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  1. Dockerfiles/
  2. install/
  3. .gitignore

Docker images for MXNET

How to use

First make sure docker is installed. The docker plugin nvidia-docker is required to run on Nvidia GPUs.

Pre-built docker containers are available at

For example, the following command launches a container with the Python package installed. It will pull the docker images from docker hub if it does not exist locally.

docker run -ti --rm mxnet/python

Then you can run MXNet in python, e.g.:

# python -c 'import mxnet as mx; a = mx.nd.ones((2,3)); print((a*2).asnumpy())'
[[ 2.  2.  2.]
 [ 2.  2.  2.]]

If the host machine has at least one GPU installed and nvidia-docker is installed, namely nvidia-docker run --rm nvidia/cuda nvidia-smi runs successfully, then you can run a container with GPU supports

nvidia-docker run -ti --rm mxnet/python:gpu

Now you can run the above example in GPU 0:

# python -c 'import mxnet as mx; a = mx.nd.ones((2,3), mx.gpu(0)); print((a*2).asnumpy())'
[[ 2.  2.  2.]
 [ 2.  2.  2.]]

Hosted containers

All images are based on Ubuntu 14.04. The gpu tag is built with CUDA 8.0 and cuDNN 5.


Hosted at

Python versions: 2.7.12 and 3.5.2.

Available tags:

  • mxnet/python
  • mxnet/python:gpu


Hosted at

R version: 3.3.3

Available tags:

  • mxnet/r-lang
  • mxnet/r-lang:gpu


Hosted at

Julia version: 0.5.1

Available tags:

  • mxnet/julia
  • mxnet/julia:gpu


Hosted at

Scala version: 2.11.8

Available tags:

  • mxnet/scala


Hosted at

Perl version: 5.18.2

Available tags:

  • mxnet/perl
  • mxnet/perl:gpu

How to build

The following command build the default Python package

./ build python cpu

Run ./ for more details. Use

Tips: The following commands stop all docker containers and delete all docker images.

docker stop $(docker ps -a -q)
docker rm $(docker ps -a -q)
docker rmi $(docker images -a -q)