Open Source Deep Learning Project: mshadow

Deep Learning Specialization on Coursera

mshadow: Matrix Shadow

Project Website: None

Github Link: https://github.com/dmlc/mshadow

Description

MShadow is a lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. The goal of mshadow is to support efficient, device invariant and simple tensor library for machine learning project that aims for maximum performance and control, while also emphasize simplicty.

MShadow also provides interface that allows writing Multi-GPU and distributed deep learning programs in an easy and unified way.

Features

Efficient: all the expression you write will be lazily evaluated and compiled into optimized code
No temporal memory allocation will happen for expression you write
mshadow will generate specific kernel for every expression you write in compile time.
Device invariant: you can write one code and it will run on both CPU and GPU
Simple: mshadow allows you to write machine learning code using expressions.
Whitebox: put a float* into the Tensor struct and take the benefit of the package, no memory allocation is happened unless explicitly called
Lightweight library: light amount of code to support frequently used functions in machine learning
Extendable: user can write simple functions that plugs into mshadow and run on GPU/CPU, no experience in CUDA is required.
MultiGPU and Distributed ML: mshadow-ps interface allows user to write efficient MultiGPU and distributed programs in an unified way.


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