Public | Automated Build

Last pushed: a year ago
Short Description
Starts a Jupyter notebook for Keras on TensorFlow or Theano and CPU or GPU.
Full Description

Using Keras via Docker

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

Installing Docker

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

Running the container

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

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

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

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

Switch between Theano and TensorFlow

$ make notebook BACKEND=theano
$ make notebook BACKEND=tensorflow

Mount a volume for external data sets

$ make DATA=~/mydata

Prints all make tasks

$ make help

You can change Theano parameters by editing /docker/theanorc.

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
Docker Pull Command
Owner
dosht
Source Repository

Comments (1)
valikund
a year ago

Hi there,

I have downloaded the image and I think the Tensorflow and cuDNN versions don't match.
The baseimage contains cuDNN v5, but the Tensorflow version needs v4.
Apart from this I really like it :D