mindspore_d_install_en.md

# MindSpore Installation Guide

This document describes how to quickly install MindSpore on an Ascend AI processor environment.

<!-- TOC -->

- [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)
        - [Configuring software package Dependencies](#configuring-software-package-dependencies)
    - [Installation Guide](#installation-guide)
        - [Installing Using Executable Files](#installing-using-executable-files)
        - [Installing Using the Source Code](#installing-using-the-source-code)
    - [Configuring Environment Variables](#configuring-environment-variables)
    - [Installation Verification](#installation-verification)
- [Installing MindInsight](#installing-mindinsight)
- [Installing MindArmour](#installing-mindarmour)

<!-- /TOC -->

## Environment Requirements

### Hardware Requirements

- Ascend 910 AI processor

  > - Reserve at least 32 GB memory for each card.

### System Requirements and Software Dependencies

| 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> - 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> **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.
- 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.

### (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)
   - [ARM Anaconda](https://github.com/Archiconda/build-tools/releases/download/0.2.3/Archiconda3-0.2.3-Linux-aarch64.sh)

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.

### Configuring software package Dependencies

 - Install the .whl package provided in Ascend 910 AI processor software package(Version:Atlas Data Center Solution V100R020C00T100). The .whl package is released with the software package. After software package is upgraded, reinstall the .whl package.

    ```bash
    pip install /usr/local/Ascend/fwkacllib/lib64/topi-{version}-py3-none-any.whl
    pip install /usr/local/Ascend/fwkacllib/lib64/te-{version}-py3-none-any.whl
    pip install /usr/local/Ascend/fwkacllib/lib64/hccl-{version}-py3-none-any.whl
    ```

## 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_ascend-{version}-cp37-cp37m-linux_{arch}.whl
    ```

### Installing Using the Source Code

The compilation and installation must be performed on the Ascend 910 AI processor environment.

1. Download the source code from the code repository.

    ```bash
    git clone https://gitee.com/mindspore/mindspore.git
    ```

2. Run the following command in the root directory of the source code to compile MindSpore:

    ```bash
    bash build.sh -e d -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 d -z -j4`.

3. Run the following command to install MindSpore:

    ```bash
    chmod +x build/package/mindspore_ascend-{version}-cp37-cp37m-linux_{arch}.whl
    pip install build/package/mindspore_ascend-{version}-cp37-cp37m-linux_{arch}.whl
    ```

## Configuring Environment Variables

- After MindSpore is installed, export runtime-related environment variables.

    ```bash
    # control log level. 0-DEBUG, 1-INFO, 2-WARNING, 3-ERROR, default level is WARNING.
    export GLOG_v=2
    # Conda environmental options
    LOCAL_ASCEND=/usr/local/Ascend # the root directory of run package
    # lib libraries that the run package depends on
    export LD_LIBRARY_PATH=${LOCAL_ASCEND}/add-ons/:${LOCAL_ASCEND}/fwkacllib/lib64:${LD_LIBRARY_PATH}
    # Environment variables that must be configured
    export TBE_IMPL_PATH=${LOCAL_ASCEND}/opp/op_impl/built-in/ai_core/tbe # TBE operator implementation tool path
    export PATH=${LOCAL_ASCEND}/fwkacllib/ccec_compiler/bin/:${PATH} # TBE operator compilation tool path
    export PYTHONPATH=${TBE_IMPL_PATH}:${PYTHONPATH} # Python library that TBE implementation depends on
    ```

## Installation Verification

- After configuring the environment variables, 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="Ascend")
    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:

    ```
    [[[ 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 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 [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. |

- 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
    ```
    > 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 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. |

- 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
   ```

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'
   ```