This package is a memory efficient implementation of cryoCARE. This setup trains a denoising U-Net for tomographic reconstruction according to the Noise2Noise training paradigm.

References:

• Tim-Oliver Buchholz, Alexander Krull, Réza Shahidi, Gaia Pigino, Gáspár Jékely, Florian Jug Content-aware image restoration for electron microscopy Methods Cell Biol. 2019;152:277-289

• cryoCARE_pip Main Site

• Module Name: cryocare_pip (see the modules page for more information)
• singlethreaded
• GPUs

Allocate an interactive session and run the program.
Sample session (user input in bold):

[user@biowulf]$ sinteractive --gres=gpu:1
salloc.exe: Pending job allocation 46116226
salloc.exe: job 46116226 queued and waiting for resources
salloc.exe: job 46116226 has been allocated resources
salloc.exe: Granted job allocation 46116226
salloc.exe: Waiting for resource configuration
salloc.exe: Nodes cn3144 are ready for job
[user@cn3144 ~]$ module load cryocare_pip
[user@cn3144 ~]$ cryoCARE_extract_train_data.py --conf train_data_config.json
[user@cn3144 ~]$ cryoCARE_train.py --conf train_config.json
[user@cn3144 ~]$ cryoCARE_predict.py --conf predict_config.json
[user@cn3144 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$

Create a batch input file (e.g. cryocare_pip.sh). For example:

#!/bin/bash
set -e
module load cryocare_pip
cryoCARE_extract_train_data.py --conf train_data_config.json
cryoCARE_train.py --conf train_config.json
cryoCARE_predict.py --conf predict_config.json

Submit this job using the Slurm sbatch command.

sbatch --partition=gpu --gres=gpu:1 --cpus-per-task=8 --mem=40g cryocare_pip.sh