NASBench: A Neural Architecture Search Dataset and Benchmark
This repository contains the code used for generating and interacting with the NASBench dataset. The dataset contains 423,624 unique neural networks exhaustively generated and evaluated from a fixed graph-based search space.
Each network is trained and evaluated multiple times on CIFAR-10 at various training budgets and we present the metrics in a queriable API. The current release contains over 5 million trained and evaluated models.
Our paper can be found at:
NAS-Bench-101: Towards Reproducible Neural Architecture Search
If you use this dataset, please cite:
@InProceedings{pmlr-v97-ying19a,
title = {{NAS}-Bench-101: Towards Reproducible Neural Architecture Search},
author = {Ying, Chris and Klein, Aaron and Christiansen, Eric and Real, Esteban and Murphy, Kevin and Hutter, Frank},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {7105--7114},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
address = {Long Beach, California, USA},
month = {09--15 Jun},
publisher = {PMLR},
url = {http://proceedings.mlr.press/v97/ying19a.html},
Dataset overview
NASBench is a tabular dataset which maps convolutional neural network architectures to their trained and evaluated performance on CIFAR-10. Specifically, all networks share the same network "skeleton", which can be seen in Figure (a) below. What changes between different models is the "module", which is a collection of neural network operations linked in an arbitrary graph-like structure.
Modules are represented by directed acyclic graphs with up to 9 vertices and 7 edges. The valid operations at each vertex are "3x3 convolution", "1x1 convolution", and "3x3 max-pooling". Figure (b) below shows an Inception-like cell within the dataset. Figure (c) shows a high-level overview of how the interior filter counts of each module are computed.
There are exactly 423,624 computationally unique modules within this search space and each one has been trained for 4, 12, 36, and 108 epochs three times each (423K * 3 * 4 = ~5M total trained models). We report the following metrics:
- training accuracy
- validation accuracy
- testing accuracy
- number of parameters
- training time
The scatterplot below shows a comparison of number of parameters, training time, and mean validation accuracy of models trained for 108 epochs in the dataset.
See our paper for more detailed information about the design of this search space, further implementation details, and more in-depth analysis.
Colab
You can directly use this dataset from Google Colaboratory without needing to install anything on your local machine. Click "Open in Colab" below:
Setup
- Clone this repo.
git clone https://github.com/google-research/nasbench
cd nasbench
- (optional) Create a virtualenv for this library.
virtualenv venv
source venv/bin/activate
- Install the project along with dependencies.
pip install -e .
Note: the only required dependency is TensorFlow. The above instructions will install the CPU version of TensorFlow to the virtualenv. For other install options, see https://www.tensorflow.org/install/.
Download the dataset
The full dataset (which includes all 5M data points at all 4 epoch lengths):
https://storage.googleapis.com/nasbench/nasbench_full.tfrecord
Size: ~1.95 GB, SHA256: 3d64db8180fb1b0207212f9032205064312b6907a3bbc81eabea10db2f5c7e9c
Subset of the dataset with only models trained at 108 epochs:
https://storage.googleapis.com/nasbench/nasbench_only108.tfrecord
Size: ~499 MB, SHA256: 4c39c3936e36a85269881d659e44e61a245babcb72cb374eacacf75d0e5f4fd1
Using the dataset
Example usage (see example.py for a full runnable example):
# Load the data from file (this will take some time)
nasbench = api.NASBench('/path/to/nasbench.tfrecord')
# Create an Inception-like module (5x5 convolution replaced with two 3x3
# convolutions).
model_spec = api.ModelSpec(
# Adjacency matrix of the module
matrix=[[0, 1, 1, 1, 0, 1, 0], # input layer
[0, 0, 0, 0, 0, 0, 1], # 1x1 conv
[0, 0, 0, 0, 0, 0, 1], # 3x3 conv
[0, 0, 0, 0, 1, 0, 0], # 5x5 conv (replaced by two 3x3's)
[0, 0, 0, 0, 0, 0, 1], # 5x5 conv (replaced by two 3x3's)
[0, 0, 0, 0, 0, 0, 1], # 3x3 max-pool
[0, 0, 0, 0, 0, 0, 0]], # output layer
# Operations at the vertices of the module, matches order of matrix
ops=[INPUT, CONV1X1, CONV3X3, CONV3X3, CONV3X3, MAXPOOL3X3, OUTPUT])
# Query this model from dataset, returns a dictionary containing the metrics
# associated with this model.
data = nasbench.query(model_spec)See nasbench/api.py for more information, including the constraints on valid
module matrices and operations.
Note: it is not required to use nasbench/api.py to work with this dataset,
you can see how to parse the dataset files from the initializer inside
nasbench/api.py and then interact the data however you'd like.
How the dataset was generated
The dataset generation code is provided for reference, but the dataset has already been fully generated.
The list of unique computation graphs evaluated in this dataset was generated
via nasbench/scripts/generate_graphs.py. Each of these graphs was evaluated
multiple times via nasbench/scripts/run_evaluation.py.
How to run the unit tests
Unit tests are included for some of the algorithmically complex parts of the code. The tests can be run directly via Python. Example:
python nasbench/tests/model_builder_test.py
Disclaimer
This is not an official Google product.