Compilation of Dockerfiles with automated builds enabled on the Docker Registry
Dockerfile Shell
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
brainstorm Remove OS-installed six for Brainstorm Aug 9, 2016
caffe Add wget to Theano, Caffe, DIGITS Jun 12, 2016
cuda-brainstorm Deprecate CUDA 7.0 Sep 15, 2017
cuda-caffe Deprecate CUDA 7.0 Sep 15, 2017
cuda-digits Deprecate CUDA 7.0 Sep 15, 2017
cuda-keras Deprecate CUDA 7.0 Sep 15, 2017
cuda-lasagne Deprecate CUDA 7.0 Sep 15, 2017
cuda-mxnet Add back wget to MXNet for MNIST demo Oct 18, 2017
cuda-neon Manually remove system six and reinstall Mar 24, 2018
cuda-pylearn2 Deprecate CUDA 7.0 Sep 15, 2017
cuda-ssh Add cuda-ssh:9.1 Jul 21, 2018
cuda-theano Fix cuDNN for Theano Mar 27, 2018
cuda-torch Downgraded cuDNN version to satisfy the requirement of Torch. Jul 28, 2018
cuda-vnc Deprecate CUDA 7.0 Sep 15, 2017
cuda Merge pull request #13 from Kaixhin/nvidia Jun 9, 2016
cudnn Add deprecation note to /cuda and /cudnn Apr 23, 2016
digits Upgrade DIGITS CPU May 9, 2018
fglab Fix quotation marks in FGLab/FGMachine Dec 7, 2015
fgmachine Add lspci for FGMachine Aug 2, 2016
keras Upgrade pip Mar 21, 2017
lasagne Add Docker badges Sep 13, 2015
localtunnel Add Docker badges Sep 13, 2015
mxnet Add back wget to MXNet for MNIST demo Oct 18, 2017
neon Manually remove system six and reinstall Mar 24, 2018
neuron Revert "Update non-DL images to 16.04" Aug 2, 2016
pylearn2 Upgrade pip Mar 21, 2017
ros Revert "Install lsb-release for ROS" Aug 2, 2016
samba Revert "Update non-DL images to 16.04" Aug 2, 2016
spearmint Revert "Install lsb-release for ROS" Aug 2, 2016
ssh Revert "Update sshd_config" Aug 2, 2016
sshx Revert "Update non-DL images to 16.04" Aug 2, 2016
theano Manually remove system six and reinstall Mar 24, 2018
torch Add --allow-root to iTorch command Feb 17, 2018
vnc-ros Revert "Install lsb-release for ROS" Aug 2, 2016
vnc Add XRDP to VNC images Dec 12, 2016
.gitignore Update cuda-torch-plus Feb 5, 2016
CITATION.md Update links to FGLab and FGMachine Docker instructions Oct 29, 2015
LICENSE.md Add note about NVIDIA Docker project Nov 5, 2015
README.md Deprecate CUDA 7.0 Sep 15, 2017

README.md

MIT License

dockerfiles

Compilation of Dockerfiles with automated builds enabled on the Docker Hub. Not suitable for production environments. These images are under continuous development, so breaking changes may be introduced.

Nearly all images are based on Ubuntu Core 14.04 LTS, built with minimising size/layers and best practices in mind. Dependencies are indicated left to right e.g. cuda-vnc is VNC built on top of CUDA. Explicit dependencies are excluded.

Graphical applications

Starting graphical (X11) applications is possible with the following commands:

docker run -it `# Running interactively, but can be replaced with -d for daemons` \
  -e DISPLAY `# Pass $DISPLAY` \
  -v=/tmp/.X11-unix:/tmp/.X11-unix `# Pass X11 socket` \
  --ipc=host `# Allows MIT-SHM` \
  <image>

General information on running desktop applications with Docker can be found in this blog post. You probably will also need to configure the X server host (xhost) to give access. For hardware acceleration on Linux, it is possible to use nvidia-docker (with an image built for NVIDIA Docker), although OpenGL is not fully supported.

On Mac OS X, use XQuartz and allow connections from network clients. Then the following can be used:

docker run -it \
  -e DISPLAY=`ifconfig en0 | grep inet | awk '$1=="inet" {print $2}'`:0 `# Use XQuartz network $DISPLAY` \
  --ipc=host \
  <image>

Daemonising containers

Most containers run as a foreground process. To daemonise (in Docker terminology, detach) such a container it is possible to use:

docker run -d <image> sh -c "while true; do sleep 1; done"

It is now possible to access the daemonised container, for example using bash:

docker exec -it <id> bash

Sibling containers

To start containers on the host from within a docker container, the container requires docker-engine installed, with the same API version as the Docker daemon on the host. The Docker socket also needs to be mounted inside the container:

-v /var/run/docker.sock:/var/run/docker.sock

CUDA

Many images rely on CUDA. These images are versioned with the corresponding tags, e.g. "8.0" and "7.5", on the Docker Hub.

These images need to be run on an Ubuntu host OS with NVIDIA Docker installed. The driver requirements can be found on the NVIDIA Docker wiki.

Deprecated images

kaixhin/cuda and kaixhin/cudnn have now been deprecated in favour of the official solution (nvidia/cuda).

Migration

In the future it will hopefully be possible to checkpoint and restore Docker containers easily using CRIU. This would alleviate some issues, such as the inability to restart a VNC image successfully.

Automated Builds

Automated Builds on the Docker Hub have several advantages, including reproducibility and security. However the build cluster has the following limits for Automated Builds:

  • 2 hours
  • 1 CPU
  • 2 GB RAM
  • 512 MB swap
  • 30 GB disk space

The main tip for keeping within the CPU and memory limits is to reduce parallelism/forking processes. Due to their logging system, redirecting stdout/stderr to /dev/null can potentially save a reasonable amount of memory.

Acknowledgements

Some Dockerfiles have been modified from the work of others. The source for these are:

Citation

If you find this useful in research please consider citing this work.