This is Repo for Kaggle Google - Fast or Slow? Predict AI Model Runtime Competition

Modified by Team Avengers

Learning Large Graph Property Prediction via Graph Segment Training

Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Dustin Zelle, Yanqi Zhou, Charith Mendis, Jure Leskovec, Bryan Perozzi

---

This is the implementation of GST+EFD in the paper Learning Large Graph Property Prediction via Graph Segment Training in PyTorch.

Dependency

The codebase is developed based on GraphGPS. Installing the environment follwoing its instructions.

Dataset

• MalNet, the split info of Malnet-Large is provided in splits folder.
• TpuGraphs.

Training

We provide several training examples with this repo:

python main.py --cfg configs/malnetlarge-GST.yaml

For TpuGraphs dataset, download the dataset following instructions here, by default, put the train/valid/test splits under the folder ./datasets/TPUGraphs/raw/npz/layout/xla/random. To run on other collections, modify source and search in in tpu_graphs.py.

You can train by invoking:

python main_tpugraphs.py --cfg configs/tpugraphs.yaml

Please change device from cuda to cpu in the yaml file if you want to try cpu only training.

To evaluate on TpuGraphs dataset, run

python test_tpugraphs.py --cfg configs/tpugraphs.yaml

If memory is not sufficient, change batch_size to 1 during evaluation. Set cfg.train.ckpt_best to True to save the best validation model during training for further evaluation.

Custom Model

To create your own custom model, you can supply a configuration (e.g., by copying configs/tpugraphs.yaml) and set the attribute type (inside of model) to some string that you register in network/custom_tpu_gnn.py.

Reference

If you find our paper and repo useful, please cite as

@article{cao2023learning,
  title={Learning Large Graph Property Prediction via Graph Segment Training},
  author={Cao, Kaidi and Phothilimthana, Phitchaya Mangpo and Abu-El-Haija, Sami and Zelle, Dustin and Zhou, Yanqi and Mendis, Charith and Leskovec, Jure and Perozzi, Bryan},
  journal={arXiv preprint arXiv:2305.12322},
  year={2023}
}