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Automated Deep Learning Projects (AutoDL-Projects) is an open source, lightweight, but useful project for everyone. This project implemented several neural architecture search (NAS) and hyper-parameter optimization (HPO) algorithms. 中文介绍见README_CN.md

Who should consider using AutoDL-Projects

  • Beginners who want to try different AutoDL algorithms
  • Engineers who want to try AutoDL to investigate whether AutoDL works on your projects
  • Researchers who want to easily implement and experiement new AutoDL algorithms.

Why should we use AutoDL-Projects

  • Simple library dependencies
  • All algorithms are in the same codebase
  • Active maintenance

AutoDL-Projects Capabilities

At this moment, this project provides the following algorithms and scripts to run them. Please see the details in the link provided in the description column.

Type ABBRV Algorithms Description
NAS TAS Network Pruning via Transformable Architecture Search NeurIPS-2019-TAS.md
DARTS DARTS: Differentiable Architecture Search ICLR-2019-DARTS.md
GDAS Searching for A Robust Neural Architecture in Four GPU Hours CVPR-2019-GDAS.md
SETN One-Shot Neural Architecture Search via Self-Evaluated Template Network ICCV-2019-SETN.md
NAS-Bench-201 NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search NAS-Bench-201.md
NATS-Bench NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size NATS-Bench.md
... ENAS / REA / REINFORCE / BOHB Please check the original papers NAS-Bench-201.md NATS-Bench.md
HPO HPO-CG Hyperparameter optimization with approximate gradient coming soon
Basic ResNet Deep Learning-based Image Classification BASELINE.md

Requirements and Preparation

Please install Python>=3.6 and PyTorch>=1.3.0. (You could also run this project in lower versions of Python and PyTorch, but may have bugs). Some visualization codes may require opencv.

CIFAR and ImageNet should be downloaded and extracted into $TORCH_HOME. Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from Google Drive (or train by yourself) and save into .latent-data.

Citation

If you find that this project helps your research, please consider citing some of the following papers:

@article{dong2020nats,
  title={{NATS-Bench}: Benchmarking NAS Algorithms for Architecture Topology and Size},
  author={Dong, Xuanyi and Liu, Lu and Musial, Katarzyna and Gabrys, Bogdan},
  journal={arXiv preprint arXiv:2009.00437},
  year={2020}
}
@inproceedings{dong2020nasbench201,
  title     = {NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search},
  author    = {Dong, Xuanyi and Yang, Yi},
  booktitle = {International Conference on Learning Representations (ICLR)},
  url       = {https://openreview.net/forum?id=HJxyZkBKDr},
  year      = {2020}
}
@inproceedings{dong2019tas,
  title     = {Network Pruning via Transformable Architecture Search},
  author    = {Dong, Xuanyi and Yang, Yi},
  booktitle = {Neural Information Processing Systems (NeurIPS)},
  pages     = {760--771},
  year      = {2019}
}
@inproceedings{dong2019one,
  title     = {One-Shot Neural Architecture Search via Self-Evaluated Template Network},
  author    = {Dong, Xuanyi and Yang, Yi},
  booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
  pages     = {3681--3690},
  year      = {2019}
}
@inproceedings{dong2019search,
  title     = {Searching for A Robust Neural Architecture in Four GPU Hours},
  author    = {Dong, Xuanyi and Yang, Yi},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages     = {1761--1770},
  year      = {2019}
}

Others

If you want to contribute to this repo, please see CONTRIBUTING.md. Besides, please follow CODE-OF-CONDUCT.md.

License

The entire codebase is under the MIT license.


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