Ubuntu EE GPU Installation With Apt

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 Volta, Kepler, or Pascal series GPU cards. This install has all of the functionality of OmniSci.

Here is a quick video overview of the installation steps.

The installation phases are:
Important The order of these instructions is significant. To avoid problems, install each component in the order presented.

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 omnisci), 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 omnisci and a user named omnisci, who will be the owner of the OmniSci database. You can create the group, user, and home directory using the useradd command with the -U and -m switches.

sudo useradd -U -m omnisci

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 https://developer.nvidia.com/cuda-zone.

Install Kernel Headers

Install kernel headers with the following command: 

sudo apt-get install linux-headers-$(uname -r)

Install CUDA

To install the CUDA package:

  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)).
  3. Install CUDA per the instructions on the NVIDIA web site.

Checkpoint

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

Note 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 6273/tcp
sudo ufw allow ssh
sudo ufw enable

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

Note Most cloud providers provide a different mechanism for handling firewall configuration. The commands above might not run in cloud deployments.

Installation

To install OmniSci:

  1. Download and add a GPG key to apt: 
    curl https://releases.omnisci.com/GPG-KEY-omnisci | sudo apt-key add -
  2. Download the OmniSci list file: 
    echo "deb https://releases.omnisci.com/ee/apt/ stable cuda" | sudo tee /etc/apt/sources.list.d/omnisci.list
  3. Use update to locate the new installation options, and then install OmniSci: 
    sudo apt update
sudo apt install omnisci

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, vi .bashrc.
  4. Edit the .bashrc file. Add the following export commands under “User specific aliases and functions.” 
    # User specific aliases and functions
export OMNISCI_USER=omnisci
export OMNISCI_GROUP=omnisci
export OMNISCI_STORAGE=/var/lib/omnisci
export OMNISCI_PATH=/opt/omnisci
export OMNISCI_LOG=/var/lib/omnisci/data/mapd_log
  5. Save the .bashrc file. For example, in vi, [esc]:x!.
  6. Open a new terminal window to use your changes.

The $OMNISCI_STORAGE directory must be dedicated to OmniSci. Do not set it to a directory shared by other packages.

Initialization

Run the systemd installer. 

cd $OMNISCI_PATH/systemd
./install_omnisci_systemd.sh

Accept the values provided (based on your environment variables) or make changes as needed. The script creates a data directory in $OMNISCI_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. If you are an OmniSci administrator, the mapd_log directory is of particular interest.

Activation

Start and use OmniSciDB and Immerse.

  1. Start OmniSciDB

    sudo systemctl start omnisci_server
sudo systemctl start omnisci_web_server

  2. Enable OmniSciDB to start automatically when the system reboots.

    sudo systemctl enable omnisci_server
sudo systemctl enable omnisci_web_server

Enter Your License Key

Validate your OmniSci instance with your license key.

  1. Copy your license key from the registration email message.
    If you have not received your license key, contact your Sales Representative or register for your 30-day trial here.
  2. Connect to Immerse using a web browser connected to your host machine on port 6273. For example, http://omnisci.mycompany.com:6273.
  3. When prompted, paste your license key in the text box and click Apply.
  4. Click Connect to start using OmniSci.

Checkpoint

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

OmniSci ships with two sample datasets of airline flight information collected in 2008, and a census of New York City trees from 2015.

  1. To install sample data, run the following command. 
    cd $OMNISCI_PATH
sudo ./insert_sample_data
  2. When prompted, enter 2 to insert the 10 thousand row flights dataset. 
    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)    NYC Tree Census (2015)    683k    nyc_trees_2015_683k    nyc_trees_2015_683k.tar.gz
  3. Connect to OmniSciDB by entering the following command in a terminal on the host machine (default password is HyperInteractive): 
    $OMNISCI_PATH/bin/omnisql
password: ••••••••••••••••
  4. Enter a SQL query such as the following: 
    omnisql> 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;

    The results should be similar to the results below.

    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

Connect to Immerse using a web browser connected to your host machine on port 6273. For example, http://omnisci.mycompany.com:6273.

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 Select Data Source.
  5. Choose the flights_2008_10k table as the data source.
  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