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* Docker image for test and experiment Keras - Docker image with CUDA support on ubuntu 14.04 - nvidia-docker script to forward the GPU to the container - MakeFile to simplify docker commands for build, run, test, ..etc - Add useful tools like jupyter notebook, ipdb, sklearn for experiments * update nvidia-docker plugin * use .theanorc in Dockerfile * Add tensorflow to the docker image * update Docker image to cuDNN v5 * test fixes * move docker to sub directory * README for docker * Fix typos * Add visualization to Dockerfile
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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