Configure a Browser IDE at the Engine Level

You can make a browser IDE available to any project within a Cloudera Data Science Workbench deployment by creating a customized engine image, installing the editor to it, and whitelisting it for a project. Additionally, browser IDEs that require root permission to install, such as RStudio, can only be used as part of a customized engine image.

When a user launches a session, they can select the customized engine with the editors available. The following steps describe how to make a customized engine image for RStudio:

  1. Create a Dockerfile for the new custom image. Note that the Base Engine Image uses Ubuntu, and you must use Base Engine Image v9 or higher.
    The following sample Dockerfile is for RStudio:
    #Dockerfile
    
    FROM docker.repository.cloudera.com/cdsw/engine:9-cml1.1
    
    WORKDIR /tmp
    
    #The RUN commands that install an editor
    #For example: RUN apt-get install myeditor
    
    RUN apt-get update && apt-get dist-upgrade -y && \
        apt-get install -y --no-install-recommends \
        libapparmor1 \
        libclang-dev \
        lsb-release \
        psmisc \
        sudo
    
    #The command that follows RUN is the same command you used to install the IDE to test it in a the session.
    RUN wget https://download2.rstudio.org/server/trusty/amd64/rstudio-server-1.2.1335-amd64.deb && \
        dpkg -i rstudio-server-1.2.1335-amd64.deb
    
    COPY rserver.conf /etc/rstudio/rserver.conf
    
    COPY rstudio-cdsw /usr/local/bin/rstudio-cdsw
    
    RUN chmod +x /usr/local/bin/rstudio-cdsw
  2. Create rserver.conf:
    # Must match CDSW_APP_PORT
    www-port=8090
    server-app-armor-enabled=0
    server-daemonize=0
    www-address=127.0.0.1
    auth-none=1
    auth-validate-users=0
    Make sure that the www-port property matches the port set in the CDSW_APP_PORT environment variable (default 8090).
  3. Create rstudio-cdsw:
    #!/bin/bash
    
    # This saves RStudio's user runtime information to /tmp, which ensures several
    # RStudio sessions can run in the same project simultaneously
    mkdir -p /tmp/rstudio/sessions/active
    mkdir -p /home/cdsw/.rstudio/sessions
    if [ -d /home/cdsw/.rstudio/sessions/active ]; then rm -rf /home/cdsw/.rstudio/sessions/active; fi
    ln -s /tmp/rstudio/sessions/active /home/cdsw/.rstudio/sessions/active
    
    # This ensures RStudio picks up the environment. This may not be necessary if
    # you are installing RStudio Professional. See
    # https://docs.rstudio.com/ide/server-pro/r-sessions.html#customizing-session-launches.
    # SPARK_DIST_CLASSPATH is treated as a special case to workaround a bug in R
    # with very long environment variables.
    env | grep -v ^SPARK_DIST_CLASSPATH >> /usr/local/lib/R/etc/Renviron.site
    echo "Sys.setenv(\"SPARK_DIST_CLASSPATH\"=\"${SPARK_DIST_CLASSPATH}\")" >> /usr/local/lib/R/etc/Rprofile.site
    
    # Now start RStudio
    /usr/sbin/rstudio-server start
  4. Build the Dockerfile:
    docker build -t <image-name>:<tag> . -f Dockerfile
    If you want to build your image on a Cloudera Machine Learning workspace, you must add the --network=host option to the build command:
    docker build --network=host -t <image-name>:<tag> . -f Dockerfile
  5. Distribute the image:
    • Push the image to a public registry such as DockerHub.

      For instructions, refer the Docker documentation.

    • Push the image to your company's Docker registry.

      When using this method, make sure to tag your image with the following schema:

      docker tag <image-name> <company-registry>/<user-name>/<image-name>:<tag>

      Once the image has been tagged properly, use the following command to push the image:

      docker push <company-registry>/<user-name>/<image-name>:<tag>
  6. Whitelist the image in Cloudera Machine Learning:
    1. Log in to the Cloudera Machine Learning web UI as a site administrator.
    2. Click Admin > Engines.
    3. Add <company-registry>/<user-name>/<image-name>:<tag> to the list of whitelisted engine images.
  7. Whitelist the new engine for a project:
    1. Go to the project Settings page.
    2. Click Engines.
    3. Select the new customized engine from the dropdown list of available Docker images. Sessions and jobs you run in your project will have access to this engine.
  8. Configure RStudio for the project. When this is done, you will be able to select RStudio from the dropdown list of editors on the Launch New Sesssion page.
    1. Go to Settings > Editors and click New Editor.
    2. Complete the fields:
      • Name: Provide a name for the editor. For example, RStudio. This is the name that appears in the dropdown menu for Editors when you start a new session.
      • Command: Enter the command to start the server for the editor.

        For example, the following command will start RStudio:

        /usr/local/bin/rstudio-cdsw
    3. Save the changes.