Create a Dockerfile for the Custom Image

First step is to select an appropriate source image for your customization. Image tags can be seen on the Session Start page on the user interface when you select a Runtime. The second step when building a customized image is to create a Dockerfile that specifies which packages you would like to install in addition to the base image.

For example, the following Dockerfile installs the telnet package, the sklearn Python package and upgraded base packages on top of an ML Runtime image released by Cloudera.

# Dockerfile
# Specify an ML Runtime base image
FROM docker.repository.cloudera.com/cloudera/cdsw/ml-runtime-jupyterlab-python3.7-standard:2021.12.1-b17
# Install telnet in the new image
RUN apt-get update && apt-get install -y --no-install-recommends telnet && apt-get clean && rm -rf /var/lib/apt/lists/*
# Upgrade packages in the base image
RUN apt-get update && apt-get upgrade -y && apt-get clean && rm -rf /var/lib/apt/lists/*
# Install the python package sklearn
RUN pip install --no-cache-dir sklearn
# Override Runtime label and environment variables metadata
ENV ML_RUNTIME_EDITION="Telnet Edition" \
       	ML_RUNTIME_SHORT_VERSION="1.0" \
        ML_RUNTIME_MAINTENANCE_VERSION=1 \
        ML_RUNTIME_DESCRIPTION="This runtime includes telnet and sklearn and upgraded packages"
ENV ML_RUNTIME_FULL_VERSION="${ML_RUNTIME_SHORT_VERSION}.${ML_RUNTIME_MAINTENANCE_VERSION}"
LABEL com.cloudera.ml.runtime.edition=$ML_RUNTIME_EDITION \
        com.cloudera.ml.runtime.full-version=$ML_RUNTIME_FULL_VERSION \
        com.cloudera.ml.runtime.short-version=$ML_RUNTIME_SHORT_VERSION \
        com.cloudera.ml.runtime.maintenance-version=$ML_RUNTIME_MAINTENANCE_VERSION \
        com.cloudera.ml.runtime.description=$ML_RUNTIME_DESCRIPTION