Simple CMAKE make project for extending PyTorch with custom cuda kernels for doing all the things that I wanna try.

pip install scikit_build_core
pip install pyproject-metadata

# Install in editable mode
pip install -v --no-build-isolation -e .

This will invoke the pyproject.toml file and build the project with the scikit-build package. Including, calling cmake and building the shared library.

So this will go and build the libdriss_torch.so shared library. In which there will be a number of ops that have been registered. In the python package driss_torch there will be small wrappers around the cpp ops with better type hints and documentation.

Could use .ini but this more fun.

The total install command from scratch can take a second. If you want a faster dev flow cd into build and once you change your sources of the lib just run ninja to rebuild the shared library. This will not work if you are adding and deleting files.

You should be able to grab the ops by doing something like

import torch
import driss_torch

a = torch.randn(10, 10, device='cuda')
b = driss_torch.add_one(b)
print (b)

pre-commit install

pytest test/

This calls cmake directly in the build dir.

# In the root directory
mkdir build && cd build

# Configure the build, to build with compute arch
cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo -GNinja  -DTORCH_CUDA_ARCH_LIST=9.0 ..