MXNet: A Scalable Deep Learning Framework

MXNet is an open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices. It is highly scalable, allowing for fast model training, and supports a flexible programming model and multiple languages. MXNet allows you to mix symbolic and imperative programming flavors to maximize both efficiency and productivity. MXNet is built on a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. The MXNet library is portable and lightweight, and it scales to multiple GPUs and multiple machines.

Setup and Installation

You can run MXNet on Amazon Linux, Ubuntu/Debian, OS X, and Windows operating systems. MXNet can be run on Docker and on Cloud like AWS. MXNet can also be run on embedded devices, such as the Raspberry Pi running Raspbian. MXNet currently supports the Python, R, Julia and Scala languages.

If you are running Python/R on Amazon Linux or Ubuntu, you can use Git Bash scripts to quickly install the MXNet libraries and all its dependencies.

Refer below for more details on setting up MXNet:

• Prerequisites for using MXNet
• Step by step instruction guide for installing MXNet
• Common installation problems

Start to use MXNet

While installation for MXNet and language package is completed, we can run following codes to verify our installation is successful.

Julia

julia> using MXNet

julia> a = mx.ones((2,3), mx.gpu())
mx.NDArray{Float32}(2,3)

julia> Array{Float32}(a * 2)
2×3 Array{Float32,2}:
 2.0  2.0  2.0
 2.0  2.0  2.0

Python

The Python interface is similar to numpy.NDArray:

   >>> import mxnet as mx
   >>> a = mx.nd.ones((2, 3), mx.gpu())
   >>> print ((a * 2).asnumpy())
   [[ 2.  2.  2.]
    [ 2.  2.  2.]]

R

   > require(mxnet)
   Loading required package: mxnet
   > a <- mx.nd.ones(c(2,3))
   > a
        [,1] [,2] [,3]
   [1,]    1    1    1
   [2,]    1    1    1
   > a + 1
        [,1] [,2] [,3]
   [1,]    2    2    2
   [2,]    2    2    2

Scala

You can perform tensor or matrix computation in pure Scala:

   scala> import ml.dmlc.mxnet._
   import ml.dmlc.mxnet._

   scala> val arr = NDArray.ones(2, 3)
   arr: ml.dmlc.mxnet.NDArray = ml.dmlc.mxnet.NDArray@f5e74790

   scala> arr.shape
   res0: ml.dmlc.mxnet.Shape = (2,3)

   scala> (arr * 2).toArray
   res2: Array[Float] = Array(2.0, 2.0, 2.0, 2.0, 2.0, 2.0)

   scala> (arr * 2).shape
   res3: ml.dmlc.mxnet.Shape = (2,3)

Recommended Starting Tutorials

• Handwritten Digit Recognition using Convolutional Neural Networks (Beginner)
• Character-level language models using LSTMs (Advanced)

Next Steps

• Setup and Installation
• Tutorials
• How To
• Architecture

MXNet Open Source Community

Broad Model Support – Train and deploy the latest deep convolutional neural networks (CNNs) and long short-term memory (LSTMs) models

 

Extensive Library of Reference Examples – Build on sample tutorials (with code), such as image classification, language modeling, neural art, and speech recognition, and more.

 

Open and Collaborative Community – Support and contributions from many top tier universities and industry partners