CentOS/RHEL Enterprise Edition 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.

https://youtu.be/7wegyPJ6PlQyoutu.be

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 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

EPEL

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. For more information on CUDA unrelated to installing OmniSci, see https://developer.nvidia.com/cuda-zone.

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 per the instructions on the NVIDIA website (https://developer.nvidia.com/cuda-downloads).

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)).

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-1.1.97.0-1.el7.noarch.rpm

2. Install the rpm file

   sudo rpm --install vulkan-filesystem-1.1.97.0-1.el7.noarch.rpm

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

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

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/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.

Installation

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

[omnisci]
name='omnisci ee - cuda'
baseurl=https://releases.omnisci.com/ee/yum/stable/cuda
enabled=1
gpgcheck=1
repo_gpgcheck=0
gpgkey=https://releases.omnisci.com/GPG-KEY-mapd

Use yum to install OmniSci:

sudo yum 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 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.

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 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.

   cd $OMNISCI_PATH
   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):

   $OMNISCI_PATH/bin/omnisql
   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,
   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 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.