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Python environments

Python is increasingly popular in a scientific environment thanks to its wide selection of well maintained modules or packages. On mistral we provide several options ranging from quick usage with a set of predefined modules up to highly customized environments for very specific requirements.

Python installations

System wide installations

The environment modules for our system wide installations follow the naming convention python3/YYYY.MM-compilerversion. The first one is python3/2020.02-gcc-9.1.0.

module load python3/2020.02-gcc-9.1.0

We start installing a new Python about every half year. This most recent installation is also available under the name

module load python3/unstable

New and updated Python modules will only be installed into the most recent installation. These updates can break your existing scripts based on python3/unstable. To avoid this possibility use an older (stable) installation.

Individual miniconda installation

For full control over every aspect of your Python environment, miniconda is a powerful option.

First download the latest 64-bit bash installer for Linux

Run the installer on mistral like so

bash ./Miniconda3-latest-Linux-x86_64.sh

Be sure to change the installation path to a project work directory, e.g.

/work/ux9999/u123456/miniconda3

You can let the installer modify your .bashrc or do it yourself. More recent versions of miniconda allow to load conda without activating the base environment. Use

conda config --set auto_activate_base false

to enable this feature.

Once miniconda's conda is available in your shell, you can start creating environments.

Basic conda commands

Create environment named my_env and install jupyter package into it.

conda create --name my_env jupyter

Show available environments.

conda info --env

Activate an environment

conda activate my_env

Show installed conda packages

conda list

Install additional package(s)

conda install numpy

More information on conda can be found in the user guide.

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