Connecting MapR Data Science Refinery to MapR Sandbox

To connect MapR Data Science Refinery to MapR Sandbox, install MapR Sandbox, configure resource settings in your virtual machine (if needed), and then set the parameters corresponding to your virtual machine environment in your docker run command.

  1. Install the sandbox using a virtual machine player
    1. You can use one of the following virtual machine players:
    2. If you are installing VirtualBox, you must configure a second network adapter using either Host-only Adapter or Bridged Adapter. For the VMware players, you can use the default network configuration.
    3. You may also need to increase the default processor and memory configurations, depending on the workload you plan to run.
  2. Determine the parameters you want to pass to Docker:
    1. See Understanding Zeppelin Docker Parameters to determine your initial parameters.
    2. Then determine the value of parameters that are specific to connecting to MapR Sandbox:
      Parameter Name Parameter Value
      MAPR_CLDB_HOSTS IP address of your virtual machine
      MAPR_HS_HOST IP address of your virtual machine
      HOST_IP

      Set this variable based on the network adapter and virtual machine player you are using:

      VirtualBox VMware
      Host-only Adapter
      IP address of the gateway interface to the virtual machine's internal network
      Bridged Adapter
      Output from the following command:
      ip r get 8.8.8.8 | awk 'NR==1 {print $NF}'
      Output from the following command:
      ip r get 8.8.8.8 | awk 'NR==1 {print $NF}'
      --add-host "<hostname>":<IP address> of your virtual machine
  3. Construct your docker run command based on Step 3.

    The following are examples of docker run commands that use VirtualBox with the different network adapters. Note the parameters highlighted in bold:

    docker run -it -p 9995:9995 \
        -e HOST_IP=192.168.192.1  \
          -p 10000-10010:10000-10010 \
          -p 11000-11010:11000-11010 \
        -e MAPR_CLUSTER=demo.mapr.com \
          -e MAPR_CLDB_HOSTS=192.168.192.100 \
        -e MAPR_CONTAINER_USER=mapr \
          -e MAPR_CONTAINER_PASSWORD=mapr \
          -e MAPR_CONTAINER_GROUP=mapr \
          -e MAPR_CONTAINER_UID=2000 \
          -e MAPR_CONTAINER_GID=2000 \
        -e MAPR_MOUNT_PATH=/mapr \
          --cap-add SYS_ADMIN \
          --cap-add SYS_RESOURCE \
          --device /dev/fuse \
        -e MAPR_HS_HOST=192.168.192.100 \
        --add-host="maprdemo:192.168.192.100" \
        --add-host="maprdemo.local:192.168.192.100" \
        maprtech/data-science-refinery:v1.2_6.0.1_5.0.0_centos7
    docker run -it -p 9995:9995 \
        -e HOST_IP=$(ip r get 8.8.8.8 | awk 'NR==1 {print $NF}')  \
          -p 10000-10010:10000-10010 \
          -p 11000-11010:11000-11010 \
        -e MAPR_CLUSTER=demo.mapr.com \
          -e MAPR_CLDB_HOSTS=10.2.13.163 \
        -e MAPR_CONTAINER_USER=mapr \
          -e MAPR_CONTAINER_PASSWORD=mapr \
          -e MAPR_CONTAINER_GROUP=mapr \
          -e MAPR_CONTAINER_UID=2000 \
          -e MAPR_CONTAINER_GID=2000 \
        -e MAPR_MOUNT_PATH=/mapr \
          --cap-add SYS_ADMIN \
          --cap-add SYS_RESOURCE \
          --device /dev/fuse \
        -e MAPR_HS_HOST=10.2.13.163 \
        --add-host="maprdemo:10.2.13.163" \
        --add-host="maprdemo.local:10.2.13.163" \
        maprtech/data-science-refinery:v1.2_6.0.1_5.0.0_centos7

After following these steps, you can refer to other MapR Data Science Refinery topics that describe how to use the resulting container.

If you encounter resource errors or hangs, you may need to increase the processor and memory configuration settings in your virtual machine player.

Important: Also, due to resource constraints, you cannot run both the Livy and Spark interpreters.