Set up overview

Before you can deploy and test models with ExecuTorch, you need to set up your local development environment. This section walks you through installing system dependencies, creating a virtual environment, and cloning the ExecuTorch repository on Ubuntu or WSL. Once complete, you’ll be ready to run TinyML models on a virtual Arm platform.

Install system dependencies

Note Make sure Python 3 is installed. It comes pre-installed on most versions of Ubuntu.

These instructions have been tested on:

  • Ubuntu 22.04 and 24.04
  • Windows Subsystem for Linux (WSL)

Run the following commands to install the dependencies:

    

        
        
sudo apt update
sudo apt install python-is-python3 python3-dev python3-venv gcc g++ make -y

    

Create a virtual environment

Create and activate a Python virtual environment:

    

        
        
python3 -m venv $HOME/executorch-venv
source $HOME/executorch-venv/bin/activate

    

Your shell prompt should now start with (executorch) to indicate the environment is active.

Install ExecuTorch

Clone the ExecuTorch repository and install dependencies:

    

        
        
cd $HOME
git clone https://github.com/pytorch/executorch.git
cd executorch

    

Set up internal submodules:

    

        
        
git submodule sync
git submodule update --init --recursive
./install_executorch.sh

    
Tip

If you encounter a stale buck environment, reset it using:

    

        
        
ps aux | grep buck
pkill -f buck

    

Verify the installation:

Check that ExecuTorch is correctly installed:

    

        
        
pip list | grep executorch

    

Expected output:

    

        
        executorch         0.8.0a0+92fb0cc

        
    

What’s next?

Now that ExecuTorch is installed, you’re ready to simulate your TinyML model on an Arm Fixed Virtual Platform (FVP). In the next section, you’ll configure and launch a Fixed Virtual Platform.

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