  Many hyperparameter optimization (HyperOpt) methods assume restricted
computing resources and mainly focus on enhancing performance. Here we propose
a novel cloud-based HyperOpt (CHOPT) framework which can efficiently utilize
shared computing resources while supporting various HyperOpt algorithms. We
incorporate convenient web-based user interfaces, visualization, and analysis
tools, enabling users to easily control optimization procedures and build up
valuable insights with an iterative analysis procedure. Furthermore, our
framework can be incorporated with any cloud platform, thus complementarily
increasing the efficiency of conventional deep learning frameworks. We
demonstrate applications of CHOPT with tasks such as image recognition and
question-answering, showing that our framework can find hyperparameter
configurations competitive with previous work. We also show CHOPT is capable of
providing interesting observations through its analysing tools
