NASLib is a Neural Architecture Search (NAS) library. Its purpose is to facilitate NAS research for the community by providing interfaces to several state-of-the-art NAS search spaces.
⚠️ This library is under construction and there is no official release yet. Feel free to play around and have a look but be aware that the APIs will be changed until we have a first release.
It is designed to be modular, extensible and easy to use.
search_space = SimpleCellSearchSpace()
optimizer = DARTSOptimizer(config)
optimizer.adapt_search_space(search_space)
trainer = Trainer(optimizer, config)
trainer.search() # Search for an architecture
trainer.evaluate() # Evaluate the best architectureFor an example file see demo.py.
For more extensive documentation see docs.
NASLib has been used to run an extensive comparison of 31 performance predictors. See the separate readme: predictors.md and our paper: How Powerful are Performance Predictors in Neural Architecture Search?
Make sure you use the latest version of pip. It makes sense to set up a virtual environment, too.
python3 -m venv naslib
source naslib/bin/activate
pip install --upgrade pip setuptools wheel
pip install cython
Clone and install.
If you plan to modify naslib consider adding the -e option for pip install.
git clone ...
cd naslib
pip install .
To validate the installation, you can run tests:
cd tests
coverage run -m unittest discover
The test coverage can be seen with coverage report.
If you use this code in your own work, please use the following bibtex entries:
@misc{naslib-2020,
title={NASLib: A Modular and Flexible Neural Architecture Search Library},
author={Ruchte, Michael and Zela, Arber and Siems, Julien and Grabocka, Josif and Hutter, Frank},
year={2020}, publisher={GitHub},
howpublished={\url{https://github.com/automl/NASLib}} }
@article{white2021powerful,
title={How Powerful are Performance Predictors in Neural Architecture Search?},
author={White, Colin and Zela, Arber and Ru, Binxin and Liu, Yang and Hutter, Frank},
journal={arXiv preprint arXiv:2104.01177},
year={2021}
}