Conformal prediction with an underlying GraphSAGE applied to dynamic and static graphs.
ssh -i .ssh/id_ed25519 ubuntu@130.61.160.82
// sudo apt install gcc
sudo apt-get update
sudo apt-get upgrade
sudo apt-get install build-essential libpq-dev libssl-dev openssl libffi-dev zlib1g-dev
sudo apt install python3-pip
// install gpu drivers
https://cloud.google.com/compute/docs/gpus/install-drivers-gpu#installation_scripts
// verify installation
sudo nvidia-smi
// conda install pytorch cudatoolkit=10.1 -c pytorch
pip3 install torch==1.8.0
python3 -c "import torch; print(torch.version.cuda)"
pip3 install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cu102.html
pip3 install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.8.0+cu102.html
pip3 install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.8.0+cu102.html
pip3 install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.8.0+cu102.html
pip3 install torch-geometric
pip3 install ogb
// might need to run `pip3 install torch==1.8.0` again, to override ogb's torch version
pip3 install matplotlib
sudo apt-get install -y xvfb
pip3 install notebook
add github public key
git clone
python3 -m notebook
ssh -L 8888:localhost:8888 -i .ssh/id_ed25519 ubuntu@130.61.160.82
go to localhost:8888
copy-paste token from jupyter notebook command
Download from VM:
scp -i .ssh/id_ed25519 -r ubuntu@130.61.254.191:msc_thesis_code/1_dynamic_experiments/output Downloads/arxiv_output
Start command in background:
nohup python3 experiments.py &
exit