Topaz 0.2.4-fosscuda-2020b-PyTorch-1.7.1Pipeline 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-fosscuda-2020b-PyTorch-1.7.1
To load the module for Topaz 0.2.4-fosscuda-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-fosscuda-2020b-PyTorch-1.7.1
BEAR Apps Version
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.
For more information visit the Topaz website.
This version of Topaz has a direct dependency on: fosscuda/2020b Python/3.8.6-GCCcore-10.2.0 PyTorch/1.7.1-fosscuda-2020b scikit-learn/0.23.2-fosscuda-2020b torchvision/0.8.2-fosscuda-2020b-PyTorch-1.7.1
Last modified on 13th July 2021