  Black-box optimization (BBO) has a broad range of applications, including
automatic machine learning, engineering, physics, and experimental design.
However, it remains a challenge for users to apply BBO methods to their
problems at hand with existing software packages, in terms of applicability,
performance, and efficiency. In this paper, we build OpenBox, an open-source
and general-purpose BBO service with improved usability. The modular design
behind OpenBox also facilitates flexible abstraction and optimization of basic
BBO components that are common in other existing systems. OpenBox is
distributed, fault-tolerant, and scalable. To improve efficiency, OpenBox
further utilizes "algorithm agnostic" parallelization and transfer learning.
Our experimental results demonstrate the effectiveness and efficiency of
OpenBox compared to existing systems.
