topaz 0.2.5-foss-2021a

A pipeline for particle detection in cryo-electron microscopy images using convolutional neural networks trained from positive and unlabeled examples. Topaz also includes methods for micrograph denoising using deep de- noising models.

Accessing topaz 0.2.5-foss-2021a

To load the module for topaz 0.2.5-foss-2021a please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):

📋 module load topaz/0.2.5-foss-2021a

There is a GPU enabled version of this module: topaz 0.2.5-foss-2021a-CUDA-11.3.1

BEAR Apps Version

2021a

Architectures

EL8-cascadelake — EL8-icelake — EL8-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

• topaz-em-0.2.5

More Information

For more information visit the topaz website.

Dependencies

This version of topaz has a direct dependency on: foss/2021a Python/3.9.5-GCCcore-10.3.0 PyTorch/1.10.0-foss-2021a scikit-learn/0.24.2-foss-2021a torchvision/0.11.1-foss-2021a-PyTorch-1.10.0

Other Versions

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

Last modified on 5th June 2024