Using Edward via Docker

This directory contains Dockerfile to make it easy to get up and running with Edward via Docker.

Installing Docker

General installation instructions are on the Docker site, but we give some quick links here:

• OSX: docker toolbox
• ubuntu

Installing NVIDIA Docker (GPU Environment)

General installation instructions are on the NVIDIA Docker site

Running the container

We are using Makefile to simplify docker commands within make commands.

CPU environment

Build the container and start a jupyter notebook

$ make notebook

Build the container and start an iPython shell

$ make ipython

Build the container and start a bash

$ make bash

Build the container and start a test

$ make test

GPU environment

Build the container and start a jupyter notebook

$ make notebook-gpu

Build the container and start an iPython shell

$ make ipython-gpu

Build the container and start a bash

$ make bash-gpu

Build the container and start a test

$ make test-gpu

For GPU support install NVidia drivers (ideally latest) and nvidia-docker. Run using

$ make notebook-gpu GPU=0 # or [ipython, bash]

Mount a volume for external data sets

$ make DATA=~/mydata

Prints all make tasks

$ make help

Note: If you would have a problem running nvidia-docker you may try the old way we have used. But it is not recommended. If you find a bug in the nvidia-docker report it there please and try using the nvidia-docker as described above.

$ export CUDA_SO=$(\ls /usr/lib/x86_64-linux-gnu/libcuda.* | xargs -I{} echo '-v {}:{}')
$ export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
$ docker run -it -p 8888:8888 $CUDA_SO $DEVICES gcr.io/tensorflow/tensorflow:latest-gpu