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Abstract
Abstract
Looking to improve your experimental workflow? Tired of dealing with all the different experimental setups?
If so, I would like to introduce you to Hydra.
Hydra elegantly configures complex applications and has loads of advantages like
easy and clear experimental configuration
no boilerplate regarding IO
dynamic composition of configurations
easy parallel running on local machine and on compute clusters
easy optimization of your function
Hydra basically is a supercharged argument parser and I will show you how to use it!
Together, we will configure a basic (Auto)ML experiment.
This tutorial can serve you as an entry point and reference for your own experimental configurations with Hydra.
Code
Code
Bio
Bio
Since 2020 Carolin has been a PhD student at the research group led by Prof. Dr. Marius Lindauer at Leibniz University Hannover which is part of the automl.org supergroup. Since then she also enjoyed collaborations with other research groups. Her focus is broadly on Automated Machine Learning, and more explicit on Dynamic Algorithm Configuration and Bayesian Optimization. Apart from the AutoML side, she is also interested in robotics and Contextual Reinforcement Learning. This might be a remnant of her Bachelor’s and Master’s studies in Mechatronics and Robotics at the Leibniz University Hannover. She is part of the development team of SMAC and worked with SMAC for numerous publications.
In general, she is driven by the love for automation and making complex algorithms more accessible.
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