  Recent advances in derivative-free optimization allow efficient approximation
of the global-optimal solutions of sophisticated functions, such as functions
with many local optima, non-differentiable and non-continuous functions. This
article describes the ZOOpt (Zeroth Order Optimization) toolbox that provides
efficient derivative-free solvers and is designed easy to use. ZOOpt provides
single-machine parallel optimization on the basis of python core and
multi-machine distributed optimization for time-consuming tasks by
incorporating with the Ray framework -- a famous platform for building
distributed applications. ZOOpt particularly focuses on optimization problems
in machine learning, addressing high-dimensional and noisy problems such as
hyper-parameter tuning and direct policy search. The toolbox is maintained
toward a ready-to-use tool in real-world machine learning tasks.
