Build the container and start jupyter lab

make notebook GPU=0

Rename the container

docker rename <original container name> keras

To start the container:

docker start keras

To stop the container:

docker stop keras

To see the notebook URL:

docker logs keras

To start bash inside running container:

docker exec -it keras bash

Taken from the official repo of Keras.

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

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

• OSX: docker toolbox
• ubuntu

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