# MindSpore Installation Guide This document describes how to quickly install MindSpore on a NVIDIA GPU environment. - [MindSpore Installation Guide](#mindspore-installation-guide) - [Environment Requirements](#environment-requirements) - [Hardware Requirements](#hardware-requirements) - [System Requirements and Software Dependencies](#system-requirements-and-software-dependencies) - [(Optional) Installing Conda](#optional-installing-conda) - [Installation Guide](#installation-guide) - [Installing Using Executable Files](#installing-using-executable-files) - [Installing Using the Source Code](#installing-using-the-source-code) - [Installation Verification](#installation-verification) - [Installing MindInsight](#installing-mindinsight) - [Installing MindArmour](#installing-mindarmour) ## Environment Requirements ### Hardware Requirements - Nvidia GPU ### System Requirements and Software Dependencies | Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies | | ---- | :--- | :--- | :--- | | MindSpore master | Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
- [OpenMPI](https://www.open-mpi.org/faq/?category=building#easy-build) 3.1.5 (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
- [NCCL](https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html#debian) 2.4.8-1 (optional, required for single-node/multi-GPU and multi-node/multi-GPU training)
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindspore/blob/r0.5/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [patch](http://ftp.gnu.org/gnu/patch/) >= 2.5
- [Autoconf](https://www.gnu.org/software/autoconf) >= 2.69
- [Libtool](https://www.gnu.org/software/libtool) >= 2.4.6-29.fc30
- [Automake](https://www.gnu.org/software/automake) >= 1.15.1
- [CUDA 9.2](https://developer.nvidia.com/cuda-92-download-archive) / [CUDA 10.1](https://developer.nvidia.com/cuda-10.1-download-archive-base)
- [CuDNN](https://developer.nvidia.com/rdp/cudnn-archive) >= 7.6
**Installation dependencies:**
same as the executable file installation dependencies. | - When the network is connected, dependency items in the `requirements.txt` file are automatically downloaded during `.whl` package installation. In other cases, you need to manually install dependency items. - MindSpore reduces dependency on Autoconf, Libtool, Automake versions for the convenience of users, default versions of these tools built in their systems are now supported. ### (Optional) Installing Conda 1. Download the Conda installation package from the following path: - [X86 Anaconda](https://www.anaconda.com/distribution/) or [X86 Miniconda](https://docs.conda.io/en/latest/miniconda.html) 2. Create and activate the Python environment. ```bash conda create -n {your_env_name} python=3.7.5 conda activate {your_env_name} ``` > Conda is a powerful Python environment management tool. It is recommended that a beginner read related information on the Internet first. ## Installation Guide ### Installing Using Executable Files - Download the .whl package from the [MindSpore website](https://www.mindspore.cn/versions/en). It is recommended to perform SHA-256 integrity verification first and run the following command to install MindSpore: ```bash pip install mindspore_gpu-{version}-cp37-cp37m-linux_{arch}.whl ``` ### Installing Using the Source Code 1. Download the source code from the code repository. ```bash git clone https://gitee.com/mindspore/mindspore.git -b r0.5 ``` 2. Run the following command in the root directory of the source code to compile MindSpore: ```bash bash build.sh -e gpu -M on -z ``` > - Before running the preceding command, ensure that the paths where the executable files `cmake` and `patch` store have been added to the environment variable PATH. > - In the `build.sh` script, the `git clone` command will be executed to obtain the code in the third-party dependency database. Ensure that the network settings of Git are correct. > - In the `build.sh` script, the default number of compilation threads is 8. If the compiler performance is poor, compilation errors may occur. You can add -j{Number of threads} in to script to reduce the number of threads. For example, `bash build.sh -e gpu -M on -z -j4`. 3. Run the following command to install MindSpore: ```bash chmod +x build/package/mindspore_gpu-{version}-cp37-cp37m-linux_{arch}.whl pip install build/package/mindspore_gpu-{version}-cp37-cp37m-linux_{arch}.whl ``` ## Installation Verification - After Installation, execute the following Python script： ```bash import numpy as np from mindspore import Tensor from mindspore.ops import functional as F import mindspore.context as context context.set_context(device_target="GPU") x = Tensor(np.ones([1,3,3,4]).astype(np.float32)) y = Tensor(np.ones([1,3,3,4]).astype(np.float32)) print(F.tensor_add(x, y)) ``` - The outputs should be same as: ```bash [[[ 2. 2. 2. 2.], [ 2. 2. 2. 2.], [ 2. 2. 2. 2.]], [[ 2. 2. 2. 2.], [ 2. 2. 2. 2.], [ 2. 2. 2. 2.]], [[ 2. 2. 2. 2.], [ 2. 2. 2. 2.], [ 2. 2. 2. 2.]]] ``` # Installing MindInsight If you need to analyze information such as model scalars, graphs, and model traceback, you can install MindInsight. ## Environment Requirements ### System Requirements and Software Dependencies | Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies | | ---- | :--- | :--- | :--- | | MindInsight master | - Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- For details about other dependency items, see [requirements.txt](https://gitee.com/mindspore/mindinsight/blob/r0.5/requirements.txt). | **Compilation dependencies:**
- [Python](https://www.python.org/downloads/) 3.7.5
- [CMake](https://cmake.org/download/) >= 3.14.1
- [GCC](https://gcc.gnu.org/releases.html) 7.3.0
- [node.js](https://nodejs.org/en/download/) >= 10.19.0
- [wheel](https://pypi.org/project/wheel/) >= 0.32.0
- [pybind11](https://pypi.org/project/pybind11/) >= 2.4.3
**Installation dependencies:**
same as the executable file installation dependencies. | - When the network is connected, dependency items in the `requirements.txt` file are automatically downloaded during .whl package installation. In other cases, you need to manually install dependency items. ## Installation Guide ### Installing Using Executable Files 1. Download the .whl package from the [MindSpore website](https://www.mindspore.cn/versions/en). It is recommended to perform SHA-256 integrity verification first and run the following command to install MindInsight: ```bash pip install mindinsight-{version}-cp37-cp37m-linux_{arch}.whl ``` 2. Run the following command. If `web address: http://127.0.0.1:8080` is displayed, the installation is successful. ```bash mindinsight start ``` ### Installing Using the Source Code 1. Download the source code from the code repository. ```bash git clone https://gitee.com/mindspore/mindinsight.git -b r0.5 ``` > You are **not** supposed to obtain the source code from the zip package downloaded from the repository homepage. 2. Install MindInsight by using either of the following installation methods: (1) Access the root directory of the source code and run the following installation command: ```bash cd mindinsight pip install -r requirements.txt python setup.py install ``` (2) Create a .whl package to install MindInsight. Access the root directory of the source code. First run the MindInsight compilation script under the `build` directory of the source code. Then run the command to install the .whl package generated into the `output` directory of the source code. ```bash cd mindinsight bash build/build.sh pip install output/mindinsight-{version}-cp37-cp37m-linux_{arch}.whl ``` 3. Run the following command. If `web address: http://127.0.0.1:8080` is displayed, the installation is successful. ```bash mindinsight start ``` # Installing MindArmour If you need to conduct AI model security research or enhance the security of the model in you applications, you can install MindArmour. ## Environment Requirements ### System Requirements and Software Dependencies | Version | Operating System | Executable File Installation Dependencies | Source Code Compilation and Installation Dependencies | | ---- | :--- | :--- | :--- | | MindArmour master | Ubuntu 18.04 x86_64 | - [Python](https://www.python.org/downloads/) 3.7.5
- MindSpore master
- For details about other dependency items, see [setup.py](https://gitee.com/mindspore/mindarmour/blob/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. ## Installation Guide ### Installing Using Executable Files 1. Download the .whl package from the [MindSpore website](https://www.mindspore.cn/versions/en). It is recommended to perform SHA-256 integrity verification first and run the following command to install MindArmour: ```bash pip install mindarmour-{version}-cp37-cp37m-linux_{arch}.whl ``` 2. Run the following command. If no loading error message such as `No module named 'mindarmour'` is displayed, the installation is successful. ```bash python -c 'import mindarmour' ``` ### Installing Using the Source Code 1. Download the source code from the code repository. ```bash git clone https://gitee.com/mindspore/mindarmour.git -b r0.5 ``` 2. Run the following command in the root directory of the source code to compile and install MindArmour: ```bash cd mindarmour python setup.py install ``` 3. Run the following command. If no loading error message such as `No module named 'mindarmour'` is displayed, the installation is successful. ```bash python -c 'import mindarmour' ```