Docker 사용하기

이미지: 도커 실행 파일 

컨테이너: 이미지 파일에서 실행된 공간 

어떤 docker가 설치되어 있는지 검색하려면  

sudo nvidia-docker images

위와 같이 나온다. 

컨테이너를 여는 명령어

nvidia-docker run nvidia/cuda CONTAINER_NAME /bin/bash

혹은 

nvidia-docker run nvidia/cuda-7.5-cudnn5-devel CONTAINER_NAME /bin/bash

여기서 뒤에 있는 /bin/bash 는 기본으로 열 프로그램? 

어떤 docker들이 현재 열려있는지 검색하려면 

sudo docker ps -a

이라고 치면 된다. 

이미 열려져있는 컨테이너에 접속하려면 

sudo docker exec -it CONTAINER_NAME /bin/bash

이라고 치면 된다. (이 때 이 CONTAINER_NAME들은 sudo docker ps -a에서 검색이 되어야 한다.)

이 환경에서 Jupyter를 쓰기 위해서는 도커 내부로 접속할 수 있게 해줘야 한다. 

이렇게 하기 위해선 

sudo docker run -p 8888:8888 -it -v "$(pwd):/workspace" --rm metal/ml /bin/zsh

위와 같이 컨테이너를 열어야 한다. 

참고 링크: http://haanjack.github.io/metal/2016-05-02-docker/

참고 링크: http://pyrasis.com/Docker/Docker-HOWTO

도커를 설치하는 것은 잘은 모르겠지만 아래 명령어로 되는 것 같다. 

sudo docker build ???

승규가 만든 DockerFile을 보자. 

FROM nvidia/cuda:7.5-cudnn5-devel

MAINTAINER Craig Citro <craigcitro@google.com>

# Pick up some TF dependencies

RUN apt-get update && apt-get install -y --no-install-recommends \

        build-essential \

        curl \

        git \

        swig \

        libcurl3-dev \

        libfreetype6-dev \

        libpng12-dev \

        libzmq3-dev \

        libtiff5-dev libjpeg8-dev \

        liblcms2-dev libwebp-dev tcl8.6-dev tk8.6-dev python-tk \

        pkg-config \

        python \

        python-dev \

        rsync \

        software-properties-common \

        unzip \

        zip \

        zlib1g-dev \

        && \

    apt-get clean && \

    rm -rf /var/lib/apt/lists/*

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

    python get-pip.py && \

    rm get-pip.py

RUN pip --no-cache-dir install \

        ipykernel \

        jupyter \

        Pillow \

        matplotlib \

        numpy \

        scipy \

        sklearn \

        && \

    python -m ipykernel.kernelspec

# Set up Bazel.

# We need to add a custom PPA to pick up JDK8, since trusty doesn't

# have an openjdk8 backport.  openjdk-r is maintained by a reliable contributor:

# Matthias Klose (https://launchpad.net/~doko).  It will do until

# we either update the base image beyond 14.04 or openjdk-8 is

# finally backported to trusty; see e.g.

#   https://bugs.launchpad.net/trusty-backports/+bug/1368094

RUN add-apt-repository -y ppa:openjdk-r/ppa && \

    apt-get update && \

    apt-get install -y --no-install-recommends openjdk-8-jdk openjdk-8-jre-headless && \

    apt-get clean && \

    rm -rf /var/lib/apt/lists/*

# Running bazel inside a `docker build` command causes trouble, cf:

#   https://github.com/bazelbuild/bazel/issues/134

# The easiest solution is to set up a bazelrc file forcing --batch.

RUN echo "startup --batch" >>/root/.bazelrc

# Similarly, we need to workaround sandboxing issues:

#   https://github.com/bazelbuild/bazel/issues/418

RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \

    >>/root/.bazelrc

ENV BAZELRC /root/.bazelrc

# Install the most recent bazel release.

ENV BAZEL_VERSION 0.3.2

WORKDIR /

RUN mkdir /bazel && \

    cd /bazel && \

    curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \

    curl -fSsL -o /bazel/LICENSE.txt https://raw.githubusercontent.com/bazelbuild/bazel/master/LICENSE.txt && \

    chmod +x bazel-*.sh && \

    ./bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \

    cd / && \

    rm -f /bazel/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh

# Download and build TensorFlow.

RUN git clone https://github.com/tensorflow/tensorflow.git && \

    cd tensorflow && \

    git checkout r0.11

WORKDIR /tensorflow

# Configure the build for our CUDA configuration.

ENV CUDA_PATH /usr/local/cuda

ENV LD_LIBRARY_PATH /usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH

# /usr/lib/x86_64-linux-gnu:

ENV CUDA_TOOLKIT_PATH /usr/local/cuda

ENV CUDNN_INSTALL_PATH /usr/lib/x86_64-linux-gnu

ENV TF_NEED_CUDA 1

ENV TF_CUDA_COMPUTE_CAPABILITIES=3.0,3.5,5.2

RUN tensorflow/tools/ci_build/builds/configured GPU \

    bazel build -c opt --config=cuda tensorflow/tools/pip_package:build_pip_package && \

    bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/pip && \

    pip install --upgrade /tmp/pip/tensorflow-*.whl && \

    rm -rf /tmp/pip && \

    rm -rf /root/.cache

# Clean up pip wheel and Bazel cache when done.

# Set up our notebook config.

COPY jupyter_notebook_config.py /root/.jupyter/

# Copy sample notebooks.

# COPY notebooks /notebooks

# Jupyter has issues with being run directly:

#   https://github.com/ipython/ipython/issues/7062

# We just add a little wrapper script.

COPY run_jupyter.sh /

# MY MODIFICATION

RUN sed 's/main$/main universe/' -i /etc/apt/sources.list && \

    apt-get update && apt-get install -y software-properties-common && \

    add-apt-repository ppa:webupd8team/java -y && \

    apt-get update && \

    echo oracle-java8-installer shared/accepted-oracle-license-v1-1 select true | /usr/bin/debconf-set-selections && \

    apt-get install -y oracle-java8-installer libxext-dev libxrender-dev libxtst-dev && \

    apt-get clean && \

    rm -rf /var/lib/apt/lists/* && \

    rm -rf /tmp/*

RUN adduser -uid 1005 -gid 100 chang

RUN usermod -aG sudo chang

RUN cp /root/.bashrc /home/chang

RUN chown chang:users /home/chang/.bashrc

USER chang

RUN echo "\n# FOR CUDA AND cudnn\n" >> /home/chang/.bashrc

RUN echo "export LD_LIBRARY_PATH="/usr/local/nvidia/lib:/usr/local/nvidia/lib64:$LD_LIBRARY_PATH"" >> /home/chang/.bashrc

RUN echo "export PATH="/usr/local/nvidia/bin:/usr/local/cuda/bin:$PATH"" >> /home/chang/.bashrc

# COPY jupyter_notebook_config.py /home/chang/.jupyter/

# COPY run_jupyter.sh /home/chang

# TensorBoard

EXPOSE 6006

# IPython

EXPOSE 8888

WORKDIR "/home/chang/notebooks"

USER root

# CMD ["/run_jupyter.sh"]