  Tuning machine learning models at scale, especially finding the right
hyperparameter values, can be difficult and time-consuming. In addition to the
computational effort required, this process also requires some ancillary
efforts including engineering tasks (e.g., job scheduling) as well as more
mundane tasks (e.g., keeping track of the various parameters and associated
results). We present Auptimizer, a general Hyperparameter Optimization (HPO)
framework to help data scientists speed up model tuning and bookkeeping. With
Auptimizer, users can use all available computing resources in distributed
settings for model training. The user-friendly system design simplifies
creating, controlling, and tracking of a typical machine learning project. The
design also allows researchers to integrate new HPO algorithms. To demonstrate
its flexibility, we show how Auptimizer integrates a few major HPO techniques
(from random search to neural architecture search). The code is available at
https://github.com/LGE-ARC-AdvancedAI/auptimizer.
