CentOS/RHEL Enterprise/Free Editions GPU with Tarball

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This is an end-to-end recipe for installing HEAVY.AI Enterprise Edition on a CentOS/RHEL 7 machine running with NVIDIA Volta, Kepler, or Pascal series GPU cards using a tarball.
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 HEAVY.AI host only runs the daemons and services required to support HEAVY.AI.
Your HEAVY.AI host is connected to the Internet.
Preparation
Prepare your Centos/RHEL 7 machine by updating your system, installing JDK and EPEL, creating the HEAVY.AI user (named heavyai), installing kernel headers, installing CUDA drivers, and enabling a firewall.
Update and Reboot
Update the entire system and reboot to activate the latest kernel.
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sudo yum update
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sudo reboot
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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:
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sudo yum install java-1.8.0-openjdk-headless
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EPEL
Install the Extra Packages for Enterprise Linux (EPEL) repository.
For CentOS, use Yum to install the epel-release package.
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sudo yum install epel-release
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Use the following install command for RHEL.
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yum install https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
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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 HEAVY.AI User
Create a group called heavyai and a user named heavyai, who will be the owner of the HEAVY.AI database. You can create the group, user, and home directory using the useradd command with the -U and -m switches.
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sudo useradd -U -m heavyai
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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 HEAVY.AI, see https://developer.nvidia.com/cuda-zone.
Install Kernel Headers
Install kernel headers and development packages:
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sudo yum install kernel-devel-$(uname -r) kernel-headers-$(uname -r)
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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:
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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
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Install the CUDA Package
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 or higher).
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
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wget http://mirror.centos.org/centos/7/os/x86_64/Packages/vulkan-filesystem-1.1.97.0-1.el7.noarch.rpm
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2.Install the rpm file
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sudo rpm --install vulkan-filesystem-1.1.97.0-1.el7.noarch.rpm
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You might see a warning similar to the following:
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warning: cuda-repo-rhel7-10.0.130-1.x86_64.rpm: Header V3 RSA/SHA512 Signature, key ID 7fa2af80: NOKEY
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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:
If you see an error like the following, the NVIDIA drivers are probably installed incorrectly:
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NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. 
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Make sure that the latest NVIDIA driver is installed and running.
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Review the Install CUDA Drivers section and correct any errors.
Install Vulkan
For HEAVY.AI Release 5.7 and higher, rendering-enabled installs require the Vulkan API:
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sudo yum install vulkan
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For more information about enabling and troubleshooting Vulkan, see Vulkan Renderer.
Firewall
If it is not installed on your host machine, install firewalld.
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sudo yum install firewalld
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sudo systemctl start firewalld
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sudo systemctl enable firewalld
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sudo systemctl status firewalld
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To use Immerse, you must prepare your host machine to accept HTTP connections. You can configure your firewall for external access.
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sudo firewall-cmd --zone=public --add-port=6273-6274/tcp --permanent
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sudo firewall-cmd --reload
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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.
Installation
These instructions follow conventions of the HEAVY.AI Engineering team. By creating an heavyai-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.
Create the heavyai-installs Directory
Use the following command to create the /opt/heavyai-installs directory.
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sudo mkdir /opt/heavyai-installs
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Download the HEAVY.AI Archive File
You can download the HEAVY.AI archive file using curl, or wget.
To download the HEAVY.AI archive file with curl, use the following command.
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sudo curl https://releases.heavy.ai/ee/tar/heavyai-ee-latest-Linux-x86_64-render.tar.gz --output  /opt/heavy-installs/heavyai.tar.gz
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To download the HEAVY.AI TAR file with wget, use the following command.
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sudo wget https://releases.heavy.ai/ee/tar/heavyai-ee-latest-Linux-x86_64-render.tar.gz -O /opt/heavyai-installs/heavyai.tar.gz
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Expand the Archive File
1.Go to the /opt/heavyai-installs directory.
2.Expand the HEAVY.AI archive file with the following command:
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sudo tar -xvf heavyai.tar.gz
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3.The expanded directory name is long and complex, with information about the version and build date. For example, the HEAVY.AI 6.0.0 directory name is the following:
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heavyai-ee-6.0.0-20190827-0f29e432f1-Linux-x86_64-render
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Go to the /opt directory and create a symlink to heavyai, using the name of the expanded directory for the current release. For example, for HEAVY.AI 6.0.0 you use the following commands:
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cd /opt
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sudo ln -s /opt/heavyai-installs/heavyai-ee-6.0.0-20190827-0f29e432f1-Linux-x86_64-render heavyai
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Configuration
Follow these steps to prepare your HEAVY.AI 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.”
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# User specific aliases and functions
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export HEAVYAI_USER=heavyai
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export HEAVYAI_GROUP=heavyai
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export HEAVYAI_STORAGE=/var/lib/heavyai
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export HEAVYAI_PATH=/opt/heavyai
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export HEAVYAI_LOG=/var/lib/heavyai/data/heavyai_log
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5.Save the .bashrc file. For example, in vi, [esc]:x!
6.Open a new terminal window to use your changes.
The $HEAVYAI_STORAGE directory must be dedicated to HEAVY.AI. Do not set it to a directory shared by other packages.
Initialization
Run the systemd installer.
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cd $HEAVYAI_PATH/systemd
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./install_heavy_systemd.sh
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You are prompted for two paths during install: HEAVYAI_PATH and HEAVYAI_STORAGE. HEAVYAI_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/heavyai. HEAVYAI_STORAGE defaults to /var/lib/heavyai.
The script creates a data directory in $HEAVYAI_STORAGE with the directories heavyai_catalogs, heavyai_data, and heavyai_export. The heavyai_import and heavyai_log directories are created when you insert data the first time. If you are an HEAVY.AI administrator, the heavyai_log directory is of particular interest.
Activation
Start and use HeavyDB and Immerse.
1.Start HeavyDB
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sudo systemctl start heavydb
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sudo systemctl start heavy_web_server
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2.Enable HeavyDB to start automatically when the system reboots.
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sudo systemctl enable heavydb
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sudo systemctl enable heavy_web_server
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Enter Your License Key
Validate your HEAVY.AI 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://heavyai.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.
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Checkpoint
To verify that everything is working correctly, load some sample data, perform an heavysql query, and generate a pointmap using 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.
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cd $HEAVYAI_PATH
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sudo ./insert_sample_data
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2.When prompted, choose dataset 2 (10 thousand rows).
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Enter dataset number to download, or 'q' to quit:
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#     Dataset           Rows    Table Name          File Name
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1)    Flights (2008)    7M      flights_2008_7M     flights_2008_7M.tar.gz
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2)    Flights (2008)    10k     flights_2008_10k    flights_2008_10k.tar.gz
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3)    NYC Tree Census (2015)    683k    nyc_trees_2015_683k    nyc_trees_2015_683k.tar.gz
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3.Connect to HeavyDB by entering the following command in a terminal on the host machine (default password is HyperInteractive):
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$HEAVYAI_PATH/bin/heavysql
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password: ••••••••••••••••
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4.Enter a SQL query such as the following, based on dataset 2 above:
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heavysql> SELECT origin_city AS "Origin", dest_city AS "Destination", AVG(airtime) AS
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"Average Airtime" FROM flights_2008_10k WHERE distance < 175 GROUP BY origin_city,
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dest_city;
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Origin|Destination|Average Airtime
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Austin|Houston|33.055556
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Norfolk|Baltimore|36.071429
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Ft. Myers|Orlando|28.666667
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Orlando|Ft. Myers|32.583333
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Houston|Austin|29.611111
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Baltimore|Norfolk|31.714286
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5.Connect to Heavy Immerse using a web browser connected to your host machine on port 6273. For example, http://heavyai.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 Select 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.
CentOS/RHEL Enterprise/Free Editions CPU with Tarball
CentOS/RHEL Open Source CPU with Tarball
Last modified 1mo ago