scikit-multiflow 0.4.1-foss-2019a-Python-3.7.2

scikit-multiflow is inspired by MOA, the most popular open source framework for machine learning for data streams, and MEKA, an open source implementation of methods for multi-label learning. scikit-multiflow is also inspired on scikit-learn, the most popular framework for machine learning in Python. Following the SciKits philosophy, scikit-multiflow is an open source machine learning framework for multi-output/multi-label and stream data.

Accessing scikit-multiflow 0.4.1-foss-2019a-Python-3.7.2

To load the module for scikit-multiflow 0.4.1-foss-2019a-Python-3.7.2 please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):

📋 module load scikit-multiflow/0.4.1-foss-2019a-Python-3.7.2

BEAR Apps Version

2019a

Architectures

EL8-cascadelakeEL8-icelakeEL8-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-multiflow-0.4.1
  • sortedcontainers 2.1.0

More Information

For more information visit the scikit-multiflow website.

Dependencies

This version of scikit-multiflow has a direct dependency on: foss/2019a matplotlib/3.0.3-foss-2019a-Python-3.7.2 Python/3.7.2-GCCcore-8.2.0 scikit-learn/0.20.3-foss-2019a

Other Versions

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

Version BEAR Apps Version
0.2.0-foss-2018b-Python-3.6.6 2018b

Last modified on 5th November 2019