Open Source Deep Learning Project: mxnet

Deep Learning Specialization on Coursera

maxnet: Flexible and Efficient Library for Deep Learning

Project Website:

Github Link:


MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of symbolic programming and imperative programming to maximize efficiency and productivity. In its core, 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. The library is portable and lightweight, and it scales to multiple GPUs and multiple machines.

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


Design notes providing useful insights that can re-used by other DL projects
Flexible configuration for arbitrary computation graph
Mix and match good flavours of programming to maximize flexibility and efficiency
Lightweight, memory efficient and portable to smart devices
Scales up to multi GPUs and distributed setting with auto parallelism
Support for python, R, C++ and Julia
Cloud-friendly and directly compatible with S3, HDFS, and Azure

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