umap-learn 0.4.6-fosscuda-2020b

Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction.

Accessing umap-learn 0.4.6-fosscuda-2020b

To load the module for umap-learn 0.4.6-fosscuda-2020b please use this command on the BEAR systems (BlueBEAR, BEARCloud VMs, and CaStLeS VMs):

module load umap-learn/0.4.6-fosscuda-2020b

There is a CPU version of this module: umap-learn 0.4.6-foss-2020b

BEAR Apps Version

2020b

Architectures

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.

More Information

For more information visit the umap-learn website.

Dependencies

This version of umap-learn has a direct dependency on: fosscuda/2020b numba/0.52.0-fosscuda-2020b Python/3.8.6-GCCcore-10.2.0 scikit-learn/0.23.2-fosscuda-2020b SciPy-bundle/2020.11-fosscuda-2020b

Required By

This version of umap-learn is a direct dependent of: cell2location/0.05-alpha-fosscuda-2020b

Other Versions

These versions of umap-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.

Version BEAR Apps Version
0.4.6-foss-2020b 2020b

Last modified on 28th July 2021