This is a Python library to easily experiment with Deep Graph Networks (DGNs) in a continual learning setting. The repository is adapted from PyDGN
(We assume git and Miniconda/Anaconda are installed)
First, make sure gcc 5.2.0 is installed: conda install -c anaconda libgcc=5.2.0. Then, echo $LD_LIBRARY_PATH should always contain :/home/[your user name]/miniconda3/lib. Then run from your terminal the following command:
source install.sh [<your_cuda_version>]
Where <your_cuda_version> is an optional argument that can be either cpu, cu92, cu101, cu102 or cu110 for Pytorch 1.7.0. If you do not provide a cuda version, the script will default to cpu. The script will create a virtual environment named cl_dgn, with all the required packages needed to run our code. Important: do NOT run this command using bash instead of source!
Remember that PyTorch MacOS Binaries dont support CUDA, install from source if CUDA is needed
For MNIST,CIFAR10, and OGBG-PPA, we will automatically use the same data splits provided in the literature (see Pytorch Geometric)
python build_dataset.py --config-file CONFIGS/config_DATA_MNIST.yml
python build_dataset.py --config-file CONFIGS/config_DATA_CIFAR10.yml
python build_dataset.py --config-file CONFIGS/config_DATA_OGBG-PPA.yml
python launch_experiment.py --config-file CONFIGS_CLDGN/config_LWF_Split_GraphSAGE_OGB.yml --splits-folder SPLITS/ --data-splits SPLITS/ogbg_ppa/ogbg_ppa_outer1_inner1.splits --data-root DATA/ --dataset-name ogbg_ppa --dataset-class data.dataset.OGBG --max-cpus 1 --max-gpus 1 --final-training-runs 5 --result-folder RESULTS_OGBG_PPA
To debug your code it is useful to add --debug to the command above. Notice, however, that the graphical interface will not work, as code will be executed sequentially. After debugging, if you need sequential execution, you can use --max-cpus 1 --max-gpus 1 --gpus-per-task [0/1] without the --debug option.
See the analogous section in the PyDGN library.