How to run a job on our computational ressources

The rationale for this procedure is to ensure data security, to keep visibility on the cluster usage and prevent maxing out cluster resources.

Apptainer makes the container safe for all the other users : unlike Docker that needs to be run as root to be able to use GPUs, Apptainer is made for HPC usage, which prevents your container or the containers for other researchers to read your files, or erase them.

SLURM allows accounting of the submitted jobs to be able to keep visibility on the cluster usage, and will allows us to adapt its configuration along the way.

Currently, you have to SSH directly on one of the compute node, either server Chacha or Disco :

$ ssh firstname.lastname@disco.hevs.ch
$ ssh firstname.lastname@chacha.hevs.ch

NOTE : Please note you have to connect from the school network or the HEVS VPN to be able to connect on those servers

TODO : Change doc when the jump host will be ready

1. On either server Chacha or Disco, you have a symlink datasets in your home directory that is linked to the local storage of the server : its purpose is to give you a proper space to put all the data you will be working on.

2. You have also another symlink shared_dataset for jobs that needs to be run on several nodes : this filesystem is shared between nodes.

3. Your .bashrc / .zshrc contains by default the variable APPTAINER_TMPDIR set to /home/user.name/.apptainer/ : this allows you to build containers without using the system /tmp that is restricted with a low quota, and use your larger dataset quota instead.

4. By default, you are the only one seeing your data : If you are working as a team on these data, please ask for a group creation so we can add members in it and apply suitable permissions.

To avoid having everyone installing their libraries installed on the system or on their user directly on the physical servers, we need you to keep them cleanly packed in a container : That way you can both install what you want inside this container, and you can do it without needing any root priviledge on the server you are sharing with other researchers.

For examples, see : How to create a simple apptainer container

To be able to run a job on the ISC Compute Center, you MUST run it under SLURM. Ressource usage is managed by Slurm on this cluster.

For examples, see : How-to create a simple SLURM job

More information on our SLURM cluster

On each server, your user is created with the following home directory :

ls -l /home/user.name
lrwxrwxrwx  1 root              root                51 Feb  4 10:21 .apptainer -> /data/disk01/apptainer/user.name/.apptainer/
lrwxrwxrwx  1 root              root                38 Feb  4 10:21 datasets -> /data/space/datasets/user.name/
lrwxrwxrwx  1 root              root                31 Feb  4 10:21 results -> /data/shared/user.name/results/
lrwxrwxrwx  1 root              root                31 Feb  4 10:22 shared_datasets -> /data/shared/user.name/shared_datasets/

• .apptainer is your cache directory for your containers, use apptainer cache clean every week to clean unused containers.
• datasets is your main directory for all your work material, do NOT use directly /home/user.name/ to put data or software on it : it would uselessly fill the root partition.
• results is located on a shared filesystem between all Slurm nodes to allow you to run jobs anywhere : as long as you write the output of your job in results, you will be able to access it whatever node the job had run on.
• shared_datasets is located on a shared filesystem between all Slurm nodes to allow you to run jobs anywhere : when your job needs to get the same data from several nodes, use this directory. NOTE : you will have less performances for fast reads/writes since it is a network shared filesystem : if this is a concern, please consider using the faster local datasets directory above and specify your work node in your sbatch using –nodelist=xxx.

On the ISC Compute Center, you need to be aware of how much time data is kept on which location.

1. Local datasets : Data life is short here, for now cleaning is not automated but will probably be in the future. Useful for a large volume of data to be processed on one server, and with the fastest I/O available.
2. Shared datasets and results : Data life is short here, for now cleaning is not automated but will probably be in the future. Useful for a large volume of data to be processed on several servers, and with reasonable I/O. (with some network latency due to NFS)
3. Filer mount : Data life is long, and backuped : this is where you can store data, results and other files for a longer time. By default you don't have a filer mount, it is the Sinf who can create you a shared directory on their main filer (filer01.hevs.ch) on their infrastructure, which is backuped and also isolated with limited access. Create this filer mount with their "Demande d'obtention de droits d'accès réseau" request

NOTE : Please note the filer storage is slow, it is not advised to run a job directly using those data.

TODO : set auto-cleaning for old data on each filesystem

1. The root filesystem (/, including /home) for every researchers is 20GB for convenience. For students, this quota is lowered to 10GB to encourage proper infrastructure usage / coding with a lower threshold.
2. On local and shared datasets, quota is set accordingly to what was requested during the access creation : either the direct value from the Access Form or it can be a lower quota depending on our current capacity.

1. Users must regularly clean the Apptainer cache using apptainer cache clean. Every week is a good starting point, we might automate cleaning if this is not respected : please don't think the container is a persistent storage for your data, it is not.
2. Don't keep data directly on your /home/user.name/ directory : you are limited to 20GB on it just for convenience : no large file should be there.
3. When you create new apptainer containers, the .sif files can be quite big : don't store them in /home/user.name/, prefer using either datasets/ or if needed, archive them in your filer mount.

1. Don't use VScode SSH plugin to avoid constant file scanning on our servers :

• https://earlruby.org/2021/06/fixing-vscode-when-it-keeps-dropping-ssh-connections/
• https://stackoverflow.com/questions/71055834/ms-vscode-cpptools-taking-a-ton-of-cpu-usage
• https://github.com/microsoft/vscode-cpptools/issues/5362
• https://learn.microsoft.com/en-us/answers/questions/1221136/visual-studio-2022-clear-local-caches

1. Currently your vscode caches are automatically removed from our servers.

If you need to use Cron to schedule something, you need to ask for your user to be added to the /etc/cron.allow whitelist.