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Chalmers BSc and MSc courses on Vera

If you are teaching a course, MSc, or BSc thesis at Chalmers you may apply for CPU, GPU, and storage resources on the Vera cluster.

You will get:

  • Access to Vera Slurm cluster for interactive use and batch jobs for teachers and students.
    • Automatic limits on how much individual students can use
  • Group storage on Cephyr accessible by all project members for sharing software and data.
  • Technical support for teachers (not students) for making jobscripts, software installs, etc.
    • Teachers are resonsible for helping their students
    • Software module tree and containers, with custom containers if needed
  • Teachers and students log in with their CID
    • SSH and remote desktop to login nodes
    • OpenOndemand portal for launching Jupyter notebooks, remote desktops, and more on compute nodes
  • Short lives accounts: student home directories will be cleared out 4 weeks after the course ends.
  • Short queue reservations for scheduled lab session upon request.

Compute time on Vera is funded by GRU.

Members

SUPR is not used, instead the teacher send a CSV file with all students to support@c3se.chalmers.se once the project is approved. You can export such a CSV file from Canvas (Course page → Grades → Export entire gradebook view), and you must send this to the C3SE-support at least 5 working days in advance. If you have a project or thesis work with few students, you can also just send the list of the CIDs that should be added.

  1. Students must pass Introduction to computer clusters The course is checked for passing grade automatically at 08:00, 10:00, 13:00 and 15:00 every day.
  2. Students should ask their teacher for support.

Applying for access

In the near future we hope to have a website up where you can apply, but in the meantime you apply via email to Mikael Enelund who handles approvals:


All fields are mandatory. For BSc, you should find your unique project code, which should look like the format: AAAX11VT26DD where AAA is your program code (DAT, TIF, etc). No spaces or dashes. For MSc and projects that don't have a unique course codes please state so.

Note that allocations on CPU and GPU are separate, if you don't apply for GPU hours your project won't be able to submit GPU jobs at all and vice-versa. Cost for a GPU-hour varies slightly by model.

Examples:

  • 50 students doing light AI work, ~4 hours per student / month = 200 GPU hours / month
  • 10 students running CFD simulations across single node jobs 64 cores for a total of 10 hours / month = 7 x 1000 core-hours/month
  • 50 students and each student shouldn't be able to exceed 2 times more than their fair share means per student limit = 4%