Our PyTorch code uses custom [CUDA extensions](https://pytorch.org/tutorials/advanced/cpp_extension.html) to speed up some of the network layers. Getting these to run can sometimes be a hassle.
This page aims to give guidance on how to diagnose and fix run-time problems related to these extensions.
## Before you start
1. Try Docker first! Ensure you can successfully run our models using the recommended Docker image. Follow the instructions in [README.md](/README.md) to get it running.
2. Can't use Docker? Read on..
## Installing dependencies
Make sure you've installed everything listed on the requirements section in the [README.md](/README.md). The key components w.r.t. custom extensions are:
- **[CUDA toolkit 11.1](https://developer.nvidia.com/cuda-toolkit)** or later (this is not the same as `cudatoolkit` from Conda).
- PyTorch invokes `nvcc` to compile our CUDA kernels.
- **ninja**
- PyTorch uses [Ninja](https://ninja-build.org/) as its build system.
The PyTorch package contains the required CUDA toolkit libraries needed to run PyTorch, so why is a separate CUDA toolkit installation required? Our models use custom CUDA kernels to implement operations such as efficient resampling of 2D images. PyTorch code invokes the CUDA compiler at run-time to compile these kernels on first-use. The tools and libraries required for this compilation are not bundled in PyTorch and thus a host CUDA toolkit installation is required.
## Things to try
- Completely remove: `$HOME/.cache/torch_extensions` (Linux) or `C:\Users\<username>\AppData\Local\torch_extensions\torch_extensions\Cache` (Windows) and re-run StyleGAN3 python code.
- Run ninja in `$HOME/.cache/torch_extensions` to see that it builds.
- Inspect the `build.ninja` in the build directories under `$HOME/.cache/torch_extensions` and check CUDA tools and versions are consistent with what you intended to use.
