分布式机器学习框架：MxNet
???? MxNet官網(wǎng)： http://mxnet.readthedocs.io/en/latest/
前言：
caffe是很優(yōu)秀的dl平臺(tái)。影響了后面很多相關(guān)框架。
cxxnet借鑒了很多caffe的思想。相比之下，cxxnet在實(shí)現(xiàn)上更加干凈，例如依賴很少，通過mshadow的模板化使得gpu和cpu代碼只用寫一份，分布式接口也很干凈。mxnet是cxxnet的下一代，目前實(shí)現(xiàn)了cxxnet所有功能，但借鑒了minerva/torch7/theano，加入更多新的功能。
目前mxnet還在快速發(fā)展中。這個(gè)月的主要方向有三，更多的binding，更好的文檔，和更多的應(yīng)用（language model、語音，機(jī)器翻譯，視頻）。地址在dmlc/mxnet · GitHub
官方簡(jiǎn)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(hào)式設(shè)計(jì)和命令式設(shè)計(jì)，來最大化效率和提高產(chǎn)出。其核心是一個(gè)動(dòng)態(tài)調(diào)度器，不停的并行執(zhí)行符號(hào)和命令操作。頂層的圖優(yōu)化層使符號(hào)執(zhí)行快速且有效。這個(gè)包輕量級(jí)可移植性好，并且可以擴(kuò)展到多GPU和多個(gè)機(jī)器。
最新發(fā)展
What's New
- MXNet Memory Monger, Training Deeper Nets with Sublinear Memory Cost
- Tutorial for NVidia GTC 2016
- Embedding Torch layers and functions in MXNet
- MXNet.js: Javascript Package for Deep Learning in Browser (without server)
- Design Note: Design Efficient Deep Learning Data Loading Module
- MXNet on Mobile Device
- Distributed Training
- Guide to Creating New Operators (Layers)
- Amalgamation and Go Binding for Predictors
- Training Deep Net on 14 Million Images on A Single Machine
- MxNet的內(nèi)存管理：子線性的內(nèi)存代價(jià)
- NVIDIA GTC2016上的 教程
- 嵌入 Torch網(wǎng)絡(luò)層和函數(shù) 到MxNet
- MxNet.js : 可運(yùn)行到瀏覽器中的javascript包
- 設(shè)計(jì)節(jié)點(diǎn)：設(shè)計(jì)有效的深度學(xué)習(xí)數(shù)據(jù)載入模型
- 移動(dòng)設(shè)備的上的 Mxnet
- 分布式訓(xùn)練方法
- 網(wǎng)絡(luò)層 的運(yùn)算符重載
- 使用一個(gè)深度網(wǎng)絡(luò) 訓(xùn)練1400萬張圖片
Contents
- Documentation and Tutorials
- Design Notes
- Code Examples
- Installation
- Pretrained Models
- Contribute to MXNet
- Frequent Asked Questions
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.
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