Docker Enterprise Edition GPU

These are the steps to installing OmniSci as a Docker container on a machine running with NVIDIA Kepler or Pascal series GPU cards.

Preparation

Prepare your host by installing NVIDIA drivers, Docker, and NVIDIA runtime.

Install NVIDIA Drivers

To install NVIDIA drivers, open a terminal window on the host. Run apt update and apt upgrade to ensure that you are using the latest operating system software.

sudo apt update
sudo apt upgrade

Use apt-get to install required utilities.

sudo apt-get install \
apt-transport-https \
ca-certificates \
curl \
gnupg-agent \
software-properties-common

Reboot your system to activate all of your changes.

sudo reboot

Install CUDA

Verify that the gcc compiler is installed with the following command.

gcc --version

If no version information returns, run the following command.

sudo apt install gcc

To install the CUDA package:

  1. Select the target platform by selecting the operating system (Linux), architecture (based on your environment), distribution (Ubuntu), version (based on your environment), and installer type (OmniSci recommends deb (network)).

  2. Install the CUDA drivers per instructions on the NVIDIA website.

  3. Reboot your system to ensure that all changes are active:

    sudo reboot

Install Docker

Remove any existing Docker installs and the legacy NVIDIA docker runtime.

sudo docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f
sudo apt-get purge nvidia-docker

Remove Docker.

sudo apt-get remove docker docker-engine docker.io containerd runc

Update with apt-get.

sudo apt-get update

Use curl to download the latest Docker version.

sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

Add Docker to your Apt repository.

sudo add-apt-repository \
"deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
stable"

Update your repository.

sudo apt-get update

Install Docker, the command line interface, and the container runtime.

sudo apt-get install docker-ce docker-ce-cli containerd.io

Optional: Run the following usermod command so that docker command execution does not require sudo privilege. Log out and log back in for the changes to take effect.

sudo usermod -aG docker $USER

Checkpoint

Verify your Docker installation.

sudo docker run hello-world

Install NVIDIA Docker Runtime

Use curl to add a gpg key:

curl -s -L https://nvidia.github.io/nvidia-container-runtime/gpgkey | \
sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)

Update your sources list:

curl -s -L https://nvidia.github.io/nvidia-container-runtime/$distribution/nvidia-container-runtime.list | \
sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list

Update apt-get and install nvidia-container-runtime:

sudo apt-get update
sudo apt-get install -y nvidia-container-runtime

Edit /etc/docker/daemon.json to add the following, and save the changes:

{
"default-runtime": "nvidia",
"runtimes": {
"nvidia": {
"path": "/usr/bin/nvidia-container-runtime",
"runtimeArgs": []
}
}
}

Restart the Docker daemon:

sudo pkill -SIGHUP dockerd

Checkpoint

Verify docker and NVIDIA runtime work together.

sudo docker run --runtime=nvidia --rm nvidia/cuda:10.0-runtime-ubuntu18.04 nvidia-smi

Standard NVIDIA-SMI output shows the GPUs in your instance.

NVIDIA SMI

Installation

Download OmniSci from DockerHub and Start OmniSci in Docker.

sudo docker run --runtime=nvidia \
-d --runtime=nvidia \
-v /var/lib/omnisci:/omnisci-storage \
-p 6273-6280:6273-6280 \
omnisci/omnisci-ee-cuda:v5.3.0

See also the note regarding the CUDA JIT Cache in Optimizing Performance.

Ensure that you have sufficient storage on the drive you choose for /var/lib/omnisci.

On startup, you receive the following error:

This is expected behavior, because OmniSci does not ship with a default omnisci.conf file. To use OmniSci with the capabilities enabled by omnisci.conf:

  1. Stop OmniSci.

  2. Create the file as described in Configuration File and place it in /var/lib/omnisci.

  3. Restart OmniSci.

For more information on CUDA driver installation, see the CUDA Installation Guide.

For more information on Docker installation, see the Docker Installation Guide.

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.

Command-Line Access

You can access the command line in the Docker image to perform configuration and run OmniSci utilities.

You need to know the container-id to access the command line. Use the command below to list the running containers.

docker container ls

You see output similar to the following.

CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
9e01e520c30c omnisci/omnisci-ee-cpu "/bin/sh -c '/omnisci..." 3 days ago Up 3 days 0.0.0.0:6273-6280->6273-6280/tcp confident_neumann

Once you have your container ID, you can access the command line using the Docker exec command. For example, here is the command to start a Bash session in the Docker instance listed above. The -it switch makes the session interactive.

docker exec -it 9e01e520c30c bash

You can end the Bash session with the exit command.

Checkpoint

To verify that all systems are go, 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 one dataset of New York City census information collected in 2015. To install the sample data, run the following command.

docker exec -it <container-id> ./insert_sample_data

Where <container-id> is the container in which OmniSci is running.

When prompted, choose whether to insert dataset 1 (7,000,000 rows), dataset 2 (10,000 rows), or dataset 3 (683,000 rows). The examples below use 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

Connect to OmniSciDB by entering the following command (default password is HyperInteractive):

docker exec -it <container-id> /omnisci/bin/omnisql
password: ••••••••••••••••

Enter a SQL query such as the following:

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;

The results should be similar to the results below.

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

Connect to Immerse using a web browser connected to your host machine on port 6273. For example, http://localhost:6273.

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 table as the data source.

  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.

4_firstScatterplot.png