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NAS-Bench-x11

NAS-Bench-x11 and the Power of Learning Curves
Shen Yan, Colin White, Yash Savani, Frank Hutter.
NeurIPS 2021.

Surrogate NAS benchmarks for multi-fidelity algorithms

We present a method to create surrogate neural architecture search (NAS) benchmarks, NAS-Bench-111, NAS-Bench-311, and NAS-Bench-NLP11, that output the full training information for each architecture, rather than just the final validation accuracy. This makes it possible to benchmark multi-fidelity techniques such as successive halving and learning curve extrapolation (LCE). Then we present a framework for converting popular single-fidelity algorithms into LCE-based algorithms.

<p align="center"> <img src="img/nbx11.png" alt="nas-bench-x11" width="70%"> </p>

Installation

Clone this repository and install its requirements.

git clone https://github.com/automl/nas-bench-x11
cd nas-bench-x11
cat requirements.txt | xargs -n 1 -L 1 pip install
pip install -e .

Download the pretrained surrogate models and place them into checkpoints/. The current models are v0.5. We will continue to improve the surrogate model by adding the sliding window noise model.

NAS-Bench-311 and NAS-Bench-NLP11 will work as is. To use NAS-Bench-111, first install NAS-Bench-101.

Using the API

The api is located in nas_bench_x11/api.py.

Here is an example of how to use the API:

from nas_bench_x11.api import load_ensemble

# load the surrogate
nb311_surrogate_model = load_ensemble('path/to/nb311-v0.5')

# define a genotype as in the original DARTS repository
from collections import namedtuple
Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat')
arch = Genotype(normal=[('sep_conv_3x3', 0), ('sep_conv_5x5', 1), ('skip_connect', 1), ('max_pool_3x3', 2), ('sep_conv_3x3', 0), ('dil_conv_5x5', 1), ('sep_conv_5x5', 2), ('dil_conv_5x5', 4)], \
                normal_concat=[2, 3, 4, 5, 6], \
                reduce=[('dil_conv_5x5', 0), ('skip_connect', 1), ('avg_pool_3x3', 0), ('sep_conv_5x5', 1), ('avg_pool_3x3', 0), ('max_pool_3x3', 2), ('sep_conv_3x3', 1), ('max_pool_3x3', 3)], \
                reduce_concat=[4, 5, 6])

# query the surrogate to output the learning curve
learning_curve = nb311_surrogate_model.predict(config=arch, representation="genotype", with_noise=True)
print(learning_curve)
# outputs: [34.50166741 44.77032749 50.62796474 ... 93.47724664]

Run NAS experiments from our paper

You will also need to download the nas-bench-301 runtime model lgb_runtime_v1.0 and place it inside a folder called nb_models.

# Supported optimizers: (rs re ls bananas)-{svr, lce}, hb, bohb 

bash naslib/benchmarks/nas/run_nb311.sh 
bash naslib/benchmarks/nas/run_nb201.sh 
bash naslib/benchmarks/nas/run_nb201_cifar100.sh 
bash naslib/benchmarks/nas/run_nb201_imagenet16-200.sh
bash naslib/benchmarks/nas/run_nb111.sh 
bash naslib/benchmarks/nas/run_nbnlp.sh

Results will be saved in results/.

Citation

@inproceedings{yan2021bench,
  title={NAS-Bench-x11 and the Power of Learning Curves},
  author={Yan, Shen and White, Colin and Savani, Yash and Hutter, Frank},
  booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
  year={2021}
}