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Last pushed: 10 hours ago
Short Description
Tensorflow w/ CUDA (GPU) + extras
Full Description

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
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mediadesignpractices
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