GNNFlow

A comprehensive framework for training graph neural networks on dynamic graphs.

NB: this is an ongoing work.

Install

Our development environment:

• Ubuntu 20.04LTS
• g++ 9.4
• CUDA 11.3 / 11.6
• cmake 3.23

Dependencies:

• torch >= 1.10
• dgl (CUDA version)

Compile and install:

python setup.py install

For debug mode,

DEBUG=1 pip install -v -e .

Prepare data

cd scripts/ && ./download_data.sh

Train

Multi-GPU single machine

Training TGN model on the REDDIT dataset with LRU feature cache (cache ratio=0.2) on four GPUs.

./scripts/run_offline.sh TGN REDDIT LRUCache 0.2 4

Distributed training

Training TGN model on the REDDIT dataset with LRU feature cache (cache ratio=0.2) and hash-based graph partitioning strategy.

./scripts/run_offline.sh TGN REDDIT LRUCache 0.2 hash