Get started today with this GPU Ready Apps Guide.
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. It 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. It is accelerated with the NVIDIA Pascal GPUs and it scales across multiple GPUs and on multiple nodes allowing you to train models faster.
Installation
System Requirements
You can run MXNet on Ubuntu/Debian, Amazon Linux, 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.
This instructions is for Ubuntu/Debian users.
The GPU-enabled version of MXNet has the following requirements:
1. 64-bit Linux
2. Python 2.x / 3.x
3. NVIDIA CUDA® 7.0 or higher (CUDA 8.0 required for Pascal GPUs)
4. NVIDIA cuDNN v4.0 (minimum) or v5.1 (recommended)
You will also need an NVIDIA GPU supporting compute capability 3.0 or higher.
Download and Installation Instructions
BELOW IS A BRIEF SUMMARY OF THE COMPILATION PROCEDURE
1. Install CUDA
To use MXNet with NVIDIA GPUs, the first step is to install the CUDA Toolkit.
2. Install cuDNN
Once the CUDA Toolkit is installed, download cuDNN v5.1 Library for Linux (note that you'll need to register for the Accelerated Computing Developer Program).
Once downloaded, uncompress the files and copy them into the CUDA Toolkit directory (assumed here to be in /usr/local/cuda/)
3. Install Dependencies
$ sudo apt-get update
$ sudo apt-get install -y git build-essential libatlas-base-dev \
libopencv-dev graphviz python-pip
$ sudo pip install setuptools numpy --upgrade
$ sudo pip install graphviz jupyter
$ sudo pip install requests # for examples, e.g., mnist, cifar
4. Install MXNet
Clone the MXNet source code repository to your computer, run the installation script, and refresh the environment variables. In addition to compiling MXNet, install python MXNet module and include the path
$ git clone https://github.com/dmlc/mxnet.git --recursive
$ cd mxnet
$ cp make/config.mk .
$ echo "USE_CUDA=1" >> config.mk # to enable CUDA
$ echo "USE_CUDA_PATH=/usr/local/cuda" >> config.mk
$ echo "USE_CUDNN=1" >> config.mk
$ make –j $(nproc)
$ cd python; sudo python setup.py install; cd .. # to install MXNet python module
$ export MXNET_HOME=$(pwd)
$ echo "export PYTHONPATH=$MXNET_HOME/python:$PYTHONPATH" >> ~/.bashrc
$ source ~/.bashrc
5. Test your installation
Check if MXNet is properly installed.
$ python
Python 2.7.6 (default, Oct 26 2016, 20:30:19)
[GCC 4.8.4] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import mxnet as mx
>>> a = mx.nd.ones((2, 3), mx.cpu())
>>> print ((a * 2).asnumpy())
[[ 2. 2. 2.]
[ 2. 2. 2.]]
>>> a = mx.nd.ones((2, 3), mx.gpu())
>>> print ((a * 2).asnumpy())
[[ 2. 2. 2.]
[ 2. 2. 2.]]
If you don't get an import error, then MXNet is ready for python.
Training Models
Now that you have successfully installed MXNet, let’s test it with a few training examples on single and multiple GPUs.
MNIST MODEL
Image classification examples:
cd example/image-classification
Train a multilayer perception on the mnist dataset and GPU 0
$ python train_mnist.py --network mlp --gpus 0
CIFAR10 MODEL
Train a 110-layer resnet on the cifar10 dataset with batch size 128 and GPU 0 and 1
$ python train_cifar10.py --network resnet --num-layers 110 \ --batch-size 128 --gpus 0,1
INCEPTION V3
To run benchmark on imagenet networks, use --benchmark 1 as the argument to train_imagenet.py:
$ cd example/image-classification # if you are at MXNET_HOME
$ python train_imagenet.py --benchmark 1 --gpus 0,1 --network inception-v3 \ --batch-size 64 --image-shape 3,299,299 --num-epochs 10 --kv-store device
Benchmarks
MXNET PERFORMANCE ON MULTI-GPU SYSTEM
Recommended System Configurations
Hardware Configuration
PC
Parameter
Specs
CPU Architecture
x86_64
System Memory
8-32GB
CPUs
2 CPU sockets (8+
cores, 2+ GHz ?)
cores, 2+ GHz ?)
GPU Model
NVIDIA
®TITAN
GPUs
1-4
Servers
Parameter
Specs
CPU Architecture
x86_64
System Memory
32 GB+
CPUs/Nodes
2 (8+ cores, 2+ GHz)
GPU Model
Tesla
®P100
GPUs/Node
1-4
Software Configuration
Software stack
Parameter
Version
OS
CentOS 6.2
GPU Driver
375.35 or newer
CUDA Toolkit
8.0 or newer
cuDNN Library
v5.0 or newer
cuDNN
2016.0.47