Rapid Experimentation with Syne Tune
The main goal of automated tuning is to help the user to find and adjust the best machine learning model as quickly as possible, given some computing resources controlled by the user. Syne Tune contains some tooling which can speed up this interactive process substantially. The user can launch many experiments in parallel, slicing the complete model selection and tuning problems into smaller parts. Comparative plots can be created from past experimental data and easily customized to specific needs.
Syne Tune’s tooling for rapid experimentation is part of the benchmarking framework, which is covered in detail in this tutorial. However, as demonstrated here, this framework is useful for experimentation beyond the comparison of different HPO algorithm. The tutorial here is self-contained, but the reader may want to consult the benchmarking tutorial for background information.
The code used in this tutorial is contained in the
Syne Tune sources, it is not
pip. You can obtain this code by installing Syne Tune from
source, but the only code that is needed is in
benchmarking.examples.demo_experiment. The final section also needs
Also, make sure to have installed the