Metadata-Version: 2.1
Name: nasbench301
Version: 0.1
Summary: A surrogate benchmark for neural architecture search
Home-page: https://github.com/automl/nasbench301
Author: AutoML Freiburg
Author-email: zimmerl@informatik.uni-freiburg.de
License: 3-clause BSD
Keywords: machine learningoptimization tuning neural architecture deep learning
Platform: Linux
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Utilities
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Requires-Python: >=3
Description-Content-Type: text/markdown
Requires-Dist: autograd (>=1.3)
Requires-Dist: click
Requires-Dist: Cython
Requires-Dist: ConfigSpace (==0.4.12)
Requires-Dist: ipython
Requires-Dist: lightgbm (>=2.3.1)
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pathvalidate
Requires-Dist: Pillow (>=7.1.2)
Requires-Dist: psutil
Requires-Dist: scikit-image
Requires-Dist: scikit-learn (>=0.23.1)
Requires-Dist: scipy
Requires-Dist: seaborn
Requires-Dist: statsmodels
Requires-Dist: tensorboard (==1.14.0)
Requires-Dist: tensorflow-estimator
Requires-Dist: tensorflow-gpu
Requires-Dist: torch (==1.5.0)
Requires-Dist: torchvision (==0.6.0)
Requires-Dist: tqdm
Requires-Dist: xgboost

# NAS-Bench-301

This repository containts code for the paper: ["NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search"](https://arxiv.org/abs/2008.09777).

The surrogate models can be downloaded on figshare. This includes the models for [v0.9](https://figshare.com/articles/software/nasbench301_models_v0_9_zip/12962432) and [v1.0](https://figshare.com/articles/software/nasbench301_models_v1_0_zip/13061510).

To install all requirements (this may take a few minutes), run

```sh
$ cat requirements.txt | xargs -n 1 -L 1 pip install
$ pip install torch-scatter==2.0.4+cu102 torch-sparse==0.6.3+cu102 torch-cluster==1.5.5+cu102 torch-spline-conv==1.2.0+cu102 -f https://pytorch-geometric.com/whl/torch-1.5.0.html
$ pip install torch-geometric
```

To run a quick example, adapt the model paths in 'nasbench301/example.py' and from the base directory run

```sh
$ export PYTHONPATH=$PWD
$ python3 nasbench301/example.py
```

## NOTE: This codebase is still subject to changes. Upcoming updates include improved versions of the surrogate models and code for all experiments from the paper. The API may still be subject to changes.


