Docker image for test and experiment Keras (#3035)

* 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
This commit is contained in:
Mostafa Abdulhamid 2016-07-27 02:29:56 +01:00 committed by François Chollet
parent 3726aba2ee
commit df84c69676
4 changed files with 135 additions and 0 deletions

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docker/Dockerfile Normal file

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FROM nvidia/cuda:7.5-cudnn5-devel
ENV CONDA_DIR /opt/conda
ENV PATH $CONDA_DIR/bin:$PATH
RUN mkdir -p $CONDA_DIR && \
echo export PATH=$CONDA_DIR/bin:'$PATH' > /etc/profile.d/conda.sh && \
apt-get update && \
apt-get install -y wget git libhdf5-dev g++ graphviz && \
wget --quiet https://repo.continuum.io/miniconda/Miniconda3-3.9.1-Linux-x86_64.sh && \
echo "6c6b44acdd0bc4229377ee10d52c8ac6160c336d9cdd669db7371aa9344e1ac3 *Miniconda3-3.9.1-Linux-x86_64.sh" | sha256sum -c - && \
/bin/bash /Miniconda3-3.9.1-Linux-x86_64.sh -f -b -p $CONDA_DIR && \
rm Miniconda3-3.9.1-Linux-x86_64.sh
ENV NB_USER keras
ENV NB_UID 1000
RUN useradd -m -s /bin/bash -N -u $NB_UID $NB_USER && \
mkdir -p $CONDA_DIR && \
chown keras $CONDA_DIR -R && \
mkdir -p /src && \
chown keras /src
USER keras
# Python
ARG python_version=3.5.1
ARG tensorflow_version=0.9.0rc0-cp35-cp35m
RUN conda install -y python=${python_version} && \
pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-${tensorflow_version}-linux_x86_64.whl && \
pip install git+git://github.com/Theano/Theano.git && \
pip install ipdb pytest pytest-cov python-coveralls coverage==3.7.1 pytest-xdist pep8 pytest-pep8 pydot_ng && \
conda install Pillow scikit-learn notebook pandas matplotlib nose pyyaml six h5py && \
pip install git+git://github.com/fchollet/keras.git && \
conda clean -yt
ADD theanorc /home/keras/.theanorc
ENV PYTHONPATH='/src/:$PYTHONPATH'
WORKDIR /src
EXPOSE 8888
CMD jupyter notebook --port=8888 --ip=0.0.0.0

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docker/Makefile Normal file

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help:
@cat Makefile
DATA?="${HOME}/Data"
GPU?=0
DOCKER_FILE=Dockerfile
DOCKER=GPU=$(GPU) nvidia-docker
BACKEND=tensorflow
TEST=tests/
SRC=$(shell dirname `pwd`)
build:
docker build -t keras --build-arg python_version=3.5 -f $(DOCKER_FILE) .
bash: build
$(DOCKER) run -it -v $(SRC):/src -v $(DATA):/data --env KERAS_BACKEND=$(BACKEND) keras bash
ipython: build
$(DOCKER) run -it -v $(SRC):/src -v $(DATA):/data --env KERAS_BACKEND=$(BACKEND) keras ipython
notebook: build
$(DOCKER) run -it -v $(SRC):/src -v $(DATA):/data --net=host --env KERAS_BACKEND=$(BACKEND) keras
test: build
$(DOCKER) run -it -v $(SRC):/src -v $(DATA):/data --env KERAS_BACKEND=$(BACKEND) keras py.test $(TEST)

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docker/README.md Normal file

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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](http://www.docker.com/).
## Installing Docker
General installation instructions are
[on the Docker site](https://docs.docker.com/installation/), but we give some
quick links here:
* [OSX](https://docs.docker.com/installation/mac/): [docker toolbox](https://www.docker.com/toolbox)
* [ubuntu](https://docs.docker.com/installation/ubuntulinux/)
## 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](https://github.com/NVIDIA/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

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docker/theanorc Normal file

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[global]
floatX = float32
optimizer=None
device = gpu