A repository for investigating graph data with Graph Neural Networks

Note that this is a limited selection of files and notebooks to demonstrate the work, due to size limitations on the original repo

Setup PyTorch using conda

Create and activate a Python 3 conda environment:

conda create -n pymira python=3.8
conda activate pymira

Install PyTorch using conda:

conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=10.2 -c pytorch

remember to check your pytorch, torchvision and cudatoolkit versions are correct before moving on, e.g. for cuda using:

python -c "import torch; print(torch.version.cuda)"

Setup PyTorch using virtualenv

Create and activate a Python 3 virtual environment:

virtualenv -p python3 <path_to_envs>/pymira
source <path_to_envs>/pymira/bin/activate

Install PyTorch using pip:

pip install torch torchvision

Install PyTorch Geometric:

Note that the versions specified in the URLs must match with the versions installed above (e.g. 102 = 10.2 for cuda)

pip install -q torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cu102.html
pip install -q torch-sparse -f https://pytorch-geometric.com/whl/torch-1.8.0+cu102.html
pip install -q torch-cluster -f https://pytorch-geometric.com/whl/torch-1.8.0+cu102.html
pip install -q torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.8.0+cu102.html   
pip install -q torch-geometric

Install additional Python packages:

pip install matplotlib jupyter pandas seaborn scikit-learn tensorboard cmake openmesh pytorch-lightning

License & Acknowledgements

This project is part of a summer MSc project at Imperial College London. Please contact the author for further details.