diff --git a/docs/source_en/architecture.md b/docs/source_en/architecture.md index cd30baf77b5b436e4761e78c2f87545ad6b36c34..0f95ac41174fee3a85c4cb00805dc320670e0244 100644 --- a/docs/source_en/architecture.md +++ b/docs/source_en/architecture.md @@ -8,7 +8,7 @@ This document describes the overall architecture of MindSpore. - + The MindSpore framework consists of the Frontend Expression layer, Graph Engine layer, and Backend Runtime layer. diff --git a/docs/source_en/benchmark.md b/docs/source_en/benchmark.md index 6c541a6755ab8ebc991e5be75c745cef167c454e..719943f87f7ef629e04c9bffdac396beaac89c7f 100644 --- a/docs/source_en/benchmark.md +++ b/docs/source_en/benchmark.md @@ -1,6 +1,6 @@ # Benchmarks - + This document describes the MindSpore benchmarks. For details about the MindSpore pre-trained model, see [Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo). diff --git a/docs/source_en/constraints_on_network_construction.md b/docs/source_en/constraints_on_network_construction.md index 69345b9878d01d5ecdcea9a647e883cb71b00a16..47bc6dc5c1b04138ff938926eb2761b474ed0295 100644 --- a/docs/source_en/constraints_on_network_construction.md +++ b/docs/source_en/constraints_on_network_construction.md @@ -23,7 +23,7 @@ - + ## Overview MindSpore can compile user source code based on the Python syntax into computational graphs, and can convert common functions or instances inherited from nn.Cell into computational graphs. Currently, MindSpore does not support conversion of any Python source code into computational graphs. Therefore, there are constraints on source code compilation, including syntax constraints and network definition constraints. As MindSpore evolves, the constraints may change. diff --git a/docs/source_en/glossary.md b/docs/source_en/glossary.md index 073a76f32a361f3227f9ab16005b811c152fe0a3..3f0f4b1471ae7a358e7e92c7041aad57b3be51d3 100644 --- a/docs/source_en/glossary.md +++ b/docs/source_en/glossary.md @@ -6,7 +6,7 @@ - + | Acronym and Abbreviation | Description | | ----- | ----- | diff --git a/docs/source_en/operator_list.md b/docs/source_en/operator_list.md index 33328e0a856194df704409f3c4eade4757096caf..7f912b97dbfe00ecb3b706b510e7442efaca82f7 100644 --- a/docs/source_en/operator_list.md +++ b/docs/source_en/operator_list.md @@ -8,7 +8,7 @@ - + ## mindspore.nn diff --git a/docs/source_en/roadmap.md b/docs/source_en/roadmap.md index 5c89ddfb0d26a29a7f515ee9e6485bf752359c89..aeec98616e8afaeee3558fa964a1a31d99b6680c 100644 --- a/docs/source_en/roadmap.md +++ b/docs/source_en/roadmap.md @@ -14,7 +14,7 @@ MindSpore's top priority plans in the year are displayed as follows. We will con - + In general, we will make continuous improvements in the following aspects: 1. Support more preset models. diff --git a/docs/source_zh_cn/architecture.md b/docs/source_zh_cn/architecture.md index 27205cde10022548ae9d25911746b1308a61956c..7f05d732c2bf31c033edd3c6a57fbad055f80f41 100644 --- a/docs/source_zh_cn/architecture.md +++ b/docs/source_zh_cn/architecture.md @@ -8,7 +8,7 @@ - + MindSpore框架架构总体分为MindSpore前端表示层、MindSpore计算图引擎和MindSpore后端运行时三层。 diff --git a/docs/source_zh_cn/benchmark.md b/docs/source_zh_cn/benchmark.md index 4d3f4efcbe1679ff1b5e76f6f277dc3aa76ade0e..fdbc9c1b4837e9b3e39cac5af2753329db5f5393 100644 --- a/docs/source_zh_cn/benchmark.md +++ b/docs/source_zh_cn/benchmark.md @@ -1,6 +1,6 @@ # 基准性能 - + 本文介绍MindSpore的基准性能。MindSpore预训练模型可参考[Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)。 diff --git a/docs/source_zh_cn/constraints_on_network_construction.md b/docs/source_zh_cn/constraints_on_network_construction.md index ff628c798272c2a83e0e7ff0aee2da47dc71c65e..4538ab05fd87322b03e87c9aaeadb76aac30770d 100644 --- a/docs/source_zh_cn/constraints_on_network_construction.md +++ b/docs/source_zh_cn/constraints_on_network_construction.md @@ -23,7 +23,7 @@ - + ## 概述 MindSpore完成从用户源码到计算图的编译，用户源码基于Python语法编写，当前MindSpore支持将普通函数或者继承自nn.Cell的实例转换生成计算图，暂不支持将任意Python源码转换成计算图，所以对于用户源码支持的写法有所限制，主要包括语法约束和网络定义约束两方面。随着MindSpore的演进，这些约束可能会发生变化。 diff --git a/docs/source_zh_cn/glossary.md b/docs/source_zh_cn/glossary.md index eb1c2cdd6ace20ac52d24438031efaaed935f64d..20aee2b801113e63c630aedc4a5ab12d521208a6 100644 --- a/docs/source_zh_cn/glossary.md +++ b/docs/source_zh_cn/glossary.md @@ -6,7 +6,7 @@ - + | 术语/缩略语 | 说明 | | ----- | ----- | diff --git a/docs/source_zh_cn/operator_list.md b/docs/source_zh_cn/operator_list.md index 128e5bc31d800ffc14436319b43505ce184f045f..de68a7d2d1e6f85648bb91ec6d4759d3193c9c37 100644 --- a/docs/source_zh_cn/operator_list.md +++ b/docs/source_zh_cn/operator_list.md @@ -8,7 +8,7 @@ - + ## mindspore.nn diff --git a/docs/source_zh_cn/roadmap.md b/docs/source_zh_cn/roadmap.md index 528182d2e3ec4224b3f07df433ea93609e939016..772a7092a1c27d4ef89dba0b26304a57eed36c17 100644 --- a/docs/source_zh_cn/roadmap.md +++ b/docs/source_zh_cn/roadmap.md @@ -23,7 +23,7 @@ - + ## 预置模型 * CV：目标检测、GAN、图像分割、姿态识别等场景经典模型。 diff --git a/install/mindspore_cpu_install.md b/install/mindspore_cpu_install.md index 45a1beee503131e6e0690bda2ba75e4980b6f6fe..431fc4dfcb81f7aecbcdc7f33ad0d903d9ba7c91 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 18.04 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 18.04 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/r0.5/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 | - 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 r0.5 ``` 2. 在源码根目录下执行如下命令编译MindSpore。 @@ -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 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- 其他依赖项参见[setup.py](https://gitee.com/mindspore/mindarmour/blob/master/setup.py) | 与可执行文件安装依赖相同 | +| MindArmour master | Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- 其他依赖项参见[setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.5/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。 @@ -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 r0.5 ``` 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 5d2eaa21209ebf7bc5c191c3846654f35c30b95c..1bc2b62cd2d9ea453202cfa6051e5d58a9b8e71c 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 18.04 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 18.04 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/r0.5/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. | - 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 r0.5 ``` 2. Run the following command in the root directory of the source code to compile MindSpore: @@ -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 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/master/setup.py). | Same as the executable file installation dependencies. | +| MindArmour master | Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.5/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. @@ -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 r0.5 ``` 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 5b33ddb3c8567ac9182297fb91ba4b93f3173497..359280013bf6e0906d3e65fdf3d867ef09f49ec0 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/r0.5/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 r0.5 ``` 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 2f1fe17739a318d9ebbc63c5d79ea46409ec662d..9de7edbcfdfba589ad80dfff4836c439d63f1540 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/r0.5/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 r0.5 ``` 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 c2e1da96b851fc03a40451c8e2742588b4a8f0fa..cd2f1d41a246d5377417ae59e4c19df964916db2 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 18.04 aarch64
- Ubuntu 18.04 x86_64
- EulerOS 2.8 aarch64
- 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 Data Center Solution V100R020C00T100）
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
- 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 Data Center Solution V100R020C00T100）
- [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
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
**安装依赖：**
与可执行文件安装依赖相同 | +| MindSpore master | - Ubuntu 18.04 aarch64
- Ubuntu 18.04 x86_64
- EulerOS 2.8 aarch64
- 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 Data Center Solution V100R020C00T100）
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.5/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 Data Center Solution V100R020C00T100）
- [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
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
**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 Data Center Solution V100R020C00T100）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。 - 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 r0.5 ``` 2. 在源码根目录下，执行如下命令编译MindSpore。 @@ -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 18.04 aarch64
- Ubuntu 18.04 x86_64
- EulerOS 2.8 aarch64
- EulerOS 2.5 x86_64
| - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindinsight/blob/master/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [node.js](https://nodejs.org/en/download/) >= 10.19.0
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [pybind11](https://pypi.org/project/pybind11/) >= 2.4.3
**安装依赖：**
与可执行文件安装依赖相同 | +| MindInsight master | - Ubuntu 18.04 aarch64
- Ubuntu 18.04 x86_64
- EulerOS 2.8 aarch64
- EulerOS 2.5 x86_64
| - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindinsight/blob/r0.5/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [node.js](https://nodejs.org/en/download/) >= 10.19.0
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [pybind11](https://pypi.org/project/pybind11/) >= 2.4.3
**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。 @@ -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 r0.5 ``` > **不能**直接在仓库主页下载zip包获取源码。 @@ -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 18.04 aarch64
- Ubuntu 18.04 x86_64
- EulerOS 2.8 aarch64
- EulerOS 2.5 x86_64
| - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- 其他依赖项参见[setup.py](https://gitee.com/mindspore/mindarmour/blob/master/setup.py) | 与可执行文件安装依赖相同 | +| MindArmour master | - Ubuntu 18.04 aarch64
- Ubuntu 18.04 x86_64
- EulerOS 2.8 aarch64
- EulerOS 2.5 x86_64
| - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- 其他依赖项参见[setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.5/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。 @@ -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 r0.5 ``` 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 1b93cae8a6785edf7a2545c65b39989bc818015f..6b215d072c6d3f51a07df8d3245aaae16a250fa5 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 18.04 aarch64
- Ubuntu 18.04 x86_64
- EulerOS 2.8 aarch64
- EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI processor software package(Version:Atlas Data Center Solution V100R020C00T100)
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
- 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 Data Center Solution V100R020C00T100)
- [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
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
**Installation dependencies:**
same as the executable file installation dependencies. | +| MindSpore master | - Ubuntu 18.04 aarch64
- Ubuntu 18.04 x86_64
- EulerOS 2.8 aarch64
- EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI processor software package(Version:Atlas Data Center Solution V100R020C00T100)
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.5/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI processor software package(Version:Atlas Data Center Solution V100R020C00T100)
- [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
- [gmp](https://gmplib.org/download/gmp/) 6.1.2
**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 Data Center Solution V100R020C00T100). 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. - 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 r0.5 ``` 2. Run the following command in the root directory of the source code to compile MindSpore: @@ -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 18.04 aarch64
- Ubuntu 18.04 x86_64
- EulerOS 2.8 aarch64
- EulerOS 2.5 x86_64
| - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindinsight/blob/master/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [node.js](https://nodejs.org/en/download/) >= 10.19.0
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [pybind11](https://pypi.org/project/pybind11/) >= 2.4.3
**Installation dependencies:**
same as the executable file installation dependencies. | +| MindInsight master | - Ubuntu 18.04 aarch64
- Ubuntu 18.04 x86_64
- EulerOS 2.8 aarch64
- EulerOS 2.5 x86_64
| - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindinsight/blob/r0.5/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [node.js](https://nodejs.org/en/download/) >= 10.19.0
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [pybind11](https://pypi.org/project/pybind11/) >= 2.4.3
**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. @@ -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 r0.5 ``` > You are **not** supposed to obtain the source code from the zip package downloaded from the repository homepage. @@ -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 18.04 aarch64
- Ubuntu 18.04 x86_64
- EulerOS 2.8 aarch64
- EulerOS 2.5 x86_64
| - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/master/setup.py). | Same as the executable file installation dependencies. | +| MindArmour master | - Ubuntu 18.04 aarch64
- Ubuntu 18.04 x86_64
- EulerOS 2.8 aarch64
- EulerOS 2.5 x86_64
| - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.5/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. @@ -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 r0.5 ``` 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 7aa1e902ac4ef75ccc4551fae18976a983dc6a24..12f3da5107fab53dad60b0f59cf24cd39a4a60d5 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 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- [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 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- [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/r0.5/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 | - 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。 - 为了方便用户使用，MindSpore降低了对Autoconf、Libtool、Automake版本的依赖，可以使用系统自带版本。 @@ -63,7 +63,7 @@ 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 r0.5 ``` 2. 在源码根目录下执行如下命令编译MindSpore。 @@ -123,7 +123,7 @@ | 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 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindinsight/blob/master/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [node.js](https://nodejs.org/en/download/) >= 10.19.0
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [pybind11](https://pypi.org/project/pybind11/) >= 2.4.3
**安装依赖：**
与可执行文件安装依赖相同 | +| MindInsight master | - Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindinsight/blob/r0.5/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [node.js](https://nodejs.org/en/download/) >= 10.19.0
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [pybind11](https://pypi.org/project/pybind11/) >= 2.4.3
**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。 @@ -148,7 +148,7 @@ 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 r0.5 ``` > **不能**直接在仓库主页下载zip包获取源码。 @@ -188,7 +188,7 @@ | 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 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- 其他依赖项参见[setup.py](https://gitee.com/mindspore/mindarmour/blob/master/setup.py) | 与可执行文件安装依赖相同 | +| MindArmour master | Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- 其他依赖项参见[setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.5/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。 @@ -213,7 +213,7 @@ 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 r0.5 ``` 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 7507771b6e5d4d491b436d75a2243322049fab4a..ba51bbc1178b680c68588c4d5360a14a32e0122b 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 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- [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 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- [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/r0.5/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 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. - MindSpore reduces dependency on Autoconf, Libtool, Automake versions for the convenience of users, default versions of these tools built in their systems are now supported. @@ -63,7 +63,7 @@ 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 r0.5 ``` 2. Run the following command in the root directory of the source code to compile MindSpore: @@ -123,7 +123,7 @@ 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 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindinsight/blob/master/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [node.js](https://nodejs.org/en/download/) >= 10.19.0
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [pybind11](https://pypi.org/project/pybind11/) >= 2.4.3
**Installation dependencies:**
same as the executable file installation dependencies. | +| MindInsight master | - Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindinsight/blob/r0.5/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [node.js](https://nodejs.org/en/download/) >= 10.19.0
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [pybind11](https://pypi.org/project/pybind11/) >= 2.4.3
**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. @@ -148,7 +148,7 @@ 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 r0.5 ``` > You are **not** supposed to obtain the source code from the zip package downloaded from the repository homepage. @@ -190,7 +190,7 @@ 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 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/master/setup.py). | Same as the executable file installation dependencies. | +| MindArmour master | Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.5/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. @@ -215,7 +215,7 @@ 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 r0.5 ``` 2. Run the following command in the root directory of the source code to compile and install MindArmour: diff --git a/resource/faq/FAQ_en.md b/resource/faq/FAQ_en.md index 5815ab26b8cfaecc15635dd13bec6e6b1fa6c772..3630c1c979b9747de4f56874d0f5dccc34d73d54 100644 --- a/resource/faq/FAQ_en.md +++ b/resource/faq/FAQ_en.md @@ -68,7 +68,7 @@ A: Please install the software manually if there is any suggestion of certain `s Q: What types of model is currently supported by MindSpore for training ? -A: MindSpore has basic support for common training scenarios, please refer to [Release note](https://gitee.com/mindspore/mindspore/blob/master/RELEASE.md) for detailed information. +A: MindSpore has basic support for common training scenarios, please refer to [Release note](https://gitee.com/mindspore/mindspore/blob/r0.5/RELEASE.md) for detailed information.
@@ -92,7 +92,7 @@ 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/master/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/master/roadmap.html) and project [Release Notes](https://gitee.com/mindspore/mindspore/blob/r0.5/RELEASE.md). ### System Support diff --git a/resource/faq/FAQ_zh_cn.md b/resource/faq/FAQ_zh_cn.md index 3ee10f6b70d94b00af22bc113e9b98e5e40f8248..68929bd7d2dd30b3c4bfdaa0b3a51c6f29d4dc6b 100644 --- a/resource/faq/FAQ_zh_cn.md +++ b/resource/faq/FAQ_zh_cn.md @@ -67,7 +67,7 @@ A：当有此提示时说明要用户安装`tclsh`；如果仍提示缺少其他 Q：MindSpore支持哪些模型的训练？ -A：MindSpore针对典型场景均有模型训练支持，支持情况详见[Release note](https://gitee.com/mindspore/mindspore/blob/master/RELEASE.md)。 +A：MindSpore针对典型场景均有模型训练支持，支持情况详见[Release note](https://gitee.com/mindspore/mindspore/blob/r0.5/RELEASE.md)。
@@ -91,7 +91,7 @@ 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/master/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/master/roadmap.html)和项目[Release note](https://gitee.com/mindspore/mindspore/blob/r0.5/RELEASE.md)获取最新信息。 ### 系统支持 diff --git a/tutorials/notebook/quick_start.ipynb b/tutorials/notebook/quick_start.ipynb index da57dbd4aa139f5e2d60a607f98a96e8e2034dfd..31fec669e4d778ced5c9901f307101763a6a3016 100644 --- a/tutorials/notebook/quick_start.ipynb +++ b/tutorials/notebook/quick_start.ipynb @@ -34,7 +34,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "说明：
你可以在这里找到完整可运行的样例代码：https://gitee.com/mindspore/docs/blob/master/tutorials/tutorial_code/lenet.py" + "说明：
你可以在这里找到完整可运行的样例代码：https://gitee.com/mindspore/docs/blob/r0.5/tutorials/tutorial_code/lenet.py" ] }, { diff --git a/tutorials/source_en/advanced_use/computer_vision_application.md b/tutorials/source_en/advanced_use/computer_vision_application.md index be0522cd85e7f938dce1dc0ec88fda95a0bd8b55..70fbc929a513e1f85da7b5676080637479361827 100644 --- a/tutorials/source_en/advanced_use/computer_vision_application.md +++ b/tutorials/source_en/advanced_use/computer_vision_application.md @@ -16,7 +16,7 @@ - + ## Overview @@ -64,7 +64,7 @@ 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/customized_debugging_information.md b/tutorials/source_en/advanced_use/customized_debugging_information.md index d7721e19b3956762b5d679dff41282949ce51f36..3e18520988367531452729ee69abfd7a74744cce 100644 --- a/tutorials/source_en/advanced_use/customized_debugging_information.md +++ b/tutorials/source_en/advanced_use/customized_debugging_information.md @@ -14,7 +14,7 @@ - + ## Overview diff --git a/tutorials/source_en/advanced_use/dashboard_and_lineage.md b/tutorials/source_en/advanced_use/dashboard_and_lineage.md index 551be944c7d5a6c88dfc5c2036e22b0db043784f..8fc357c4b43e562ec08bcb65450096abbe99ea28 100644 --- a/tutorials/source_en/advanced_use/dashboard_and_lineage.md +++ b/tutorials/source_en/advanced_use/dashboard_and_lineage.md @@ -21,7 +21,7 @@ - + ## Overview Scalars, images, computational graphs, and model hyperparameters during training are recorded in files and can be viewed on the web page. diff --git a/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md b/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md index 1142684a4ef8d9f5b052995d07280ac6c9c7d00d..569b3a8efd3f4461b0a330fc2a47235965070689 100644 --- a/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md +++ b/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md @@ -11,7 +11,7 @@ - + ## Overview diff --git a/tutorials/source_en/advanced_use/differential_privacy.md b/tutorials/source_en/advanced_use/differential_privacy.md index 50717af4aa584d0f93e7b5a8d2f0dd41ccbaf8de..b38b5c1034f75ec777608747fad85129c078f83a 100644 --- a/tutorials/source_en/advanced_use/differential_privacy.md +++ b/tutorials/source_en/advanced_use/differential_privacy.md @@ -14,7 +14,7 @@ - + ## Overview diff --git a/tutorials/source_en/advanced_use/distributed_training.md b/tutorials/source_en/advanced_use/distributed_training.md index 20cd61d6253bb7392aa9e8c422aeedadfb102ee4..eb249ee93c3cad904e5a316c36903ddc9d506bd6 100644 --- a/tutorials/source_en/advanced_use/distributed_training.md +++ b/tutorials/source_en/advanced_use/distributed_training.md @@ -18,7 +18,7 @@ - + ## Overview In deep learning, the increasing number of datasets and parameters prolongs the training time and requires more hardware resources, becoming a training bottleneck. Parallel distributed training is an important optimization method for training, which can reduce requirements on hardware, such as memory and computing performance. Based on different parallel principles and modes, parallelism is generally classified into the following types: @@ -34,7 +34,7 @@ MindSpore also provides the parallel distributed training function. It supports This tutorial describes how to train the ResNet-50 network in data parallel and automatic parallel modes on MindSpore. > The example in this tutorial applies to hardware platforms based on the Ascend 910 AI processor, whereas does not support CPU and GPU scenarios. -> Download address of the complete sample code: +> Download address of the complete sample code: ## Preparations @@ -177,7 +177,7 @@ Different from the single-node system, the multi-node system needs to transfer t ## Defining the Network -In data parallel and automatic parallel modes, the network definition method is the same as that in a single-node system. The reference code is as follows: +In data parallel and automatic parallel modes, the network definition method is the same as that in a single-node system. The reference code is as follows: ## Defining the Loss Function and Optimizer diff --git a/tutorials/source_en/advanced_use/mixed_precision.md b/tutorials/source_en/advanced_use/mixed_precision.md index b88cbd8ec2b4dc17f8dd86e58432df7ab1fd96d7..1f09f5bca7fdf8d25a7a4a74d6132b54db2e2c88 100644 --- a/tutorials/source_en/advanced_use/mixed_precision.md +++ b/tutorials/source_en/advanced_use/mixed_precision.md @@ -10,7 +10,7 @@ - + ## Overview diff --git a/tutorials/source_en/advanced_use/model_security.md b/tutorials/source_en/advanced_use/model_security.md index 0b4990aace93c35300d1726b516c767480dec688..7774c16c1ae22c985ce2603058f17ef0e43baac8 100644 --- a/tutorials/source_en/advanced_use/model_security.md +++ b/tutorials/source_en/advanced_use/model_security.md @@ -15,7 +15,7 @@ - + ## Overview diff --git a/tutorials/source_en/advanced_use/network_migration.md b/tutorials/source_en/advanced_use/network_migration.md index 9f396fe5480fbc544b0baf578da8103cb0385450..656ba30e10f2908600ecf94fbdc57092bdd2c158 100644 --- a/tutorials/source_en/advanced_use/network_migration.md +++ b/tutorials/source_en/advanced_use/network_migration.md @@ -79,7 +79,7 @@ The ResNet-50 network migration and training on the Ascend 910 is used as an exa num_shards=device_num, shard_id=rank_id) ``` - Then, perform data augmentation, data cleaning, and batch processing. For details about the code, see . + Then, perform data augmentation, data cleaning, and batch processing. For details about the code, see . 3. Build a network. @@ -214,7 +214,7 @@ The ResNet-50 network migration and training on the Ascend 910 is used as an exa 6. Build the entire network. - The [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py) network structure is formed by connecting multiple defined subnets. Follow the rule of defining subnets before using them and define all the subnets used in the `__init__` and connect subnets in the `construct`. + The [ResNet-50](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/resnet/src/resnet.py) network structure is formed by connecting multiple defined subnets. Follow the rule of defining subnets before using them and define all the subnets used in the `__init__` and connect subnets in the `construct`. 7. Define a loss function and an optimizer. diff --git a/tutorials/source_en/advanced_use/nlp_application.md b/tutorials/source_en/advanced_use/nlp_application.md index 1d2b11258bcb4644dd55b84a49ebac3af1c35431..cd7becd13c40aa6368263b4f1827ca255b9a88c9 100644 --- a/tutorials/source_en/advanced_use/nlp_application.md +++ b/tutorials/source_en/advanced_use/nlp_application.md @@ -20,7 +20,7 @@ - + ## Overview diff --git a/tutorials/source_en/advanced_use/on_device_inference.md b/tutorials/source_en/advanced_use/on_device_inference.md index f3ddc120b3c3bcea04bc95c931aeb638aa8b2611..f8c130951834fec999103a264e067d81bc2c06dd 100644 --- a/tutorials/source_en/advanced_use/on_device_inference.md +++ b/tutorials/source_en/advanced_use/on_device_inference.md @@ -11,7 +11,7 @@ - + ## Overview @@ -60,7 +60,7 @@ 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 r0.5 ``` 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/advanced_use/performance_profiling.md b/tutorials/source_en/advanced_use/performance_profiling.md index 2162cbcb5a9feb19c0268a512ff14596507e72f6..f251dd4c31df0f5a3503cf3440727948441dfd7b 100644 --- a/tutorials/source_en/advanced_use/performance_profiling.md +++ b/tutorials/source_en/advanced_use/performance_profiling.md @@ -16,7 +16,7 @@ - + ## Overview Performance data like operators' execution time are recorded in files and can be viewed on the web page, this can help the user optimize the performance of neural networks. MindInsight Profiler can only support the Ascend chip now. diff --git a/tutorials/source_en/quick_start/quick_start.md b/tutorials/source_en/quick_start/quick_start.md index 500c9614e3fa7e445a33a0822c73895495ead5d6..9fdc7698c3f373f3db24f5661c4dff907bd9130b 100644 --- a/tutorials/source_en/quick_start/quick_start.md +++ b/tutorials/source_en/quick_start/quick_start.md @@ -24,7 +24,7 @@ - + ## Overview @@ -38,7 +38,7 @@ 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_en/use/custom_operator.md b/tutorials/source_en/use/custom_operator.md index c6f4296a3df4ed91a1834627fa4e5e79b5b90425..db1224ffff99eb1d1ddd0ac7130a49f599ef130d 100644 --- a/tutorials/source_en/use/custom_operator.md +++ b/tutorials/source_en/use/custom_operator.md @@ -14,7 +14,7 @@ - + ## Overview diff --git a/tutorials/source_en/use/data_preparation/converting_datasets.md b/tutorials/source_en/use/data_preparation/converting_datasets.md index 11ea33c866010d2275ede0a1831a1da03e1b1320..966794ca3030d28915bd726d2504cf4f9cc99047 100644 --- a/tutorials/source_en/use/data_preparation/converting_datasets.md +++ b/tutorials/source_en/use/data_preparation/converting_datasets.md @@ -14,7 +14,7 @@ - + ## Overview diff --git a/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md b/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md index ee669c4d3452f00e8ba08b36ed877348a3b6ac94..6fa0aec89dd5ae9478ee90f86d3ef352b1ebbca6 100644 --- a/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md +++ b/tutorials/source_en/use/data_preparation/data_processing_and_augmentation.md @@ -16,7 +16,7 @@ - + ## Overview diff --git a/tutorials/source_en/use/data_preparation/loading_the_datasets.md b/tutorials/source_en/use/data_preparation/loading_the_datasets.md index 4ffaf9de19e9d997bfb30ac93723f44a4026af01..2041da36da29ba046ca1123beb94398d28ac2faf 100644 --- a/tutorials/source_en/use/data_preparation/loading_the_datasets.md +++ b/tutorials/source_en/use/data_preparation/loading_the_datasets.md @@ -13,7 +13,7 @@ - + ## Overview diff --git a/tutorials/source_en/use/multi_platform_inference.md b/tutorials/source_en/use/multi_platform_inference.md index 373dc9b634b7ce3a531d58d77582c12f88a10326..e8731b46b91152190c39f12ab7e661c7075c6a33 100644 --- a/tutorials/source_en/use/multi_platform_inference.md +++ b/tutorials/source_en/use/multi_platform_inference.md @@ -8,7 +8,7 @@ - + ## Overview @@ -16,7 +16,7 @@ Models based on MindSpore training can be used for inference on different hardwa 1. Inference on the Ascend 910 AI processor - MindSpore provides the `model.eval` API for model validation. You only need to import the validation dataset. The processing method of the validation dataset is the same as that of the training dataset. For details about the complete code, see . + MindSpore provides the `model.eval` API for model validation. You only need to import the validation dataset. The processing method of the validation dataset is the same as that of the training dataset. For details about the complete code, see . ```python res = model.eval(dataset) diff --git a/tutorials/source_en/use/saving_and_loading_model_parameters.md b/tutorials/source_en/use/saving_and_loading_model_parameters.md index 78fc04726f1a0977a87396c7c0ef02082efb852e..2bd4bb7806eb1520e76cf1d93723a21224d19728 100644 --- a/tutorials/source_en/use/saving_and_loading_model_parameters.md +++ b/tutorials/source_en/use/saving_and_loading_model_parameters.md @@ -13,7 +13,7 @@ - + ## Overview diff --git a/tutorials/source_zh_cn/advanced_use/aware_quantization.md b/tutorials/source_zh_cn/advanced_use/aware_quantization.md index fcb5a520ae3ec77bbeb5c01c00a873643cc89805..9f831fe06f5b7f0aec9f2ae6933d7a97cf56caeb 100644 --- a/tutorials/source_zh_cn/advanced_use/aware_quantization.md +++ b/tutorials/source_zh_cn/advanced_use/aware_quantization.md @@ -12,7 +12,7 @@ - + ## 概述 diff --git a/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md b/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md index 6aaf7eb9e7b1628b60282eee3ce068133b8950bf..63bf4733a8b3d289e4a61d680012c5087be1bbcc 100644 --- a/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md +++ b/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md @@ -26,7 +26,7 @@ - + ## 概述 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 d8839c1b946e1f51615f5aa23e9d652531e6b8bc..6497d34f07a5851053844b12f1b0b5c4ae6e944a 100644 --- a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md +++ b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md @@ -16,7 +16,7 @@ - + ## 概述 @@ -65,7 +65,7 @@ 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/customized_debugging_information.md b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md index 9febbe7c2eb790fc25fd2eca73334faf30515f07..2e25b94c22824c023c10dc570fa141a0e0c80124 100644 --- a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md +++ b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md @@ -13,7 +13,7 @@ - + ## 概述 diff --git a/tutorials/source_zh_cn/advanced_use/dashboard_and_lineage.md b/tutorials/source_zh_cn/advanced_use/dashboard_and_lineage.md index 97512495b4624ca4172d5a9ae1f7de68667766fc..d962ca944bc9fa21e90e1878d094c3e4b3522923 100644 --- a/tutorials/source_zh_cn/advanced_use/dashboard_and_lineage.md +++ b/tutorials/source_zh_cn/advanced_use/dashboard_and_lineage.md @@ -21,7 +21,7 @@ - + ## 概述 训练过程中的标量、图像、计算图以及模型超参等信息记录到文件中，通过可视化界面供用户查看。 diff --git a/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md b/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md index 08ac3f3f4d8f89b5fe56791ece4f90250c49d125..c5a3e29d10b51bb821b2dc39a9679426a8eaaa9d 100644 --- a/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md +++ b/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md @@ -11,7 +11,7 @@ - + ## 概述 diff --git a/tutorials/source_zh_cn/advanced_use/differential_privacy.md b/tutorials/source_zh_cn/advanced_use/differential_privacy.md index 84c381f7cfc2ade4fe58fe837697b83fb2f81b64..7dbaf65499ec54ffeb547aea3bb87fce69d70b11 100644 --- a/tutorials/source_zh_cn/advanced_use/differential_privacy.md +++ b/tutorials/source_zh_cn/advanced_use/differential_privacy.md @@ -1,6 +1,6 @@ # 机器学习中的差分隐私 - + ## 概述 diff --git a/tutorials/source_zh_cn/advanced_use/distributed_training.md b/tutorials/source_zh_cn/advanced_use/distributed_training.md index 52627db1c5abfa1614582dcab03a7571d2cbff70..4044307d695d7d6818af0b76f3d3bd84de3881a6 100644 --- a/tutorials/source_zh_cn/advanced_use/distributed_training.md +++ b/tutorials/source_zh_cn/advanced_use/distributed_training.md @@ -18,7 +18,7 @@ - + ## 概述 在深度学习中，当数据集和参数量的规模越来越大，训练所需的时间和硬件资源会随之增加，最后会变成制约训练的瓶颈。分布式并行训练，可以降低对内存、计算性能等硬件的需求，是进行训练的重要优化手段。根据并行的原理及模式不同，业界主流的并行类型有以下几种： @@ -34,7 +34,7 @@ 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 @@ -175,7 +175,7 @@ def create_dataset(data_path, repeat_num=1, batch_size=32, rank_id=0, rank_size= ## 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/graph_kernel_fusion.md b/tutorials/source_zh_cn/advanced_use/graph_kernel_fusion.md index 3f53b6c3fc50167bace2807e8678b14eb9ed3848..294d91046ed49b7714ae8528b70c3337af44561b 100644 --- a/tutorials/source_zh_cn/advanced_use/graph_kernel_fusion.md +++ b/tutorials/source_zh_cn/advanced_use/graph_kernel_fusion.md @@ -12,7 +12,7 @@ - + ## 概述 diff --git a/tutorials/source_zh_cn/advanced_use/mixed_precision.md b/tutorials/source_zh_cn/advanced_use/mixed_precision.md index 4cacb8c7a40d5cd55ea31bbc7a078f6dab3ab10e..4df6ab97dfe93bc43bbe9a81894480691e4d3670 100644 --- a/tutorials/source_zh_cn/advanced_use/mixed_precision.md +++ b/tutorials/source_zh_cn/advanced_use/mixed_precision.md @@ -10,7 +10,7 @@ - + ## 概述 diff --git a/tutorials/source_zh_cn/advanced_use/model_security.md b/tutorials/source_zh_cn/advanced_use/model_security.md index 9daf517e31f8ea9c295ecc3fc9b993eab417148a..198b20eb4bb4443b44ceadbb775895ad13b3f052 100644 --- a/tutorials/source_zh_cn/advanced_use/model_security.md +++ b/tutorials/source_zh_cn/advanced_use/model_security.md @@ -15,7 +15,7 @@ - + ## 概述 diff --git a/tutorials/source_zh_cn/advanced_use/network_migration.md b/tutorials/source_zh_cn/advanced_use/network_migration.md index 8d3f574dbe4a90fec2c34772db6d2cdd12426fba..6d0aa8f27f431248d3f0615e7c4580961203f3df 100644 --- a/tutorials/source_zh_cn/advanced_use/network_migration.md +++ b/tutorials/source_zh_cn/advanced_use/network_migration.md @@ -17,7 +17,7 @@ - + ## 概述 @@ -77,7 +77,7 @@ MindSpore与TensorFlow、PyTorch在网络结构组织方式上，存在一定差 num_shards=device_num, shard_id=rank_id) ``` - 然后对数据进行了数据增强、数据清洗和批处理等操作。代码详见。 + 然后对数据进行了数据增强、数据清洗和批处理等操作。代码详见。 3. 构建网络。 @@ -210,7 +210,7 @@ MindSpore与TensorFlow、PyTorch在网络结构组织方式上，存在一定差 6. 构造整网。 - 将定义好的多个子网连接起来就是整个[ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py)网络的结构了。同样遵循先定义后使用的原则，在`__init__`中定义所有用到的子网，在`construct`中连接子网。 + 将定义好的多个子网连接起来就是整个[ResNet-50](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/resnet/src/resnet.py)网络的结构了。同样遵循先定义后使用的原则，在`__init__`中定义所有用到的子网，在`construct`中连接子网。 7. 定义损失函数和优化器。 diff --git a/tutorials/source_zh_cn/advanced_use/nlp_application.md b/tutorials/source_zh_cn/advanced_use/nlp_application.md index ace94f759da81b922d1654991efa567987cfb8cb..f42511a1a9f4a1ca161b1aa66f8e433a676ffec4 100644 --- a/tutorials/source_zh_cn/advanced_use/nlp_application.md +++ b/tutorials/source_zh_cn/advanced_use/nlp_application.md @@ -20,7 +20,7 @@ - + ## 概述 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 eb224de00025b55d2015db23265a7012b98c07c7..7de3f8f563cd4c2eee1e6048217590ae866d9de6 100644 --- a/tutorials/source_zh_cn/advanced_use/on_device_inference.md +++ b/tutorials/source_zh_cn/advanced_use/on_device_inference.md @@ -11,7 +11,7 @@ - + ## 概述 @@ -59,7 +59,7 @@ 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 r0.5 ``` 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/advanced_use/performance_profiling.md b/tutorials/source_zh_cn/advanced_use/performance_profiling.md index ee36edfad680f2f50a2c4cf7e9c3e9bc6f3ac334..8cb5ad4b5b70ff1ea2d5e825adf239a4715e1809 100644 --- a/tutorials/source_zh_cn/advanced_use/performance_profiling.md +++ b/tutorials/source_zh_cn/advanced_use/performance_profiling.md @@ -17,7 +17,7 @@ - + ## 概述 将训练过程中的算子耗时等信息记录到文件中，通过可视化界面供用户查看分析，帮助用户更高效地调试神经网络性能。目前仅支持在Ascend芯片上的性能调试。 diff --git a/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md b/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md index 8b6a147e3140b500abfd9bee18bdc7c70d390e5a..3f74f6b32189798a6d9d1f6f506d5ff0c4cdcc8c 100644 --- a/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md +++ b/tutorials/source_zh_cn/advanced_use/use_on_the_cloud.md @@ -24,7 +24,7 @@ - + ## 概述 diff --git a/tutorials/source_zh_cn/quick_start/quick_start.md b/tutorials/source_zh_cn/quick_start/quick_start.md index 19a7c8e7a442b7760079e5297f2f1eb8788c8c8c..a06cb6ea4ba8c76f5492522ad5199f3010381b3b 100644 --- a/tutorials/source_zh_cn/quick_start/quick_start.md +++ b/tutorials/source_zh_cn/quick_start/quick_start.md @@ -24,9 +24,9 @@ - +    - + ## 概述 @@ -40,7 +40,7 @@ 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。 diff --git a/tutorials/source_zh_cn/quick_start/quick_video.md b/tutorials/source_zh_cn/quick_start/quick_video.md index ccc368390d3236920e52914bf6640ea35df81120..5f0e8be9f6c195e9398cef2331036baea5e83e8a 100644 --- a/tutorials/source_zh_cn/quick_start/quick_video.md +++ b/tutorials/source_zh_cn/quick_start/quick_video.md @@ -43,7 +43,7 @@ -**查看代码**： +**查看代码**： ### 模型参数保存与加载 diff --git a/tutorials/source_zh_cn/use/custom_operator.md b/tutorials/source_zh_cn/use/custom_operator.md index 064b271fd8daedef06cebffb2b552f8322daebd1..891c6502a879687606d69d59aa044bef9a5a330a 100644 --- a/tutorials/source_zh_cn/use/custom_operator.md +++ b/tutorials/source_zh_cn/use/custom_operator.md @@ -14,7 +14,7 @@ - + ## 概述 diff --git a/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md b/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md index ba744652da3054a25d64a8d55e95dfbf84da3071..84f91840551297c65bb37ac44ddbcc55c17045f1 100644 --- a/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md +++ b/tutorials/source_zh_cn/use/data_preparation/converting_datasets.md @@ -14,7 +14,7 @@ - + ## 概述 diff --git a/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md b/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md index 190440fb1e9addb7a4a13d6b2498da3fd143ac71..a5537f1e96a110c002998e721867e33da0ea6967 100644 --- a/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md +++ b/tutorials/source_zh_cn/use/data_preparation/data_processing_and_augmentation.md @@ -16,7 +16,7 @@ - + ## 概述 diff --git a/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md b/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md index 26ddc142db4beb0018256f866b200ed3c5c4493f..974d59331bdcc56af4ca9451761512f2bf8c9118 100644 --- a/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md +++ b/tutorials/source_zh_cn/use/data_preparation/loading_the_datasets.md @@ -13,7 +13,7 @@ - + ## 概述 diff --git a/tutorials/source_zh_cn/use/multi_platform_inference.md b/tutorials/source_zh_cn/use/multi_platform_inference.md index 3d2bb3fb10cb68f63dc43af2084ed4d5940870cc..6df770d36d9ea008e6e26d7991ff12f74a460375 100644 --- a/tutorials/source_zh_cn/use/multi_platform_inference.md +++ b/tutorials/source_zh_cn/use/multi_platform_inference.md @@ -19,7 +19,7 @@ - + ## 概述 @@ -60,7 +60,7 @@ CPU | ONNX格式 | 支持ONNX推理的runtime/SDK，如TensorRT。 ``` 其中， `model.eval`为模型验证接口，对应接口说明：。 - > 推理样例代码：。 + > 推理样例代码：。 2. 使用`model.predict`接口来进行推理操作。 ```python diff --git a/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md b/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md index dc4848e1872805f99426f8cd7ec15baa30b076c2..111c493105d535e7107add358b8dfb2f6cf0b317 100644 --- a/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md +++ b/tutorials/source_zh_cn/use/saving_and_loading_model_parameters.md @@ -13,7 +13,7 @@ - + ## 概述