Apache MXNet is a deep learning framework designed for both efficiency and
flexibility. It allows you to mix symbolic and imperative programming to
maximize efficiency and productivity. At its core, MXNet contains a dynamic
dependency scheduler that automatically parallelizes both symbolic and
imperative operations on the fly. A graph optimization layer on top of that
makes symbolic execution fast and memory efficient. MXNet is portable and
lightweight, scaling effectively to multiple GPUs and multiple machines.

MXNet is more than a deep learning project. It is a collection of blue prints
and guidelines for building deep learning systems, and interesting insights of
DL systems for hackers.