Efficient Guided Evolution for Neural Architecture Search

Usage

Create a conda environment using the env.yml file

conda env create -f env.yml

Activate the environment and follow the instructions to install

conda activate gea

Install nasbench (see https://github.com/google-research/nasbench)

Download the NDS data from https://github.com/facebookresearch/nds and place the json files in path_to_code/nds_data/
Download the NASbench101 data (see https://github.com/google-research/nasbench)
Download the NASbench201 data (see https://github.com/D-X-Y/NAS-Bench-201)

Reproduce all of the results by running

./run.sh

The code is licensed under the MIT licence.

Acknowledgements

This repository makes liberal use of code from the AutoDL library, NAS-Bench-201, NAS-Bench-101 and NAS-WOT. We are grateful to the authors for making the implementations publicly available.

Citing us

If you use or build on our work, please consider citing us:

@inproceedings{gea2021,
    title={Guided Evolution for Neural Architecture Search},
    author={Vasco Lopes and Miguel Santos and Bruno Degardin and Luís A. Alexandre},
    year={2021},
    booktitle={Advances in Neural Information Processing Systems 35 (NeurIPS) - New In ML}
}