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 package for R : http://igotit.tistory.com/1056

mxnet main site : http://mxnet.io/ 
pdf 문서 : MXNet.pdf


MXNet, 아마존의 립러닝 플랫폼으로 채택됨


기사 1 (2016년 11월 ) : http://www.itworld.co.kr/news/102258
 

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




 


 


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Posted by 리치굿맨

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