We have built Docker images containing ParaView 5.5 which support ParaViewWeb applications. These images currently come in two flavors: One for NVidia GPUs w/ EGL rendering support, and another with support for OSMesa (including the
swr rendering backends). You can find these images here. Each of the flavors mentioned above exposes Visualizer as a standalone deployment.
For this setup we have used an AWS EC2 instance with an NVidia GPU, and running Ubuntu 16.04. Below we describe the steps. Note that if your instance doesn’t have an NVidia GPU, you can still try the
osmesa image. For that, just skip the instructions having to do with graphics card driver installation and
nvidia-docker2 package installation, and use the
osmesa run example at the bottom of this document. All the rest of the steps should remain the same.
Update system packages:
sudo apt-get update sudo apt-get upgrade sudo apt-get dist-upgrade sudo reboot
Install graphics card drivers
sudo apt-get install nvidia-384 sudo reboot
Check graphics card driver installation:
nvidia-smi -q | head
To install Docker, first install their official GPG key:
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
Check it by verifying the last 8 characters:
sudo apt-key fingerprint 0EBFCD88
Now set up the “stable” repository:
sudo add-apt-repository \
Then update the package index and install Docker community edition:
sudo apt-get update
Note that the
nvidia-docker2 package is now deprecated (as of Docker 19.03), and the current way of installing nvidia support for gpus within containers is as follows:
# Add the package repositories
See the source of that documentation here for full details.
Verify that it’s working:
sudo docker run --rm --gpus all nvidia/cuda:9.0-base nvidia-smi
We installed Apache on the instance to act as the front-end for our webapps. All we needed was the
apache2 package and some confguration.
sudo apt-get install apache2
Now we can configure that webserver. First enable some extra modules, saving the restart for last:
sudo a2enmod vhost_alias sudo a2enmod proxy sudo a2enmod proxy_http sudo a2enmod proxy_wstunnel
Create a simple virtual host configuration:
sudo vim /etc/apache2/sites-available/001-pvw-forward.conf
Just paste this content there for a start:
Now create the simple website directory structure and possibly add a basic landing page:
mkdir -p /home/ubuntu/paraviewweb-docker/testsite/www mkdir /home/ubuntu/paraviewweb-docker/testsite/logs vim /home/ubuntu/paraviewweb-docker/testsite/www/index.html
Here you could copy the following example landing page, though it’s not really necessary for validation of the ParaViewWeb Docker image:
Now disable the default site, and enable the one you created above, and finally restart the webserver:
sudo a2dissite 000-default sudo a2ensite 001-pvw-forward sudo service apache2 restart
All that’s left is to run the docker image as follows:
sudo docker run --gpus all \
Do not forget to replace
<host-data-directory> with some real directory where your datasets are located, and replace
<ec2-hostname-or-ip[:port]> with the actual hostname or IP address (and possibly port) of the instance.
Some other run examples follow. To run the osmesa image, you don’t need the
sudo docker run \
Additionally, extra arguments can be passed to the
pvpython process that will be launched by providing the “EXTRA_PVPYTHON_ARGS” environment variable in the docker command. For example, you could pick the
swr rendering backend while at the same time preventing loading of ParaView registry values like this:
sudo docker run \
Note in the above that the extra pvpython args are comma-separated, with no additional spaces inserted.
If you chose to create a simple landing page, just point your browser at the running instance now:
Otherwise, you can point straight through to ParaViewWeb Visualizer:
Please contact us if you need help with deployment or custom development.