From 9a98294430350c655c0ccd124d368a4b59bad275 Mon Sep 17 00:00:00 2001 From: SebastianHan Date: Fri, 29 May 2020 11:56:44 +0800 Subject: [PATCH] master to r0.3 --- docs/source_en/architecture.md | 2 +- docs/source_en/benchmark.md | 4 ++-- .../constraints_on_network_construction.md | 2 +- docs/source_en/glossary.md | 2 +- docs/source_en/network_list.md | 22 +++++++++---------- docs/source_en/operator_list.md | 2 +- docs/source_en/roadmap.md | 2 +- docs/source_zh_cn/architecture.md | 2 +- docs/source_zh_cn/benchmark.md | 4 ++-- .../constraints_on_network_construction.md | 2 +- docs/source_zh_cn/glossary.md | 2 +- docs/source_zh_cn/network_list.md | 22 +++++++++---------- docs/source_zh_cn/operator_list.md | 2 +- docs/source_zh_cn/roadmap.md | 2 +- install/mindspore_cpu_install.md | 8 +++---- install/mindspore_cpu_install_en.md | 8 +++---- install/mindspore_cpu_win_install.md | 4 ++-- install/mindspore_cpu_win_install_en.md | 4 ++-- install/mindspore_d_install.md | 12 +++++----- install/mindspore_d_install_en.md | 12 +++++----- install/mindspore_gpu_install.md | 12 +++++----- install/mindspore_gpu_install_en.md | 12 +++++----- resource/faq/FAQ_en.md | 6 ++--- resource/faq/FAQ_zh_cn.md | 6 ++--- tutorials/notebook/quick_start.ipynb | 2 +- .../computer_vision_application.md | 4 ++-- .../customized_debugging_information.md | 2 +- .../debugging_in_pynative_mode.md | 2 +- .../advanced_use/distributed_training.md | 6 ++--- .../source_en/advanced_use/mixed_precision.md | 2 +- .../source_en/advanced_use/model_security.md | 4 ++-- .../advanced_use/network_migration.md | 12 +++++----- .../source_en/advanced_use/nlp_application.md | 4 ++-- .../advanced_use/on_device_inference.md | 2 +- .../advanced_use/visualization_tutorials.md | 2 +- .../source_en/quick_start/quick_start.md | 4 ++-- tutorials/source_en/use/custom_operator.md | 8 +++---- .../data_preparation/converting_datasets.md | 2 +- .../data_processing_and_augmentation.md | 2 +- .../data_preparation/loading_the_datasets.md | 2 +- .../source_en/use/multi_platform_inference.md | 4 ++-- .../saving_and_loading_model_parameters.md | 2 +- .../checkpoint_for_hybrid_parallel.md | 2 +- .../computer_vision_application.md | 4 ++-- .../customized_debugging_information.md | 2 +- .../debugging_in_pynative_mode.md | 2 +- .../advanced_use/differential_privacy.md | 2 +- .../advanced_use/distributed_training.md | 6 ++--- .../advanced_use/mixed_precision.md | 2 +- .../advanced_use/model_security.md | 4 ++-- .../advanced_use/network_migration.md | 12 +++++----- .../advanced_use/nlp_application.md | 4 ++-- .../advanced_use/on_device_inference.md | 2 +- .../advanced_use/use_on_the_cloud.md | 6 ++--- .../advanced_use/visualization_tutorials.md | 2 +- .../source_zh_cn/quick_start/quick_start.md | 4 ++-- .../source_zh_cn/quick_start/quick_video.md | 2 +- tutorials/source_zh_cn/use/custom_operator.md | 6 ++--- .../data_preparation/converting_datasets.md | 2 +- .../data_processing_and_augmentation.md | 2 +- .../data_preparation/loading_the_datasets.md | 2 +- .../use/multi_platform_inference.md | 4 ++-- .../saving_and_loading_model_parameters.md | 2 +- 63 files changed, 148 insertions(+), 148 deletions(-) diff --git a/docs/source_en/architecture.md b/docs/source_en/architecture.md index cd30baf77b..07ec27ec7f 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 51fd7faaef..ec74eb51fd 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 69345b9878..7f92721aab 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 e7ff6e22bf..6cb4b9145a 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 f1a5056d5e..0adb641b55 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 c4f0453e9e..8fbcd43733 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 5c89ddfb0d..7d008b39d4 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 27205cde10..b69bd1daeb 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 264a5f6d69..1976258e5d 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 ff628c7982..fd8a1a4def 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 7d6c684e57..ddadfecf61 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 8c952fa85f..25fdee6677 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 26aebd483f..082d5a37fd 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 528182d2e3..a3283a1fc7 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 7cacc407ef..6c3e8be5a3 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 f8a3c44643..b856dcdd1c 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 505b3bf649..0b7bdd0d8d 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 300d0b2738..8903b47c13 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 26132ba7e9..6f4d0741ce 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 745715ba9d..86fbf76c0d 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 537b32933a..47178d7dc2 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 4c41f09d87..f1e705ff33 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 7fa3b30347..d51a0e4f7c 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 db448da184..8e4e58760d 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 63056b51e9..7253291341 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 d45edca3a9..456fe086a1 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 2294d12580..5b65734171 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 d6a06a1239..117aabcdaa 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 8d45551e76..7ca77d5533 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 5ff32468c5..010d023886 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 3d0909f862..b9669e8850 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 d5c91ee304..f9982b1943 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 5f3138e406..00da384b51 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 210fd4985a..bac6a4df87 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 5880bb1ad1..63c43222d3 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 b8e7c80786..b5b09176a4 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 6869c08b02..a7e9024a3a 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 8a05cd641a..8bad8429af 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 9da14fb7c9..cfbf2ab33c 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 6a47a1a22b..a4e22ee6cf 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 eb311cca1b..6ab2aa30e3 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 9732e57432..e2fa683286 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 570ea81dea..c18ca9c002 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 745ce939f5..fbc5490627 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 316a15643e..2cdf09c4aa 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 08ac3f3f4d..096d16d70f 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 2535f2f381..b5c6226636 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 dab0586678..14704fec00 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 95939b844f..7ddeb1d19b 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 f5420ab586..23769f448e 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 b427c91d45..dd8b164999 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 ea9cb10078..a648342651 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 17be02578e..12c7af2ea9 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 05573286c7..91615a90f7 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 68433db670..1a42a04c8b 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 a608fb0b72..87777cdd0f 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 9604103021..650c0e6166 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 18004d1f88..eb0ff265f0 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 93634166c2..82d7763fe2 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 ee3ae3d198..83dff300fa 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 3186acaa2b..0fce07d2a2 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 f1b8f5a219..a1c2a9335b 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 b5822be706..0f5b5d492d 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 -- Gitee