindspore.cn/api/en/0.3.0-alpha/api/python/mindspore/mindspore.ops.html#mindspore.ops.constexpr) to calculate operators.
 ### Other Constraints

--- a/docs/source_en/operator_list.md
+++ b/docs/source_en/operator_list.md
--- a/docs/source_zh_cn/conf.py
+++ b/docs/source_zh_cn/conf.py
@@ -20,7 +20,7 @@ copyright = '2020, MindSpore'
 author = 'MindSpore'
 # The full version, including alpha/beta/rc tags
-release = 'master'
+release = '0.3.0-alpha'
 # -- General configuration ---------------------------------------------------

--- a/docs/source_zh_cn/constraints_on_network_construction.md
+++ b/docs/source_zh_cn/constraints_on_network_construction.md
@@ -143,8 +143,8 @@
 ## 网络定义约束
 ### 整网实例类型
-* 带[@ms_function](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.html#mindspore.ms_function)装饰器的普通Python函数。
+* 带[@ms_function](https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/api/python/mindspore/mindspore.html#mindspore.ms_function)装饰器的普通Python函数。
-* 继承自[nn.Cell](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Cell)的Cell子类。
+* 继承自[nn.Cell](https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/api/python/mindspore/mindspore.nn.html#mindspore.nn.Cell)的Cell子类。
 ### 网络输入类型
 * 整网的训练数据输入参数只能是Tensor类型。
@@ -157,13 +157,13 @@
 | 类别                 | 内容
 | :-----------         |:--------
-| `Cell`实例           |[mindspore/nn/*](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html)、自定义[Cell](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Cell)。
+| `Cell`实例           |[mindspore/nn/*](https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/api/python/mindspore/mindspore.nn.html)、自定义[Cell](https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/api/python/mindspore/mindspore.nn.html#mindspore.nn.Cell)。
 | `Cell`实例的成员函数 | Cell的construct中可以调用其他类成员函数。
 | 函数                 | 自定义Python函数、前文中列举的系统函数。
 | dataclass实例        | 使用@dataclass装饰的类。
-| Primitive算子        |[mindspore/ops/operations/*](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html)
+| Primitive算子        |[mindspore/ops/operations/*](https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/api/python/mindspore/mindspore.ops.operations.html)
-| Composite算子        |[mindspore/ops/composite/*](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.composite.html)
+| Composite算子        |[mindspore/ops/composite/*](https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/api/python/mindspore/mindspore.ops.composite.html)
-| constexpr生成算子    |使用[@constexpr](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.html#mindspore.ops.constexpr)生成的值计算算子。
+| constexpr生成算子    |使用[@constexpr](https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/api/python/mindspore/mindspore.ops.html#mindspore.ops.constexpr)生成的值计算算子。
 ### 其他约束

--- a/docs/source_zh_cn/operator_list.md
+++ b/docs/source_zh_cn/operator_list.md
--- 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 <br> - 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt) | **编译依赖：**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> **安装依赖：**<br> 与可执行文件安装依赖相同 |
+| MindSpore 0.3.0-alpha | Ubuntu 16.04（及以上） x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt) | **编译依赖：**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> **安装依赖：**<br> 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。
@@ -97,7 +97,7 @@
 | 版本号                 | 操作系统            | 可执行文件安装依赖                                           | 源码编译安装依赖         |
 | ---------------------- | :------------------ | :----------------------------------------------------------- | :----------------------- |
-| MindArmour 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[setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.3/setup.py) | 与可执行文件安装依赖相同 |
+| MindArmour 0.3.0-alpha | Ubuntu 16.04（及以上） x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore 0.3.0-alpha<br> - 其他依赖项参见[setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.3/setup.py) | 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setup.py中的依赖项，其余情况需自行安装。

--- 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 <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> same as the executable file installation dependencies. |
+| MindSpore 0.3.0-alpha | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> 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.
@@ -97,7 +97,7 @@ If you need to conduct AI model security research or enhance the security of the
 | Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
 | ---- | :--- | :--- | :--- |
-| MindArmour 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 [setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.3/setup.py). | Same as the executable file installation dependencies. |
+| MindArmour 0.3.0-alpha | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore 0.3.0-alpha <br> - For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.3/setup.py). | Same as the executable file installation dependencies. |
 - When the network is connected, dependency items in the setup.py file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items.

--- 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 <br> - 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt) | **编译依赖：**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [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 <br> - [ActivePerl](http://downloads.activestate.com/ActivePerl/releases/5.24.3.2404/ActivePerl-5.24.3.2404-MSWin32-x64-404865.exe) 5.24.3.2404 <br> - [CMake](https://cmake.org/download/) 3.14.1 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> **安装依赖：**<br> 与可执行文件安装依赖相同 |
+| MindSpore 0.3.0-alpha | Windows 10 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt) | **编译依赖：**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [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 <br> - [ActivePerl](http://downloads.activestate.com/ActivePerl/releases/5.24.3.2404/ActivePerl-5.24.3.2404-MSWin32-x64-404865.exe) 5.24.3.2404 <br> - [CMake](https://cmake.org/download/) 3.14.1 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <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。

--- 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 <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [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 <br> - [ActivePerl](http://downloads.activestate.com/ActivePerl/releases/5.24.3.2404/ActivePerl-5.24.3.2404-MSWin32-x64-404865.exe) 5.24.3.2404 <br> - [CMake](https://cmake.org/download/) 3.14.1 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> **Installation dependencies:**<br> same as the executable file installation dependencies. |
+| MindSpore 0.3.0-alpha | Windows 10 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [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 <br> - [ActivePerl](http://downloads.activestate.com/ActivePerl/releases/5.24.3.2404/ActivePerl-5.24.3.2404-MSWin32-x64-404865.exe) 5.24.3.2404 <br> - [CMake](https://cmake.org/download/) 3.14.1 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <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.

--- 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） aarch64 <br> - Ubuntu 16.04（及以上） x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI处理器配套软件包（对应版本Atlas T 1.1.T107） <br> - 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt) | **编译依赖：**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI处理器配套软件包（对应版本Atlas T 1.1.T107） <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> **安装依赖：**<br> 与可执行文件安装依赖相同 |
+| MindSpore 0.3.0-alpha | - Ubuntu 16.04（及以上） aarch64 <br> - Ubuntu 16.04（及以上） x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI处理器配套软件包（对应版本Atlas T 1.1.T107） <br> - 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt) | **编译依赖：**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI处理器配套软件包（对应版本Atlas T 1.1.T107） <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> **安装依赖：**<br> 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。
@@ -160,7 +160,7 @@
 | 版本号 | 操作系统 | 可执行文件安装依赖 | 源码编译安装依赖 |
 | ---- | :--- | :--- | :--- |
-| MindInsight master | - Ubuntu 16.04（及以上） aarch64 <br> - Ubuntu 16.04（及以上） x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 <br> | - [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/r0.3/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 |
+| MindInsight 0.3.0-alpha | - Ubuntu 16.04（及以上） aarch64 <br> - Ubuntu 16.04（及以上） x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 <br> | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore 0.3.0-alpha <br> - 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindinsight/blob/r0.3/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。
@@ -225,7 +225,7 @@
 | 版本号 | 操作系统 | 可执行文件安装依赖 | 源码编译安装依赖 |
 | ---- | :--- | :--- | :--- |
-| MindArmour master | - Ubuntu 16.04（及以上） aarch64 <br> - Ubuntu 16.04（及以上） x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 <br> | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore master <br> - 其他依赖项参见[setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.3/setup.py) | 与可执行文件安装依赖相同 |
+| MindArmour 0.3.0-alpha | - Ubuntu 16.04（及以上） aarch64 <br> - Ubuntu 16.04（及以上） x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 <br> | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore 0.3.0-alpha <br> - 其他依赖项参见[setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.3/setup.py) | 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setup.py中的依赖项，其余情况需自行安装。

--- 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 aarch64 <br> - Ubuntu 16.04 or later x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI processor software package(Version:Atlas T 1.1.T107) <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI processor software package(Version:Atlas T 1.1.T107) <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> **Installation dependencies:**<br> same as the executable file installation dependencies. |
+| MindSpore 0.3.0-alpha | - Ubuntu 16.04 or later aarch64 <br> - Ubuntu 16.04 or later x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI processor software package(Version:Atlas T 1.1.T107) <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI processor software package(Version:Atlas T 1.1.T107) <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> **Installation dependencies:**<br> 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.
@@ -159,7 +159,7 @@ If you need to analyze information such as model scalars, graphs, and model trac
 | Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
 | ---- | :--- | :--- | :--- |
-| MindInsight master | - Ubuntu 16.04 or later aarch64 <br> - Ubuntu 16.04 or later x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 <br> | - [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/r0.3/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. |
+| MindInsight 0.3.0-alpha | - Ubuntu 16.04 or later aarch64 <br> - Ubuntu 16.04 or later x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 <br> | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore 0.3.0-alpha <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindinsight/blob/r0.3/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.
@@ -226,7 +226,7 @@ If you need to conduct AI model security research or enhance the security of the
 | Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
 | ---- | :--- | :--- | :--- |
-| MindArmour master | - Ubuntu 16.04 or later aarch64 <br> - Ubuntu 16.04 or later x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 <br> | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore master <br> - For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.3/setup.py). | Same as the executable file installation dependencies. |
+| MindArmour 0.3.0-alpha | - Ubuntu 16.04 or later aarch64 <br> - Ubuntu 16.04 or later x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 <br> | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore 0.3.0-alpha <br> - For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.3/setup.py). | Same as the executable file installation dependencies. |
 - When the network is connected, dependency items in the setup.py file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items.

--- 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 <br> - [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base) <br> - [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6 <br> - [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） <br> - [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） <br> - 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt) | **编译依赖：**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> - [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69 <br> - [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30 <br> - [Automake](https://www.gnu.org/software/automake) >= 1.15.1 <br> - [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base) <br> - [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6 <br> **安装依赖：**<br> 与可执行文件安装依赖相同 |
+| MindSpore 0.3.0-alpha | Ubuntu 16.04（及以上） x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base) <br> - [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6 <br> - [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） <br> - [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） <br> - 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt) | **编译依赖：**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> - [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69 <br> - [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30 <br> - [Automake](https://www.gnu.org/software/automake) >= 1.15.1 <br> - [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base) <br> - [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6 <br> **安装依赖：**<br> 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。
@@ -124,7 +124,7 @@
 | 版本号 | 操作系统 | 可执行文件安装依赖 | 源码编译安装依赖 |
 | ---- | :--- | :--- | :--- |
-| 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/r0.3/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 |
+| MindInsight 0.3.0-alpha | - Ubuntu 16.04（及以上） x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore 0.3.0-alpha <br> - 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindinsight/blob/r0.3/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。
@@ -189,7 +189,7 @@
 | 版本号                 | 操作系统            | 可执行文件安装依赖                                           | 源码编译安装依赖         |
 | ---------------------- | :------------------ | :----------------------------------------------------------- | :----------------------- |
-| MindArmour 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[setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.3/setup.py) | 与可执行文件安装依赖相同 |
+| MindArmour 0.3.0-alpha | Ubuntu 16.04（及以上） x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore 0.3.0-alpha <br> - 其他依赖项参见[setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.3/setup.py) | 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setup.py中的依赖项，其余情况需自行安装。

--- 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 <br> - [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base) <br> - [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6 <br> - [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) <br> - [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) <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> - [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69 <br> - [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30 <br> - [Automake](https://www.gnu.org/software/automake) >= 1.15.1 <br> - [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base) <br> - [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6 <br> **Installation dependencies:**<br> same as the executable file installation dependencies. |
+| MindSpore 0.3.0-alpha | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base) <br> - [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6 <br> - [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) <br> - [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) <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> - [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69 <br> - [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30 <br> - [Automake](https://www.gnu.org/software/automake) >= 1.15.1 <br> - [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base) <br> - [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6 <br> **Installation dependencies:**<br> 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.
@@ -124,7 +124,7 @@ If you need to analyze information such as model scalars, graphs, and model trac
 | 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/r0.3/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. |
+| MindInsight 0.3.0-alpha | - Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore 0.3.0-alpha <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindinsight/blob/r0.3/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.
@@ -191,7 +191,7 @@ If you need to conduct AI model security research or enhance the security of the
 | Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
 | ---- | :--- | :--- | :--- |
-| MindArmour 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 [setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.3/setup.py). | Same as the executable file installation dependencies. |
+| MindArmour 0.3.0-alpha | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore 0.3.0-alpha <br> - For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.3/setup.py). | Same as the executable file installation dependencies. |
 - When the network is connected, dependency items in the setup.py file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items.

--- a/resource/faq/FAQ_en.md
+++ b/resource/faq/FAQ_en.md
@@ -92,13 +92,13 @@ A: MindSpore provides pluggable device management interface so that developer co
 Q: What hardware does MindSpore require?
-A: Currently, you can try out MindSpore through Docker images on laptops or in environments with GPUs. Some models in MindSpore Model Zoo support GPU-based training and inference, and other models are being improved. For distributed parallel training, MindSpore supports multi-GPU training. You can obtain the latest information from [RoadMap](https://www.mindspore.cn/docs/en/master/roadmap.html) and project [Release Notes](https://gitee.com/mindspore/mindspore/blob/r0.3/RELEASE.md).
+A: Currently, you can try out MindSpore through Docker images on laptops or in environments with GPUs. Some models in MindSpore Model Zoo support GPU-based training and inference, and other models are being improved. For distributed parallel training, MindSpore supports multi-GPU training. You can obtain the latest information from [RoadMap](https://www.mindspore.cn/docs/en/0.3.0-alpha/roadmap.html) and project [Release Notes](https://gitee.com/mindspore/mindspore/blob/r0.3/RELEASE.md).
 ### System Support
 Q: Does MindSpore support Windows 10?
-A: The MindSpore CPU version can be installed on Windows 10. For details about the installation procedure, see tutorials on the [MindSpore official website](https://www.mindspore.cn/tutorial/en/master/advanced_use/mindspore_cpu_win_install.html).
+A: The MindSpore CPU version can be installed on Windows 10. For details about the installation procedure, see tutorials on the [MindSpore official website](https://www.mindspore.cn/tutorial/en/0.3.0-alpha/advanced_use/mindspore_cpu_win_install.html).
 ### Programming Language
@@ -122,7 +122,7 @@ A: The MindSpore framework does not support FCA. For semantic models, you can ca
 Q: Where can I view the sample code or tutorial of MindSpore training and inference?
-A: Please visit the [MindSpore official website](https://www.mindspore.cn/tutorial/en/master/index.html).
+A: Please visit the [MindSpore official website](https://www.mindspore.cn/tutorial/en/0.3.0-alpha/index.html).
 ## Features
@@ -140,7 +140,7 @@ A: Automatic parallelism on CPUs and GPUs are being improved. You are advised to
 Q: What is the relationship between MindSpore and ModelArts? Can MindSpore be used on ModelArts?
-A: ModelArts is an online training and inference platform on HUAWEI CLOUD. MindSpore is a Huawei deep learning framework. You can view the tutorials on the [MindSpore official website](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/use_on_the_cloud.html) to learn how to train MindSpore models on ModelArts.
+A: ModelArts is an online training and inference platform on HUAWEI CLOUD. MindSpore is a Huawei deep learning framework. You can view the tutorials on the [MindSpore official website](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/advanced_use/use_on_the_cloud.html) to learn how to train MindSpore models on ModelArts.
 ## Capabilities
@@ -152,7 +152,7 @@ A: The TensorFlow's object detection pipeline API belongs to the TensorFlow's Mo
 Q: How do I migrate scripts or models of other frameworks to MindSpore?
-A: For details about script or model migration, please visit the [MindSpore official website](https://www.mindspore.cn/tutorial/en/master/advanced_use/network_migration.html).
+A: For details about script or model migration, please visit the [MindSpore official website](https://www.mindspore.cn/tutorial/en/0.3.0-alpha/advanced_use/network_migration.html).
 <br/>

--- a/resource/faq/FAQ_zh_cn.md
+++ b/resource/faq/FAQ_zh_cn.md
@@ -91,13 +91,13 @@ A：MindSpore提供了可插拔式的设备管理接口，其他计算单元（
 Q：MindSpore需要什么硬件支持？
-A：目前笔记本电脑或者有GPU的环境，都可以通过Docker镜像来试用。当前MindSpore Model Zoo中有部分模型已经支持GPU的训练和推理，其他模型也在不断地进行完善。在分布式并行训练方面，MindSpore当前支持GPU多卡训练。你可以通过[RoadMap](https://www.mindspore.cn/docs/zh-CN/master/roadmap.html)和项目[Release note](https://gitee.com/mindspore/mindspore/blob/r0.3/RELEASE.md)获取最新信息。
+A：目前笔记本电脑或者有GPU的环境，都可以通过Docker镜像来试用。当前MindSpore Model Zoo中有部分模型已经支持GPU的训练和推理，其他模型也在不断地进行完善。在分布式并行训练方面，MindSpore当前支持GPU多卡训练。你可以通过[RoadMap](https://www.mindspore.cn/docs/zh-CN/0.3.0-alpha/roadmap.html)和项目[Release note](https://gitee.com/mindspore/mindspore/blob/r0.3/RELEASE.md)获取最新信息。
 ### 系统支持
 Q：MindSpore是否支持Windows 10？
-A：MindSpore CPU版本已经支持在Windows 10系统中安装，具体安装步骤可以查阅[MindSpore官网教程](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mindspore_cpu_win_install.html)。
+A：MindSpore CPU版本已经支持在Windows 10系统中安装，具体安装步骤可以查阅[MindSpore官网教程](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/advanced_use/mindspore_cpu_win_install.html)。
 ### 编程语言扩展
@@ -121,7 +121,7 @@ A：MindSpore框架本身并不需要支持FCA。对于语义类模型，用户
 Q：从哪里可以查看MindSpore训练及推理的样例代码或者教程？
-A：可以访问[MindSpore官网教程](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)。
+A：可以访问[MindSpore官网教程](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/index.html)。
 ## 特性
@@ -139,7 +139,7 @@ A：自动并行特性对CPU GPU的支持还在完善中。推荐用户在Ascend
 Q：MindSpore与ModelArts是什么关系，在ModelArts中能使用MindSpore吗？
-A：ModelArts是华为公有云线上训练及推理平台，MindSpore是华为深度学习框架，可以查阅[MindSpore官网教程](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/use_on_the_cloud.html)，教程中详细展示了用户如何使用ModelArts来做MindSpore的模型训练。
+A：ModelArts是华为公有云线上训练及推理平台，MindSpore是华为深度学习框架，可以查阅[MindSpore官网教程](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/advanced_use/use_on_the_cloud.html)，教程中详细展示了用户如何使用ModelArts来做MindSpore的模型训练。
 ## 能力
@@ -151,7 +151,7 @@ A：TensorFlow的对象检测Pipeline接口属于TensorFlow Model模块。待Min
 Q：其他框架的脚本或者模型怎么迁移到MindSpore？
-A：关于脚本或者模型迁移，可以查询MindSpore官网中关于[网络迁移](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/network_migration.html)的介绍。
+A：关于脚本或者模型迁移，可以查询MindSpore官网中关于[网络迁移](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/advanced_use/network_migration.html)的介绍。
 <br/>

--- a/tutorials/source_en/advanced_use/checkpoint_for_hybrid_parallel.md
+++ b/tutorials/source_en/advanced_use/checkpoint_for_hybrid_parallel.md
@@ -313,7 +313,7 @@ User process:
 3. Execute stage 2 training: There are two devices in stage 2 training environment. The weight shape of the MatMul operator on each device is \[4, 8]. Load the initialized model parameter data from the integrated checkpoint file and then perform training.
-> For details about the distributed environment configuration and training code, see [Distributed Training](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
+> For details about the distributed environment configuration and training code, see [Distributed Training](https://www.mindspore.cn/tutorial/en/0.3.0-alpha/advanced_use/distributed_training.html).
 >
 > This document provides the example code for integrating checkpoint files and loading checkpoint files before distributed training. The code is for reference only.

--- a/tutorials/source_en/advanced_use/network_migration.md
+++ b/tutorials/source_en/advanced_use/network_migration.md
@@ -29,9 +29,9 @@ Before you start working on your scripts, prepare your operator assessment and h
 ### Operator Assessment
-Analyze the operators contained in the network to be migrated and figure out how does MindSpore support these operators based on the [Operator List](https://www.mindspore.cn/docs/en/master/operator_list.html).
+Analyze the operators contained in the network to be migrated and figure out how does MindSpore support these operators based on the [Operator List](https://www.mindspore.cn/docs/en/0.3.0-alpha/operator_list.html).
-Take ResNet-50 as an example. The two major operators [Conv](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Conv2d) and [BatchNorm](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.BatchNorm2d) exist in the MindSpore Operator List.
+Take ResNet-50 as an example. The two major operators [Conv](https://www.mindspore.cn/api/en/0.3.0-alpha/api/python/mindspore/mindspore.nn.html#mindspore.nn.Conv2d) and [BatchNorm](https://www.mindspore.cn/api/en/0.3.0-alpha/api/python/mindspore/mindspore.nn.html#mindspore.nn.BatchNorm2d) exist in the MindSpore Operator List.
 If any operator does not exist, you are advised to perform the following operations:
@@ -63,11 +63,11 @@ The ResNet-50 network migration and training on the Ascend 910 is used as an exa
 1. Import MindSpore modules.
-   Import the corresponding MindSpore modules based on the required APIs. For details about the module list, see <https://www.mindspore.cn/api/en/master/index.html>.
+   Import the corresponding MindSpore modules based on the required APIs. For details about the module list, see <https://www.mindspore.cn/api/en/0.3.0-alpha/index.html>.
 2. Load and preprocess a dataset.
-   Use MindSpore to build the required dataset. Currently, MindSpore supports common datasets. You can call APIs in the original format, `MindRecord`, and `TFRecord`. In addition, MindSpore supports data processing and data augmentation. For details, see the [Data Preparation](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/data_preparation.html).
+   Use MindSpore to build the required dataset. Currently, MindSpore supports common datasets. You can call APIs in the original format, `MindRecord`, and `TFRecord`. In addition, MindSpore supports data processing and data augmentation. For details, see the [Data Preparation](https://www.mindspore.cn/tutorial/en/0.3.0-alpha/use/data_preparation/data_preparation.html).
   In this example, the CIFAR-10 dataset is loaded, which supports both single-GPU and multi-GPU scenarios.
@@ -235,7 +235,7 @@ The ResNet-50 network migration and training on the Ascend 910 is used as an exa
   loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
   ```
-   You can use a built-in assessment method of `Model` by setting the [metrics](https://www.mindspore.cn/tutorial/en/master/advanced_use/customized_debugging_information.html#mindspore-metrics) attribute.
+   You can use a built-in assessment method of `Model` by setting the [metrics](https://www.mindspore.cn/tutorial/en/0.3.0-alpha/advanced_use/customized_debugging_information.html#mindspore-metrics) attribute.
   ```python
   model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
@@ -264,17 +264,17 @@ The accuracy optimization process is as follows:
 #### On-Cloud Integration
-Run your scripts on ModelArts. For details, see [Using MindSpore on Cloud](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/use_on_the_cloud.html).
+Run your scripts on ModelArts. For details, see [Using MindSpore on Cloud](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/advanced_use/use_on_the_cloud.html).
 ### Inference Phase
-Models trained on the Ascend 910 AI processor can be used for inference on different hardware platforms. Refer to the [Multi-platform Inference Tutorial](https://www.mindspore.cn/tutorial/en/master/use/multi_platform_inference.html) for detailed steps.
+Models trained on the Ascend 910 AI processor can be used for inference on different hardware platforms. Refer to the [Multi-platform Inference Tutorial](https://www.mindspore.cn/tutorial/en/0.3.0-alpha/use/multi_platform_inference.html) for detailed steps.
 ## Examples
 1. [Common network script examples](https://gitee.com/mindspore/mindspore/tree/r0.3/example)
-2. [Common dataset examples](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/loading_the_datasets.html)
+2. [Common dataset examples](https://www.mindspore.cn/tutorial/en/0.3.0-alpha/use/data_preparation/loading_the_datasets.html)
 3. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/r0.3/mindspore/model_zoo)
--- a/tutorials/source_en/conf.py
+++ b/tutorials/source_en/conf.py
@@ -21,7 +21,7 @@ copyright = '2020, MindSpore'
 author = 'MindSpore'
 # The full version, including alpha/beta/rc tags
-release = 'master'
+release = '0.3.0-alpha'
 # -- General configuration ---------------------------------------------------

--- a/tutorials/source_en/quick_start/quick_start.md
+++ b/tutorials/source_en/quick_start/quick_start.md
@@ -83,7 +83,7 @@ Currently, the `os` libraries are required. For ease of understanding, other req
 import os
 ```
-For details about MindSpore modules, search on the [MindSpore API Page](https://www.mindspore.cn/api/en/master/index.html).
+For details about MindSpore modules, search on the [MindSpore API Page](https://www.mindspore.cn/api/en/0.3.0-alpha/index.html).
 ### Configuring the Running Information
@@ -178,7 +178,7 @@ In the preceding information:
 Perform the shuffle and batch operations, and then perform the repeat operation to ensure that data during an epoch is unique.
-> MindSpore supports multiple data processing and augmentation operations, which are usually combined. For details, see section "Data Processing and Augmentation" in the MindSpore Tutorials (https://www.mindspore.cn/tutorial/en/master/use/data_preparation/data_processing_and_augmentation.html).
+> MindSpore supports multiple data processing and augmentation operations, which are usually combined. For details, see section "Data Processing and Augmentation" in the MindSpore Tutorials (https://www.mindspore.cn/tutorial/en/0.3.0-alpha/use/data_preparation/data_processing_and_augmentation.html).
 ## Defining the Network

--- a/tutorials/source_en/use/defining_the_network.rst
+++ b/tutorials/source_en/use/defining_the_network.rst
@@ -4,5 +4,5 @@ Defining the Network
 .. toctree::
  :maxdepth: 1
-  Network List <https://www.mindspore.cn/docs/en/master/network_list.html>
+  Network List <https://www.mindspore.cn/docs/en/0.3.0-alpha/network_list.html>
  custom_operator
\ No newline at end of file
--- a/tutorials/source_en/use/multi_platform_inference.md
+++ b/tutorials/source_en/use/multi_platform_inference.md
@@ -26,16 +26,16 @@ Models based on MindSpore training can be used for inference on different hardwa
 2. Inference on the Ascend 310 AI processor
-   1. Export the ONNX or GEIR model by referring to the [Export GEIR Model and ONNX Model](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html#geironnx).
+   1. Export the ONNX or GEIR model by referring to the [Export GEIR Model and ONNX Model](https://www.mindspore.cn/tutorial/en/0.3.0-alpha/use/saving_and_loading_model_parameters.html#geironnx).
   2. For performing inference in the cloud environment, see the [Ascend 910 training and Ascend 310 inference samples](https://support.huaweicloud.com/bestpractice-modelarts/modelarts_10_0026.html). For details about the bare-metal environment (compared with the cloud environment where the Ascend 310 AI processor is deployed locally), see the description document of the Ascend 310 AI processor software package.
 3. Inference on a GPU
-   1. Export the ONNX model by referring to the [Export GEIR Model and ONNX Model](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html#geironnx).
+   1. Export the ONNX model by referring to the [Export GEIR Model and ONNX Model](https://www.mindspore.cn/tutorial/en/0.3.0-alpha/use/saving_and_loading_model_parameters.html#geironnx).
   2. Perform inference on the NVIDIA GPU by referring to [TensorRT backend for ONNX](https://github.com/onnx/onnx-tensorrt).
 ## On-Device Inference
-The On-Device Inference is based on the MindSpore Predict. Please refer to [On-Device Inference Tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/on_device_inference.html) for details.
+The On-Device Inference is based on the MindSpore Predict. Please refer to [On-Device Inference Tutorial](https://www.mindspore.cn/tutorial/en/0.3.0-alpha/advanced_use/on_device_inference.html) for details.
--- a/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md
+++ b/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md
@@ -316,7 +316,7 @@ load_param_into_net(opt, param_dict)
 3. 执行阶段2训练：阶段2为2卡训练环境，每卡上MatMul算子weight的shape为[4, 8]，从合并后的CheckPoint文件加载初始化模型参数数据，之后执行训练。
-> 具体分布式环境配置和训练部分代码，此处不做详细说明，可以参考[分布式并行训练](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/distributed_training.html)
+> 具体分布式环境配置和训练部分代码，此处不做详细说明，可以参考[分布式并行训练](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/advanced_use/distributed_training.html)
 章节。
 >
 > 本文档附上对CheckPoint文件做合并处理以及分布式训练前加载CheckPoint文件的示例代码，仅作为参考，实际请参考具体情况实现。

--- a/tutorials/source_zh_cn/advanced_use/network_migration.md
+++ b/tutorials/source_zh_cn/advanced_use/network_migration.md
@@ -29,9 +29,9 @@
 ### 算子评估
-分析待迁移的网络中所包含的算子，结合[MindSpore算子支持列表](https://www.mindspore.cn/docs/zh-CN/master/operator_list.html)，梳理出MindSpore对这些算子的支持程度。
+分析待迁移的网络中所包含的算子，结合[MindSpore算子支持列表](https://www.mindspore.cn/docs/zh-CN/0.3.0-alpha/operator_list.html)，梳理出MindSpore对这些算子的支持程度。
-以ResNet-50为例，[Conv](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.Conv2d)和[BatchNorm](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.nn.html#mindspore.nn.BatchNorm2d)是其中最主要的两个算子，它们已在MindSpore支持的算子列表中。
+以ResNet-50为例，[Conv](https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/api/python/mindspore/mindspore.nn.html#mindspore.nn.Conv2d)和[BatchNorm](https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/api/python/mindspore/mindspore.nn.html#mindspore.nn.BatchNorm2d)是其中最主要的两个算子，它们已在MindSpore支持的算子列表中。
 如果发现没有对应算子，建议：
 - 使用其他算子替换：分析算子实现公式，审视是否可以采用MindSpore现有算子叠加达到预期目标。
@@ -61,11 +61,11 @@ MindSpore与TensorFlow、PyTorch在网络结构组织方式上，存在一定差
 1. 导入MindSpore模块。
-    根据所需使用的接口，导入相应的MindSpore模块，模块列表详见<https://www.mindspore.cn/api/zh-CN/master/index.html>。
+    根据所需使用的接口，导入相应的MindSpore模块，模块列表详见<https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/index.html>。
 2. 加载数据集和预处理。
-    使用MindSpore构造你需要使用的数据集。目前MindSpore已支持常见数据集，你可以通过原始格式、`MindRecord`、`TFRecord`等多种接口调用，同时还支持数据处理以及数据增强等相关功能，具体用法可参考[准备数据教程](https://www.mindspore.cn/tutorial/zh-CN/master/use/data_preparation/data_preparation.html)。
+    使用MindSpore构造你需要使用的数据集。目前MindSpore已支持常见数据集，你可以通过原始格式、`MindRecord`、`TFRecord`等多种接口调用，同时还支持数据处理以及数据增强等相关功能，具体用法可参考[准备数据教程](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/use/data_preparation/data_preparation.html)。
    本例中加载了Cifar-10数据集，可同时支持单卡和多卡的场景。
@@ -231,7 +231,7 @@ MindSpore与TensorFlow、PyTorch在网络结构组织方式上，存在一定差
    loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
    ```
-    如果希望使用`Model`内置的评估方法，则可以使用[metrics](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/customized_debugging_information.html#mindspore-metrics)属性设置希望使用的评估方法。
+    如果希望使用`Model`内置的评估方法，则可以使用[metrics](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/advanced_use/customized_debugging_information.html#mindspore-metrics)属性设置希望使用的评估方法。
    ```python
    model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
@@ -259,16 +259,16 @@ MindSpore与TensorFlow、PyTorch在网络结构组织方式上，存在一定差
 #### 云上集成
-请参考[在云上使用MindSpore](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/use_on_the_cloud.html)，将你的脚本运行在ModelArts。
+请参考[在云上使用MindSpore](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/advanced_use/use_on_the_cloud.html)，将你的脚本运行在ModelArts。
 ### 推理阶段
-在Ascend 910 AI处理器上训练后的模型，支持在不同的硬件平台上执行推理。详细步骤可参考[多平台推理教程](https://www.mindspore.cn/tutorial/zh-CN/master/use/multi_platform_inference.html)。
+在Ascend 910 AI处理器上训练后的模型，支持在不同的硬件平台上执行推理。详细步骤可参考[多平台推理教程](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/use/multi_platform_inference.html)。
 ## 样例参考
 1. [常用网络脚本样例](https://gitee.com/mindspore/mindspore/tree/r0.3/example)
-2. [常用数据集读取样例](https://www.mindspore.cn/tutorial/zh-CN/master/use/data_preparation/loading_the_datasets.html)
+2. [常用数据集读取样例](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/use/data_preparation/loading_the_datasets.html)
 3. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/r0.3/mindspore/model_zoo)
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--- a/tutorials/source_zh_cn/conf.py
+++ b/tutorials/source_zh_cn/conf.py
@@ -21,7 +21,7 @@ copyright = '2020, MindSpore'
 author = 'MindSpore'
 # The full version, including alpha/beta/rc tags
-release = 'master'
+release = '0.3.0-alpha'
 # -- General configuration ---------------------------------------------------

--- a/tutorials/source_zh_cn/quick_start/quick_start.md
+++ b/tutorials/source_zh_cn/quick_start/quick_start.md
@@ -84,7 +84,7 @@
 import os
 ```
-详细的MindSpore的模块说明，可以在[MindSpore API页面](https://www.mindspore.cn/api/zh-CN/master/index.html)中搜索查询。
+详细的MindSpore的模块说明，可以在[MindSpore API页面](https://www.mindspore.cn/api/zh-CN/0.3.0-alpha/index.html)中搜索查询。
 ### 配置运行信息
@@ -179,7 +179,7 @@ def create_dataset(data_path, batch_size=32, repeat_size=1,
 先进行shuffle、batch操作，再进行repeat操作，这样能保证1个epoch内数据不重复。
-> MindSpore支持进行多种数据处理和增强的操作，各种操作往往组合使用，具体可以参考[数据处理与数据增强](https://www.mindspore.cn/tutorial/zh-CN/master/use/data_preparation/data_processing_and_augmentation.html)章节。
+> MindSpore支持进行多种数据处理和增强的操作，各种操作往往组合使用，具体可以参考[数据处理与数据增强](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/use/data_preparation/data_processing_and_augmentation.html)章节。
 ## 定义网络

--- a/tutorials/source_zh_cn/use/defining_the_network.rst
+++ b/tutorials/source_zh_cn/use/defining_the_network.rst
@@ -4,5 +4,5 @@
 .. toctree::
  :maxdepth: 1
-  网络支持 <https://www.mindspore.cn/docs/zh-CN/master/network_list.html>
+  网络支持 <https://www.mindspore.cn/docs/zh-CN/0.3.0-alpha/network_list.html>
  custom_operator
\ No newline at end of file
--- a/tutorials/source_zh_cn/use/multi_platform_inference.md
+++ b/tutorials/source_zh_cn/use/multi_platform_inference.md
@@ -29,16 +29,16 @@ res = model.eval(dataset)
 ## Ascend 310 AI处理器上推理
-1. 参考[模型导出](https://www.mindspore.cn/tutorial/zh-CN/master/use/saving_and_loading_model_parameters.html#geironnx)生成ONNX或GEIR模型。
+1. 参考[模型导出](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/use/saving_and_loading_model_parameters.html#geironnx)生成ONNX或GEIR模型。
 2. 云上环境请参考[Ascend910训练和Ascend310推理的样例](https://support.huaweicloud.com/bestpractice-modelarts/modelarts_10_0026.html)完成推理操作。裸机环境（对比云上环境，即本地有Ascend 310 AI 处理器）请参考Ascend 310 AI处理器配套软件包的说明文档。
 ## GPU上推理
-1. 参考[模型导出](https://www.mindspore.cn/tutorial/zh-CN/master/use/saving_and_loading_model_parameters.html#geironnx)生成ONNX模型。
+1. 参考[模型导出](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/use/saving_and_loading_model_parameters.html#geironnx)生成ONNX模型。
 2. 参考[TensorRT backend for ONNX](https://github.com/onnx/onnx-tensorrt)，在Nvidia GPU上完成推理操作。
 ## 端侧推理
-端侧推理需使用MindSpore Predict推理引擎，详细操作请参考[端侧推理教程](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/on_device_inference.html)。
+端侧推理需使用MindSpore Predict推理引擎，详细操作请参考[端侧推理教程](https://www.mindspore.cn/tutorial/zh-CN/0.3.0-alpha/advanced_use/on_device_inference.html)。
\ No newline at end of file