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A lightweight distributed GNN library for full batch node property prediction.

Features/Changelog

  • Complete refactoring of CAGNET.
  • Distributed utilities such as log, timer, etc.
  • Node feature cached training.
  • Partitioned graph cache on disk.
  • More datasets. Most large graphs from pyg, dgl, ogb supported.
  • Training depends on pytorch only.
  • Distributed GAT training.
  • Latest pytorch version supported.
  • CSR graph supported.
  • Half precision training supported.

Getting started

  1. Setup a clean environment.
conda create --name gnn
conda activate gnn
  1. Install pytorch (needed for training) and other libraries (needed for downloading datasets).
// Cuda 10:
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch-lts
conda install -c dglteam dgl-cuda10.2
conda install pyg -c pyg -c conda-forge
pip install ogb

// Cuda 11:
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c nvidia
conda install -c dglteam dgl-cuda11.1
conda install pyg -c pyg -c conda-forge
pip install ogb
  1. Compile and install spmm. (Optional. CUDA dev environment needed.)
cd spmm_cpp
python setup.py install
  1. Prepare datasets (edit the code according to your needs).
//This may take a while.
python prepare_data.py
  1. Train.
python main.py

Experiments for Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks

  1. Check the steps in Getting started .
  2. Check dataset, epoch, and num of GPUs in main.py.
  3. Check model settings in dist_train.py
  4. Check cache methods in models.
  5. Run and see the result.

Contact

Contact chenzhao@ust.hk for any problems.