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https://github.com/D-X-Y/AutoDL-Projects
Automated deep learning algorithms implemented in PyTorch.
https://github.com/D-X-Y/AutoDL-Projects
autodl automl nas neural-architecture-search pytorch
Last synced: 1 day ago
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Automated deep learning algorithms implemented in PyTorch.
- Host: GitHub
- URL: https://github.com/D-X-Y/AutoDL-Projects
- Owner: D-X-Y
- License: mit
- Created: 2019-01-31T14:30:50.000Z (almost 6 years ago)
- Default Branch: main
- Last Pushed: 2022-04-24T22:18:16.000Z (over 2 years ago)
- Last Synced: 2024-11-05T03:42:18.125Z (5 days ago)
- Topics: autodl, automl, nas, neural-architecture-search, pytorch
- Language: Python
- Homepage:
- Size: 11 MB
- Stars: 1,570
- Watchers: 43
- Forks: 283
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGE-LOG.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: .github/CODE-OF-CONDUCT.md
Awesome Lists containing this project
README
---------
[](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.
δΈζδ»η»θ§[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.
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
**First of all**, please use `pip install .` to install `xautodl` library.
Please install `Python>=3.6` and `PyTorch>=1.5.0`. (You could use 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 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:
```
@inproceedings{dong2021autohas,
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},
booktitle = {2nd Workshop on Neural Architecture Search at International Conference on Learning Representations (ICLR)},
year = {2021}
}
@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).