https://github.com/dmlc/mshadow

Matrix Shadow:Lightweight CPU/GPU Matrix and Tensor Template Library in C++/CUDA for (Deep) Machine Learning
https://github.com/dmlc/mshadow

Last synced: about 2 months ago
JSON representation

Matrix Shadow:Lightweight CPU/GPU Matrix and Tensor Template Library in C++/CUDA for (Deep) Machine Learning

• Host: GitHub
• URL: https://github.com/dmlc/mshadow
• Owner: dmlc
• License: other
• Archived: true
• Created: 2013-12-05T22:47:13.000Z (almost 11 years ago)
• Default Branch: master
• Last Pushed: 2019-08-04T00:45:20.000Z (over 5 years ago)
• Last Synced: 2024-09-21T21:32:44.043Z (about 2 months ago)
• Language: C++
• Homepage:
• Size: 1.44 MB
• Stars: 1,108
• Watchers: 102
• Forks: 431
• Open Issues: 37
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGES.md
  • License: LICENSE

Awesome Lists containing this project

README

        
mshadow: Matrix Shadow

======

This code base has been donated to the Apache MXNet project per [#373](https://github.com/dmlc/mshadow/issues/373), and repo is deprecated. Future development should continue in Apache MXNet.

[![Build Status](https://travis-ci.org/dmlc/mshadow.svg?branch=master)](https://travis-ci.org/dmlc/mshadow)

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 simplicity.

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

* [Contributors](https://github.com/tqchen/mshadow/graphs/contributors)

* [Tutorial](guide)

* [Documentation](doc)

* [Parameter Server Interface for GPU Tensor](guide/mshadow-ps)

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.

Version

-------

* This version mshadow-2.x, there are a lot of changes in the interface and it is not backward compatible with mshadow-1.0

  - If you use older version of cxxnet, you will need to use the legacy mshadow code

* For legacy code, refer to [Here](https://github.com/tqchen/mshadow/releases/tag/v1.1)

* Change log in [CHANGES.md](CHANGES.md)

Projects Using MShadow

----------------------

* [MXNet: Efficient and Flexible Distributed Deep Learning Framework](https://github.com/dmlc/mxnet)

* [CXXNet: A lightweight  C++ based deep learnig framework](https://github.com/dmlc/cxxnet)