  In this proceeding, we will briefly review our recent progress on
implementing deep learning to relativistic hydrodynamics. We will demonstrate
that a successfully designed and trained deep neural network, called {\tt
stacked U-net}, can capture the main features of the non-linear evolution of
hydrodynamics, which could also rapidly predict the final profiles for various
testing initial conditions.
