Custom CUDA Kernel for PyTorch

This codebase demonstrates how to:

• implement a simple CUDA kernel function
• interface it to PyTorch using pybind11 / ATen

Shortly I will add more code to this extension module that:

• demonstrates some common CUDA/GPU optimization techniques
• implements a custom layer of a PyTorch neural network

To Build:

• Create a Python virtualenv based on Python 3.8 and activate it.

    pushd cuda-torch-custom-kernel
    bash ../pyvenv.sh 3.8.0 .
    source ./venv3.8.0/bin/activate
  

• Install requirements:

    pip3 install -r requirements.txt
  

• Build extension module:

    python3 setup.py install --force
  

From a terminal window, launch JupyterLAB and load up test.ipynb - run that...

    jupyter lab