This repository contains the relevant training and testing scipts for the BOSHNAS tool for efficient neural architecture search. It is based on the naszilla/naszilla repo.

• Environment setup
• Run experiments
• Developer
• Cite this work
• License

Clone this repository and install its requirements (which includes nasbench, nas-bench-201, and nasbench301). It may take a few minutes.

git clone https://github.com/JHA-Lab/boshnas.git
cd boshnas

For pip, use the requirements.txt file.

cat requirements.txt | xargs -n 1 -L 1 pip install
pip install -e .

If you use conda, you can use the environment setup script.

source env_setup.sh

Next, download the NAS benchmark dataset (either with the terminal command below, or from the website (nasbench):

wget https://storage.googleapis.com/nasbench/nasbench_only108.tfrecord

To run various NAS algorithms on the NASBench-101 dataset, use the script naszilla/run_experiments.py.

cd naszilla
python run_experiments.py --algo_params <algo_params> --queries 50 --trials 1

Here, <algo_params> can be one of: simple_algos, all_algos, sota_algos (which only contains BANANAS and BOSHNAS), local_search_variants, random, evolution, bananas, gp_bo, dngo, bohamiann, local_search, nasbot, gcn_predictor, and boshnas. Other evaluation flags can be seen by running the command python run_experiments.py --help.

Shikhar Tuli and Shreshth Tuli. For any questions, comments or suggestions, please reach out at [email protected].

Cite our work using the following bitex entry:

@article{tuli2022jair,
      title={{FlexiBERT}: Are Current Transformer Architectures too Homogeneous and Rigid?}, 
      author={Tuli, Shikhar and Dedhia, Bhishma and Tuli, Shreshth and Jha, Niraj K.},
      year={2022},
      eprint={2205.11656},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

This work is used in jha-lab/txf_design-space. Other works that leverage BOSHNAS and its extensions include:

@article{tuli2022codebench,
  title={CODEBench: A Neural Architecture and Hardware Accelerator Co-Design Framework},
  author={Tuli, Shikhar and Li, Chia-Hao and Sharma, Ritvik and Jha, Niraj K},
  journal={ACM Transactions on Embedded Computing Systems},
  publisher={ACM New York, NY}
}

@article{tuli2023transcode,
      title={{TransCODE}: Co-design of Transformers and Accelerators for Efficient Training and Inference}, 
      author={Tuli, Shikhar and Jha, Niraj K.},
      year={2023},
      eprint={2303.14882},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

BSD-3-Clause. Copyright (c) 2022, Shikhar Tuli and Jha Lab. All rights reserved.

See License file for more details.