Copyright (C) 2019 AutoML Group Freiburg
This an alpha version of Auto-PyTorch with improved API. So far, Auto-PyTorch supports tabular data (classification, regression). We plan to enable image data and time-series data.
Find the documentation here
We recommend using Anaconda for developing as follows:
# Following commands assume the user is in a cloned directory of Auto-Pytorch
# We also need to initialize the automl_common repository as follows
# You can find more information about this here:
# https://github.com/automl/automl_common/
git submodule update --init --recursive
# Create the environment
conda create -n autopytorch python=3.8
conda activate autopytorch
conda install swig
cat requirements.txt | xargs -n 1 -L 1 pip install
python setup.py install
If you want to contribute to Auto-PyTorch, clone the repository and checkout our current development branch
$ git checkout developmentThis program is free software: you can redistribute it and/or modify it under the terms of the Apache license 2.0 (please see the LICENSE file).
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
You should have received a copy of the Apache license 2.0 along with this program (see LICENSE file).
Please refer to the branch TPAMI.2021.3067763 to reproduce the paper Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL.
@article{zimmer-tpami21a,
author = {Lucas Zimmer and Marius Lindauer and Frank Hutter},
title = {Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2021},
note = {IEEE early access; also available under https://arxiv.org/abs/2006.13799},
pages = {1-12}
}@incollection{mendoza-automlbook18a,
author = {Hector Mendoza and Aaron Klein and Matthias Feurer and Jost Tobias Springenberg and Matthias Urban and Michael Burkart and Max Dippel and Marius Lindauer and Frank Hutter},
title = {Towards Automatically-Tuned Deep Neural Networks},
year = {2018},
month = dec,
editor = {Hutter, Frank and Kotthoff, Lars and Vanschoren, Joaquin},
booktitle = {AutoML: Methods, Sytems, Challenges},
publisher = {Springer},
chapter = {7},
pages = {141--156}
}Auto-PyTorch is developed by the AutoML Group of the University of Freiburg.