/mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more

Primary LanguageC++OtherNOASSERTION

for Deep Learning

Build Status Documentation Status GitHub license

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 together to maximize the efficiency and your productivity. In its core, a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer is build on top, which makes symbolic execution fast and memory efficient. The library is portable and lightweight, and is ready 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.

Join the chat at https://gitter.im/dmlc/mxnet

What's New

Contents

Features

  • Open sourced design note on useful insights that can re-used by general DL projects.
  • Flexible configuration, for arbitrary computation graph.
  • Mix and Maximize 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 python, R, C++, Julia,
  • Cloud friendly, and directly compatible with S3, HDFS, AZure

Bug Reporting

License

© Contributors, 2015. Licensed under an Apache-2.0 license.

Reference Paper

Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. In Neural Information Processing Systems, Workshop on Machine Learning Systems, 2015

History

MXNet is initiated and designed in collaboration by authors from cxxnet, minerva and purine2. The project reflects what we have learnt from the past projects. It combines important flavour of the existing projects, being efficient, flexible and memory efficient.