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Building an ExecuTorch iOS Demo App

Welcome to the tutorial on setting up the ExecuTorch iOS Demo App!

This app uses the MobileNet v3 model to process live camera images leveraging three different backends: XNNPACK, Core ML and Metal Performance Shaders (MPS) (Xcode 15+ and iOS 17+ only).

Prerequisites

Before we start, make sure you have the following tools installed:

1. Xcode 15 and Command Line Tools

Install Xcode 15 from the Mac App Store and then install the Command Line Tools using the terminal:

xcode-select --install

2. Python 3.10+

Python 3.10 or above, along with pip, should be pre-installed on MacOS 13.5+. If needed, download Python and install it. Verify the Python and pip versions using these commands:

which python3 pip
python3 --version
pip --version

3. Set Up ExecuTorch

Clone ExecuTorch and set up the environment as explained in the Building from Source tutorial:

git clone -b viable/strict https://github.com/pytorch/executorch.git && cd executorch

python3 -m venv .venv && source .venv/bin/activate && pip install --upgrade pip

./install_executorch.sh

4. Backend Dependencies

Install additional dependencies for Core ML and MPS backends:

./backends/apple/coreml/scripts/install_requirements.sh
./backends/apple/mps/install_requirements.sh

5. Clone the Demo App

git clone --depth 1 https://github.com/pytorch-labs/executorch-examples.git

Models and Labels

Now, let's move on to exporting and bundling the MobileNet v3 model.

1. Export Model

Export the MobileNet v3 model with Core ML, MPS and XNNPACK backends, and move the exported model to a specific location where the Demo App will pick them up:

MODEL_NAME="mv3"

python3 -m examples.portable.scripts.export --model_name="$MODEL_NAME"
python3 -m examples.apple.coreml.scripts.export --model_name="$MODEL_NAME"
python3 -m examples.apple.mps.scripts.mps_example --model_name="$MODEL_NAME"
python3 -m examples.xnnpack.aot_compiler --model_name="$MODEL_NAME" --delegate

APP_PATH="executorch-examples/apple/ExecuTorchDemo/ExecuTorchDemo"

mkdir -p "$APP_PATH/Resources/Models/MobileNet/"
mv $MODEL_NAME*.pte "$APP_PATH/Resources/Models/MobileNet/"

2. Download Labels

Download the MobileNet model labels required for image classification:

curl https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt \
  -o "$APP_PATH/Resources/Models/MobileNet/imagenet_classes.txt"

Build and Run the App

We're almost done! Now, we just need to open the project in Xcode, run the tests, and finally run the app.

1. Open Project in Xcode

Double-click on the project file under executorch-examples/apple/ExecuTorchDemo or run the command:

open $APP_PATH.xcodeproj

2. Run Tests

You can run tests on Simulaltor directly in Xcode with Cmd + U or use the command line:

xcrun simctl create executorch "iPhone 15"
xcodebuild clean test \
     -project $APP_PATH.xcodeproj \
     -scheme App \
     -destination name=executorch
xcrun simctl delete executorch

3. Run App

Finally, connect the device, set up Code Signing in Xcode, and then run the app using Cmd + R. Try installing a Release build for better performance.

Congratulations! You've successfully set up the ExecuTorch iOS Demo App. Now, you can explore and enjoy the power of ExecuTorch on your iOS device!

Learn more about Using ExecuTorch on iOS.