Dockerfile.dev

# This builds a DL *development* docker image with TF-GPU, Keras, Jupyter 
# It's assumed that a host volume will be mapped to /mnt/

# Start from tensorflow official image for gpu with python 3 as base
FROM tensorflow/tensorflow:1.13.1-gpu-py3

# Note that the TF 13.1 version only supports python 3.5 and ubuntu 16.04

# Install necessary *nix tools and pip3
RUN apt-get update && \
    apt-get install -y vim && \
    apt-get install -y git-core && \
    apt-get install -y python3-pip && \
    apt-get install -y python3-matplotlib && \
    apt-get install -y python3-pil && \
    apt-get install -y libsndfile1 && \
    apt-get install -y ffmpeg && \
    apt-get install -y wget && \
    apt-get install -y unzip

# Update pip
RUN pip install --upgrade pip
RUN pip install --upgrade setuptools

# Install jupyter notebook
RUN pip3 install jupyter -U && \
    pip3 install jupyterlab

# Install all dependency libs for the project.

COPY requirements.pip /
RUN pip3 install -r ./requirements.pip

# Install and configure the SSH Server

RUN apt-get update && apt-get install -y openssh-server
RUN systemctl enable ssh
RUN sed -i 's/PermitRootLogin prohibit-password/PermitRootLogin yes/' /etc/ssh/sshd_config

COPY ./keys/spyros_id_rsa.pub /tmp/tmp.pub
RUN mkdir -p ~/.ssh && chmod 700 ~/.ssh && cat /tmp/tmp.pub >> ~/.ssh/authorized_keys && chmod 600 ~/.ssh/authorized_keys && rm -f /tmp/tmp.pub

COPY ./keys/biseda_id_rsa.pub /tmp/tmp.pub
RUN mkdir -p ~/.ssh && chmod 700 ~/.ssh && cat /tmp/tmp.pub >> ~/.ssh/authorized_keys && chmod 600 ~/.ssh/authorized_keys && rm -f /tmp/tmp.pub

RUN service ssh restart

#Expose ports for jupyter lab and mlflow tracking
EXPOSE 8888
EXPOSE 4040
EXPOSE 6006
EXPOSE 22

ENTRYPOINT ["bash"]

# Just keeping this as a command to run if jupyter needs to be invoked by default at the start of the container
#ENTRYPOINT ["jupyter", "lab","--ip=0.0.0.0","--allow-root"]