42 lines
1.3 KiB
Markdown
42 lines
1.3 KiB
Markdown
# Neural Architecture Search Without Training
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:warning: Note: this repository has been updated to reflect the [second version](https://arxiv.org/abs/2006.04647) of the paper
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For the [original version of the paper](https://arxiv.org/abs/2006.04647v1), refer to the tag [v1.0](https://github.com/BayesWatch/nas-without-training/releases/tag/v1.0).:warning:
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## Usage
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Create a conda environment using the env.yml file
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```bash
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conda env create -f env.yml
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```
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Activate the environment and follow the instructions to install
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Install nasbench (see https://github.com/google-research/nasbench)
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Download the NDS data from https://github.com/facebookresearch/nds and place the json files in naswot-codebase/nds_data/
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Download the NASbench101 data (see https://github.com/google-research/nasbench)
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Download the NASbench201 data (see https://github.com/D-X-Y/NAS-Bench-201)
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Reproduce all of the results by running
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```bash
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./scorehook.sh
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```
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The code is licensed under the MIT licence.
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## Citing us
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If you use or build on our work, please consider citing us:
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```bibtex
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@inproceedings{mellor2021neural,
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title={Neural Architecture Search without Training},
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author={Joseph Mellor and Jack Turner and Amos Storkey and Elliot J. Crowley},
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year={2021},
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booktitle={International Conference on Machine Learning}
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}
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```
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