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.
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.