ParaViewWeb with Apptainer (singularity)
Attention: ParaViewWeb is different than the regular ParaView software. ParaViewWeb is a Web framework to build applications with interactive scientific visualization inside the Web browser. In case you want to run regular ParaView, use it inside X2Go or follow the guide on how to run it using X11 Forwarding.
Running ParaView using remote desktop software on our clusters is far from ideal because it adds an unnecessary layer of virtualization, making the application run slower and taxing the server and users running other programs.
Running ParaView through a container has a few advantages:
You do not rely on IT support to install a particular version of the software;
It is possible to run the latest version, as long as the container image is also updated;
You can specify exactly how much resources you need (including CPUs and also GPUs where available) and they will be allocated to your project;
It runs much better on your, already familiar, browser;
There is no need to maintain old software that will probably stop working within time;
You can use the same container image on different hosts (i.e. what is described here can be adapted for other platforms), and always remain in the exact same software environment.
In this guide, we are going to use containers provided on NVIDIA NGC, which is a hub for GPU-optimized software for deep learning, machine learning, and HPC: https://catalog.ngc.nvidia.com/orgs/nvidia-hpcvis/containers/paraview
NOTE: We are going to use
$USERWORK for space purposes. Please, remember that this folder is subject to automatic clean up: https://documentation.sigma2.no/files_storage/clusters.html#user-work-area . It might be necessary to download the container again at some point in the future (which will be available as long as Nvidia maintains it) but, DO NOT store important data under this directory.
Pulling ParaView image
First, log in to your preferred server via SSH (in this example, we are going to use Fram):
ssh -i ~/.ssh/ssh_key firstname.lastname@example.org
The first time, you will have to pull the container image, and since these can be quite large it is often better not to use your $HOME but the $USERWORK instead:
Also, let’s set some variables so there won’t be issues while downloading the container:
mkdir -p $USERWORK/.apptainer export APPTAINER_CACHEDIR=$USERWORK/.apptainer export APPTAINER_TMPDIR=$USERWORK/.apptainer
Now, pull a ParaView container image with Apptainer:
apptainer pull docker://nvcr.io/nvidia-hpcvis/paraview:egl-py3-5.11.0
This will create a
.sif file in the directory from where you pulled it (you can rename this file as much as you want, and also move it where you want, it will still work):
ls -lrtah paraview*
WARNING: If you want to run a different ParaView version, you can do so by replacing the url after “docker://”, copying the new one from here: https://catalog.ngc.nvidia.com/orgs/nvidia-hpcvis/containers/paraview/tags.
However, if you do this, be careful to use the correct PATH for Paraview because for tags
egl-py3-5.8.0, Paraview was installed in
/opt/paraview whereas for tags
egl-5.6.0rc it is installed in
/usr/local/paraview, so modify the PATH in “Apptainer exec command” accordingly.
Allocating resources for the project
Log in again to your preferred server and run the following command:
salloc --nodes=1 --ntasks-per-node=1 --cpus-per-task=1 --time=00:30:00 --qos=devel --account=nnxxxxk
Please, note that here we are asking 1 CPU only for 30 minutes in the Devel queue. If you need more resources and time, adjust the parameters accordingly.
The output will be similar to this one:
salloc: Pending job allocation 5442258 salloc: job 5442258 queued and waiting for resources salloc: job 5442258 has been allocated resources salloc: Granted job allocation 5442258 salloc: Waiting for resource configuration salloc: Nodes c84-5 are ready for job
NOTE: Remember which node was allocated for the job, it will be needed later. In this case, the allocated node was “c84-5”. Also, you can allocate resources after logging in with SSH or right before executing the container in the step below.
Running the container
Select a random port number, say 7412. Also, for this guide, we will assume your data is located in
REMEMBER: Please, adjust the command accordingly with the chosen port, data directory and the Paraview image you want to use. If you see an error because the port is already in use, select another port number.
apptainer exec --bind $USERWORK/data:/data $USERWORK/paraview_egl-py3-5.11.0.sif /usr/local/paraview/bin/pvpython /usr/local/paraview/share/paraview-5.11/web/visualizer/server/pvw-visualizer.py --content /usr/local/paraview/share/paraview-5.11/web/visualizer/www --port 7412 --data /data -i 0.0.0.0
The command above is binding the port and the data folder to the container, so that it can see the information outside of it (by default, a container is relatively isolated from “outside world”, meaning we have to specify which folders from the host machine we want to “see” from inside the container).
The first folder
$USERWORK/data is only known outside the container and
/data is only known inside the container, we are binding them together with
--bind $USERWORK/data:/data but it is the same folder therefore changes made in
/data are actually done to
$USERWORK/data and hence permanent.
From a second terminal window, log in again to the server but, this time, forwarding the port you used for the container:
ssh -L 7412:localhost:7412 -i ~/.ssh/ssh_key email@example.com
Now, forward again the same port from the compute node that you were allocated, run the following:
ssh -L 7412:localhost:7412 c84-5.
Remember to replace the last part (c84-5) with the allocated node in the beginning
Finally, on your computer’s browser, type the following address (replacing the chosen port):
You should see ParaViewWeb interface loading on your browser.