In this section, you will prepare a development environment to compile a machine learning model.
ExecuTorch is a lightweight runtime designed for efficient execution of PyTorch models on resource-constrained devices. It enables machine learning inference on embedded and edge platforms, making it well-suited for Arm-based hardware. Since Arm processors are widely used in mobile, IoT, and embedded applications, ExecuTorch leverages Arm’s efficient CPU architectures to deliver optimized performance while maintaining low power consumption. By integrating with Arm’s compute libraries, it ensures smooth execution of AI workloads on Arm-powered devices, from Cortex-M microcontrollers to Cortex-A application processors.
These instructions have been tested on Ubuntu 22.04, 24.04, and on Windows Subsystem for Linux (WSL).
Python3 is required and comes installed with Ubuntu, but some additional packages are needed:
sudo apt update
sudo apt install python-is-python3 python3-dev python3-venv gcc g++ make -y
Create a Python virtual environment using python venv:
python3 -m venv $HOME/executorch-venv
source $HOME/executorch-venv/bin/activate
The prompt of your terminal now has (executorch) as a prefix to indicate the virtual environment is active.
From within the Python virtual environment, run the commands below to download the ExecuTorch repository and install the required packages:
cd $HOME
git clone https://github.com/pytorch/executorch.git
cd executorch
Run the commands below to set up the ExecuTorch internal dependencies:
git submodule sync
git submodule update --init
./install_executorch.sh
If you run into an issue of buck running in a stale environment, reset it by running the following instructions:
ps aux | grep buck
pkill -f buck
After running the commands, executorch should be listed upon running pip list:
pip list | grep executorch
executorch 0.6.0a0+3eea1f1
Proceed to the next section to learn about and set up the virtualized hardware.