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.
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.
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,
Create a new dashboard and a pointmap to verify that backend rendering is working.
- Click New Dashboard.
- Select the flights_2008_10K table as the datasource.
- Click Connect to Table.
- Click Add Chart.
- Click SCATTER.
- Click X Axis +Add Measure.
- Click Y Axis +Add Measure.
The resulting chart shows, unsurprisingly, that there is a correlation between departure delay and arrival delay.