Hey! You might have seen my recent post about having Dockerized some software called GraphLab Create (together with IPython) for a machine learning course I was taking. As it happens, I've found that image so useful for other, generic ML work that I've pared it down to its IPython/Anaconda bundle only. So I'd like to introduce the super-simple but super-useful Dockerized IPython / Anaconda project!
This repo includes a couple of useful scripts: One for building the image (
build.sh) and one for running the resultant image as a container (
run.sh). Just run the build script and then the run script (and optionally provide a directory to mount into the container for data files) and you're all set! Note: Either your specified directory or current working directory will be mounted to
/data as a volume into the container! Also, your IPython Notebook may include
import statements which reference functions inside files in your new
/data volume directory. This means you will need to change any path references to include
/data, and specifically add the
/data directory to your import path by adding this to the top of your IPython Notebook:
import sys sys.path.insert(0, '/data') ## this relative dir won't work: # data_dir = 'foo/dataset.1' ## so we just add /data to the front: data_dir = '/data/foo/dataset.1'
Finally, I try to be readable, but take a look at my earlier post linked above if you want a breakdown of what's going on in there. Have fun!