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CentOS/RHEL Enterprise/Free Editions GPU with Yum

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 Yum.

Here is a short video overview of the installation process.

The order of these instructions is significant. To avoid problems, install each component in the order presented.


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.


Prepare your Centos/RHEL machine by updating your system, installing EPEL, creating the OmniSci user (named omnisci), 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


Install the Extra Packages for Enterprise Linux (EPEL) repository.

For CentOS, use Yum to install the epel-release package.

sudo yum install epel-release

Use the following install command for RHEL.

yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm

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.

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.

Install Kernel Headers

Install kernel headers and development packages:

sudo yum install kernel-devel-$(uname -r) kernel-headers-$(uname -r)

If installing 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

Install CUDA

Install the CUDA package for your platform and operating system according to the instructions on the NVIDIA website (https://developer.nvidia.com/cuda-downloads).

  1. Select the Product Type as Data center / Tesla.

  2. Select the correct Product Series and Product Type for your installation.

  3. In the Operating System dropdown list, select Linux 64-bit.

  4. In the CUDA Toolkit dropdown list, click a supported version (11.0).

  5. Click Search.

  6. On the resulting page, verify the download information and click Download.

If installing on RHEL, you need to obtain and install the vulkan-filesystem package manually. Perform these additional steps:

  1. Download the rpm file:

    wget http://mirror.centos.org/centos/7/os/x86_64/Packages/vulkan-filesystem-
  2. Install the rpm file:

    sudo rpm --install vulkan-filesystem-

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.


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:

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.


If it is not installed on your host machine, install firewalld.

sudo yum install firewalld
sudo systemctl start firewalld
sudo systemctl enable firewalld
sudo systemctl status firewalld

To use Immerse, you must prepare your host machine to accept HTTP connections. Configure your firewall for external access:

sudo firewall-cmd --zone=public --add-port=6273-6274/tcp --permanent
sudo firewall-cmd --reload

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

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


Create a repo file at /etc/yum.repos.d/omnisci.repo with the OmniSci repository specification:

name='omnisci ee - cuda'

Use yum to install OmniSci:

sudo yum install omnisci


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 enter[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.


Run the systemd installer.

cd $OMNISCI_PATH/systemd

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.


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. Log into Immerse by entering the default username (admin) and password (HyperInteractive), and then clicking Connect.



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

  1. OmniSci ships with two sample datasets of airline flight information collected in 2008, and a census of New York City trees. To install the sample data, run the following command.

    sudo ./insert_sample_data
  2. When prompted, choose dataset 2.

    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):

    password: ••••••••••••••••
  4. Enter a SQL query such as the following, based on dataset 2 above:

    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,
    Origin|Destination|Average Airtime
    Ft. Myers|Orlando|28.666667
    Orlando|Ft. Myers|32.583333
  5. Connect to Immerse using a web browser connected to your host machine on port 6273. For example, http://omnisci.mycompany.com:6273.

  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 table as the data source.

    6. Click X Axis +Add Measure.

    7. Choose depdelay.

    8. Click Y Axis +Add Measure.

    9. Choose arrdelay.

  7. The resulting chart shows, unsurprisingly, that there is a correlation between departure delay and arrival delay.