# 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](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_infer_model/DXYs/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} } ```