Docker CE GPU Installation Recipe

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

Here is a quick video overview of the installation process.

Install Docker and nvidia-docker.

Note: nvidia-docker has a dependency on nvidia-modprobe. If you receive the message: Error: Could not load UVM kernel module. Is nvidia-modprobe installed? you can install nvidia-modprobe using the following commands.

sudo apt-add-repository multiverse
sudo apt update
sudo apt install nvidia-modprobe

Open a new terminal window and enter the following command:

nvidia-docker run -d -v $HOME/mapd-docker-storage:/mapd-storage -p 9090-9092:9090-9092 -v /usr/share/glvnd/egl_vendor.d:/usr/share/glvnd/egl_vendor.d mapd/mapd-ce-cuda

When the installation is complete, Docker runs the MapD server and MapD web server automatically.

Command Line Access

You can access the command line in the Docker image to perform configuration and run MapD 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 will receive output similar to the following.

CONTAINER ID        IMAGE               COMMAND                  CREATED             STATUS              PORTS                                            NAMES
9e01e520c30c        mapd/mapd-ce-gpu    "/bin/sh -c '/mapd..."   3 days ago          Up 3 days           0.0.0.0:9090-9092->9090-9092/tcp                 confident_neumann

Access the command line in your Docker image using the following command.

docker exec -it <container-id> bash

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

Checkpoint

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 from the Docker image command line.

cd $MAPD_PATH
sudo ./insert_sample_data

When prompted, choose whether to insert dataset 1 (7 million rows) or dataset 2 (10 thousand rows). The examples below use the smaller 10 thousand row dataset.

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 (default password is HyperInteractive):

$MAPD_PATH/bin/mapdql
password: ••••••••••••••••

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,
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 9092. For example, http://localhost:9092.

Create a new dashboard and a pointmap to verify that backend rendering is working.

  1. Click New Dashboard.
  2. Select the flights_2008_10K table as the datasource.
  3. Click Connect to Table.
  4. Click Add Chart.
  5. Click SCATTER.
  6. Click X Axis +Add Measure.
  7. Choose arrdelay.
  8. Click Y Axis +Add Measure.
  9. Choose depdelay.

The resulting chart shows, unsurprisingly, that there is a correlation between departure delay and arrival delay.

../../_images/firstScatterplot.png