Package: xautodl
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.mdWho should consider using AutoDL-ProjectsBeginners who want totry different AutoDL algorithmsEngineers who want totry AutoDLto investigate whether AutoDL works on your projectsResearchers who want toeasilyimplement and experiementnewAutoDL algorithms.Why should we use AutoDL-ProjectsSimple library dependenciesAll algorithms are in the same codebaseActive maintenanceAutoDL-Projects CapabilitiesAt 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.TypeABBRVAlgorithmsDescriptionNASTASNetwork Pruning via Transformable Architecture SearchNeurIPS-2019-TAS.mdDARTSDARTS: Differentiable Architecture SearchICLR-2019-DARTS.mdGDASSearching for A Robust Neural Architecture in Four GPU HoursCVPR-2019-GDAS.mdSETNOne-Shot Neural Architecture Search via Self-Evaluated Template NetworkICCV-2019-SETN.mdNAS-Bench-201NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture SearchNAS-Bench-201.mdNATS-BenchNATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and SizeNATS-Bench.md...ENAS / REA / REINFORCE / BOHBPlease check the original papersNAS-Bench-201.mdNATS-Bench.mdHPOHPO-CGHyperparameter optimization with approximate gradientcoming soonBasicResNetDeep Learning-based Image ClassificationBASELINE.mdRequirements and PreparationFirst of all, please usepython setup.py installto installxautodllibrary.Please installPython>=3.6andPyTorch>=1.5.0. (You could use lower versions of Python and PyTorch, but may have bugs).
Some visualization codes may requireopencv.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 fromGoogle Drive(or train by yourself) and save into.latent-data.Please usegit 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 XAutoDLto download this repo with submodules.CitationIf 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}
}OthersIf you want to contribute to this repo, please seeCONTRIBUTING.md.
Besides, please followCODE-OF-CONDUCT.md.We useblackfor Python code formatter.
Please useblack . -l 88.LicenseThe entire codebase is under theMIT license.