datmo/mxnet

ai-docker-files - https://hub.docker.com/u/datmo/

The aim of this repository is to have one place location to find Dockerfiles for most AI frameworks and tools. The docker images are pushed to public dockerhub/datmo repository. This is used along with datmo workflow https://github.com/datmo/datmo

Structure

1. Base image: There are base image for CPU and GPU, followed by com***ation of py2.7 and py3.5

   a. datmo/python-base:py35-cpu
   b. datmo/python-base:py35-gpu
   c. datmo/python-base:py27-cpu
   d. datmo/python-base:py27-gpu

2. Environments: There are two ways in creating an environment,

   a. Using the above base image, environments are created. (e.g. datmo/keras-tensorflow:py27-cpu). This is reliable since these are by datmo team.
   b. Using any dockerhub image, (e.g. kaggle/python:latest)

3. Using environments as the base image, workspaces are added. Currently, there are three workspace. a. Jupyter Notebook b. Jupyterlab c. RStudio

How to Build a new Environment?

There are two approaches in creating an environment.

1. Using the base images provided by datmo. These are following images,

   i. datmo/python-base:py35-cpu
   ii. datmo/python-base:py35-gpu
   iii. datmo/python-base:py27-cpu
   iv. datmo/python-base:py27-gpu

You can now create new installation as follows,

# the tag z is dependent on py27, py35 and cpu, gpu. one of the above images
FROM datmo/python-base:z    

# To install a python package
RUN pip install <python-package-name>

# To install apt-get package
RUN apt-get install <package-name>

This is a reliable image for ubuntu OS since these are maintained by datmo team. We also accepts PRs for any new environments.

2.Using any dockerhub image, (e.g. kaggle/python:latest) You can create new environment over this as the base image with new installation as follows. In order to install any other packages over this base image, you can use apt-get or pip package manager for ubuntu, apk for alpine images, yum for CentOS. These options are not completely exhasutive and you can use other package manager based on your base image.

# an example for the base docker images is x/y:z, eg: kaggle/python:latest
FROM x/y:z      

How to plugin workspaces to your own Environment?

1. You can add Jupyter notebook to an environment from option 2 (above) along with files in workspace-patches, by adding the following code to your base environment docker image, Please make sure, you have pip and apt-get package manager installed on the base image.

    # Jupyter
    RUN pip --no-cache-dir install \
            ipykernel \
            jupyter \
            && \
        python -m ipykernel.kernelspec
    
    # Set up our notebook config.
    COPY jupyter_notebook_config_py2.py /root/.jupyter/
    RUN mv /root/.jupyter/jupyter_notebook_config_py2.py /root/.jupyter/jupyter_notebook_config.py
    
    # 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 /
    RUN chmod +x /run_jupyter.sh
    
    # IPython
    EXPOSE 8888
   

2. You can add Jupyterlab to an environment along with files in workspace-patches, by adding the following code to your base environment docker image,

    # Jupyter
    RUN pip --no-cache-dir install \
            ipykernel \
            jupyter \
            && \
        python -m ipykernel.kernelspec
    
    # Set up our notebook config.
    COPY jupyter_notebook_config_py2.py /root/.jupyter/
    RUN mv /root/.jupyter/jupyter_notebook_config_py2.py /root/.jupyter/jupyter_notebook_config.py
    
    # 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 /
    RUN chmod +x /run_jupyter.sh
    
    # Jupyter lab
    RUN pip install jupyterlab==0.32.1
    
    # IPython
    EXPOSE 8888
   

3. You can add RStudio to an environment along with files in workspace-patches, by adding the following code to your base or environment docker image,

    # Rstudio
    ENV DEBIAN_FRONTEND noninteractive
    ENV CRAN_URL https://cloud.r-project.org/
    
    RUN set -e \
          && ln -sf /***/bash /***/sh
    
    RUN set -e \
          && apt-get -y update \
          && apt-get -y dist-upgrade \
          && apt-get -y install apt-transport-https gdebi-core libapparmor1 libcurl4-openssl-dev \
                                libssl-dev libxml2-dev pandoc r-base \
          && apt-get -y autoremove \
          && apt-get clean
    
    RUN set -e \
          && R -e "\
          update.packages(ask = FALSE, repos = '${CRAN_URL}'); \
          pkgs <- c('dbplyr', 'devtools', 'docopt', 'doParallel', 'foreach', 'gridExtra', 'rmarkdown', 'tidyverse'); \
          install.packages(pkgs = pkgs, dependencies = TRUE, repos = '${CRAN_URL}'); \
          sapply(pkgs, require, character.only = TRUE);"
    
    RUN set -e \
          && curl -sS https://s3.amazonaws.com/rstudio-server/current.ver \
            | xargs -I {} curl -sS [***]{}-amd64.deb -o /tmp/rstudio.deb \
          && gdebi -n /tmp/rstudio.deb \
          && rm -rf /tmp/rstudio.deb
    
    RUN set -e \
          && useradd -m -d /home rstudio \
          && echo rstudio:rstudio \
            | chpasswd
    
    # expose for rstudio
    EXPOSE 8787
   

Reference

• Nvidia docker images
  • https://hub.docker.com/r/nvidia/cuda/
  • https://github.com/NVIDIA/nvidia-docker/wiki/Third-party
  • https://devblogs.nvidia.com/parallelforall/nvidia-docker-gpu-server-application-deployment-made-easy/
• Tensorflow
  • https://www.tensorflow.org/install/install_linux
  • https://github.com/floydhub/dockerfiles
  • https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/docker
• Keras
  • https://keras.io/#installation
• Caffe
  • https://github.com/BVLC/caffe/tree/master/docker
• Pytorch
  • https://github.com/pytorch/pytorch/blob/master/Dockerfile
• Spacy
  • [***]
• XGBoost
  • This Dockerfile is for libraries such as sklearn, pandas, scipy and xgboost
  • https://xgboost.readthedocs.io/en/latest/
• kaggle
  • Gives the environment of running projects in kaggle environment along with jupyter notebook capability