#Machine Learning Docker Images
Machine learning tools are notorious for having byzantine dependencies and academic code quality. This makes them easy to install and configure across a variety of machines and operating systems.
Currently supported tools
- Deeplearning4j
- GraphLab
- H2O
- Julia
- MLlib
- Theano
- Torch7
- Vowpal Wabbit (VW)
Getting started
- First step is to Install Docker on Mac OS X.
- Once Boot2Docker has been installed, launch it from Spotlight
- In the terminal window with the title "Boot2Docker for OSX" go to the jetpack directory and start the build process (be patient, the builds can take some time)
To build an individual image, provide it as an argument to the build.sh script.
./build.sh julia (or theano, graphlab, h2o, mllib ...)
to run the docker image
./run.sh julia (or theano, graphlab, h2o, mllib ...)
Troubleshooting
Pulling Images
If you are having trouble with the build command, try the pre-built images
docker pull startupml/julia
docker pull startupml/theano
docker pull startupml/graphlab
docker pull startupml/h2o
docker pull startupml/mllib
...
Starting Over
to clean up (kill, remove container and remove image)
./clean.sh
Linux specific
The docker daemon must run as root. To be able to run the docker
client as a normal user, add that user to the docker group. To do so
for the current user, run: sudo gpasswd -a ${USER} docker.
See docker documentation
for more detail.
No space left on device
Getting “no space left on device” errors with Boot2Docker?
Boot2Docker issues
Consider using Kitematic
