# mxnet

**Repository Path**: daqingba/mxnet

## Basic Information

- **Project Name**: mxnet
- **Description**: Efficient and Flexible Distributed Deep Learning Framework, for python, R, Julia and more
- **Primary Language**: C++
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-06-06
- **Last Updated**: 2024-06-10

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<img src=https://raw.githubusercontent.com/dmlc/dmlc.github.io/master/img/logo-m/mxnet2.png width=135/> for Deep Learning
=====

[![Build Status](https://travis-ci.org/dmlc/mxnet.svg?branch=master)](https://travis-ci.org/dmlc/mxnet)
[![Documentation Status](https://readthedocs.org/projects/mxnet/badge/?version=latest)](http://mxnet.readthedocs.org/en/latest/)
[![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE)

MXNet is a deep learning framework designed for both *efficiency* and *flexibility*.
It allows you to mix the [flavours](http://mxnet.readthedocs.org/en/latest/program_model.html) of
deep learning programs together to maximize the efficiency and your productivity.


What's New
----------
* [LSTM Example by using symbolic API](https://github.com/dmlc/mxnet/tree/master/example/rnn)
* [MXNet R Package brings Deep learning for R!](https://github.com/dmlc/mxnet/tree/master/R-package)
* [Note on Dependency Engine for Deep Learning](http://mxnet.readthedocs.org/en/latest/developer-guide/note_engine.html)

Contents
--------
* [Documentation and Tutorials](http://mxnet.readthedocs.org/en/latest/)
* [Open Source Design Notes](http://mxnet.readthedocs.org/en/latest/#open-source-design-notes)
* [Code Examples](example)
* [Installation](http://mxnet.readthedocs.org/en/latest/build.html)
* [Features](#features)
* [Contribute to MXNet](http://mxnet.readthedocs.org/en/latest/contribute.html)
* [License](#license)

Features
--------
* To Mix and Maximize
  - Mix all flavours of programming models to maximize flexibility and efficiency.
* Lightweight, scalable and memory efficient.
  - Minimum build dependency, scales to multi-GPUs with very low memory usage.
* Auto parallelization
  - Write numpy-style ndarray GPU programs, which will be automatically parallelized.
* Language agnostic
  - With support for python, c++, R, more to come.
* Cloud friendly
  - Directly load/save from S3, HDFS, AZure
* Easy extensibility
  - Extending no requirement on GPU programming.

Bug Reporting
-------------
* For reporting bugs please use the [mxnet/issues](https://github.com/dmlc/mxnet/issues) page.

License
-------
© Contributors, 2015. Licensed under an [Apache-2.0](https://github.com/dmlc/mxnet/blob/master/LICENSE) license.


History
-------
MXNet is initiated and designed in collaboration by authors from [cxxnet](https://github.com/dmlc/cxxnet), [minerva](https://github.com/dmlc/minerva) and [purine2](https://github.com/purine/purine2). The project reflects what we have learnt from the past projects. It combines important flavour of the existing projects, being efficient, flexible and memory efficient.