_ __ __ _ /\ | | | \/ | | / \ _ _| |_ ___ | \ / | | / /\ \| | | | __/ _ \| |\/| | | / ____ \ |_| | || (_) | | | | |____ /_/ \_\__,_|\__\___/|_| |_|______|
AutoML: taking the human expert out of the loop
AutoML.org is no longer updated. We moved all information over to ml4aad.org/automl.
HPOlib is a hyperparameter optimization library. It provides a common interface to three state of the art hyperparameter optimization packages: SMAC, Spearmint and Hyperopt. Moreover, the library provides optimization benchmarks which can be used to compare different hyperparameter optimization packages and to establish standard test routines for hyperparameter optimization packages.
Please post questions in our google group.
At the time of writing, HPOlib contains three hyperparameter optimization algorithms and six different benchmarks. In order to make HPOlib a standard library for testing hyperparameter optimization software for machine learning, we want HPOlib to include more optimization packages and benchmarks.
If you have written a machine learning algorithm or work on a machine learning problem which you think is worth including into HPOlib, please look here to find out how to add it to the library. If you have a new benchmark, please
Ideally, the benchmark falls into one of these categories:
and is available on github or a different open source collaboration platform.
If you have written a hyperparemeter optimization package and want to:
then you can find an explanation of the interface here. If you have an optimizer, please send us an e-mail (you can find the address at the bottom of this page) so we can discuss the further integration into the HPOlib.
We would like to hear your comments, remarks, advice or feature requests. Please feel free to contact us via e-mail (you can find the address at the bottom of this page) or post in the google group.
Visit our google group on google.com