MXNet - 轻量级、便携、灵活的分布式/移动深度学习框架

MXNet - 轻量级、便携、灵活的分布式/移动深度学习框架

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.

What's New

MXNet Memory Monger, Training Deeper Nets with Sublinear Memory CostTutorial for NVidia GTC 2016Embedding Torch layers and functions in MXNetMXNet.js: Javascript Package for Deep Learning in Browser (without server) Design Note: Design Efficient Deep Learning Data Loading ModuleMXNet on Mobile DeviceDistributed TrainingGuide to Creating New Operators (Layers)Amalgamation and Go Binding for PredictorsTraining Deep Net on 14 Million Images on A Single Machine

Contents

Documentation and TutorialsDesign NotesCode ExamplesInstallationPretrained ModelsContribute to MXNetFrequent Asked Questions

Features

Design notes providing useful insights that can re-used by other DL projectsFlexible configuration for arbitrary computation graphMix and match good flavours of programming to maximize flexibility and efficiencyLightweight, memory efficient and portable to smart devicesScales up to multi GPUs and distributed setting with auto parallelismSupport for python, R, C++ and JuliaCloud-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 and purine2. 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.

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