How to use the docker container for the course

We provide a docker image oduerr/tf_docker:tf1_py3 with Tensorflow (v1.0.0) , TFLearn, Keras, and many other pre-installed python libraries (numpy, pandas).

Installation of docker

The official installation guide can be found at: https://docs.docker.com/engine/installation/
For hints how to use docker on Windows see here
In case that the docker installation does not work we have an addition installation guide for installing Tensorflow directly on Windows.

Running the container

In the docker command line execute:

docker run -p 8888:8888 -p 6006:6006 -it oduerr/tf_docker:tf1_py3

open http://localhost:8888/?token=tensorchiefs or http://192.168.99.100:8888/tree?token=tensorchiefs(for windows) in the browser.

Running with a linked file system.

If you want to access a directory here (/Users/oli/Documents/workspace/dl_tutorial/) from inside the docker container execute:

docker run -p 8888:8888 -p 6006:6006 -v /Users/oli/Documents/workspace/dl_tutorial/:/notebooks/local/ -it oduerr/tf_docker:tf1_py3

Updating

Please make sure to use the latest container by updating it using

docker pull oduerr/tf_docker:tf1_py3

Other useful hints for docker

Starting in bash

In case you want to not start the jupyter notebook sever automatically but want a bash shell do:

docker run -p 8888:8888 -p 6006:6006 -it oduerr/tf_docker:tf1_py3 bash

Local vs Inside container

The entry before the colon ‘:’ is on the local machine, the one after it inside the container. Examples:

  docker run -p 4242:8888 -it oduerr/tf_docker:tf1_py3 #4242 is the port on the local machine, 8888 inside the container
  docker run -v /tmp/dl_tutorial/:/notebooks/dl_tutorial/ #/tmp/dl_tutorial is on local machine

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