diff --git a/docs/source_en/architecture.md b/docs/source_en/architecture.md index cd30baf77b5b436e4761e78c2f87545ad6b36c34..07ec27ec7fc86aa64e10b85a7961fd0b1c1ef340 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 51fd7faaef4c4e91382a85d00ca102fcc764e04d..ec74eb51fdf6b4ac28f7d9c4d323bcd730f06e8e 100644 --- a/docs/source_en/benchmark.md +++ b/docs/source_en/benchmark.md @@ -1,9 +1,9 @@ # 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/mindspore/model_zoo). +For details about the MindSpore pre-trained model, see [Model Zoo](https://gitee.com/mindspore/mindspore/tree/r0.3/mindspore/model_zoo). ## Training Performance diff --git a/docs/source_en/constraints_on_network_construction.md b/docs/source_en/constraints_on_network_construction.md index 69345b9878d01d5ecdcea9a647e883cb71b00a16..7f92721aab39304111e2e05cb0d28780938e2cc8 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 e7ff6e22bf9044da6ee020530cc091822a124eac..6cb4b9145a483ba6574f4ea4c84a6a563969826b 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/network_list.md b/docs/source_en/network_list.md index f1a5056d5e322fc5e2af6f72235d11ffe1584c72..0adb641b55db5567b6e6f3837b34c01e7a526a13 100644 --- a/docs/source_en/network_list.md +++ b/docs/source_en/network_list.md @@ -1,16 +1,16 @@ # Network List - + | Domain | Sub Domain | Network | Ascend | GPU | CPU |:------ |:------| :----------- |:------ |:------ |:----- -|Computer Version (CV) | Image Classification | [AlexNet](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/alexnet.py) | Supported | Supported | Doing -| Computer Version (CV) | Image Classification | [GoogleNet](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/googlenet.py) | Supported | Doing | Doing -| Computer Version (CV) | Image Classification | [LeNet](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/lenet.py) | Supported | Supported | Supported -| Computer Version (CV) | Image Classification | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/resnet.py) | Supported | Doing | Doing -|Computer Version (CV) | Image Classification | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/resnet.py) | Supported |Doing | Doing -| Computer Version (CV) | Image Classification | [VGG16](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/vgg.py) | Supported | Doing | Doing -| Computer Version (CV) | Mobile Image Classification
Image Classification
Semantic Tegmentation | [MobileNetV2](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/mobilenet.py) | Supported | Doing | Doing -|Computer Version (CV) | Targets Detection | [SSD](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/ssd.py) | Supported |Doing | Doing -| Computer Version (CV) | Targets Detection | [YoloV3](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/yolov3.py) | Supported | Doing | Doing -| Natural Language Processing (NLP) | Natural Language Understanding | [BERT](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/Bert_NEZHA/bert_model.py) | Supported | Doing | Doing \ No newline at end of file +|Computer Version (CV) | Image Classification | [AlexNet](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/alexnet.py) | Supported | Supported | Doing +| Computer Version (CV) | Image Classification | [GoogleNet](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/googlenet.py) | Supported | Doing | Doing +| Computer Version (CV) | Image Classification | [LeNet](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/lenet.py) | Supported | Supported | Supported +| Computer Version (CV) | Image Classification | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/resnet.py) | Supported | Doing | Doing +|Computer Version (CV) | Image Classification | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/resnet.py) | Supported |Doing | Doing +| Computer Version (CV) | Image Classification | [VGG16](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/vgg.py) | Supported | Doing | Doing +| Computer Version (CV) | Mobile Image Classification
Image Classification
Semantic Tegmentation | [MobileNetV2](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/mobilenet.py) | Supported | Doing | Doing +|Computer Version (CV) | Targets Detection | [SSD](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/ssd.py) | Supported |Doing | Doing +| Computer Version (CV) | Targets Detection | [YoloV3](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/yolov3.py) | Supported | Doing | Doing +| Natural Language Processing (NLP) | Natural Language Understanding | [BERT](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/Bert_NEZHA/bert_model.py) | Supported | Doing | Doing \ No newline at end of file diff --git a/docs/source_en/operator_list.md b/docs/source_en/operator_list.md index c4f0453e9e8f46030a98dd725a9250e83d0ff0a3..8fbcd43733102564ae378dec3ed690a5c244be53 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..7d008b39d4975823f89c871971825286dfb5563e 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..b69bd1daeb06abc76f3caff49a8e71767d2786b6 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 264a5f6d69fa784a8c41f9105eb6035fbf835b2a..1976258e5dcd33289edff8c06b9bc001f23e93ce 100644 --- a/docs/source_zh_cn/benchmark.md +++ b/docs/source_zh_cn/benchmark.md @@ -1,8 +1,8 @@ # 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/mindspore/model_zoo)。 +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/r0.3/mindspore/model_zoo)。 ## 训练性能 diff --git a/docs/source_zh_cn/constraints_on_network_construction.md b/docs/source_zh_cn/constraints_on_network_construction.md index ff628c798272c2a83e0e7ff0aee2da47dc71c65e..fd8a1a4def2b1a2326f30dab5f6dcf68073f2fc2 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 7d6c684e5762d279df4f758d6ead44807fe26e03..ddadfecf61795d3fefc5d248c9d54c39cfe35101 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/network_list.md b/docs/source_zh_cn/network_list.md index 8c952fa85f55c82f0cc08d6573acc1f4ac2213df..25fdee66770e6815c15c2d0a41607e459320c829 100644 --- a/docs/source_zh_cn/network_list.md +++ b/docs/source_zh_cn/network_list.md @@ -1,16 +1,16 @@ # 网络支持 - + | 领域 | 子领域 | 网络 | Ascend | GPU | CPU |:------ |:------| :----------- |:------ |:------ |:----- -|计算机视觉（CV） | 图像分类（Image Classification） | [AlexNet](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/alexnet.py) | Supported | Supported | Doing -| 计算机视觉（CV） | 图像分类（Image Classification） | [GoogleNet](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/googlenet.py) | Supported | Doing | Doing -| 计算机视觉（CV） | 图像分类（Image Classification） | [LeNet](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/lenet.py) | Supported | Supported | Supported -| 计算机视觉（CV） | 图像分类（Image Classification） | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/resnet.py) | Supported | Doing | Doing -|计算机视觉（CV） | 图像分类（Image Classification） | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/resnet.py) | Supported |Doing | Doing -| 计算机视觉（CV） | 图像分类（Image Classification） | [VGG16](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/vgg.py) | Supported | Doing | Doing -| 计算机视觉（CV） | 移动端图像分类（Mobile Image Classification）
目标检测（Image Classification）
语义分割（Semantic Tegmentation） | [MobileNetV2](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/mobilenet.py) | Supported | Doing | Doing -|计算机视觉（CV） | 目标检测（Targets Detection） | [SSD](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/ssd.py) | Supported |Doing | Doing -| 计算机视觉（CV） | 目标检测（Targets Detection） | [YoloV3](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/yolov3.py) | Supported | Doing | Doing -| 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding） | [BERT](https://gitee.com/mindspore/mindspore/blob/master/mindspore/model_zoo/Bert_NEZHA/bert_model.py) | Supported | Doing | Doing \ No newline at end of file +|计算机视觉（CV） | 图像分类（Image Classification） | [AlexNet](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/alexnet.py) | Supported | Supported | Doing +| 计算机视觉（CV） | 图像分类（Image Classification） | [GoogleNet](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/googlenet.py) | Supported | Doing | Doing +| 计算机视觉（CV） | 图像分类（Image Classification） | [LeNet](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/lenet.py) | Supported | Supported | Supported +| 计算机视觉（CV） | 图像分类（Image Classification） | [ResNet-50](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/resnet.py) | Supported | Doing | Doing +|计算机视觉（CV） | 图像分类（Image Classification） | [ResNet-101](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/resnet.py) | Supported |Doing | Doing +| 计算机视觉（CV） | 图像分类（Image Classification） | [VGG16](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/vgg.py) | Supported | Doing | Doing +| 计算机视觉（CV） | 移动端图像分类（Mobile Image Classification）
目标检测（Image Classification）
语义分割（Semantic Tegmentation） | [MobileNetV2](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/mobilenet.py) | Supported | Doing | Doing +|计算机视觉（CV） | 目标检测（Targets Detection） | [SSD](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/ssd.py) | Supported |Doing | Doing +| 计算机视觉（CV） | 目标检测（Targets Detection） | [YoloV3](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/yolov3.py) | Supported | Doing | Doing +| 自然语言处理（NLP） | 自然语言理解（Natural Language Understanding） | [BERT](https://gitee.com/mindspore/mindspore/blob/r0.3/mindspore/model_zoo/Bert_NEZHA/bert_model.py) | Supported | Doing | Doing \ No newline at end of file diff --git a/docs/source_zh_cn/operator_list.md b/docs/source_zh_cn/operator_list.md index 26aebd483f53703ad858b1741ec4d08e58129756..082d5a37fdccbff1ef8d1c4b76e10e0e6f2191a9 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..a3283a1fc76a5a3973af63d9bd36bf0bec417153 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 7cacc407ef36cf42dae674d1450f2c851fa3fc5c..6c3e8be5a3ab5a0999880c803ed8dc93732c486c 100644 --- a/install/mindspore_cpu_install.md +++ b/install/mindspore_cpu_install.md @@ -21,7 +21,7 @@ | 版本号 | 操作系统 | 可执行文件安装依赖 | 源码编译安装依赖 | | ---- | :--- | :--- | :--- | -| MindSpore master | Ubuntu 16.04（及以上） x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
**安装依赖：**
与可执行文件安装依赖相同 | +| MindSpore master | Ubuntu 16.04（及以上） x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
**安装依赖：**
与可执行文件安装依赖相同 | - Ubuntu版本为18.04时，GCC 7.3.0可以直接通过apt命令安装。 - 在联网状态下，安装whl包时会自动下载requirements.txt中的依赖项，其余情况需自行安装。 @@ -62,7 +62,7 @@ 1. 从代码仓下载源码。 ```bash - git clone https://gitee.com/mindspore/mindspore.git + git clone https://gitee.com/mindspore/mindspore.git -b r0.3 ``` 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 16.04（及以上） 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 16.04（及以上） 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/r0.3/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。 @@ -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 r0.3 ``` 2. 在源码根目录下，执行如下命令编译并安装MindArmour。 diff --git a/install/mindspore_cpu_install_en.md b/install/mindspore_cpu_install_en.md index f8a3c44643e67466ce5f394a34848ac7968dca4f..b856dcdd1c60a72277b28169608ce0a0c400d5b5 100644 --- a/install/mindspore_cpu_install_en.md +++ b/install/mindspore_cpu_install_en.md @@ -21,7 +21,7 @@ This document describes how to quickly install MindSpore on a Ubuntu system with | Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies | | ---- | :--- | :--- | :--- | -| MindSpore master | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
same as the executable file installation dependencies. | +| MindSpore master | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
same as the executable file installation dependencies. | - When Ubuntu version is 18.04, GCC 7.3.0 can be installed by using apt command. - When the network is connected, dependency items in the requirements.txt file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items. @@ -62,7 +62,7 @@ This document describes how to quickly install MindSpore on a Ubuntu system with 1. Download the source code from the code repository. ```bash - git clone https://gitee.com/mindspore/mindspore.git + git clone https://gitee.com/mindspore/mindspore.git -b r0.3 ``` 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 16.04 or later 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 16.04 or later 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/r0.3/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. @@ -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 r0.3 ``` 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 505b3bf6499be7b089b28b229dc0b13f5542db99..0b7bdd0d8d15073cac308d5111eb1ec29431636f 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/r0.3/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 r0.3 ``` 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 300d0b2738608606e9f520d0ab240c8689e26bd0..8903b47c13787c605ca18ce94121cc058171895c 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/r0.3/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 r0.3 ``` 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 26132ba7e98f49993f249a1456d0f96aae234936..6f4d0741ce6cfe653106f08068b0bccb1b7d47a2 100644 --- a/install/mindspore_d_install.md +++ b/install/mindspore_d_install.md @@ -33,7 +33,7 @@ | 版本号 | 操作系统 | 可执行文件安装依赖 | 源码编译安装依赖 | | ---- | :--- | :--- | :--- | -| MindSpore master | - Ubuntu 16.04（及以上） aarch64
- Ubuntu 16.04（及以上） 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 T 1.1.T107）
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI处理器配套软件包（对应版本Atlas T 1.1.T107）
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
**安装依赖：**
与可执行文件安装依赖相同 | +| MindSpore master | - Ubuntu 16.04（及以上） aarch64
- Ubuntu 16.04（及以上） 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 T 1.1.T107）
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI处理器配套软件包（对应版本Atlas T 1.1.T107）
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
**安装依赖：**
与可执行文件安装依赖相同 | - 确认当前用户有权限访问Ascend 910 AI处理器配套软件包（对应版本Atlas T 1.1.T107）的安装路径`/usr/local/Ascend`，若无权限，需要root用户将当前用户添加到`/usr/local/Ascend`所在的用户组，具体配置请详见配套软件包的说明文档。 - Ubuntu版本为18.04时，GCC 7.3.0可以直接通过apt命令安装。 @@ -82,7 +82,7 @@ 1. 从代码仓下载源码。 ```bash - git clone https://gitee.com/mindspore/mindspore.git + git clone https://gitee.com/mindspore/mindspore.git -b r0.3 ``` 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 16.04（及以上） aarch64
- Ubuntu 16.04（及以上） 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 16.04（及以上） aarch64
- Ubuntu 16.04（及以上） 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/r0.3/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 r0.3 ``` > **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 16.04（及以上） aarch64
- Ubuntu 16.04（及以上） 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 16.04（及以上） aarch64
- Ubuntu 16.04（及以上） 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/r0.3/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。 @@ -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 r0.3 ``` 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 745715ba9d42aaf6173248fcb574a25854237f34..86fbf76c0d57f25260ad12fa7cd8167b31dffc77 100644 --- a/install/mindspore_d_install_en.md +++ b/install/mindspore_d_install_en.md @@ -32,7 +32,7 @@ This document describes how to quickly install MindSpore on an Ascend AI process | Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies | | ---- | :--- | :--- | :--- | -| MindSpore master | - Ubuntu 16.04 or later aarch64
- Ubuntu 16.04 or later 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 T 1.1.T107)
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI processor software package(Version:Atlas T 1.1.T107)
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
**Installation dependencies:**
same as the executable file installation dependencies. | +| MindSpore master | - Ubuntu 16.04 or later aarch64
- Ubuntu 16.04 or later 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 T 1.1.T107)
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- Ascend 910 AI processor software package(Version:Atlas T 1.1.T107)
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
**Installation dependencies:**
same as the executable file installation dependencies. | - Confirm that the current user has the right to access the installation path `/usr/local/Ascend `of Ascend 910 AI processor software package(Version:Atlas T 1.1.T107). If not, the root user needs to add the current user to the user group where `/usr/local/Ascend` is located. For the specific configuration, please refer to the software package instruction document. - When Ubuntu version is 18.04, GCC 7.3.0 can be installed by using apt command. @@ -81,7 +81,7 @@ The compilation and installation must be performed on the Ascend 910 AI processo 1. Download the source code from the code repository. ```bash - git clone https://gitee.com/mindspore/mindspore.git + git clone https://gitee.com/mindspore/mindspore.git -b r0.3 ``` 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 16.04 or later aarch64
- Ubuntu 16.04 or later 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 16.04 or later aarch64
- Ubuntu 16.04 or later 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/r0.3/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 r0.3 ``` > 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 16.04 or later aarch64
- Ubuntu 16.04 or later 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 16.04 or later aarch64
- Ubuntu 16.04 or later 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/r0.3/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. @@ -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 r0.3 ``` 2. Run the following command in the root directory of the source code to compile and install MindArmour: diff --git a/install/mindspore_gpu_install.md b/install/mindspore_gpu_install.md index 537b32933aced82e98e0debc2a5239e3a2922af0..47178d7dc2d0ffe81ce3b7dbe09c77b7afda1b8a 100644 --- a/install/mindspore_gpu_install.md +++ b/install/mindspore_gpu_install.md @@ -28,7 +28,7 @@ | 版本号 | 操作系统 | 可执行文件安装依赖 | 源码编译安装依赖 | | ---- | :--- | :--- | :--- | -| MindSpore master | Ubuntu 16.04（及以上） x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
- [OpenMPI](https://www.open-mpi.org/faq/?category=building#easy-build) 3.1.5 （可选，单机多卡/多机多卡训练需要）
- [NCCL](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) 2.4.8-1 （可选，单机多卡/多机多卡训练需要）
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
- [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69
- [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30
- [Automake](https://www.gnu.org/software/automake) >= 1.15.1
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
**安装依赖：**
与可执行文件安装依赖相同 | +| MindSpore master | Ubuntu 16.04（及以上） x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
- [OpenMPI](https://www.open-mpi.org/faq/?category=building#easy-build) 3.1.5 （可选，单机多卡/多机多卡训练需要）
- [NCCL](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) 2.4.8-1 （可选，单机多卡/多机多卡训练需要）
- 其他依赖项参见[requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt) | **编译依赖：**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
- [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69
- [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30
- [Automake](https://www.gnu.org/software/automake) >= 1.15.1
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
**安装依赖：**
与可执行文件安装依赖相同 | - Ubuntu版本为18.04时，GCC 7.3.0可以直接通过apt命令安装。 - 在联网状态下，安装whl包时会自动下载requirements.txt中的依赖项，其余情况需自行安装。 @@ -64,7 +64,7 @@ 1. 从代码仓下载源码。 ```bash - git clone https://gitee.com/mindspore/mindspore.git + git clone https://gitee.com/mindspore/mindspore.git -b r0.3 ``` 2. 在源码根目录下执行如下命令编译MindSpore。 @@ -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
- 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 16.04（及以上） 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/r0.3/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。 @@ -149,7 +149,7 @@ 1. 从代码仓下载源码。 ```bash - git clone https://gitee.com/mindspore/mindinsight.git + git clone https://gitee.com/mindspore/mindinsight.git -b r0.3 ``` > **不能**直接在仓库主页下载zip包获取源码。 @@ -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
- 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 16.04（及以上） 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/r0.3/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。 @@ -214,7 +214,7 @@ 1. 从代码仓下载源码。 ```bash - git clone https://gitee.com/mindspore/mindarmour.git + git clone https://gitee.com/mindspore/mindarmour.git -b r0.3 ``` 2. 在源码根目录下，执行如下命令编译并安装MindArmour。 diff --git a/install/mindspore_gpu_install_en.md b/install/mindspore_gpu_install_en.md index 4c41f09d87c8ad1f8cc16651314a7fbf4bb3dd4a..f1e705ff333b08fbed297712876e68215d98e433 100644 --- a/install/mindspore_gpu_install_en.md +++ b/install/mindspore_gpu_install_en.md @@ -28,7 +28,7 @@ This document describes how to quickly install MindSpore on a NVIDIA GPU environ | Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies | | ---- | :--- | :--- | :--- | -| MindSpore master | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
- [OpenMPI](https://www.open-mpi.org/faq/?category=building#easy-build) 3.1.5 (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
- [NCCL](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) 2.4.8-1 (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
- [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69
- [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30
- [Automake](https://www.gnu.org/software/automake) >= 1.15.1
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
**Installation dependencies:**
same as the executable file installation dependencies. | +| MindSpore master | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
- [OpenMPI](https://www.open-mpi.org/faq/?category=building#easy-build) 3.1.5 (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
- [NCCL](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) 2.4.8-1 (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.3/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
- [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69
- [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30
- [Automake](https://www.gnu.org/software/automake) >= 1.15.1
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
**Installation dependencies:**
same as the executable file installation dependencies. | - When Ubuntu version is 18.04, GCC 7.3.0 can be installed by using apt command. - When the network is connected, dependency items in the requirements.txt file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items. @@ -64,7 +64,7 @@ This document describes how to quickly install MindSpore on a NVIDIA GPU environ 1. Download the source code from the code repository. ```bash - git clone https://gitee.com/mindspore/mindspore.git + git clone https://gitee.com/mindspore/mindspore.git -b r0.3 ``` 2. Run the following command in the root directory of the source code to compile MindSpore: @@ -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
- 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 16.04 or later 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/r0.3/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. @@ -149,7 +149,7 @@ If you need to analyze information such as model scalars, graphs, and model trac 1. Download the source code from the code repository. ```bash - git clone https://gitee.com/mindspore/mindinsight.git + git clone https://gitee.com/mindspore/mindinsight.git -b r0.3 ``` > You are **not** supposed to obtain the source code from the zip package downloaded from the repository homepage. @@ -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
- 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 16.04 or later 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/r0.3/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. @@ -216,7 +216,7 @@ If you need to conduct AI model security research or enhance the security of the 1. Download the source code from the code repository. ```bash - git clone https://gitee.com/mindspore/mindarmour.git + git clone https://gitee.com/mindspore/mindarmour.git -b r0.3 ``` 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 7fa3b3034787ff8ea08b355864c47e199bc1cfb6..d51a0e4f7c2867c39f38d25ed08158ecbd70ee09 100644 --- a/resource/faq/FAQ_en.md +++ b/resource/faq/FAQ_en.md @@ -68,13 +68,13 @@ 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/r0.3/RELEASE.md) for detailed information.
Q: What are the available recommendation or text generation networks or models provided by MindSpore? -A: Currently, recommendation models such as Wide & Deep, DeepFM, and NCF are under development. In the natural language processing (NLP) field, Bert\_NEZHA is available and models such as MASS are under development. You can rebuild the network into a text generation network based on the scenario requirements. Please stay tuned for updates on the [MindSpore Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo). +A: Currently, recommendation models such as Wide & Deep, DeepFM, and NCF are under development. In the natural language processing (NLP) field, Bert\_NEZHA is available and models such as MASS are under development. You can rebuild the network into a text generation network based on the scenario requirements. Please stay tuned for updates on the [MindSpore Model Zoo](https://gitee.com/mindspore/mindspore/tree/r0.3/mindspore/model_zoo). ### Backend Support @@ -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/r0.3/RELEASE.md). ### System Support diff --git a/resource/faq/FAQ_zh_cn.md b/resource/faq/FAQ_zh_cn.md index db448da184e2b5370da5245b4992fa995be8aa38..8e4e58760d6a53114b5722f7fb588d511a65efbf 100644 --- a/resource/faq/FAQ_zh_cn.md +++ b/resource/faq/FAQ_zh_cn.md @@ -67,13 +67,13 @@ 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/r0.3/RELEASE.md)。
Q：MindSpore有哪些现成的推荐类或生成类网络或模型可用？ -A：目前正在开发Wide & Deep、DeepFM、NCF等推荐类模型，NLP领域已经支持Bert_NEZHA，正在开发MASS等模型，用户可根据场景需要改造为生成类网络，可以关注[MindSpore Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)。 +A：目前正在开发Wide & Deep、DeepFM、NCF等推荐类模型，NLP领域已经支持Bert_NEZHA，正在开发MASS等模型，用户可根据场景需要改造为生成类网络，可以关注[MindSpore Model Zoo](https://gitee.com/mindspore/mindspore/tree/r0.3/mindspore/model_zoo)。 ### 后端支持 @@ -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/r0.3/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 63056b51e9e1d918aebddf555b26de472407af97..7253291341f5a16d8ee3df957cc37b6c5ab1b483 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/r0.3/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 d45edca3a947ddc005d8bfe37689e8d5f368b6eb..456fe086a13e3439ca5ad2d0045d0a45c5197f8f 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 2294d1258012e3a64fda921494941f46bc5decc1..5b6573417144e4f8e5223a0ef821db3ec0f69b46 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/debugging_in_pynative_mode.md b/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md index d6a06a1239974235583b7c531b1d6c005489383d..117aabcdaaa8a9f09a5018e65d2195e08709b719 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/distributed_training.md b/tutorials/source_en/advanced_use/distributed_training.md index 8d45551e76b9c03202191f3bcf624e0f9622e4be..7ca77d55332135f949be2e4f211f0528531a5686 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 5ff32468c5cb0d015c44c6dc757f4c4ffa223bcf..010d0238867d20407f4eeffca5556fd9c7e3fd88 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 3d0909f862e559b96edc1829c8a4684d4eec34b7..b9669e88502a2d892d0ccc9d00637eeecfcc8fe7 100644 --- a/tutorials/source_en/advanced_use/model_security.md +++ b/tutorials/source_en/advanced_use/model_security.md @@ -15,7 +15,7 @@ - + ## Overview @@ -29,7 +29,7 @@ At the beginning of AI algorithm design, related security threats are sometimes This section describes how to use MindArmour in adversarial attack and defense by taking the Fast Gradient Sign Method (FGSM) attack algorithm and Natural Adversarial Defense (NAD) algorithm as examples. -> The current sample is for CPU, GPU and Ascend 910 AI processor. You can find the complete executable sample code at： +> The current sample is for CPU, GPU and Ascend 910 AI processor. You can find the complete executable sample code at： > - mnist_attack_fgsm.py: contains attack code. > - mnist_defense_nad.py: contains defense code. diff --git a/tutorials/source_en/advanced_use/network_migration.md b/tutorials/source_en/advanced_use/network_migration.md index d5c91ee3040bcbae535cd818831dc7c4d1312578..f9982b1943095cbc4681493a92eb58ca2e596a4f 100644 --- a/tutorials/source_en/advanced_use/network_migration.md +++ b/tutorials/source_en/advanced_use/network_migration.md @@ -17,7 +17,7 @@ - + ## Overview @@ -57,7 +57,7 @@ Prepare the hardware environment, find a platform corresponding to your environm MindSpore differs from TensorFlow and PyTorch in the network structure. Before migration, you need to clearly understand the original script and information of each layer, such as shape. -> You can also use [MindConverter Tool](https://gitee.com/mindspore/mindinsight/tree/master/mindinsight/mindconverter) to automatically convert the PyTorch network definition script to MindSpore network definition script. +> You can also use [MindConverter Tool](https://gitee.com/mindspore/mindinsight/tree/r0.3/mindinsight/mindconverter) to automatically convert the PyTorch network definition script to MindSpore network definition script. The ResNet-50 network migration and training on the Ascend 910 is used as an example. @@ -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/mindspore/model_zoo/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/r0.3/mindspore/model_zoo/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. @@ -272,9 +272,9 @@ Models trained on the Ascend 910 AI processor can be used for inference on diffe ## Examples -1. [Common network script examples](https://gitee.com/mindspore/mindspore/tree/master/example) +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) -3. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo) +3. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/r0.3/mindspore/model_zoo) diff --git a/tutorials/source_en/advanced_use/nlp_application.md b/tutorials/source_en/advanced_use/nlp_application.md index 5f3138e406c11951cce61bc5e58925415f714d59..00da384b517288774d7308a937d0b3c0db62788d 100644 --- a/tutorials/source_en/advanced_use/nlp_application.md +++ b/tutorials/source_en/advanced_use/nlp_application.md @@ -20,7 +20,7 @@ - + ## Overview @@ -85,7 +85,7 @@ Currently, MindSpore GPU supports the long short-term memory (LSTM) network for Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used for processing and predicting an important event with a long interval and delay in a time sequence. For details, refer to online documentation. 3. After the model is obtained, use the validation dataset to check the accuracy of model. -> The current sample is for the Ascend 910 AI processor. You can find the complete executable sample code at： +> The current sample is for the Ascend 910 AI processor. You can find the complete executable sample code at： > - main.py: code file, including code for data preprocessing, network definition, and model training. > - config.py: some configurations on the network, including the batch size and number of training epochs. diff --git a/tutorials/source_en/advanced_use/on_device_inference.md b/tutorials/source_en/advanced_use/on_device_inference.md index 210fd4985a0e11612c591dd507fa55bb8496eaf4..bac6a4df8701f77b28d2388ba12645cb28122038 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 diff --git a/tutorials/source_en/advanced_use/visualization_tutorials.md b/tutorials/source_en/advanced_use/visualization_tutorials.md index 5880bb1ad106171c831ea1bb5313ad083369c58b..63c43222d345ff7787231db1d1dca6db4aff7d53 100644 --- a/tutorials/source_en/advanced_use/visualization_tutorials.md +++ b/tutorials/source_en/advanced_use/visualization_tutorials.md @@ -23,7 +23,7 @@ - + ## 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/quick_start/quick_start.md b/tutorials/source_en/quick_start/quick_start.md index b8e7c807865fea8a7ed67422e35c82903c98983f..b5b09176a4c039919e03da3f172564980b798168 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 6869c08b02fad72675f1222a9b947eeedda56a19..a7e9024a3a34c6844bfbf6b0bce9569f6baeaa6d 100644 --- a/tutorials/source_en/use/custom_operator.md +++ b/tutorials/source_en/use/custom_operator.md @@ -9,12 +9,12 @@ - [Implementing a TBE Operator](#implementing-a-tbe-operator) - [Registering the Operator Information](#registering-the-operator-information) - [Example](#example) - - [Using a Custom Operator](#using-a-custom-operator) + - [Using Custom Operators](#using-custom-operators) - [Defining the bprop Function for an Operator](#defining-the-bprop-function-for-an-operator) - + ## Overview @@ -27,14 +27,14 @@ The related concepts are as follows: - Operator implementation: describes the implementation of the internal computation logic for an operator through the DSL API provided by the Tensor Boost Engine (TBE). The TBE supports the development of custom operators based on the Ascend AI chip. You can apply for Open Beta Tests (OBTs) by visiting . - Operator information: describes basic information about a TBE operator, such as the operator name and supported input and output types. It is the basis for the backend to select and map operators. -This section takes a Square operator as an example to describe how to customize an operator. For details, see cases in [tests/st/ops/custom_ops_tbe](https://gitee.com/mindspore/mindspore/tree/master/tests/st/ops/custom_ops_tbe) in the MindSpore source code. +This section takes a Square operator as an example to describe how to customize an operator. For details, see cases in [tests/st/ops/custom_ops_tbe](https://gitee.com/mindspore/mindspore/tree/r0.3/tests/st/ops/custom_ops_tbe) in the MindSpore source code. ## Registering the Operator Primitive The primitive of an operator is a subclass inherited from `PrimitiveWithInfer`. The type name of the subclass is the operator name. The definition of the custom operator primitive is the same as that of the built-in operator primitive. -- The attribute is defined by the input parameter of the constructor function `__init__()`. The operator in this test case has no attribute. Therefore, `__init__()` has only one input parameter. For details about test cases in which operators have attributes, see [custom add3](https://gitee.com/mindspore/mindspore/tree/master/tests/st/ops/custom_ops_tbe/cus_add3.py) in the MindSpore source code. +- The attribute is defined by the input parameter of the constructor function `__init__()`. The operator in this test case has no attribute. Therefore, `__init__()` has only one input parameter. For details about test cases in which operators have attributes, see [custom add3](https://gitee.com/mindspore/mindspore/tree/r0.3/tests/st/ops/custom_ops_tbe/cus_add3.py) in the MindSpore source code. - The input and output names are defined by the `init_prim_io_names()` function. - The shape inference method of the output tensor is defined in the `infer_shape()` function, and the dtype inference method of the output tensor is defined in the `infer_dtype()` function. diff --git a/tutorials/source_en/use/data_preparation/converting_datasets.md b/tutorials/source_en/use/data_preparation/converting_datasets.md index 8a05cd641a1f9a3ec373af47a898d4ca6b7bb00e..8bad8429af0950d002093e9285f9c5195ea12a7a 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 9da14fb7c9b0347214b1ae81ab43415e5efcbc28..cfbf2ab33c25472872eead643a5df9f9adeed594 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 6a47a1a22b3b896b632aaf386421184f93306909..a4e22ee6cfc928dc74d5e88cce4b4d056771f7e3 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 eb311cca1b51faefe29be5c556c3dd01c97bd7d7..6ab2aa30e373e9f7767df985011c0368398bfbd6 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 9732e574324ae96f31e264d9fb8283eee229051d..e2fa683286662786ae571011719482cc492c4dce 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/checkpoint_for_hybrid_parallel.md b/tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md index 570ea81deaee0d471d83b6e6a96efbb9238d3d1e..c18ca9c0029038cdddeb51c1bdbee6b4fda078e1 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 745ce939f540a44e10d161b924cd4ef28d10e246..fbc5490627a613853fd8379bb1487b64a0c55114 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 316a15643e24c75ae012ce2d7d046baf833ac929..2cdf09c4aa61b067e3842db815f617e609429d9f 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/debugging_in_pynative_mode.md b/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md index 08ac3f3f4d8f89b5fe56791ece4f90250c49d125..096d16d70ff54d2e70cb0749d22ae4b8152e0409 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 2535f2f38194d47e056c8cd6c803c3e58beb04fc..b5c622663615db716cfbbecde270e13442bca108 100644 --- a/tutorials/source_zh_cn/advanced_use/differential_privacy.md +++ b/tutorials/source_zh_cn/advanced_use/differential_privacy.md @@ -25,7 +25,7 @@ MindArmour的差分隐私模块Differential-Privacy，实现了差分隐私优 这里以LeNet模型，MNIST 数据集为例，说明如何在MindSpore上使用差分隐私优化器训练神经网络模型。 -> 本例面向Ascend 910 AI处理器，支持PYNATIVE_MODE，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，支持PYNATIVE_MODE，你可以在这里下载完整的样例代码： ## 实现阶段 diff --git a/tutorials/source_zh_cn/advanced_use/distributed_training.md b/tutorials/source_zh_cn/advanced_use/distributed_training.md index dab05866784faa207701883517327640db44fc8a..14704fec005d723e2b6a48fd55ff84364d8a649e 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/mixed_precision.md b/tutorials/source_zh_cn/advanced_use/mixed_precision.md index 95939b844f94b5cb56c1465a1de43f332459c560..7ddeb1d19b75ca0c23faa392558b6c5821aea43e 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 f5420ab586408c8841c15e690cefd98f2a86f44e..23769f448e0e8e87ecc107cde6656c2de9661884 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 @@ -28,7 +28,7 @@ AI算法设计之初普遍未考虑相关的安全威胁，使得AI算法的判 这里通过图像分类任务上的对抗性攻防，以攻击算法FGSM和防御算法NAD为例，介绍MindArmour在对抗攻防上的使用方法。 -> 本例面向CPU、GPU、Ascend 910 AI处理器，你可以在这里下载完整的样例代码： +> 本例面向CPU、GPU、Ascend 910 AI处理器，你可以在这里下载完整的样例代码： > - mnist_attack_fgsm.py：包含攻击代码。 > - mnist_defense_nad.py：包含防御代码。 diff --git a/tutorials/source_zh_cn/advanced_use/network_migration.md b/tutorials/source_zh_cn/advanced_use/network_migration.md index b427c91d45c24eb8048dce50db6c03c0705138a0..dd8b164999e02e922b3f47f1d3a3d7e2c8ba7e67 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 @@ -55,7 +55,7 @@ MindSpore与TensorFlow、PyTorch在网络结构组织方式上，存在一定差别，迁移前需要对原脚本有较为清晰的了解，明确地知道每一层的shape等信息。 -> 你也可以使用[MindConverter工具](https://gitee.com/mindspore/mindinsight/tree/master/mindinsight/mindconverter)实现PyTorch网络定义脚本到MindSpore网络定义脚本的自动转换。 +> 你也可以使用[MindConverter工具](https://gitee.com/mindspore/mindinsight/tree/r0.3/mindinsight/mindconverter)实现PyTorch网络定义脚本到MindSpore网络定义脚本的自动转换。 下面，我们以ResNet-50的迁移，并在Ascend 910上训练为例： @@ -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/mindspore/model_zoo/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/r0.3/mindspore/model_zoo/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。 @@ -267,8 +267,8 @@ MindSpore与TensorFlow、PyTorch在网络结构组织方式上，存在一定差 ## 样例参考 -1. [常用网络脚本样例](https://gitee.com/mindspore/mindspore/tree/master/example) +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) -3. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo) \ No newline at end of file +3. [Model Zoo](https://gitee.com/mindspore/mindspore/tree/r0.3/mindspore/model_zoo) \ No newline at end of file diff --git a/tutorials/source_zh_cn/advanced_use/nlp_application.md b/tutorials/source_zh_cn/advanced_use/nlp_application.md index ea9cb100787c9c393d6faf4a64da7d5ab6778466..a648342651820010c24de1445249d4d4ab911906 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 @@ -85,7 +85,7 @@ $F1分数 = (2 * Precision * Recall) / (Precision + Recall)$ > LSTM（Long short-term memory，长短期记忆）网络是一种时间循环神经网络，适合于处理和预测时间序列中间隔和延迟非常长的重要事件。具体介绍可参考网上资料，在此不再赘述。 3. 得到模型之后，使用验证数据集，查看模型精度情况。 -> 本例面向GPU硬件平台，你可以在这里下载完整的样例代码： +> 本例面向GPU硬件平台，你可以在这里下载完整的样例代码： > - main.py：代码文件，包括数据预处理、网络定义、模型训练等代码。 > - config.py：网络中的一些配置，包括batch size、进行几次epoch训练等。 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 17be02578ea5ecdeadb445ff0cc0f7bc628a4e37..12c7af2ea962fbdd133de4063e6ccb7a3bd5e146 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 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 05573286c76ac728f30261093d2868fc156f63f9..91615a90f7d6f9c0fe8e43518f7582a66d3fedf9 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 @@ -69,7 +69,7 @@ ModelArts使用对象存储服务（Object Storage Service，简称OBS）进行 ### 执行脚本准备 新建一个自己的OBS桶（例如：resnet50-train），在桶中创建代码目录（例如：resnet50_cifar10_train），并将以下目录中的所有脚本上传至代码目录： -> 脚本使用ResNet-50网络在CIFAR-10数据集上进行训练，并在训练结束后验证精度。脚本可以在ModelArts采用`1*Ascend`或`8*Ascend`两种不同规格进行训练任务。 +> 脚本使用ResNet-50网络在CIFAR-10数据集上进行训练，并在训练结束后验证精度。脚本可以在ModelArts采用`1*Ascend`或`8*Ascend`两种不同规格进行训练任务。 为了方便后续创建训练作业，先创建训练输出目录和日志输出目录，本示例创建的目录结构如下： @@ -108,7 +108,7 @@ ModelArts使用对象存储服务（Object Storage Service，简称OBS）进行 ### 适配OBS数据 MindSpore暂时没有提供直接访问OBS数据的接口，需要通过MoXing提供的API与OBS交互。ModelArts训练脚本在容器中执行，通常选用`/cache`目录作为容器数据存储路径。 -> 华为云MoXing提供了丰富的API供用户使用，本示例中仅需要使用`copy_parallel`接口。 +> 华为云MoXing提供了丰富的API供用户使用，本示例中仅需要使用`copy_parallel`接口。 1. 将OBS中存储的数据下载至执行容器。 diff --git a/tutorials/source_zh_cn/advanced_use/visualization_tutorials.md b/tutorials/source_zh_cn/advanced_use/visualization_tutorials.md index 68433db67048974e61a3e9822b01b834be57e73a..1a42a04c8b697d3a7378e70da659597e09475a89 100644 --- a/tutorials/source_zh_cn/advanced_use/visualization_tutorials.md +++ b/tutorials/source_zh_cn/advanced_use/visualization_tutorials.md @@ -28,7 +28,7 @@ - + ## 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/quick_start/quick_start.md b/tutorials/source_zh_cn/quick_start/quick_start.md index a608fb0b72c7df5fb60f3aa39f82be3f039c64d1..87777cdd0f97ca316752a1ebf2b3e9947e0bdf01 100644 --- a/tutorials/source_zh_cn/quick_start/quick_start.md +++ b/tutorials/source_zh_cn/quick_start/quick_start.md @@ -24,7 +24,7 @@ - + ## 概述 @@ -38,7 +38,7 @@ 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 960410302134a8e051b026ea79b5312f35dd7236..650c0e61665f1c4c5b30ed2e6cd57f51808ca59d 100644 --- a/tutorials/source_zh_cn/quick_start/quick_video.md +++ b/tutorials/source_zh_cn/quick_start/quick_video.md @@ -43,4 +43,4 @@ -**查看代码**： \ No newline at end of file +**查看代码**： \ No newline at end of file diff --git a/tutorials/source_zh_cn/use/custom_operator.md b/tutorials/source_zh_cn/use/custom_operator.md index 18004d1f8817d6f9a7f32095065aa4da4e16ecc8..eb0ff265f01af433d813279a28712622da9291bb 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 @@ -27,14 +27,14 @@ - 算子实现：通过TBE（Tensor Boost Engine）提供的特性语言接口，描述算子内部计算逻辑的实现。TBE提供了开发昇腾AI芯片自定义算子的能力。你可以在页面申请公测。 - 算子信息：描述TBE算子的基本信息，如算子名称、支持的输入输出类型等。它是后端做算子选择和映射时的依据。 -本文将以自定义Square算子为例，介绍自定义算子的步骤。更多详细内容可参考MindSpore源码中[tests/st/ops/custom_ops_tbe](https://gitee.com/mindspore/mindspore/tree/master/tests/st/ops/custom_ops_tbe)下的用例。 +本文将以自定义Square算子为例，介绍自定义算子的步骤。更多详细内容可参考MindSpore源码中[tests/st/ops/custom_ops_tbe](https://gitee.com/mindspore/mindspore/tree/r0.3/tests/st/ops/custom_ops_tbe)下的用例。 ## 注册算子原语 每个算子的原语是一个继承于`PrimitiveWithInfer`的子类，其类型名称即是算子名称。 自定义算子原语与内置算子原语的接口定义完全一致： -- 属性由构造函数`__init__()`的入参定义。本用例的算子没有属性，因此`__init__()`没有额外的入参。带属性的用例可参考MindSpore源码中的[custom add3](https://gitee.com/mindspore/mindspore/tree/master/tests/st/ops/custom_ops_tbe/cus_add3.py)用例。 +- 属性由构造函数`__init__()`的入参定义。本用例的算子没有属性，因此`__init__()`没有额外的入参。带属性的用例可参考MindSpore源码中的[custom add3](https://gitee.com/mindspore/mindspore/tree/r0.3/tests/st/ops/custom_ops_tbe/cus_add3.py)用例。 - 输入输出的名称通过`init_prim_io_names()`函数定义。 - 输出Tensor的shape推理方法在`infer_shape()`函数中定义，输出Tensor的dtype推理方法在`infer_dtype()`函数中定义。 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 93634166c29eaec6dbfd8451f65115ee769dbede..82d7763fe203b6d4fe5fe205240714bc1c5ef184 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 ee3ae3d198d1be2f5111cbaedaa89f305070d4f5..83dff300fa3809e06b07fa6d42e8d22907924426 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 3186acaa2bd0c2779064fc2ae411bd324d8898f2..0fce07d2a2f624973c05a5c1773cdd907362a11c 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 f1b8f5a219ca32f2edba1c34f5f42a41a01e900f..a1c2a9335bfbdd59fcabdbcdc655ab1d09d9e10c 100644 --- a/tutorials/source_zh_cn/use/multi_platform_inference.md +++ b/tutorials/source_zh_cn/use/multi_platform_inference.md @@ -11,7 +11,7 @@ - + ## 概述 @@ -19,7 +19,7 @@ ## Ascend 910 AI处理器上推理 -MindSpore提供了`model.eval()`接口来进行模型验证，你只需传入验证数据集即可，验证数据集的处理方式与训练数据集相同。完整代码请参考。 +MindSpore提供了`model.eval()`接口来进行模型验证，你只需传入验证数据集即可，验证数据集的处理方式与训练数据集相同。完整代码请参考。 ```python res = model.eval(dataset) 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 b5822be706518c0502cbc68fdc12dcc61361b4e6..0f5b5d492dfda66b67534e33356a1202a9d2f093 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