Docker/Pods/Deep Learning

For this, I used the docker image from docker hub, waleedka/modern-deep-learning.

Basics

Running the Deep Learning Container

Let's start with how we get this deep learning docker container up and running.

Start by installing Docker: Docker/Installing

Next, this deep learning container can run a Jupyter notebook server, which runs on port 8888 by default, so we'll pass the container's port 8888 through to the host machine's port 8888:

$ docker run -it -p 8888:8888 waleedka/modern-deep-learning

This is great, but unfortunately any changes we make or notebooks we create will disappear with our container, so we'll need to figure out data volumes.

For the time being, let's start by testing out the container and making sure the software components work.

Then we'll figure out a schema for data volumes, and how we get data into and out of our deep learning container.

Testing it out

To take this for a test drive, run the above command. This will give you a bash terminal on the docker container, where we can run a Jupyter notebook:

$ docker run -it -p 8888:8888 waleedka/modern-deep-learning
root@a944863bc1e6:~# jupyter notebook

Now on the host machine, we can navigate to localhost:8888 and see a Jupyter notebook server up and running. This is exposing the container's file system and any notebooks running in the container. This container runs Python 3 only.

Create a new Python notebook, and try importing a few libraries:

import numpy
import scipy
import sklearn
import theano
import tensorflow
import pandas
import matplotlib
import keras