CentOS/RHEL Enterprise Edition GPU with Tarball
Last updated
Last updated
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 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 7 machine by updating your system, installing JDK and EPEL, creating the OmniSci user (named omnisci), installing kernel headers, installing CUDA drivers, and enabling a firewall.
Update the entire system and reboot to activate the latest kernel.
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.
Open a terminal on the host machine.
Install the headless JDK using the following command:
Install the Extra Packages for Enterprise Linux (EPEL) repository.
For CentOS, use Yum to install the epel-release package.
Use the following install command for RHEL.
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 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.
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 and development packages:
If installing kernel headers does not work correctly, follow these steps instead:
Identify the Linux kernel you are using by issuing the uname -r command.
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:
To install the CUDA package:
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)).
Install CUDA per the instructions on the NVIDIA website.
If installing on RHEL, you need to obtain and install the vulkan-filesystem package manually. Perform these additional steps:
Download the rpm file
Install the rpm file
You might see a warning similar to the following:
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:
Review the Install CUDA Drivers section and correct any errors.
If it is not installed on your host machine, install firewalld.
To use Immerse, you must prepare your host machine to accept HTTP connections. You can configure your firewall for external access.
For more information, see https://fedoraproject.org/wiki/Firewalld?rd=FirewallD.
Most cloud providers provide a different mechanism for handling firewall configuration. The commands above might not run in cloud deployments.
These instructions follow conventions of the OmniSci Engineering team. By creating an omnisci-installs directory and using a symbolic link that points to the current version, you can conveniently roll back to a previous version in the unlikely event that you would want to do so.
Use the following command to create the /opt/omnisci-installs directory.
You can download the OmniSci archive file using curl, or wget.
To download the OmniSci archive file with curl, use the following command.
To download the OmniSci TAR file with wget, use the following command.
Go to the /opt/omnisci-installs directory.
Expand the OmniSci archive file with the following command:
The expanded directory name is long and complex, with information about the version and build date. For example, the OmniSci 4.8.1 directory name is the following:
Go to the /opt directory and create a symlink to omnisci, using the name of the expanded directory for the current release. For example, for OmniSci 4.8.1, you use the following commands:
Follow these steps to prepare your OmniSci environment.
For convenience, you can update .bashrc with the required environment variables.
Open a terminal window.
Enter cd ~/ to go to your home directory.
Open .bashrc in a text editor. For example, vi .bashrc.
Edit the .bashrc file. Add the following export commands under “User specific aliases and functions.”
Save the .bashrc file. For example, in vi, [esc]:x!
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.
You are prompted for two paths during install: OMNISCI_PATH and OMNISCI_STORAGE. OMNISCI_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/omnisci. OMNISCI_STORAGE defaults to /var/lib/omnisci.
The script creates a data directory in $OMNISCI_STORAGE with the directories mapd_catalogs, mapd_data, and mapd_export. The 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.
Start OmniSciDB
Enable OmniSciDB to start automatically when the system reboots.
Validate your OmniSci instance with your license key.
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.
Connect to Immerse using a web browser connected to your host machine on port 6273. For example, http://omnisci.mycompany.com:6273.
When prompted, paste your license key in the text box and click Apply.
Click Connect to start using OmniSci.
To verify that everything is working correctly, 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. To install sample data, run the following command.
When prompted, choose dataset 2 (10 thousand rows).
Connect to OmniSciDB by entering the following command in a terminal on the host machine (default password is HyperInteractive):
Enter a SQL query such as the following, based on dataset 2 above:
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.
Click New Dashboard.
Click Add Chart.
Click SCATTER.
Click Select Data Source.
Choose the flights_2008_10k or flights_2008_7M table as the data source, depending on the dataset you selected for ingest.
Click X Axis +Add Measure.
Choose depdelay.
Click Y Axis +Add Measure.
Choose arrdelay.
The resulting chart shows, unsurprisingly, that there is a correlation between departure delay and arrival delay.
