scikit-learn 0.24.2-foss-2021a

There is a newer install of scikit-learn

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 0.24.2-foss-2021a

To load the module for scikit-learn 0.24.2-foss-2021a please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):

📋 module load scikit-learn/0.24.2-foss-2021a

BEAR Apps Version

2021a

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.

More Information

For more information visit the scikit-learn website.

Dependencies

This version of scikit-learn has a direct dependency on: foss/2021a Python/3.9.5-GCCcore-10.3.0 SciPy-bundle/2021.05-foss-2021a

Required By

This version of scikit-learn is a direct dependent of: CellBender/0.2.0-foss-2021a-CUDA-11.3.1 CellBender/0.2.0-foss-2021a CellRank/1.5.1-foss-2021a Megalodon/2.4.2-foss-2021a Open3D/0.15.1-foss-2021a Optuna/2.9.1-foss-2021a pept/0.4.1-foss-2021a PyTorch-Geometric/2.0.4-foss-2021a-CUDA-11.3.1 Remora/0.1.2-foss-2021a scanpy/1.8.1-foss-2021a Scipion/3.1.0-foss-2021a-CUDA-11.3.1 Scipion/3.0.12-foss-2021a-CUDA-11.3.1 scVelo/0.2.4-foss-2021a SimPEG/0.18.1-foss-2021a topaz/0.2.5-foss-2021a topaz/0.2.5-foss-2021a-CUDA-11.3.1 velocyto/0.17.17-foss-2021a

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 22nd September 2021