cryoDRGN 0.3.5-fosscuda-2020bcryoDRGN: Deep Reconstructing Generative Networks for cryo-EM heterogeneous reconstruction. CryoDRGN is a neural network based algorithm for heterogeneous cryo-EM reconstruction. In particular, the method models a continuous distribution over 3D structures by using a neural network based representation for the volume.
Accessing cryoDRGN 0.3.5-fosscuda-2020b
To load the module for cryoDRGN 0.3.5-fosscuda-2020b please use this command on the BEAR systems (BlueBEAR, BEARCloud VMs, and CaStLeS VMs):
module load cryoDRGN/0.3.5-fosscuda-2020b
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.
- colorlover 0.3.0
- cryodrgn 0.3.5
- cufflinks 0.17.3
- pynndescent 0.5.2
For more information visit the cryoDRGN website.
This version of cryoDRGN has a direct dependency on: fosscuda/2020b IPython/7.18.1-GCCcore-10.2.0 JupyterLab/2.2.8-GCCcore-10.2.0 matplotlib/3.3.3-fosscuda-2020b numba/0.52.0-fosscuda-2020b plotly.py/4.14.3-GCCcore-10.2.0 Python/3.8.6-GCCcore-10.2.0 PyTorch/1.7.1-fosscuda-2020b scikit-learn/0.23.2-fosscuda-2020b SciPy-bundle/2020.11-fosscuda-2020b Seaborn/0.11.1-fosscuda-2020b
Last modified on 6th May 2022