Skip to content

Python Data

Collection of Python libraries for data analytics and machine learning.

News

2.5.2024 Installed python-data/3.10-24.04 with newer packages of popular Python modules.

28.11.2023 Installed python-data/3.10-23.11 with newer packages of popular Python modules.

28.11.2023 Installed python-data/3.10-23.11 with newer packages of popular Python modules.

4.7.2023 Installed python-data/3.10-23.07 with newer packages of popular Python modules.

28.10.2022 Module python-data/3.8 was added for those who specifically need Python 3.8.

Available

Versions are numbered as X.Z-YY.MM, where X.Z is the version of the Python interpreter, and YY.MM is the year and month of the installation. Typically the module will include the newest versions of libraries at installation time, to the extent software dependencies allow.

Current versions are:

  • python-data/3.10-24.04: installed in April 2024, includes for example Scikit-learn 1.4.2, SciPy 1.13.0, Pandas 2.2.2 and JupyterLab 4.1.6.

  • python-data/3.10-23.11: installed in November 2023, includes for example Scikit-learn 1.3.2, SciPy 1.11.4, Pandas 2.1.3 and JupyterLab 4.0.9.

  • python-data/3.10-23.07: installed in July 2023, includes for example Scikit-learn 1.2.2, SciPy 1.11.1, Pandas 2.0.3 and JupyterLab 4.0.2.

  • (default version) python-data/3.10-22.09 or python-data/3.10: installed in September 2022, includes for example Scikit-learn 1.1.2, SciPy 1.9.1, Pandas 1.4.4 and JupyterLab 3.4.6.
  • python-data/3.9-22.04 or python-data/3.9: installed in April 2022, includes for example Scikit-learn 1.0.2, SciPy 1.8.0, Pandas 1.4.2 and JupyterLab 3.3.4.
  • python-data/3.8-22.10 or python-data/3.8: added for those who specifically need Python 3.8.

Python-data tries to include a comprehensive selection of Python libraries for data analytics and machine learning, for example:

If you find that some package is missing, you can often install it yourself with pip install --user. See our Python documentation for more information on how to install packages yourself.

It is also possible to use Python virtual environments. To create a virtual environment use the command python3 -m venv --system-site-packages venv.

If you think that some important package should be included in the module provided by CSC, please contact our servicedesk. Note that some machine learning frameworks have their own specific modules, for example: PyTorch, TensorFlow, JAX, and RAPIDS.

Note about multi-threading

Loading the python-data module will set the environment variable OMP_NUM_THREADS=1, which essentially disables OpenMP multi-threading support. This is a reasonable setting in most cases, and fixes some issues related to multi-processing runs. If you know that you need to use OpenMP multi-threading, please set this variable manually, for example in your Slurm job script:

export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK

License

All packages are licensed under various free and open source licenses (FOSS).

Usage

To use this software on Puhti, initialize it with:

module load python-data

to access the default version, or if you wish to have a specific version (see above for available versions):

module load python-data/3.10-2023.11

If you just want the most recent version with a specific Python version, you can also run:

module load python-data/3.10

This will show all available versions:

module avail python-data

To check the exact packages and versions included in the loaded module you can run:

list-packages

Warning

Note that Puhti login nodes are not intended for heavy computing, please use slurm batch jobs instead. See our instructions on how to use the batch job system.

Please also check CSC's general Python documentation.

Local storage

Some nodes in Puhti have fast local storage which is useful for IO-intensive applications. See our general instructions on how to take the fast local storage into use.