BEAR Applications

umap-learn 0.5.3-foss-2022a

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 0.5.3-foss-2022a

To load the module for umap-learn 0.5.3-foss-2022a please use this command on the BEAR systems (BlueBEAR, BEARCloud VMs, and CaStLeS VMs):

module load bear-apps/2022a
module load umap-learn/0.5.3-foss-2022a

BEAR Apps Version

2022a

Architectures

EL8-cascadelake — EL8-haswell — EL8-icelake

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.

Extensions

• pynndescent 0.5.7
• umap-learn-0.5.3

More Information

For more information visit the umap-learn website.

Dependencies

This version of umap-learn has a direct dependency on: foss/2022a LLVM/12.0.1-GCCcore-11.3.0 numba/0.56.4-foss-2022a Python/3.10.4-GCCcore-11.3.0 scikit-learn/1.1.2-foss-2022a SciPy-bundle/2022.05-foss-2022a tqdm/4.64.0-GCCcore-11.3.0

Required By

This version of umap-learn is a direct dependent of: scanpy/1.9.1-foss-2022a

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.

Last modified on 27th February 2023