前言：

caffe是很優秀的dl平臺。影響了後面很多相關框架。

cxxnet借鑑了很多caffe的思想。相比之下，cxxnet在實現上更加乾淨，例如依賴很少，通過mshadow的模板化使得gpu和cpu程式碼只用寫一份，分散式介面也很乾淨。

mxnet是cxxnet的下一代，目前實現了cxxnet所有功能，但借鑑了minerva/torch7/theano，加入更多新的功能。

1. ndarray程式設計介面，類似matlab/numpy.ndarray/torch.tensor。獨有優勢在於通過背後的engine可以在效能上和記憶體使用上更優
2. symbolic介面。這個可以使得快速構建一個神經網路，和自動求導。
3. 更多binding 目前支援比較好的是python，馬上會有julia和R
4. 更加方便的多卡和多機執行
5. 效能上更優。目前mxnet比cxxnet快40%，而且gpu記憶體使用少了一半。

目前mxnet還在快速發展中。這個月的主要方向有三，更多的binding，更好的文件，和更多的應用（language model、語音，機器翻譯，視訊）。地址在dmlc/mxnet · GitHub

官方簡介：           

       MXNet is a deep learning framework designed for both efficiency andflexibility.It allows you tomix

theflavours of symbolicprogramming 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 ofblue prints and guidelines for buildingdeep learning system, and interesting insights of DL systems for hackers.

      MxNet混合了符號式設計和命令式設計，來最大化效率和提高產出。其核心是一個動態排程器，不停的並行執行符號和命令操作。頂層的圖優化層使符號執行快速且有效。這個包輕量級可移植性好，並且可以擴充套件到多GPU和多個機器。

      MxNet不僅是一個深度學習工程，並且是一個為構建DL系統提供藍圖和指導的集合，並且為hackers 提供了一個有趣的視野。

最新發展

What's New

Contents

Features

• 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

Ask Questions

• Please use mxnet/issues for how to use mxnet and reporting bugs

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 the authors of cxxnet, minerva andpurine2. The project reflects what we have learnt from the past projects. It combines important flavours of the existing projects for efficiency, flexibility and memory efficiency.