4-env-setup-execut.md

title	Set up your ExecuTorch environment
weight	4
layout	learningpathall

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

{{< notice Note >}} Make sure Python 3 is installed. It comes pre-installed on most versions of Ubuntu. {{< /notice >}}

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

{{% notice Tip %}} If you encounter a stale buck environment, reset it using:

ps aux | grep buck
pkill -f buck

{{% /notice %}}

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.