# Set up your ExecuTorch environment

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/)
- [Overview](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/2-overview/)
- [Understand the ExecuTorch workflow](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/3-executorch-workflow/)
- [Set up your ExecuTorch environment](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/4-env-setup-execut/)
- [Set up the Corstone-320 Fixed Virtual Platform](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/5-env-setup-fvp/)
- [Deploy and run Mobilenet V2 on the Corstone-320 FVP](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/6-run-model/)
- [Enable GUI and deploy a model on Corstone-320 FVP](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/7-configure-fvp-gui/)
- [Evaluate Ethos-U Performance](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/8-evaluate-output/)
- [Next Steps](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/_next-steps/)

## 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:
```bash
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:
```bash
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:
```bash
cd $HOME
git clone [https://github.com/pytorch/executorch.git](https://github.com/pytorch/executorch.git)
cd executorch
git checkout release/1.0
```
Set up internal submodules:
```bash
git submodule sync
git submodule update --init --recursive
./install_executorch.sh
```
> **Tip**  
> If you encounter a stale `buck` environment, reset it using:
```bash
ps aux | grep buck
pkill -f buck
```

## Verify the installation:
Check that ExecuTorch is correctly installed:
```bash
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.