CentOS/RHEL 7 EE GPU Install With Tarball

Note MapD has been rebranded to OmniSci.

This is an end-to-end recipe for installing OmniSci Enterprise Edition on a CentOS/RHEL 7 machine running with NVIDIA Volta, Kepler, or Pascal series GPU cards using a tarball.

Here is a quick video overview of the installation process.

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” CentOS/RHEL 7 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 Centos/RHEL 7 machine by updating your system, installing JDK and EPEL, creating the OmniSci user (named mapd), installing kernel headers, installing CUDA drivers, and enabling a firewall.

Update and Reboot

Update the entire system and reboot to activate the latest kernel.

sudo yum update
sudo reboot

JDK

Follow these instructions to install a headless JDK and configure an environment variable with a path to the library. The “headless” Java Development Kit does not provide support for keyboard, mouse, or display systems. It has fewer dependencies and is best suited for a server host. For more information, see https://openjdk.java.net.

  1. Open a terminal on the host machine.
  2. Install the headless JDK using the following command:
    sudo yum install java-1.8.0-openjdk-headless

EPEL

Install the Extra Packages for Enterprise Linux (EPEL) repository. RHEL-based distributions require Dynamic Kernel Module Support (DKMS) to build the GPU driver kernel modules. For more information, see https://fedoraproject.org/wiki/EPEL.

Use Yum to install the epel-release package.

sudo yum install epel-release

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. Install kernel headers and development packages:
    sudo yum install kernel-devel-$(uname -r) kernel-headers-$(uname -r)
  2. Reboot your system to ensure that the kernel is up to date:
    sudo reboot
Important If this procedure to install kernel headers does not work correctly, follow these steps instead:
  1. Identify the Linux kernel you are using by issuing the uname -r command.
  2. Use the name of the kernel (3.10.0-862.11.6.el7.x86_64 in the following code example) to install kernel headers and development packages:
    sudo yum install kernel-devel-3.10.0-862.11.6.el7.x86_64 kernel-headers-3.10.0-862.11.6.el7.x86_64
  3. Reboot your system 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 (CentOS or RHEL), version (7), and installer type (OmniSci recommends rpm (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 (in this example, https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-repo-rhel7-10.0.130-1.x86_64.rpm):
    • curl:
      curl -O https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-repo-rhel7-10.0.130-1.x86_64.rpm
    • wget:
      wget https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-repo-rhel7-10.0.130-1.x86_64.rpm
  5. Install the CUDA drivers using the filename you just downloaded (cuda-repo-rhel7-10.0.130-1.x86_64.rpm in the previous step):
    sudo rpm --install <file_name>
    sudo yum clean expire-cache
    sudo yum install cuda-drivers
  6. Reboot your system to ensure that all changes are active.
    sudo reboot
    
Note You might see a warning similar to the following:
warning: cuda-repo-rhel7-10.0.130-1.x86_64.rpm: Header V3 RSA/SHA512 Signature, key ID 7fa2af80: NOKEY
Ignore it for now; you can verify CUDA driver installation at the Checkpoint.

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.

Firewall

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

sudo firewall-cmd --zone=public --add-port=9092/tcp --permanent
sudo firewall-cmd --reload

For more information, see https://fedoraproject.org/wiki/Firewalld?rd=FirewallD.

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

Installation

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

  1. Create an installs directory in your home folder:
    cd ~
    sudo mkdir installs
    cd installs
  2. Download the desired version of OmniSci from the URL provided you by your Account Executive.
  3. Expand the OmniSci archive file in the installs directory with the following command: 
    sudo tar -xvf <file_name>.tar.gz
  4. List the contents of the installs directory and copy the name of the directory created when expanding the archive. For example:
    mapd-ee-4.3.0-20181119-b7f85d00bd-Linux-x86_64-render
  5. Go to the opt folder and create a symbolic link to the directory you just copied:
    cd /opt
    ln -s ~/installs/<onmisci_directory> mapd

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

Run the systemd installer. This script requires sudo access. You might be prompted for a password.

cd $MAPD_PATH/systemd
sudo ./install_mapd_systemd.sh

You are prompted for two paths during install: MAPD_PATH and MAPD_STORAGE. MAPD_PATH must be the same as the location of the symbolic link you created in step 5 of the installation process and the environment variable you just created. In a standard installation, that path is /opt/mapd. MAPD_STORAGE defaults to /var/lib/mapd.

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 of particular interest to OmniSci administrators.

Activation

Start and use OmniSci Core and Immerse.

  1. Start OmniSci Core

    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