Tag: container

Running analysis in docker container


There are generally 4 steps to create an analytics environment on your server that will be separate from the rest of the system. By running your analysis within a container, it might reduce the risk of crashing the server because you might have used up all the resouce and cause the server to freeze up.

  1. Install virtualbox (to create a docker machine)

  2. Install docker-machine

  3. Create a docker-machine this will be the machine to run your container

  4. Map your command to run in the container. Following the kaggle/python tutorial.

Install virtualbox-qt

sudo apt-get install virtualbox-qt

Install docker-machine

curl -L https://github.com/docker/machine/releases/download/v0.8.0/docker-machine-`uname -s-uname -m` > /usr/local/bin/docker-machine && \ chmod +x /usr/local/bin/docker-machine

create a docker-machine

This will create a separate docker machine called docker2

docker-machine create -d virtualbox --virtualbox-disk-size "100000" --virtualbox-cpu-count "8" --virtualbox-memory "32092" docker2
docker-machine start docker2

We then need to specify a new destination where docker container will run, i.e. on docker2

eval $(docker-machine env docker2)

See <https://docs.docker.com/machine/install-machine/&gt; for more info

run kaggle/python

You’re now at a point where you can run stuff in the container. Here’s an extra step that will make it super easy: put these lines in your .bashrc file (or the Windows equivalent)

kpython(){
docker run -v $PWD:/tmp/working -w=/tmp/working --rm -it kaggle/python python "$@"
}
ikpython() {
docker run -v $PWD:/tmp/working -w=/tmp/working --rm -it kaggle/python ipython
}
kjupyter() {
(sleep 3 && open "http://$(docker-machine ip docker2):8888")&
docker run -v $PWD:/tmp/working -w=/tmp/working -p 8888:8888 --rm -it kaggle/python jupyter notebook --no-browser --ip="*" --notebook-dir=/tmp/working
}

Reference

<http://blog.kaggle.com/2016/02/05/how-to-get-started-with-data-science-in-containers/&gt;

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