Bringing PyTorch to iOS: A guide by Tao Xu

PyTorch is a popular open-source machine learning framework that has gained significant traction in the deep learning community. PyTorch is known for its ease of use, flexibility, and dynamic computation graph, making it a popular choice for researchers and practitioners alike. In this tutorial, we will walk through the process of integrating PyTorch into an iOS app, allowing you to run deep learning models on mobile devices.

1. Requirements
   Before we get started, you will need to have the following software installed on your development machine:

• Xcode: Xcode is the IDE used for iOS app development. You can download it from the Mac App Store.
• PyTorch: You can install PyTorch by following the instructions on the official PyTorch website.
• CMake: CMake is a cross-platform build system that we will use to build the PyTorch library for iOS. You can download CMake from the official website.

1. Building PyTorch for iOS
   To use PyTorch in an iOS app, we need to build the PyTorch library for the ARM64 architecture that is used in iOS devices. Here are the steps to build PyTorch for iOS:

• Clone the PyTorch repository from GitHub:

  git clone --recursive https://github.com/pytorch/pytorch
  cd pytorch

• Create a build directory and run CMake to configure the build:

  mkdir build_ios
  cd build_ios
  cmake -DCMAKE_TOOLCHAIN_FILE=../tools/cmake/toolchains/ios.toolchain.cmake -DIOS_PLATFORM=OS64COMBINED ..

• Build the PyTorch library for iOS:

  make

• Once the build is complete, you will find the PyTorch library (libtorch.a) in the pytorch/build_ios/lib directory.

1. Integrating PyTorch into an iOS App
   Now that we have built the PyTorch library for iOS, we can integrate it into an iOS app. Here are the steps to do so:

• Create a new iOS project in Xcode and add the PyTorch library (libtorch.a) to your project. You can do this by dragging the library file into the Frameworks section of your Xcode project.

• Link the required frameworks to your project by going to the Build Phases tab of your project settings and adding the following frameworks:

  • Accelerate.framework
  • Metal.framework
  • QuartzCore.framework
  • libbz2.dylib
  • libz.dylib

• Import the PyTorch header files into your project. You can do this by adding the following line to your Objective-C bridging header file:

  #import <torch/script.h>

• Write code to load and run a PyTorch model in your iOS app. Here is an example of how you can load a pre-trained model and make predictions using PyTorch:

  
  // Load a pre-trained model
  torch::jit::script::Module module = torch::jit::load("model.pt");

// Create an input tensor
at::Tensor input = at::randn({1, 3, 224, 224});

// Run the model
at::Tensor output = module.forward({input}).toTensor();

- Finally, build and run your iOS app on a simulator or a physical iOS device to test the PyTorch integration.

4. Troubleshooting
If you encounter any issues during the integration of PyTorch into your iOS app, here are some common troubleshooting steps:

- Make sure that you have linked all the required frameworks and added the PyTorch library to your project correctly.
- Check that the PyTorch headers are included in your project and that you have added the necessary import statements to your code.
- Verify that the PyTorch model you are loading is compatible with the version of PyTorch you have built for iOS.

By following this tutorial, you should be able to integrate PyTorch into your iOS app and run deep learning models on mobile devices. PyTorch on iOS opens up a world of possibilities for deploying machine learning models in real-world applications, and with the right tools and knowledge, you can harness the power of deep learning on your iOS device.