df84c69676
* 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
47 lines
1.5 KiB
Docker
47 lines
1.5 KiB
Docker
FROM nvidia/cuda:7.5-cudnn5-devel
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ENV CONDA_DIR /opt/conda
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ENV PATH $CONDA_DIR/bin:$PATH
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RUN mkdir -p $CONDA_DIR && \
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echo export PATH=$CONDA_DIR/bin:'$PATH' > /etc/profile.d/conda.sh && \
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apt-get update && \
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apt-get install -y wget git libhdf5-dev g++ graphviz && \
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wget --quiet https://repo.continuum.io/miniconda/Miniconda3-3.9.1-Linux-x86_64.sh && \
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echo "6c6b44acdd0bc4229377ee10d52c8ac6160c336d9cdd669db7371aa9344e1ac3 *Miniconda3-3.9.1-Linux-x86_64.sh" | sha256sum -c - && \
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/bin/bash /Miniconda3-3.9.1-Linux-x86_64.sh -f -b -p $CONDA_DIR && \
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rm Miniconda3-3.9.1-Linux-x86_64.sh
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ENV NB_USER keras
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ENV NB_UID 1000
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RUN useradd -m -s /bin/bash -N -u $NB_UID $NB_USER && \
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mkdir -p $CONDA_DIR && \
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chown keras $CONDA_DIR -R && \
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mkdir -p /src && \
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chown keras /src
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USER keras
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# Python
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ARG python_version=3.5.1
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ARG tensorflow_version=0.9.0rc0-cp35-cp35m
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RUN conda install -y python=${python_version} && \
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pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-${tensorflow_version}-linux_x86_64.whl && \
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pip install git+git://github.com/Theano/Theano.git && \
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pip install ipdb pytest pytest-cov python-coveralls coverage==3.7.1 pytest-xdist pep8 pytest-pep8 pydot_ng && \
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conda install Pillow scikit-learn notebook pandas matplotlib nose pyyaml six h5py && \
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pip install git+git://github.com/fchollet/keras.git && \
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conda clean -yt
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ADD theanorc /home/keras/.theanorc
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ENV PYTHONPATH='/src/:$PYTHONPATH'
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WORKDIR /src
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EXPOSE 8888
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CMD jupyter notebook --port=8888 --ip=0.0.0.0
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