scikit-learn 1.4.2-gfbf-2023a

Scikit-learn integrates machine learning algorithms in the tightly-knit scientific Python world, building upon numpy, scipy, and matplotlib. As a machine-learning module, it provides versatile tools for data mining and analysis in any field of science and engineering. It strives to be simple and efficient, accessible to everybody, and reusable in various contexts.

Accessing scikit-learn 1.4.2-gfbf-2023a

To load the module for scikit-learn 1.4.2-gfbf-2023a please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):

📋 module load bear-apps/2023a
module load scikit-learn/1.4.2-gfbf-2023a

BEAR Apps Version

2023a

Architectures

EL8-cascadelake — EL8-icelake — EL8-sapphirerapids

The listed architectures consist of two part: OS-CPU. The OS used is represented by EL and there are several different processor (CPU) types available on BlueBEAR. More information about the processor types on BlueBEAR is available on the BlueBEAR Job Submission page.

Extensions

• scikit-learn-1.4.2
• sklearn 0.0

More Information

For more information visit the scikit-learn website.

Dependencies

This version of scikit-learn has a direct dependency on: Cython/3.0.8-GCCcore-12.3.0 gfbf/2023a Python/3.11.3-GCCcore-12.3.0 Python-bundle-PyPI/2023.06-GCCcore-12.3.0 SciPy-bundle/2023.07-gfbf-2023a

Required By

This version of scikit-learn is a direct dependent of: GRAPE/0.2.4-foss-2023a HDBSCAN/0.8.39-gfbf-2023a PhyKIT/1.19.9-foss-2023a scikit_allel/1.3.11-foss-2023a

Other Versions

These versions of scikit-learn are available on the BEAR systems (BlueBEAR and BEAR Cloud VMs). These will be retained in accordance with our Applications Support and Retention Policy.

Last modified on 28th June 2024