MXNet. Deep Learning API.

	Overview
	주요특징. - Flexible and Efficient Library for Deep Learning - mxnet 소스 : C++11 - Portable 실행 : Runs on CPUs or GPUs, on clusters, servers, desktops, or mobile phones - Cloud 실행 : Supports distributed training on multiple CPU/GPU machines, including AWS, GCE, Azure, and Yarn clusters - API 언어 : C++ , R , Python, Javascript, Scala, Julia, Matlab. -- mxnet C++ API : http://mxnet.io/api/c++/index.html  -- mxnet package for R : http://igotit.tistory.com/1056 mxnet main site : http://mxnet.io/  github : https://github.com/dmlc/mxnet pdf 문서 : MXNet.pdf Guide to MXnet : http://home.cse.ust.hk/~xshiab/data/MXNet.pdf MXNet, 아마존의 립러닝 플랫폼으로 채택됨 기사 1 (2016년 11월 ) : http://www.itworld.co.kr/news/102258 기사 2 (2017년 1월 ) : http://www.zdnet.co.kr/news/news_view.asp?artice_id=20170131112615&type=det&re=   MXNet Function Block Above image shows major modules/components of the MXNet system and their interaction. The modules are: Runtime Dependency Engine: Schedules and executes the operations according to their read/write dependency. Storage Allocator: Efficiently allocate and recycles memory blocks for GPU and CPU. Resource Manager: Manage global resources such as random number generator, temporal space. NDArray: Dynamic asynchronous n-dimensional arrays, provide flexible imperative programs for MXNet. Symbolic Execution: Static symbolic graph executor, provide efficient symbolic graph execution and optimization. Operator: Operators that defines static forward and gradient calculation(backprop). SimpleOp: Operators that extend to NDArray operators and symbolic operators in a unified fashion. Symbol Construction: Symbolic construction, provide a way to construct computation graph(net configuration) KVStore: Key-value store interface for easy parameter synchronizations. Data Loading(IO): Efficient distributed data loading and augmentation from : http://mxnet.io/architecture/overview.html

 

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