TorchGNN-MLP

Integrated example of MLP functionality in ROOT's TorchGNN.

Dependencies

• A Python virtual environment with:
  • PyTorch
  • ROOT
• C++
  • LibTorch: Can be downloaded from the PyTorch homepage. We used version TorchLib 2.0.1 (cxx11 ABI).
  • BLAS: We used OpenBLAS.

How to run

The model can be trained and parsed by running the Python code MLP_generator.py.

The C++ benchmarking code main.cxx can be compiled and run in the following way:

cd code_directory
mkdir build
cd build
cmake -DCMAKE_PREFIX_PATH="path/to/libtorch" -DCMAKE_BUILD_TYPE="Release" ..
make
export OMP_NUM_THREADS=1
./TorchGNN

Alternatively, one can use the provided bash script collect_statistics.sh, to collect all statistics mentioned in the report. Make sure to change the CMAKE_PREFIX_PATH variable and activate the virtual environment before calling the bash script.