1. Create docker container with conda environments: baseline and inkstream.
cd docker
docker build -t inkstream_image .
docker run --name inkstream_container --gpus all --shm-size=32g -it inkstream_image
2. Run InkStream:
python inkstream_<gcn/sage/gin>.py --dataset <cora/PubMed/yelp/reddit/products/papers> --model <GCN/SAGE/GIN> --save_int --aggr <min/max/mean/sum> --perbatch <number of changed edges> --stream <mix/add/delete>
e.g.,
python inkstream_gcn.py --dataset cora --model GCN --save_int --aggr min --perbatch 100 --stream mix
Note:
Inkstream can only be executed in `inkstream` conda environment inside the docker, as we made modifications to the torch_geometric library. Baseline methods should be executed in `baseline` conda environment.
3. Run baseline (k-hop):
conda activate baseline
python timing_original.py --dataset cora --model GCN --aggr min --perbatch 100 --stream mix --range affected