Linux

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. ...

We are using a solution based on ThinLinc from Cendio to easily connect to a remote Linux desktop inside the D-MATH network from anywhere. Requirements You need either a Wi-Fi connection at ETH, as described here (with the “math-guest” profile), or a working VPN connection to the D-MATH network, as described here (with the “math-guest” profile) and a ThinLinc client for your operating system (external link) Note: on our managed computers, the ThinLinc client is already installed If you do not want or cannot install the ThinLinc client on your (private) device, you may try to use it via browser at this URL:https://tango.math.ethz.ch (the correct Wi-Fi or the VPN connection is still needed!). This solution may also work on a tablet. ...

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: ...

Develop moodle Resources: https://github.com/moodlehq/moodle-docker https://github.com/moodlehq https://hub.docker.com/r/moodlehq/moodle-php-apache The following setup can be used to set up a moodle development environment, with rootless podman: # D-MATH link podman-compose ln -s /usr/bin/podman-compose ~/bin/docker-compose # D-MATH clone moodle-docker repo git clone https://github.com/moodlehq/moodle-docker.git cd moodle-docker # D-MATH set variables export MOODLE_DOCKER_DB_PORT=15432 export MOODLE_DOCKER_APP_VERSION=18080 export MOODLE_DOCKER_SELENIUM_VNC_PORT=15900 export MOODLE_DOCKER_WEB_PORT=18000 # Change ./moodle to your /path/to/moodle if you already have it checked out export MOODLE_DOCKER_WWWROOT=./moodle # D-MATH add custom configuration cat <<EOF > local.yml services: webserver: volumes: - "${MOODLE_DOCKER_WWWROOT}/../moodledata:/var/www/moodledata" db: environment: PGDATA: /var/lib/postgresql/data/pgdata volumes: - "${MOODLE_DOCKER_WWWROOT}/../database:/var/lib/postgresql/data" EOF # Choose a db server (Currently supported: pgsql, mariadb, mysql, mssql, oracle) export MOODLE_DOCKER_DB=pgsql # D-MATH creating files with group access for moodle code umask 007 # Get Moodle code, you could select another version branch (skip this if you already got the code) git clone -b MOODLE_404_STABLE git://git.moodle.org/moodle.git $MOODLE_DOCKER_WWWROOT # D-MATH add folder for moodle data mkdir moodledata mkdir database # D-MATH setgid for automatic setting group permission chmod 2770 moodle chmod 2770 moodledata chmod 2770 database # Ensure customized config.php for the Docker containers is in place cp config.docker-template.php $MOODLE_DOCKER_WWWROOT/config.php # D-MATH set the permission to 33 inside the container for moodle code podman unshare chown 33 -R moodle podman unshare chown 33 -R moodledata podman unshare chown 999 -R database # Start up containers, choose docker.io to download the images bin/moodle-docker-compose up -d # Shut down and destroy containers #bin/moodle-docker-compose down Above we set the webserver port to 18000 so this means you now need to connect to http://localhost:18000 in your browser to see the application. ...

If you want to develop something for kubernetes, you may need to run minikube. This can be done on our linux installations. The following commands should start a working “kubernetes cluster” on your installation: mkdir ~/bin/ wget https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64 -O ~/bin/minikube chmod 755 ~/bin/minikube cd ~/bin/ ./minikube config set rootless true ./minikube config set driver podman ./minikube config set container-runtime containerd ./minikube start --cni calico After minikube has started, you can check all running pods with the following: ...

This describes how to create a local folder in which you can move emails, which will still be available in the evolution mail client. So the mails will be removed from the mailserver and saved into a folder, which you have to backup. Requirements: You already use evolution as your mail client You make backups of your local files and folders First create a folder in the filesystem, which will hold your mails: ...

To use docker at D-MATH is to use podman -> https://podman.io/ With podman you can use all docker commands just write podman instead of docker in the command, all the parameters stay the same. With podman we are able to provide a possibility that a user without administrative rights can download and run docker images. Example 1 The following example will run the latest docker image of alpine linux (https://hub.docker.com/_/alpine). podman run -ti -v $(pwd):/home/test alpine:latest ...