This is an end-to-end recipe for installing HEAVY.AI on a Red Hat Enterprise 8.x machine using CPU and GPU devices.
The order of these instructions is significant. To avoid problems, install each component in the order presented.
The same instructions can be used to install on RL / RHEL 9, which some minor modifications.
These instructions assume the following:
You are installing a "clean" Rocky Linux / RHEL 8 host machine with only the operating system.
Your HEAVY.AI host only runs the daemons and services required to support HEAVY.AI.
Your HEAVY.AI host is connected to the Internet.
Prepare your machine by updating your system and optionally enabling or configuring a firewall.
Update the entire system and reboot the system if needed.
Install the utilities needed to create HEAVY.AI repositories and download installation binaries.
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:
Create a group called heavyai and a user named heavyai, who will own HEAVY.AI software and data on the file system.
You can create the group, user, and home directory using the useradd command with the --user-group and --create-home switches:
Set a password for the user using the passwd command.
Log in with the newly created user.
There are two ways to install the heavy.ai software
DNF Installation To install software using DNF's package manager, you can utilize DNF's package management capabilities to search for and then install the desired software. This method provides a convenient and efficient way to manage software installations and dependencies on your system.
Tarball Installation Installing via a tarball involves obtaining a compressed archive file (tarball) from the software's official source or repository. After downloading the tarball, you would need to extract its contents and follow the installation instructions provided by the software developers. This method allows for manual installation and customization of the software.
Using the DNF package manager for installation is highly recommended due to its ability to handle dependencies and streamline the installation process, making it a preferred choice for many users.
If your system includes NVIDIA GPUs but the drivers are not installed, it is advisable to install them before proceeding with the suite installation.
See Install NVIDIA Drivers and Vulkan on Rocky Linux and RHEL for details.
Create a DNF repository depending on the edition (Enterprise, Free, or Open Source) and execution device (GPU or CPU) you will use.
Add the GPG-key to the newly added repository.
Use DNF to install the latest version of HEAVY.AI.
You can use the DNF package manager to list the available packages when installing a specific version of HEAVY.AI, such as when a multistep upgrade is necessary, or a specific version is needed for any other reason.
sudodnf --showduplicateslistheavyai
Select the version needed from the list (e.g. 7.0.0) and install using the command.
sudodnfinstallheavyai-7.0.0_20230501_be4f51b048-1.x86_64
Let's begin by creating the installation directory.
Download the archive and install the latest version of the software. The appropriate archive is downloaded based on the edition (Enterprise, Free, or Open Source) and the device used for runtime.
Follow these steps to configure your HEAVY.AI environment.
For your convenience, you can update .bashrc with these environment variables
Although this step is optional, you will find references to the HEAVYAI_BASE and HEAVYAI_PATH variables. These variables contain the paths where configuration, license, and data files are stored and the location of the software installation. It is strongly recommended that you set them up.
Run the script that will initialize the HEAVY.AI services and database storage located in the systemd folder.
Accept the default values provided or make changes as needed.
This step will take a few minutes if you are installing a CUDA-enabled version of the software because the shaders must be compiled.
The script creates a data directory in $HEAVYAI_BASE/storage (typically /var/lib/heavyai) with the directories catalogs, dataand log, which will contain the metadata, the data of the database tables, and the log files from Immerse's web server and the database.
The log folder is particularly important for database administrators. It contains data about the system's health, performance, and user activities.
The first step to activate the system is starting HeavyDB and the Web Server service that Heavy Immerse needs. ¹
Heavy Immerse is not available in the OS Edition.
Start the services and enable the automatic startup of the service at reboot and start the HEAVY.AI services.
If a firewall is not already installed and you want to harden your system, install and start firewalld.
To use Heavy Immerse or other third-party tools, you must prepare your host machine to accept incoming HTTP(S) connections. Configure your firewall for external access:
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.
If you are on Enterprise or Free Edition, you need to validate your HEAVY.AI instance with your license key. You can skip this section if you are using Open Source Edition. ²
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 Heavy Immerse using a web browser connected to your host machine on port 6273. For example, http://heavyai.mycompany.com:6273.
When prompted, paste your license key in the text box and click Apply.
Log into Heavy Immerse by entering the default username (admin) and password (HyperInteractive), and then click Connect.
The $HEAVYAI_BASE directory must be dedicated to HEAVYAI; do not set it to a directory shared by other packages.
To verify that everything is working, load some sample data, perform a heavysql query, and generate a Pointmap using Heavy Immerse. ¹
HEAVY.AI 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.
Connect to HeavyDB by entering the following command in a terminal on the host machine (default password is HyperInteractive):
anEnter a SQL query such as the following:
The results should be similar to the results below.
After installing Enterprise or Free Edition, check if Heavy Immerse is running as intended.
Connect to Heavy Immerse using a web browser connected to your host machine on port 6273. For example, http://heavyai.mycompany.com:6273.
Log into Heavy Immerse by entering the default username (admin) and password (HyperInteractive), and then click Connect.
Create a new dashboard and a Scatter Plot to verify that backend rendering is working.
Click New Dashboard.
Click Add Chart.
Click SCATTER.
Click Add Data Source.
Choose the flights_2008_10k table as the data source.
Click X Axis +Add Measure.
Choose depdelay.
Click Y Axis +Add Measure.
Choose arrdelay.
Click Size +Add Measure.
Choose airtime.
Click Color +Add Measure.
Choose dest_state.
The resulting chart clearly demonstrates that there is a direct correlation between departure delay and arrival delay. This insight can help in identifying areas for improvement and implementing strategies to minimize delays and enhance overall efficiency.
Create a new dashboard and a Table chart to verify that Heavy Immerse is working.
Click New Dashboard.
Click Add Chart.
Click Bubble.
Click Select Data Source.
Choose the flights_2008_10k table as the data sour
Click Add Dimension.
Choose carrier_name.
Click Add Measure.
Choose depdelay.
Click Add Measure.
Choose arrdelay.
Click Add Measure.
Choose #Records.
The resulting chart shows, unsurprisingly, that also the average departure delay is correlated to the average of arrival delay, while there is quite a difference between Carriers.
