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Welcome to DEHB's documentation!#

Introduction#

DEHB was designed to be an algorithm for Hyper Parameter Optimization (HPO). DEHB uses Differential Evolution (DE) under-the-hood as an Evolutionary Algorithm to power the black-box optimization that HPO problems pose. DE is a black-box optimization algorithm that generates candidate configurations \(x\), to the black-box function \(f(x)\), that is being optimized. The \(x\) is evaluated by the black-box and the corresponding response \(y\) is made available to the DE algorithm, which can then use this observation (\(x\), \(y\)), along with previous such observations, to suggest a new candidate \(x\) for the next evaluation. DEHB also uses Hyperband along with DE, to allow for cheaper approximations of the actual evaluations of \(x\).

dehb is a python package implementing the DEHB algorithm. It offers an intuitive interface to optimize user-defined problems using DEHB.

This documentation explains how to use dehb and demonstrates its features. In the following section you will be guided how to install the dehb package and how to use it in your own projects. Examples with more hands-on material can be found in the examples folder.

Installation#

To start using the dehb package, you can install it via pip. You can either install the package right from git or install it as an editable package to modify the code and rerun your experiments:

# Install from pypi
pip install dehb

From Source

To install directly from from source

git clone https://github.com/automl/DEHB.git
pip install -e DEHB  # -e stands for editable, lets you modify the code and rerun things

Using DEHB#

DEHB allows users to either utilize the Ask & Tell interface for manual task distribution or leverage the built-in functionality (run) to set up a Dask cluster autonomously. Please refer to our Getting Started examples.

Contributing#

Please have a look at our contributing guidelines.

To cite the paper or code#

If you use DEHB in one of your research projects, please cite our paper(s): 

@inproceedings{awad-ijcai21,
  author    = {N. Awad and N. Mallik and F. Hutter},
  title     = {{DEHB}: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization},
  pages     = {2147--2153},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {ijcai.org},
  editor    = {Z. Zhou},
  year      = {2021}
}