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tg_all_machine_learning_cuda8_cudnn6_tg4.Dockerfile
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#first build this one
# nvidia-docker build -t tg/all_machine_learning:4 -f tg_all_machine_learning_cuda8_cudnn6_tg4.Dockerfile .
#then
# nvidia-docker build -t tg/base_all_machine_learning: -f tg_base_all_machine_learning_.Dockerfile .
#original at https://github.com/saiprashanths/dl-docker/edit/master/Dockerfile.gpu
FROM nvidia/cuda:8.0-cudnn6-devel
#also available , cuda8 + cudnn6
MAINTAINER Sai Soundararaj <saip@outlook.com>
#update these if cuda8 cudnn5 works
ARG THEANO_VERSION=rel-0.8.2
ARG TENSORFLOW_VERSION=0.8. #pip installs the latest version (currently 1.3 as of 31 aug 2017)
ARG TENSORFLOW_ARCH=gpu
ARG KERAS_VERSION=1.0.3
ARG LASAGNE_VERSION=v0.1
ARG TORCH_VERSION=latest
ARG CAFFE_VERSION=master
ARG YOLO_VERSION=master
#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
#libopenjpeg5 not 2 for ubuntu16
RUN apt-get update && apt-get install -y \
bc \
build-essential \
cmake \
curl \
g++ \
gfortran \
git \
libffi-dev \
libfreetype6-dev \
libhdf5-dev \
libjpeg-dev \
liblcms2-dev \
libopenblas-dev \
liblapack-dev \
libopenjpeg5 \
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 TensorFlow
#see https://www.tensorflow.org/install/install_linux
RUN echo hello
RUN apt-get update #this fixes prob in line below
RUN apt-get install -y libcupti-dev #wtf The command '/bin/sh -c apt-get install -y libcupti-dev' returned a non-zero code: 100
RUN apt-get install -y python-pip python-dev
RUN pip install tensorflow
#RUN pip --no-cache-dir install \
# https://storage.googleapis.com/tensorflow/linux/${TENSORFLOW_ARCH}/tensorflow-${TENSORFLOW_VERSION}-cp27-none-linux_x86_64.whl
# Install dependencies for Caffe
RUN apt-get update && apt-get install -y \
libboost-all-dev \
libgflags-dev \
libgoogle-glog-dev \
libhdf5-serial-dev \
libleveldb-dev \
liblmdb-dev \
libopencv-dev \
libprotobuf-dev \
libsnappy-dev \
protobuf-compiler \
&& \
apt-get clean && \
apt-get autoremove && \
rm -rf /var/lib/apt/lists/*
# Install Caffe
RUN git clone -b ${CAFFE_VERSION} --depth 1 https://github.com/BVLC/caffe.git /root/caffe && \
cd /root/caffe && \
cat python/requirements.txt | xargs -n1 pip install && \
mkdir build && cd build && \
#### below is the only line jr changed from the original at https://github.com/saiprashanths/dl-docker/edit/master/Dockerfile.gpu
cmake -DUSE_CUDNN=1 -DBLAS=Open -DBUILD_python=ON -DBUILD_python_layer=ON .. && \
make -j"$(nproc)" all && \
make install
# Set up Caffe environment variables
ENV CAFFE_ROOT=/root/caffe
ENV PYCAFFE_ROOT=$CAFFE_ROOT/python
ENV PYTHONPATH=$PYCAFFE_ROOT:$PYTHONPATH \
PATH=$CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
# Install Theano and set up Theano config (.theanorc) for CUDA and OpenBLAS
RUN pip --no-cache-dir install git+git://github.com/Theano/Theano.git@${THEANO_VERSION} && \
\
echo "[global]\ndevice=gpu\nfloatX=float32\noptimizer_including=cudnn\nmode=FAST_RUN \
\n[lib]\ncnmem=0.95 \
\n[nvcc]\nfastmath=True \
\n[blas]\nldflag = -L/usr/lib/openblas-base -lopenblas \
\n[DebugMode]\ncheck_finite=1" \
> /root/.theanorc
# Install Keras
RUN pip --no-cache-dir install git+git://github.com/fchollet/keras.git@${KERAS_VERSION}
# Install Lasagne
RUN pip --no-cache-dir install git+git://github.com/Lasagne/Lasagne.git@${LASAGNE_VERSION}
# Install Torch #
#this isnt working since install.sh has a sudo command
#RUN git clone https://github.com/torch/distro.git /root/torch --recursive
#WORKDIR /root/torch
##RUN bash install-deps && \
## yes no | ./install.sh
#RUN bash install-deps ./install.sh
# 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 nn && \
# luarocks install cutorch && \
# luarocks install cunn && \
\
# cd /root && git clone https://github.com/soumith/cudnn.torch.git && cd cudnn.torch && \
# git checkout R4 && \
# luarocks make && \
\
# cd /root && git clone https://github.com/facebook/iTorch.git && \
# cd iTorch && \
# luarocks make
#yolo
RUN git clone https://github.com/pjreddie/darknet /root/darknet
WORKDIR /root/darknet
#set opencv on allowing viewing images/detections - prob. not necessary
#actually dont do this as it automatically pos a window which docker doesnt like
#RUN sed -i.bak 's/OPENCV=0/OPENCV=1/' Makefile
#set gpu on
RUN sed -i.bak 's/GPU=0/GPU=1/' Makefile
RUN sed -i.bak 's/CUDNN=0/CUDNN=1/' Makefile
RUN make
#get yolo weights
RUN wget http://pjreddie.com/media/files/yolo.weights
#those are prob same as https://pjreddie.com/media/files/yolo.weights
#conv weights from extraction model
RUN wget https://pjreddie.com/media/files/darknet19_448.conv.23
# Jupyter has issues with being run directly: https://github.com/ipython/ipython/issues/7062
#COPY run_jupyter.sh /root/
# Expose Ports for TensorBoard (6006), Ipython (8888)
EXPOSE 6006 8888
WORKDIR "/root"
CMD ["/bin/bash"]