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.
@@ -21,7 +21,7 @@ This document describes how to quickly install MindSpore on a Ubuntu system with
| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
| ---- | :--- | :--- | :--- |
| MindSpore master | Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <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 18.04 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/r0.5/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. |
- 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.
2. Run the following command in the root directory of the source code to compile MindSpore:
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@@ -97,7 +97,7 @@ If you need to conduct AI model security research or enhance the security of the
| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
| ---- | :--- | :--- | :--- |
| MindArmour master | Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore master <br> - For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/master/setup.py). | Same as the executable file installation dependencies. |
| MindArmour master | Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore master <br> - For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.5/setup.py). | Same as the executable file installation dependencies. |
- When the network is connected, dependency items in the `setup.py` file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items.
...
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@@ -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.
@@ -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/r0.5/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.
- 确认当前用户有权限访问Ascend 910 AI处理器配套软件包（对应版本Atlas Data Center Solution V100R020C00T100）的安装路径`/usr/local/Ascend`，若无权限，需要root用户将当前用户添加到`/usr/local/Ascend`所在的用户组，具体配置请详见配套软件包的说明文档。
@@ -32,7 +32,7 @@ This document describes how to quickly install MindSpore on an Ascend AI process
| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
| ---- | :--- | :--- | :--- |
| MindSpore master | - Ubuntu 18.04 aarch64 <br> - Ubuntu 18.04 x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI processor software package(Version:Atlas Data Center Solution V100R020C00T100) <br> - [gmp](https://gmplib.org/download/gmp/) 6.1.2 <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 Data Center Solution V100R020C00T100) <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> - [gmp](https://gmplib.org/download/gmp/) 6.1.2 <br>**Installation dependencies:**<br> same as the executable file installation dependencies. |
| MindSpore master | - Ubuntu 18.04 aarch64 <br> - Ubuntu 18.04 x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI processor software package(Version:Atlas Data Center Solution V100R020C00T100) <br> - [gmp](https://gmplib.org/download/gmp/) 6.1.2 <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.5/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - Ascend 910 AI processor software package(Version:Atlas Data Center Solution V100R020C00T100) <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> - [gmp](https://gmplib.org/download/gmp/) 6.1.2 <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 Data Center Solution V100R020C00T100). If not, the root user needs to add the current user to the user group where `/usr/local/Ascend` is located. For the specific configuration, please refer to the software package instruction document.
- GCC 7.3.0 can be installed by using apt command.
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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.
- 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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@@ -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.
> You are **not** supposed to obtain the source code from the zip package downloaded from the repository homepage.
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@@ -226,7 +226,7 @@ If you need to conduct AI model security research or enhance the security of the
| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
| ---- | :--- | :--- | :--- |
| MindArmour master | - Ubuntu 18.04 aarch64 <br> - Ubuntu 18.04 x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 <br> | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore master <br> - For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/master/setup.py). | Same as the executable file installation dependencies. |
| MindArmour master | - Ubuntu 18.04 aarch64 <br> - Ubuntu 18.04 x86_64 <br> - EulerOS 2.8 aarch64 <br> - EulerOS 2.5 x86_64 <br> | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore master <br> - For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.5/setup.py). | Same as the executable file installation dependencies. |
- When the network is connected, dependency items in the `setup.py` file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items.
...
...
@@ -251,7 +251,7 @@ If you need to conduct AI model security research or enhance the security of the
1. Download the source code from the code repository.
- When the network is connected, dependency items in the `requirements.txt` file are automatically downloaded during `.whl` package installation. In other cases, you need to manually install dependency items.
- MindSpore reduces dependency on Autoconf, Libtool, Automake versions for the convenience of users, default versions of these tools built in their systems are now supported.
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@@ -63,7 +63,7 @@ This document describes how to quickly install MindSpore on a NVIDIA GPU environ
1. Download the source code from the code repository.
2. Run the following command in the root directory of the source code to compile MindSpore:
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@@ -123,7 +123,7 @@ If you need to analyze information such as model scalars, graphs, and model trac
| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
| ---- | :--- | :--- | :--- |
| MindInsight master | - Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore master <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindinsight/blob/master/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [node.js](https://nodejs.org/en/download/) >= 10.19.0 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [pybind11](https://pypi.org/project/pybind11/) >= 2.4.3 <br>**Installation dependencies:**<br> same as the executable file installation dependencies. |
| MindInsight master | - Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore master <br> - For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindinsight/blob/r0.5/requirements.txt). | **Compilation dependencies:**<br> - [Python](https://www.python.org/downloads/) 3.7.5 <br> - [CMake](https://cmake.org/download/) >= 3.14.1 <br> - [GCC](https://gcc.gnu.org/releases.html) 7.3.0 <br> - [node.js](https://nodejs.org/en/download/) >= 10.19.0 <br> - [wheel](https://pypi.org/project/wheel/) >= 0.32.0 <br> - [pybind11](https://pypi.org/project/pybind11/) >= 2.4.3 <br>**Installation dependencies:**<br> same as the executable file installation dependencies. |
- When the network is connected, dependency items in the `requirements.txt` file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items.
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@@ -148,7 +148,7 @@ If you need to analyze information such as model scalars, graphs, and model trac
1. Download the source code from the code repository.
> You are **not** supposed to obtain the source code from the zip package downloaded from the repository homepage.
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@@ -190,7 +190,7 @@ If you need to conduct AI model security research or enhance the security of the
| Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies |
| ---- | :--- | :--- | :--- |
| MindArmour master | Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore master <br> - For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/master/setup.py). | Same as the executable file installation dependencies. |
| MindArmour master | Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5 <br> - MindSpore master <br> - For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/r0.5/setup.py). | Same as the executable file installation dependencies. |
- When the network is connected, dependency items in the `setup.py` file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items.
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@@ -215,7 +215,7 @@ If you need to conduct AI model security research or enhance the security of the
1. Download the source code from the code repository.
@@ -68,7 +68,7 @@ A: Please install the software manually if there is any suggestion of certain `s
Q: What types of model is currently supported by MindSpore for training ?
A: MindSpore has basic support for common training scenarios, please refer to [Release note](https://gitee.com/mindspore/mindspore/blob/master/RELEASE.md) for detailed information.
A: MindSpore has basic support for common training scenarios, please refer to [Release note](https://gitee.com/mindspore/mindspore/blob/r0.5/RELEASE.md) for detailed information.
<br/>
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@@ -92,7 +92,7 @@ A: MindSpore provides pluggable device management interface so that developer co
Q: What hardware does MindSpore require?
A: Currently, you can try out MindSpore through Docker images on laptops or in environments with GPUs. Some models in MindSpore Model Zoo support GPU-based training and inference, and other models are being improved. For distributed parallel training, MindSpore supports multi-GPU training. You can obtain the latest information from [RoadMap](https://www.mindspore.cn/docs/en/master/roadmap.html) and project [Release Notes](https://gitee.com/mindspore/mindspore/blob/master/RELEASE.md).
A: Currently, you can try out MindSpore through Docker images on laptops or in environments with GPUs. Some models in MindSpore Model Zoo support GPU-based training and inference, and other models are being improved. For distributed parallel training, MindSpore supports multi-GPU training. You can obtain the latest information from [RoadMap](https://www.mindspore.cn/docs/en/master/roadmap.html) and project [Release Notes](https://gitee.com/mindspore/mindspore/blob/r0.5/RELEASE.md).
A：目前笔记本电脑或者有GPU的环境，都可以通过Docker镜像来试用。当前MindSpore Model Zoo中有部分模型已经支持GPU的训练和推理，其他模型也在不断地进行完善。在分布式并行训练方面，MindSpore当前支持GPU多卡训练。你可以通过[RoadMap](https://www.mindspore.cn/docs/zh-CN/master/roadmap.html)和项目[Release note](https://gitee.com/mindspore/mindspore/blob/master/RELEASE.md)获取最新信息。
A：目前笔记本电脑或者有GPU的环境，都可以通过Docker镜像来试用。当前MindSpore Model Zoo中有部分模型已经支持GPU的训练和推理，其他模型也在不断地进行完善。在分布式并行训练方面，MindSpore当前支持GPU多卡训练。你可以通过[RoadMap](https://www.mindspore.cn/docs/zh-CN/master/roadmap.html)和项目[Release note](https://gitee.com/mindspore/mindspore/blob/r0.5/RELEASE.md)获取最新信息。
@@ -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： <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/r0.5/tutorials/tutorial_code/resnet>.
The key parts of the task process code are explained below.
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:
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@@ -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: <https://gitee.com/mindspore/docs/blob/master/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py>
> Download address of the complete sample code: <https://gitee.com/mindspore/docs/blob/r0.5/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py>
## Preparations
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@@ -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: <https://gitee.com/mindspore/docs/blob/master/tutorials/tutorial_code/resnet/resnet.py>
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: <https://gitee.com/mindspore/docs/blob/r0.5/tutorials/tutorial_code/resnet/resnet.py>
@@ -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 <https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/dataset.py>.
Then, perform data augmentation, data cleaning, and batch processing. For details about the code, see <https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/resnet/src/dataset.py>.
3. Build a network.
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@@ -214,7 +214,7 @@ The ResNet-50 network migration and training on the Ascend 910 is used as an exa
6. Build the entire network.
The [ResNet-50](https://gitee.com/mindspore/mindspore/blob/master/model_zoo/resnet/src/resnet.py) network structure is formed by connecting multiple defined subnets. Follow the rule of defining subnets before using them and define all the subnets used in the `__init__` and connect subnets in the `construct`.
The [ResNet-50](https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/resnet/src/resnet.py) network structure is formed by connecting multiple defined subnets. Follow the rule of defining subnets before using them and define all the subnets used in the `__init__` and connect subnets in the `construct`.
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.
Performance data like operators' execution time are recorded in files and can be viewed on the web page, this can help the user optimize the performance of neural networks. MindInsight Profiler can only support the Ascend chip now.
@@ -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 <https://gitee.com/mindspore/mindspore/blob/master/model_zoo/lenet/eval.py>.
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 <https://gitee.com/mindspore/mindspore/blob/r0.5/model_zoo/lenet/eval.py>.
