- 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

 

 

 

 

 

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