#Machine Learning Tools in Docker
Machine learning tools are notorious for having byzantine dependencies and often academic code quality. This makes them hard to install and configure correctly across different machines and operating systems. At Startup.ML we have been using Docker to simplify the process of getting these tools on our machines.
- GraphLab
- H2O
- Julia
- MLlib
- Theano
- Torch7
- Vowpal Wabbit (VW)
- 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 ...)
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
...
to clean up (kill, remove container and remove image)
./clean.sh
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.