Probabilistic Neural Architecture Search with Deep Graph Generation

Dependencies

Python 3.8, PyTorch>(1.8.0)

pip install -r requirements.txt

DATASET Preparation

put nasbench101 file to ./data/nasbench_only108.tfrecord unzip nasbench201 .tar files to data folder: ./data/nasbench_201

Run Demos

To run NB101 experiments: python run_nas_multiple.py -c config/nb101.yaml

To run NB201 experiments: python run_nas_multiple.py -c config/nb201_*.yaml

Cite

Please cite our paper if you use this code in your research work.

@article{li2022graphpnas,
  title={GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Models},
  author={Li, Muchen and Liu, Jeffrey Yunfan and Sigal, Leonid and Liao, Renjie},
  journal={arXiv preprint arXiv:2211.15155},
  year={2022}
}

Questions/Bugs

Please submit a Github issue