An overview of our IPM-MPNN
Simply install from the existing file conda env create -f environment.yml or install the required envs manually:
conda create -y -n ipmgnn python=3.10
conda activate ipmgnn
conda install pytorch==2.0.0 pytorch-cuda=11.8 -c pytorch -c nvidia
conda install pyg -c pyg
pip install https://data.pyg.org/whl/torch-2.0.0%2Bcu118/torch_scatter-2.1.1%2Bpt20cu118-cp310-cp310-linux_x86_64.whl
pip install https://data.pyg.org/whl/torch-2.0.0%2Bcu118/torch_sparse-0.6.17%2Bpt20cu118-cp310-cp310-linux_x86_64.whl
pip install ml-collections
pip install wandb
See run/main.sh for the commands and hyperparameters. Note that the small and large instances of the same type share the same configs, unless otherwise stated.
If your GPU run out of memory on large instances, use the --micro_batch trick, which accumulates a few batches before updating the model params. You can set e.g. --micro_batch 2 --batchsize 4 which is in theory equivalent to --batchsize 8
We provide bipartite graphs as ablation to our tripartite approach. Which can also be considered as the implementation of the baseline from this paper. See run/bipartite.sh.
We compare our MPNN approach with the neural-ODE-inspired method. See run/ode.sh.