如何在安装了Jupyter笔记本和Keras的情况下构建自定义docker映像？

我正在尝试构建一个映像，该映像将包含Jupyter笔记本和Keras，以便在docker容器中运行我创建的ML模型。这是我现在的Dockerfile文件

Dockerfile

ARG cuda_version=9.0
ARG cudnn_version=7
FROM nvidia/cuda:${cuda_version}-cudnn${cudnn_version}-devel
# Install system packages
RUN apt-get update && apt-get install -y --no-install-recommends \
      bzip2 \
      g++ \
      git \
      graphviz \
      libgl1-mesa-glx \
      libhdf5-dev \
      openmpi-bin \
      wget && \
    rm -rf /var/lib/apt/lists/*

# Install conda
ENV CONDA_DIR /opt/conda
ENV PATH $CONDA_DIR/bin:$PATH

RUN wget --quiet --no-check-certificate https://repo.continuum.io/miniconda/Miniconda3-4.2.12-Linux-x86_64.sh && \
    echo "c59b3dd3cad550ac7596e0d599b91e75d88826db132e4146030ef471bb434e9a *Miniconda3-4.2.12-Linux-x86_64.sh" | sha256sum -c - && \
    /bin/bash /Miniconda3-4.2.12-Linux-x86_64.sh -f -b -p $CONDA_DIR && \
    rm Miniconda3-4.2.12-Linux-x86_64.sh && \
    echo export PATH=$CONDA_DIR/bin:'$PATH' > /etc/profile.d/conda.sh

# Install Python packages and keras
ENV NB_USER keras
ENV NB_UID 1000

RUN useradd -m -s /bin/bash -N -u $NB_UID $NB_USER && \
    chown $NB_USER $CONDA_DIR -R && \
    mkdir -p /src && \
    chown $NB_USER /src

USER $NB_USER

ARG python_version=3.6

RUN conda config --append channels conda-forge
RUN conda install -y python=${python_version} && \
    pip install --upgrade pip && \
    pip install \
      sklearn_pandas \
      tensorflow-gpu \
      cntk-gpu && \
    conda install \
      bcolz \
      h5py \
      json \ 
      cx_Oracle \
      matplotlib \
      numpy \
      mkl \
      nose \
      notebook \
      Pillow \
      pandas \
      pydot \
      pygpu \
      pyyaml \
      scikit-learn \
      six \
      theano \
      mkdocs \
      && \
    git clone git://github.com/keras-team/keras.git /src && pip install -e /src[tests] && \
    pip install git+git://github.com/keras-team/keras.git && \
    conda clean -yt

ADD theanorc /home/keras/.theanorc

ENV LC_ALL=C.UTF-8
ENV LANG=C.UTF-8

ENV PYTHONPATH='/src/:$PYTHONPATH'
# Working directory
WORKDIR /data

# COPYING CURRENT DIRECTORY INTO /data folder in container
COPY ./lstm_real_deal.ipynb /data

EXPOSE 8888

CMD jupyter notebook --port=8888 --ip=0.0.0.0

然后，我按以下顺序运行docker命令：

docker build.

（我在docker文件所在的目录中，因此它正在正确构建）

docker run-d-p 8888:8888[图片]

然后在本地机器上转到0.0.0.0:8888，我似乎无法连接到Jupyter笔记本

对Dockerfile的任何改进建议也将不胜感激