How to install STM32 model zoo

This article explains how to install the STM32 model zoo in ST environment

1. Setting up proxy and SSL variables

If you are running behind a proxy, you may face different issues when you try to install the python packages with the pip command or when you will try to access the STM32Cube.AI Developer cloud. To avoid these issues or to install python packages using pip, require to set the environment variables named as `http_proxy`, `https_proxy`, and eventually `NO_SSL_VERIFY`. As a prerequisite, you need to get your proxy url and port as well as the credentials to access it. On the below sections:

• Replace username with the your proxy login username, and password with your proxy password.

• UTF-8 encode the password if it has any special characters. If the password contains some special character such as @, %, & or! you need to UTF-8 encode these characters. For example, if your password is Edge@AI, this will become Edge%40AI. You can use this link for the encoding.

• Replace the email address with your own email address.
• Replace the proxy.url.com with the proxy url and port with the port used.

1.1. Setting Environment variables using GUI for Windows (option 1)

Open the environment variable dialog by pressing the Windows button:

entering the term “environment variables” in the search bar. Open the application “Edit the System Environment Variables” or “Edit your account Environment Variables”.

Then click on the new button to add a new environment variable:

• Repeat the same process for adding another variable called https_proxy.
• Note use the same string for both proxies to avoid certificate issues. e.g., even for the variable https_proxy, the value will be http://username:password@proxy.url.com:port
• You may need also to add a variable to disable SSL Verification. Click >New
  • Variable name: NO_SSL_VERIFY
  • Variable value: 1

  After entering these three variables the variables should look something like this:

Click OK to save the environment variables.

Bonus:

In the context of the STM32 model zoo, users are required to create a secure connection with the STM32Cube.AI Developer Cloud, before they can execute any operation, i.e., analyze, benchmark, quantize. This required them to enter the username and password for my-ST account in the prompt window. To avoid entering the credentials every time, users can add two environment variables to have store their username and password and name them `stmai_username` and `stmai_password` following the method described above.

In the model zoo context, the code will look for these environment variables and will always use these to enter them automatically at the time of connection creation and users will not have to enter the username and password again and again. However, the variables have to be exactly named as described here.  

1.2. Setting Environment variables using CLI (option 2)

Alternatively, one can set the environment variables using a terminal command prompt.

For Windows Command prompt, execute following commands:

set http_proxy="http://username:password@proxy.url.com:port"
set https_proxy= "http://username:password@proxy.url.com:port”

Eventually:

set NO_SSL_VERIFY="1"
set stmai_username="user.name@example.com"
set stmai_password="myST_password"

You should be able to verify that the environment variables have been set by issuing an echo command in::

echo %variable_name%

To set the environment variable permanently, instead of using set, use setx:

setx http_proxy http://username:password@proxy.url.com:port 

You should be able to verify that the environment variables have been set by issuing an echo command in::

echo %variable_name%

For a using a Powershell prompt:

$env:http_proxy = 'http://username:password@proxy.url.com:port'
$env:https_proxy= 'https://username:password@proxy.url.com:port'

Eventually:

$env:NO_SSL_VERIFY ='1'
$env:stmai_username = 'user.name@example.com'
$env:stmai_password = 'myST_password'

To set the environment variable permanently, instead of using $env, use the SetEnvironmentVariable method of the System.Environment class: [System.Environment]::SetEnvironmentVariable('http_proxy','http://username:password@proxy.url.com:port',[System.EnvironmentVariableTarget]::User)) You should be able to verify that the environment variables have been set by issuing an echo command in::

$env:variable_name

Setting Environment Variables in Linux For setting the environment variables on the machines using Linux OS, open a terminal and enter:

export https_proxy='http://username:password.@proxy.url.com:port' 
export http_proxy=' http://username:password.@proxy.url.com:port'

Eventually:

export SSL_VERIFY=false
export stmai_username=’user.name@example.com’
export stmai_password=’myST_password’

To set the environment variable permanently, you must add the following lines in .bashrc file. This can be done by first issuing command:

$ nano ~/.bashrc

This will open .bashrc file in the editable mode. Navigate to the end of .bashrc file and add the previous lines (starting with export). Once these lines are added, press ‘CTRL + S’ button and then ‘CTRL + X’ to exit. Finally, to take these changes into account run following command:

$ source ~/.bashrc

• The stmai_username is the login for your myST account.
• The stmai_password is the password for your myST account.
• The password in the stmai_password string does not need to be encoded.

2. Install STM32Cube.AI

2.1. STM32Cube.AI Developer Cloud (option 1)

You can use only the STM32Cube.AI Developer Cloud without the need to have a local installation. However this means you always have an internet access.

Create an account on myST and then sign in to STM32Cube.AI Developer Cloud to be able access the service.

2.2. Install STM32Cube.AI through CubeMX (option 2)

To install STM32Cube.AI locally, follow the instruction here: How to install X-CUBE-AI through STM32CubeMX. This is the recommended installation process as it will work either with X-CUBE-AI as a plugin to STM32CubeMX or by accessing directly the Command Line Interface executable.

The windows CLI executable stm32ai.exe is installed by default in the following path

C:/Users/username/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/version/Utilities/windows/stm32ai.exe 

Replacing "username" by your windows user directory name and "version" by the version you are using, i.e. 8.1.0.

Warning: if there are some white spaces in the paths for STM32Cube.AI this may result in errors. Make sure to install in a path without white space.

Warning

If there are some white spaces in the paths for STM32Cube.AI this may result in errors. Make sure to install in a path without white space.

2.3. STM32Cube.AI Command Line Interface only (option 3)

Alternatively, you can install only the STM32Cube.AI Command Line Interface

Download the latest version of STM32Cube.AI for your Operating System. For Windows: Extract and unzip the package stm32ai-windows-8.1.0.zip in a directory such as C:/stm32ai.

The path to the executable will then be C:/stm32ai/windows/stm32ai.exe For Linux: Extract and unzip the package stm32ai-windows-8.1.0.zip in a directory, for instance $HOME/stm32ai. Then execute:

cd $HOME/stm32ai
chmod 644 en.en.x-cube-ai-v8-1-0-linux.zip
unzip en.en.x-cube-ai-v8-1-0-linux.zip
mv STMicroelectronics.X-CUBE-AI.8.1.0.pack STMicroelectronics.X-CUBE-AI.8.1.0.zip
unzip STMicroelectronics.X-CUBE-AI.8.1.0.zip -d X-CUBE-AI.8.1.0
unzip stm32ai-linux-8.1.0.zip -d X-CUBE-AI.8.1.0/Utilities

Warning: if there are some white spaces in the paths for STM32Cube.AI this may result in errors. Make sure to install in a path without white space.

Warning

If there are some white spaces in the paths for STM32Cube.AI this may result in errors. Make sure to install in a path without white space.

3. Install STM32 model zoo

3.1. Install with git clone (option 1)

For Windows, install git for Windows. For Linux, install git through apt-get:

$ sudo apt-get update 
$ sudo apt-get install git

For using git behind proxy, optionally if the proxy variables as not been configured for your terminal prompt, add following lines to the git config file. If the file does not exist already create an empty file. For WIndows, the file should be located under "C:/Users/username/.gitconfig".

For Linux, the file should be located under .git/config

[user]
    name = FirstName SecondName
    email = email@domain.com
[http]
    sslVerify = false
    proxy = http://username:password.@proxy.url.com:port
[https]
    sslVerify = false
    proxy = http://username:password.@proxy.url.com:port

Go to the directory where you want to download the model zoo script. You can use a Powershell or Command prompt. For WIndows, you can alternatively right click to open a git bash window:

For Linux, open a terminal.

Then enter git clone https://github.com/STMicroelectronics/stm32ai-modelzoo.git in terminal to clone the repository.

Then enter git clone https://github.com/STMicroelectronics/stm32ai-modelzoo.git in terminal to clone the repository.

3.2. Install from a ZIP (option 2)

An easy way to download without the need to install git for Windows is to download the repository as a ZIP archive. Go to stm32ai-modelzoo github and click on Download ZIP from the menu ""Code"":

Then extract to your target directory.

4. Installing Python environment

4.1. Prerequisites

• If you don't have python installed already, you can download and install it from here.
  • Python Version <= 3.10 is required to be able to use required version of TensorFlow later, we recommend Python v3.9 or v3.10.

• • For Windows systems make sure to check the Add python.exe to PATH option during the installation process.
  • Having multiple versions installed can be a problem, so it is recommended to remove the paths for the other Python versions installed and keep only the one installed for STM32 Model Zoo.
  • For Linux add the path of the Python installation to your paths using export command or modifying ~/.bashrc file.
• When using GPU, make sure to install the GPU drivers. For NVIDIA GPUs, please refer to https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html to install CUDA and CUDNN.
• On Windows OS, it is not recommended to use WSL as it can result in suboptimal acceleration for training on GPU.

4.2. Python environment creation

4.2.1. Python virtual environment (option 1)

• For Windows, open a Powershell or Command prompt terminal.
• For Linux, open a terminal.
• Navigate to the model zoo directory and create a python virtual environment for the project using venv:

cd stm32ai-modelzoo
python -m venv st_zoo

• Activate your virtual environment.
• For Windows run:

st_zoo\Scripts\activate.bat

• For Linux run:

source st_zoo/bin/activate

• Then install all the necessary python packages. A list of all the required python packages is provided in requirement.txt file.

pip install -r requirements.txt

4.2.2. Python conda environment (option 2)

Or create a conda virtual environment for the project. Install Miniconda.

If you are using conda environment, optionally if the proxy variables as not been configured for your terminal prompt, you can also use the .condarc file to store the proxy information This can be done by creating a .condarc file and place it in the path C:/Users/username/ for Windows and $HOME for Linux. The .condarc file must contain:

proxy_servers: 
   https: http://username:UTF-8-EncodedPassword.@proxy.url.com::8080
   http: http://username:UTF-8-EncodedPassword@proxy.url.com::8080
ssl_verify: false

Note: see first section for the UTF encoding and proxy url.

• For Windows, open an Anaconda Powershell Prompt. For Linux open a terminal.
• Go to the model zoo folder:

cd stm32ai-modelzoo

• Create a conda environment:

conda create -n st_zoo

• Activate your virtual environment:

conda activate st_zoo

• Install python 3.10 from conda-forge open source repository:

conda install -c conda-forge pyhton=3.10

conda create --name st_zoo python=3.10.x

• If using NVIDIA GPU, install cudatoolkit and cudnn and add to conda path:

conda install -c conda-forge cudatoolkit=11.8 cudnn

• Add cudatoolkit and cudnn to path permanently:

mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

• Then install all the necessary python packages, the requirement file contains it all.

pip install -r requirements.txt

5. Installing STM32CubeIDE

For training a model, you do not need to rebuilt a STM32 firmware, but to deploy on a Getting Started Application package you need to be able to built the corresponding firmware. This can only be done locally through STM32CubeIDE. You need then to install STM32CubeIDE following the instruction of the STM32CubeIDE installation guide.

Warning: if there are some white spaces in the paths for STM32Cube.AI or STM32CubeIDE this may result in errors. Make sure to install in a path without white space.

Warning

If there are some white spaces in the paths for STM32Cube.AI or STM32CubeIDE this may result in errors. Make sure to install in a path without white space.

Note: The deployment of the models works even without having the local installation of STM32Cube.AI.

6. Updating model zoo yaml file with installation path

When using local installation either for training or deployment, the yaml file shall be updated with the correct paths. Please update the variables with your installation paths:

 path_to_stm32ai: C:/ST/STM32CubeAI/en.x-cube-ai-windows_v7.3.0/windows/stm32ai.exe
 path_to_cubeIDE: C:/ST/STM32CubeIDE_1.10.1/STM32CubeIDE/stm32cubeide.exe