Ubuntu EE GPU Installation With Tarball

Note MapD has been rebranded to OmniSci.

This is an end-to-end recipe for installing OmniSci Enterprise Edition on an Ubuntu machine running with NVIDIA Kepler or Pascal series GPU cards. This install has all of the functionality of OmniSci.

Here is a quick video overview of the installation process.

The installation phases are:

Note: The order of these instructions is significant. Please install each component in the order presented to prevent aggravated hair loss.

Assumptions

These instructions assume the following:
  • You are installing on a “clean” Ubuntu host machine with only the operating system installed.
  • Your OmniSci host only runs the daemons and services required to support OmniSci.
  • Your OmniSci host is connected to the Internet.

Preparation

Prepare your Ubuntu machine by updating your system, creating the OmniSci user (named mapd), installing kernel headers, installing CUDA drivers, and enabling the firewall.

Update and Reboot

  1. Update the entire system: 
    sudo apt update
sudo apt upgrade
  2. Install a “headless” Java Runtime Environment: 
    sudo apt install default-jre-headless
  3. Verify that the apt-transport-https utility is installed: 
    sudo apt install apt-transport-https
  4. Reboot to activate the latest kernel: 
    sudo reboot

Create the OmniSci User

Create a group called mapd and a user named mapd, who will be the owner of the OmniSci database. You can create both the group and user with the useradd command and the -U switch.

sudo useradd -U mapd

Install CUDA Drivers

CUDA is a parallel computing platform and application programming interface (API) model. It uses a CUDA-enabled graphics processing unit (GPU) for general purpose processing. The CUDA platform provides direct access to the GPU virtual instruction set and parallel computation elements. For more information on CUDA unrelated to installing OmniSci, see http://www.nvidia.com/object/cuda_home_new.html.

Install Kernel Headers

  1. Identify the Linux kernel you are using by issuing the uname -r command.
  2. Use the name of the kernel (4.15.0-1021-aws in the following code example) to install kernel headers and development packages: 
    sudo apt-get install linux-headers-4.15.0-1021-aws
  3. Reboot to ensure that the kernel is up to date: 
    sudo reboot

Install the Drivers

OmniSci requires only the CUDA drivers and not the entire CUDA package. To install the drivers:

  1. Go to https://developer.nvidia.com/cuda-downloads.
  2. Select the target platform by selecting the operating system (Linux), architecture (based on your environment), distribution (Ubuntu), version (based on your environment), and installer type (OmniSci recommends deb (network)).

    CUDA install

  3. In Download Installer..., right-click the Download button and copy the link location of the Base Installer. Do not use the installation instructions on the CUDA site.

    CUDA base installer

  4. Use one of the following methods to download the installer from the command line, using the download link you copied (https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb, in this example):
    • curl: 
      sudo curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb
    • wget: 
      sudo wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb

      If wget is not installed in your environment, use sudo apt install wget to install it.

  5. Install the CUDA drivers: 
    sudo dpkg -i cuda-repo-ubuntu1804_10.0.130-1_amd64.deb
  6. If you do not have the public CUDA GPG key installed, run the installation command provided by NVIDIA; for example: 
    sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
  7. Update the local repository cache: 
    sudo apt update
  8. Install the CUDA Toolkit and GPU drivers: 
    sudo apt install cuda-drivers linux-image-extra-virtual
  9. Reboot your system to ensure that all changes are active: 
    sudo reboot

Checkpoint

  1. Run nvidia-smi to verify that your drivers are installed correctly and recognize the GPUs in your environment. Depending on your environment, you should see something like this to verify that your NVIDIA GPUs and drivers are present:

    NVIDIA SMI

  2. If you see an error like the following, the NVIDIA drivers are probably installed incorrectly: 
    NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running
    Review the Install CUDA Drivers section and correct any errors.

Enable the Firewall

To use Immerse, you must prepare your host machine to accept HTTP connections. You can configure your firewall for external access.

sudo ufw disable
sudo ufw allow 9092/tcp
sudo ufw allow ssh
sudo ufw enable

For more information, see https://help.ubuntu.com/lts/serverguide/firewall.html.

Installation

You install the OmniSci application itself by expanding a TAR file.

  1. Download the desired version of OmniSci from the URL provided you by your Account Executive.
  2. Create your $MAPD_PATH directory. OmniSci recommends /opt/mapd.
  3. Expand the OmniSci archive file in the $MAPD_PATH directory with the following command: 
    sudo tar -xvf <file_name>.tar.gz

Configuration

Follow these steps to prepare your OmniSci environment.

Set Environment Variables

For convenience, you can update .bashrc with the required environment variables.

  1. Open a terminal window.
  2. Enter cd ~/ to go to your home directory.
  3. Open .bashrc in a text editor. For example, sudo gedit .bashrc.
  4. Edit the .bashrc file. Add the following export commands under “User specific aliases and functions.” 
    # User specific aliases and functions
export MAPD_USER=mapd
export MAPD_GROUP=mapd
export MAPD_STORAGE=/var/lib/mapd
export MAPD_PATH=/opt/mapd
export MAPD_LOG=/var/lib/mapd/data/mapd_log
  5. Save the .bashrc file.
  6. Open a new terminal window to use your changes.

The $MAPD_STORAGE directory must be dedicated to OmniSci: do not set it to a directory shared by other packages.

Initialization

This step initializes the database and prepares systemd commands for OmniSci.

  1. Run the systemd installer. This script requires sudo access. You might be prompted for a password. Accept the values provided (based on your environment variables) or make changes as needed. The script creates a data directory in $MAPD_STORAGE with the directories mapd_catalogs, mapd_data, and mapd_export. mapd_import and mapd_log directories are created when you insert data the first time. The mapd_log directory is the one of most interest to a OmniSci administrator.

    cd $MAPD_PATH/systemd
sudo ./install_mapd_systemd.sh

Activation

Start and use OmniSci Core and Immerse.

  1. Start OmniSci Core

    cd $MAPD_PATH
sudo systemctl start mapd_server
sudo systemctl start mapd_web_server

  2. Enable OmniSci Core to start when the system reboots.

    sudo systemctl enable mapd_server
sudo systemctl enable mapd_web_server

Checkpoint

To verify that everything is working correctly, load some sample data, perform a mapdql query, and generate a pointmap using Immerse.

  1. OmniSci ships with two sample datasets of airline flight information collected in 2008. To install the sample data, run the following command. 
    cd $MAPD_PATH
sudo ./insert_sample_data
  2. When prompted, choose whether to insert dataset 1 (7 million rows) or dataset 2 (10 thousand rows). 
    Enter dataset number to download, or 'q' to quit:
#     Dataset           Rows    Table Name          File Name
1)    Flights (2008)    7M      flights_2008_7M     flights_2008_7M.tar.gz
2)    Flights (2008)    10k     flights_2008_10k    flights_2008_10k.tar.gz
  3. Connect to OmniSci Core by entering the following command in a terminal on the host machine (default password is HyperInteractive): 
    $MAPD_PATH/bin/mapdql
password: ••••••••••••••••
  4. Enter a SQL query such as the following, based on dataset 2 above: 
    mapdql> SELECT origin_city AS "Origin", dest_city AS "Destination", AVG(airtime) AS
"Average Airtime" FROM flights_2008_10k WHERE distance < 175 GROUP BY origin_city,
dest_city;
Origin|Destination|Average Airtime
Austin|Houston|33.055556
Norfolk|Baltimore|36.071429
Ft. Myers|Orlando|28.666667
Orlando|Ft. Myers|32.583333
Houston|Austin|29.611111
Baltimore|Norfolk|31.714286
  5. Connect to Immerse using a web browser connected to your host machine on port 9092. For example, http://omnisci.mycompany.com:9092.
  6. Create a new dashboard and a Scatter Plot to verify that backend rendering is working.
    1. Click New Dashboard.
    2. Click Add Chart.
    3. Click SCATTER.
    4. Click Add Data Source.
    5. Choose the flights_2008_10k or flights_2008_7M table as the data source, depending on the dataset you selected for ingest.
    6. Click X Axis +Add Measure.
    7. Choose depdelay.
    8. Click Y Axis +Add Measure.
    9. Choose arrdelay.
    The resulting chart shows, unsurprisingly, that there is a correlation between departure delay and arrival delay. 4_firstScatterplot.png