Apache MXNet 一个轻量级，便携式，灵活的分布式/移动深度学习框架

Apache MXNet 一个轻量级，便携式，灵活的分布式/移动深度学习框架

Apache MXNet (incubating) for Deep Learning

Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains 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. MXNet is portable and lightweight, scaling effectively 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 systems, and interesting insights of DL systems for hackers.

What's New

Version 0.11.0 Release - MXNet 0.11.0 Release.Apache Incubator - We are now an Apache Incubator project.Version 0.10.0 Release - MXNet 0.10.0 Release.Version 0.9.3 Release - First 0.9 official release.Version 0.9.1 Release (NNVM refactor) - NNVM branch is merged into master now. An official release will be made soon.Version 0.8.0 ReleaseUpdated Image Classification with new Pre-trained ModelsPython Notebooks for How to Use MXNetMKLDNN for Faster CPU PerformanceMXNet 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)Go binding for inferenceAmalgamation and Go Binding for Predictors - OutdatedLarge Scale Image Classification

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 imperative and symbolic 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, Scala, 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

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 emerged from a collaboration by the authors of cxxnet, minerva, and purine2. The project reflects what we have learned from the past projects. MXNet combines aspects of each of these projects to achieve flexibility, speed, and memory efficiency.

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