CentOS 7 EE GPU Install With Tarball¶
This is an end-to-end recipe for installing MapD Enterprise Edition on a CentOS 7 machine running with NVIDIA Kepler or Pascal series GPU cards using a tarball.
Here is a quick video overview of the installation process.
Note: The order of these instructions is significant. Please install each component in the order presented to prevent aggravated hair loss.
- These instructions assume the following:
- You are installing on a “clean” CentOS 7 host machine with only the operating system installed.
- Your MapD host only runs the daemons and services required to support MapD.
- Your MapD host is connected to the Internet.
Prepare your Centos 7 machine by installing JDK, EPEL, and CUDA, and enabling a firewall.
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 http://openjdk.java.net/.
Open a terminal on the host machine.
Install the headless JDK using the following command:
sudo yum install java-1.8.0-openjdk-headless
Install the Extra Packages for Enterprise Linux (EPEL) repository. RHEL-based distributions require Dynamic Kernel Module Support (DKMS) in order to build the GPU driver kernel modules. For more information, see https://fedoraproject.org/wiki/EPEL.
sudo yum install epel-release
Update and Reboot¶
Update the entire system and reboot to activate the latest kernel.
sudo yum update
Create the MapD User¶
mapd group and
mapd user, who will be the owner of the MapD database. You can create both the group and user with the
useradd command and the
sudo useradd -U mapd
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 gives direct access to the GPU virtual instruction set and parallel computation elements. For more information on CUDA, see http://www.nvidia.com/object/cuda_home_new.html.
MapD does not require the entire CUDA package, only the CUDA drivers. Without following the installation instructions on the CUDA site, download the CUDA RPM for network install (https://developer.nvidia.com/cuda-downloads).
curl -O -u mapd http://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-repo-rhel7-<VERSION INFO, for example 8.0.61-1.x86_64>.rpm
Use the following commands to install CUDA drivers:
sudo rpm --install cuda-repo-rhel7-<VERSION INFO, for example 8.0.61-1.x86_64>.rpm
sudo yum clean expire-cache
sudo yum install cuda-drivers
Reboot your system to ensure that all changes are active.
/usr/lib64/ and verify that the file
libcuda.so is in that location.
To use Immerse, you must prepare your host machine to accept HTTP connections. You can configure your firewall for external access.
sudo firewall-cmd --zone=public --add-port=9092/tcp --permanent
sudo firewall-cmd --reload
For more information, see https://fedoraproject.org/wiki/Firewalld?rd=FirewallD.
You install the MapD application itself by expanding a TAR file.
Download the MapD archive using the username and password provided by your sales representative.
Expand the archive in the
~/installsdirectory with the following command.
tar -xvf <file_name>.tar.gz
Create a symbolic link. The symbolic link lets you navigate to the MapD directory without typing the lengthy directory name, and lets you upgrade to a new version by redirecting the link.
cd ~/ ln -s ~/installs/<MapD Directory> mapd
For example, for MapD version 3.2.0, the symbolic link command was:
ln -s ~/installs/mapd-ee-3.2.0-20170817-8ab3117-Linux-x86_64-cpu mapd
These are the steps to prepare your MapD environment.
Use the following command to create the
mapd user. The -U switch also creates the
sudo useradd -U mapd
Set Environment Variables¶
For convenience, you can update bash.rc with the required environment variables.
Go to your home directory.
Use ctrl-h to show hidden files.
.bashrcfile. Add the following export commands under “User specific aliases and functions.”
# User specific aliases and functions export MAPD_USER=mapd export MAPD_GROUP=mapd export MAPD_STORAGE=/var/lib/mapd export MAPD_PATH=/home/mapd/mapd
Open a new terminal window to use your changes.
The $MAPD_STORAGE directory must be dedicated to MapD: do not set it to a directory shared by other packages.
These are the steps to initialize the database and prepare
systemd commands for MapD.
This step initializes the database and prepares
systemd commands for MapD.
systemdinstaller. This script requires
sudoaccess. You might be prompted for a password. Accept the values provided (based on your environment variables) or make changes as needed. The script creates a data directory in $MAPD_STORAGE with the directories
mapd_logdirectories are created when you insert data the first time. The
mapd_logdirectory is the one of most interest to a MapD administrator.
cd $MAPD_PATH/systemd ./install_mapd_systemd.sh
Start and use MapD Core and Immerse.
Start MapD Core
sudo systemctl start mapd_server sudo systemctl start mapd_web_server
Enable MapD Core to start when the system reboots.
sudo systemctl enable mapd_server sudo systemctl enable mapd_web_server
To verify that all systems are go, load some sample data, perform a
mapdql query, and generate a pointmap using Immerse.
MapD ships with two sample datasets of airline flight information collected in 2008. To install the sample data, run the following command.
When prompted, choose whether to insert dataset 1 (7 million rows) or dataset 2 (10 thousand rows).
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
Connect to MapD Core by entering the following command in a terminal on the host machine (default password is HyperInteractive):
Enter a SQL query such as the following:
mapdql> 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,
Connect to Immerse using a web browser connected to your host machine on port 9092. For example,
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.
The resulting chart shows, unsurprisingly, that there is a correlation between departure delay and arrival delay.