mediadesignpractices/tensorflow repository overview

⁠Using Tensorflow via Docker

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

⁠Running the container

We are using Makefile to simplify docker commands within make commands. You can download⁠ the makefile from git.

# curl -O https://raw.githubusercontent.com/mediadesignpractices/docker/master/tensorflow/Makefile

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

Mount a volume for external data sets

$ make DATA=~/mydata

Prints all make tasks

$ make help

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

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

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