There is a newer version of scikit-learnScikit-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 0.21.3-fosscuda-2019b-Python-3.7.4
To load the module for scikit-learn 0.21.3-fosscuda-2019b-Python-3.7.4 please use this command on the BEAR systems (BlueBEAR, BEARCloud VMs, and CaStLeS VMs):
module load scikit-learn/0.21.3-fosscuda-2019b-Python-3.7.4
There is a CPU version of this module: scikit-learn 0.21.3-foss-2019b-Python-3.7.4
BEAR Apps Version
EL8-haswell (GPUs: NVIDIA P100)
The listed architectures consist of two part: OS-CPU.
- BlueBEAR: The OS used on BlueBEAR 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.
- BEAR and CaStLeS Cloud VMs: These VMs can have one of two OSes. Those with access to a BEAR Cloud or CaStLeS VM should check that the listed architectures for an application include the OS of VM being used. The VMs, irrespective of OS, will use the haswell CPU type.
For more information visit the scikit-learn website.
This version of scikit-learn has a direct dependency on: fosscuda/2019b Python/3.7.4-GCCcore-8.3.0 SciPy-bundle/2019.10-fosscuda-2019b-Python-3.7.4
This version of scikit-learn is a direct dependent of: keras-tuner/1.0.2-fosscuda-2019b-Python-3.7.4 PyTorch-Geometric/1.4.3-fosscuda-2019b-Python-3.7.4-PyTorch-1.4.0
These versions of scikit-learn are available on the BEAR systems (BlueBEAR, BEARCloud VMs, and CaStLeS VMs). These will be retained in accordance with our Applications Support and Retention Policy.
Last modified on 13th May 2020