Computing

The D-MATH is a shareholder of the HPC cluster Euler. All our resources are in the single share “ls_math” (which is also the default for our members). Important: all users need to request the access supported by a member (Professor, Senior Scientist) of D-MATH. Note: On 7 January 2026, the resources of “es_math” have been transferred to “ls_math”. This will lead to a more efficient Euler usage for our department. Computing resources The “ls_math” share provides access to 1’504 CPU cores, 5’248 GB of system RAM, 108 GPUs, and 3’968 GB of VRAM. These resources consist of: ...

Central Clients are powerful computers in the server room, running Fedora. The software available is the same as on the Linux desktops at the D-MATH. We distinguish between two types of Central Clients: Compute Clients and Load Clients. Compute Clients Note: The central IT services have a very powerful HPC-cluster called Euler. Application area: compute jobs (!) like R, Python or Matlab which produce heavy load or use a lot of memory. Important: Run your compute jobs with scheduling priority 7. ...

Euler Cluster https://jupyter.euler.hpc.ethz.ch/ This service is made for developing and debugging your code , not to run your code on the cluster. If you wish to run it, please use the SLURM queue system. Please only request multiple cores if you are planning to run some code that can make use of multiple cores. For more information go to the following website: https://docs.hpc.ethz.ch/services/jupyterhub ~/.config/euler/jupyterhub I want to load a cluster module / I want to activate a virtualenv / Jupyterlab is missing some features This script will be sourced (. ~/.config/euler/jupyterhub/jupyterlabrc) before starting any service. ...

https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix It is necessary that the required CUDA library is installed in the system, normally this is under ls -lad /usr/local/cuda*. …in a python project https://docs.astral.sh/uv/guides/integration/pytorch/ First create a new uv project: export UV_CACHE_DIR=/scratch/userdata/${USER}/CACHE export UV_NO_CACHE=1 cd /scratch/userdata/${USER} # uv will create a virtual env for each project uv init new-uv-project cd new-uv-project Customise the file pyproject.toml: [project] # the following supports CUDA 12.8 w/ CUDNN 9.10.2.21 requires-python = ">=3.10,<=3.15" dependencies = [ "torch>=2.9,<=2.10", "torchvision", "torchaudio", ] [tool.uv.sources] torch = { index = "pytorch" } [[tool.uv.index]] name = "pytorch" # experimental with 2.9 + 2.10 #url = "https://download.pytorch.org/whl/cu130" # stable support url = "https://download.pytorch.org/whl/cu128" explicit = true Now add packages defined in the pyproject.toml to the new project, the packages will be installed inside a virtual environment: ...

Resources: https://www.tensorflow.org/guide/gpu https://docs.astral.sh/uv/ To be able to use tensorflow you need to install it for your project. We recommend to use uv for creating your tensorflow python project. Setup your project uv will create and use a python virtual environment transparently so there is no need to create one separately. Use the storage on /scratch/userdata/$USER for your project, this is on the local disk of the ada-Xs as not all have the same python versions so the virtual environment must be recreated for every ada-X. ...

With Environment Modules, or simply called Modules, it is possible to dynamically expand the active user environment and to remove it again without closing the shell. At the D-MATH modules are used for: providing applications in different versions, customising the personal library environment, setting environment presets for institutes and groups. Modules are grouped by their functionality in different folders. These module collections are referenced by their MODULEPATH. Default collections Every bash-shell starts with two collections. ...

https://hub.docker.com/r/sagemath/sagemath/ Since Fedora 39 sagemath is not included as a package anymore. There are several possibilities you can use sagemath on Fedora, let’s highlight two: Using the official docker image Installing it with conda Podman sagemath commandline As our installation provides a fully configured rootless podman configuration, you can for example use the following commands to run the sagemath image: # connect to a central client ssh ada-X # start tmux, alternativly you can use screen tmux # create a .sage folder mkdir $HOME/.sage # run sage with your home mounted to /home/sage/mydata and a mounted .sage podman run --user 0 -ti -v $HOME/.sage:/home/sage/.sage -v $HOME:/home/sage/mydata sagemath/sagemath:latest # to detach from the tmux session use the following keystrokes #CTRL+B then D To add an additional folder you can put another -v parameter, for example: ...

The suggested way to run your simulations on a remote host (let’s say for instance on ada-18) is to use screen, which allows you to: use multiple shell windows from a single SSH session keep a shell active even through network disruptions disconnect and re-connect to a shell session from multiple locations run a long running process without maintaining an active shell session. Start screen Enter in a terminal screen or – better – start a screen session by giving it a name (for instance “sim1”): ...