BEAR Applications

Topaz 0.2.4-foss-2020b-PyTorch-1.7.1

Pipeline for particle picking in cryo-electron microscopy images using convolutional neural networks trained from positive and unlabeled examples. Also featuring micrograph and tomogram denoising with DNNs.

Accessing Topaz 0.2.4-foss-2020b-PyTorch-1.7.1

To load the module for Topaz 0.2.4-foss-2020b-PyTorch-1.7.1 please use this command on the BEAR systems (BlueBEAR, BEARCloud VMs, and CaStLeS VMs):

module load Topaz/0.2.4-foss-2020b-PyTorch-1.7.1

There is a GPU enabled version of this module: Topaz 0.2.4-fosscuda-2020b-PyTorch-1.7.1

BEAR Apps Version

2020b

Architectures

EL8-cascadelake — EL8-haswell

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 Topaz website.

Dependencies

This version of Topaz has a direct dependency on: foss/2020b Python/3.8.6-GCCcore-10.2.0 PyTorch/1.7.1-foss-2020b scikit-learn/0.23.2-foss-2020b torchvision/0.8.2-foss-2020b-PyTorch-1.7.1

Other Versions

These versions of Topaz 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 13th July 2021