scikit-multiflow 0.4.1-foss-2019a-Python-3.7.2scikit-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, BEAR Cloud VMs, and CaStLeS VMs):
module load scikit-multiflow/0.4.1-foss-2019a-Python-3.7.2
BEAR Apps Version
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 Cloud and CaStLeS 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.
- sortedcontainers 2.1.0
For more information visit the scikit-multiflow website.
These versions of scikit-multiflow are available on the BEAR systems (BlueBEAR, BEAR Cloud VMs, and CaStLeS VMs). These will be retained in accordance with our Applications Support and Retention Policy.
Last modified on 5th November 2019