xautodl/docs/ICLR-2019-DARTS.md

DARTS: Differentiable Architecture Search

DARTS: Differentiable Architecture Search is accepted by ICLR 2019. In this paper, Hanxiao proposed a differentiable neural architecture search method, named as DARTS. Recently, DARTS becomes very popular due to its simplicity and performance.

Run DARTS on the NAS-Bench-201 search space

CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V2.sh cifar10 1 -1
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh     cifar10 1 -1

Run the first-order DARTS on the NASNet/DARTS search space

This command will start to use the first-order DARTS to search architectures on the DARTS search space.

CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/DARTS1V-search-NASNet-space.sh cifar10 -1

After searching, if you want to train the searched architecture found by the above scripts, you need to add the config of that architecture (will be printed in log) in genotypes.py. In future, I will add a more eligent way to train the searched architecture from the DARTS search space.

Citation

@inproceedings{liu2019darts,
  title     = {{DARTS}: Differentiable architecture search},
  author    = {Liu, Hanxiao and Simonyan, Karen and Yang, Yiming},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2019}
}