<p align="center">
<img src="https://xuanyidong.com/resources/images/AutoDL-log.png" width="400"/>
</p>
---------
[](LICENSE.md)
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](https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/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.
<table>
<tbody>
<tr align="center" valign="bottom">
<th>Type</th>
<th>ABBRV</th>
<th>Algorithms</th>
<th>Description</th>
</tr>
<tr> <!-- (1-st row) -->
<td rowspan="6" align="center" valign="middle" halign="middle"> NAS </td>
<td align="center" valign="middle"> TAS </td>
<td align="center" valign="middle"> <a href="https://arxiv.org/abs/1905.09717">Network Pruning via Transformable Architecture Search</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NeurIPS-2019-TAS.md">NeurIPS-2019-TAS.md</a> </td>
</tr>
<tr> <!-- (2-nd row) -->
<td align="center" valign="middle"> DARTS </td>
<td align="center" valign="middle"> <a href="https://arxiv.org/abs/1806.09055">DARTS: Differentiable Architecture Search</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/ICLR-2019-DARTS.md">ICLR-2019-DARTS.md</a> </td>
</tr>
<tr> <!-- (3-nd row) -->
<td align="center" valign="middle"> GDAS </td>
<td align="center" valign="middle"> <a href="https://arxiv.org/abs/1910.04465">Searching for A Robust Neural Architecture in Four GPU Hours</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/CVPR-2019-GDAS.md">CVPR-2019-GDAS.md</a> </td>
</tr>
<tr> <!-- (4-rd row) -->
<td align="center" valign="middle"> SETN </td>
<td align="center" valign="middle"> <a href="https://arxiv.org/abs/1910.05733">One-Shot Neural Architecture Search via Self-Evaluated Template Network</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/ICCV-2019-SETN.md">ICCV-2019-SETN.md</a> </td>
</tr>
<tr> <!-- (5-th row) -->
<td align="center" valign="middle"> NAS-Bench-201 </td>
<td align="center" valign="middle"> <a href="https://openreview.net/forum?id=HJxyZkBKDr"> NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NAS-Bench-201.md">NAS-Bench-201.md</a> </td>
</tr>
<tr> <!-- (6-th row) -->
<td align="center" valign="middle"> NATS-Bench </td>
<td align="center" valign="middle"> <a href="https://xuanyidong.com/assets/projects/NATS-Bench"> NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NATS-Bench.md">NATS-Bench.md</a> </td>
</tr>
<tr> <!-- (7-th row) -->
<td align="center" valign="middle"> ... </td>
<td align="center" valign="middle"> ENAS / REA / REINFORCE / BOHB </td>
<td align="center" valign="middle"> Please check the original papers </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NAS-Bench-201.md">NAS-Bench-201.md</a> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NATS-Bench.md">NATS-Bench.md</a> </td>
</tr>
<tr> <!-- (start second block) -->
<td rowspan="1" align="center" valign="middle" halign="middle"> HPO </td>
<td align="center" valign="middle"> HPO-CG </td>
<td align="center" valign="middle"> Hyperparameter optimization with approximate gradient </td>
<td align="center" valign="middle"> coming soon </a> </td>
</tr>
<tr> <!-- (start third block) -->
<td rowspan="1" align="center" valign="middle" halign="middle"> Basic </td>
<td align="center" valign="middle"> ResNet </td>
<td align="center" valign="middle"> Deep Learning-based Image Classification </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/BASELINE.md">BASELINE.md</a> </a> </td>
</tr>
</tbody>
</table>
## 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](https://drive.google.com/open?id=1ANmiYEGX-IQZTfH8w0aSpj-Wypg-0DR-) (or train by yourself) and save into `.latent-data`.
Please use
```
git clone --recurse-submodules git@github.com:D-X-Y/AutoDL-Projects.git XAutoDL
git clone --recurse-submodules https://github.com/D-X-Y/AutoDL-Projects.git XAutoDL
```
to download this repo with submodules.
## Citation
If you find that this project helps your research, please consider citing the related paper:
```
@article{dong2020autohas,
title={{AutoHAS}: Efficient Hyperparameter and Architecture Search},
author={Dong, Xuanyi and Tan, Mingxing and Yu, Adams Wei and Peng, Daiyi and Gabrys, Bogdan and Le, Quoc V},
journal={arXiv preprint arXiv:2006.03656},
year={2020}
}
@article{dong2021nats,
title = {{NATS-Bench}: Benchmarking NAS Algorithms for Architecture Topology and Size},
author = {Dong, Xuanyi and Liu, Lu and Musial, Katarzyna and Gabrys, Bogdan},
doi = {10.1109/TPAMI.2021.3054824},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
year = {2021},
note = {\mbox{doi}:\url{10.1109/TPAMI.2021.3054824}}
}
@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](.github/CONTRIBUTING.md).
Besides, please follow [CODE-OF-CONDUCT.md](.github/CODE-OF-CONDUCT.md).
We use [`black`](https://github.com/psf/black) for Python code formatter.
Please use `black . -l 88`.
# License
The entire codebase is under the [MIT license](LICENSE.md).