@@ -21,7 +21,7 @@ This document describes how to quickly install MindSpore on a Ubuntu system with
| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
| ---- | :--- | :--- | :--- |
| MindSpore master | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> same as the executable file installation dependencies. |
| MindSpore master | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.2/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> same as the executable file installation dependencies. |
- When Ubuntu version is 18.04, GCC 7.3.0 can be installed by using apt command.
- When the network is connected, dependency items in the requirements.txt file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items.
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@@ -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.
@@ -20,7 +20,7 @@ This document describes how to quickly install MindSpore on a Windows system wit
| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
| ---- | :--- | :--- | :--- |
| MindSpore master | Windows 10 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [MinGW-W64 GCC-7.3.0](https://sourceforge.net/projects/mingw-w64/files/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/7.3.0/threads-posix/seh/x86_64-7.3.0-release-posix-seh-rt_v5-rev0.7z) x86_64-posix-seh <br> - [ActivePerl](http://downloads.activestate.com/ActivePerl/releases/5.24.3.2404/ActivePerl-5.24.3.2404-MSWin32-x64-404865.exe) 5.24.3.2404 <br> - [CMake](https://cmake.org/download/) 3.14.1 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br>**Installation dependencies:**<br> same as the executable file installation dependencies. |
| MindSpore master | Windows 10 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.2/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [MinGW-W64 GCC-7.3.0](https://sourceforge.net/projects/mingw-w64/files/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/7.3.0/threads-posix/seh/x86_64-7.3.0-release-posix-seh-rt_v5-rev0.7z) x86_64-posix-seh <br> - [ActivePerl](http://downloads.activestate.com/ActivePerl/releases/5.24.3.2404/ActivePerl-5.24.3.2404-MSWin32-x64-404865.exe) 5.24.3.2404 <br> - [CMake](https://cmake.org/download/) 3.14.1 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br>**Installation dependencies:**<br> same as the executable file installation dependencies. |
- When the network is connected, dependency items in the requirements.txt file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items.
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@@ -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.
@@ -32,7 +32,7 @@ This document describes how to quickly install MindSpore on an Ascend AI process
| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
| ---- | :--- | :--- | :--- |
| MindSpore master | - Ubuntu 16.04 or later x86_64 <br> - EulerOS 2.8 arrch64 <br> - EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI processor software package(Version:Atlas T 1.1.T107) <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI processor software package(Version:Atlas T 1.1.T107) <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br>**Installation dependencies:**<br> same as the executable file installation dependencies. |
| MindSpore master | - Ubuntu 16.04 or later x86_64 <br> - EulerOS 2.8 arrch64 <br> - EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI processor software package(Version:Atlas T 1.1.T107) <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.2/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI processor software package(Version:Atlas T 1.1.T107) <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br>**Installation dependencies:**<br> same as the executable file installation dependencies. |
- Confirm that the current user has the right to access the installation path `/usr/local/Ascend `of Ascend 910 AI processor software package(Version:Atlas T 1.1.T107). If not, the root user needs to add the current user to the user group where `/usr/local/Ascend` is located. For the specific configuration, please refer to the software package instruction document.
- When Ubuntu version is 18.04, GCC 7.3.0 can be installed by using apt command.
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@@ -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.
@@ -28,7 +28,7 @@ This document describes how to quickly install MindSpore on a NVIDIA GPU environ
| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
| ---- | :--- | :--- | :--- |
| MindSpore master | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)<br> - [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6 <br> - [OpenMPI](https://www.open-mpi.org/faq/?category=building#easy-build) 3.1.5 (optional, required for single-node/multi-GPU and multi-node/multi-GPU training) <br> - [NCCL](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) 2.4.8-1 (optional, required for single-node/multi-GPU and multi-node/multi-GPU training) <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/master/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> - [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69 <br> - [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30 <br> - [Automake](https://www.gnu.org/software/automake) >= 1.15.1 <br> - [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)<br> - [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6 <br>**Installation dependencies:**<br> same as the executable file installation dependencies. |
| MindSpore master | Ubuntu 16.04 or later x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)<br> - [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6 <br> - [OpenMPI](https://www.open-mpi.org/faq/?category=building#easy-build) 3.1.5 (optional, required for single-node/multi-GPU and multi-node/multi-GPU training) <br> - [NCCL](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) 2.4.8-1 (optional, required for single-node/multi-GPU and multi-node/multi-GPU training) <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.2/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5 <br> - [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69 <br> - [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30 <br> - [Automake](https://www.gnu.org/software/automake) >= 1.15.1 <br> - [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)<br> - [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6 <br>**Installation dependencies:**<br> same as the executable file installation dependencies. |
- When Ubuntu version is 18.04, GCC 7.3.0 can be installed by using apt command.
- When the network is connected, dependency items in the requirements.txt file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items.
...
...
@@ -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.
@@ -62,7 +62,7 @@ Next, let's use MindSpore to solve the image classification task. The overall pr
5. Call the high-level `Model` API to train and save the model file.
6. Load the saved model for inference.
> This example is for the hardware platform of the Ascend 910 AI processor. You can find the complete executable sample code at： <https://gitee.com/mindspore/docs/blob/master/tutorials/tutorial_code/resnet>.
> This example is for the hardware platform of the Ascend 910 AI processor. You can find the complete executable sample code at： <https://gitee.com/mindspore/docs/blob/r0.2/tutorials/tutorial_code/resnet>.
The key parts of the task process code are explained below.
In this tutorial, we will learn how to train the ResNet-50 network in `DATA_PARALLEL` or `AUTO_PARALLEL` mode on MindSpore.
> The current sample is for the Ascend 910 AI processor. CPU and GPU processors are not supported for now.
> You can find the complete executable sample code at：<https://gitee.com/mindspore/docs/blob/master/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py>.
> You can find the complete executable sample code at：<https://gitee.com/mindspore/docs/blob/r0.2/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py>.
@@ -27,7 +27,7 @@ At the beginning of AI algorithm design, related security threats are sometimes
This section describes how to use MindArmour in adversarial attack and defense by taking the Fast Gradient Sign Method (FGSM) attack algorithm and Natural Adversarial Defense (NAD) algorithm as examples.
> The current sample is for CPU, GPU and Ascend 910 AI processor. You can find the complete executable sample code at：<https://gitee.com/mindspore/docs/tree/master/tutorials/tutorial_code/model_safety>
> The current sample is for CPU, GPU and Ascend 910 AI processor. You can find the complete executable sample code at：<https://gitee.com/mindspore/docs/tree/r0.2/tutorials/tutorial_code/model_safety>
3. Run the following command in the root directory of the source code to compile MindSpore Predict: -I indicates options for compiling MindSpore Predict and the parameter is the target platform architecture. Currently, only the Android arm64 platform is supported.
