Dockerfile.gpu

FROM nvidia/cuda:8.0-cudnn5-devel

MAINTAINER Gregory Johnson <gregj@alleninstitute.org>

ARG TORCH_VERSION=latest


#RUN echo -e "\n**********************\nNVIDIA Driver Version\n**********************\n" && \
#	cat /proc/driver/nvidia/version && \
#	echo -e "\n**********************\nCUDA Version\n**********************\n" && \
#	nvcc -V && \
#	echo -e "\n\nBuilding your Deep Learning Docker Image...\n"

# Install some dependencies
RUN apt-get update && apt-get install -y \
		sudo \
		bc \
		build-essential \
		cmake \
		curl \
		g++ \
		gfortran \
		git \
		libffi-dev \
		libfreetype6-dev \
		libhdf5-dev \
		libjpeg-dev \
		liblcms2-dev \
		libopenblas-dev \
		liblapack-dev \
		libpng12-dev \
		libssl-dev \
		libtiff5-dev \
		libwebp-dev \
		libzmq3-dev \
		nano \
		pkg-config \
		python-dev \
		software-properties-common \
		unzip \
		vim \
		wget \
		zlib1g-dev \
		&& \
	apt-get clean && \
	apt-get autoremove && \
	rm -rf /var/lib/apt/lists/* && \
# Link BLAS library to use OpenBLAS using the alternatives mechanism (https://www.scipy.org/scipylib/building/linux.html#debian-ubuntu)
	update-alternatives --set libblas.so.3 /usr/lib/openblas-base/libblas.so.3

# Install pip
RUN curl -O https://bootstrap.pypa.io/get-pip.py && \
	python get-pip.py && \
	rm get-pip.py

# Add SNI support to Python
RUN pip --no-cache-dir install \
		pyopenssl \
		ndg-httpsclient \
		pyasn1

# Install useful Python packages using apt-get to avoid version incompatibilities with Tensorflow binary
# especially numpy, scipy, skimage and sklearn (see https://github.com/tensorflow/tensorflow/issues/2034)
RUN apt-get update && apt-get install -y \
		python-numpy \
		python-scipy \
		python-nose \
		python-h5py \
		python-skimage \
		python-matplotlib \
		python-pandas \
		python-sklearn \
		python-sympy \
		&& \
	apt-get clean && \
	apt-get autoremove && \
	rm -rf /var/lib/apt/lists/*

# Install other useful Python packages using pip
RUN pip --no-cache-dir install --upgrade ipython && \
	pip --no-cache-dir install \
		Cython \
		ipykernel \
		jupyter \
		path.py \
		Pillow \
		pygments \
		six \
		sphinx \
		wheel \
		zmq \
		&& \
	python -m ipykernel.kernelspec



# Install Torch
RUN git clone https://github.com/torch/distro.git ~/torch --recursive && \
	cd /root/torch && \
	bash install-deps && \
	./install.sh -y

# Export the LUA evironment variables manually
ENV LUA_PATH='/root/.luarocks/share/lua/5.1/?.lua;/root/.luarocks/share/lua/5.1/?/init.lua;/root/torch/install/share/lua/5.1/?.lua;/root/torch/install/share/lua/5.1/?/init.lua;./?.lua;/root/torch/install/share/luajit-2.1.0-beta1/?.lua;/usr/local/share/lua/5.1/?.lua;/usr/local/share/lua/5.1/?/init.lua' \
	LUA_CPATH='/root/.luarocks/lib/lua/5.1/?.so;/root/torch/install/lib/lua/5.1/?.so;./?.so;/usr/local/lib/lua/5.1/?.so;/usr/local/lib/lua/5.1/loadall.so' \
	PATH=/root/torch/install/bin:$PATH \
	LD_LIBRARY_PATH=/root/torch/install/lib:$LD_LIBRARY_PATH \
	DYLD_LIBRARY_PATH=/root/torch/install/lib:$DYLD_LIBRARY_PATH
ENV LUA_CPATH='/root/torch/install/lib/?.so;'$LUA_CPATH

# Install the latest versions of nn, cutorch, cunn, cuDNN bindings and iTorch
RUN luarocks install torch && \
	luarocks install nn && \
	luarocks install cutorch && \
	luarocks install cunn && \
	\
	cd /root && git clone https://github.com/soumith/cudnn.torch.git && cd cudnn.torch && \
	luarocks make && \
	\
	cd /root && git clone https://github.com/facebook/iTorch.git && \
	cd iTorch && \
	luarocks make && \
	luarocks install colormap && \
	luarocks install optnet && \
	luarocks install hdf5
	
RUN luarocks install torchx
RUN luarocks install dpnn
RUN luarocks install optim

# Set up notebook config
COPY jupyter_notebook_config.py /root/.jupyter/

# Jupyter has issues with being run directly: https://github.com/ipython/ipython/issues/7062
COPY run_jupyter.sh /root/

# Expose Ports for Ipython (8888)
EXPOSE 8888

WORKDIR "/root"
CMD ["/bin/bash"]