  MXNet is a multi-language machine learning (ML) library to ease the
development of ML algorithms, especially for deep neural networks. Embedded in
the host language, it blends declarative symbolic expression with imperative
tensor computation. It offers auto differentiation to derive gradients. MXNet
is computation and memory efficient and runs on various heterogeneous systems,
ranging from mobile devices to distributed GPU clusters.
  This paper describes both the API design and the system implementation of
MXNet, and explains how embedding of both symbolic expression and tensor
operation is handled in a unified fashion. Our preliminary experiments reveal
promising results on large scale deep neural network applications using
multiple GPU machines.
