Smriprep is a computational pipeline for preprocessing of structural MRI datasets. The output of smriprep can be used as an input to dmriprep and fmriprep.

• Smriprep Documentation
• Github page

• Module Name: smriprep (see the modules page for more information)
• smriprep is a scientific pipeline with the potential to overload Biowulf's central filesystem. To avoid filesystem issues we recommend the following:
  1. Limit I/O with central Biowulf directories (e.g., /data, /home) by making use of local disk (/lscratch). You can make use of lscratch to store temporary working directories by directly assigning the temporary work directory of smriprep (-w option) to /lscratch/$SLURM_JOB_ID (remember to allocate enough space in /lscratch).
  2. Estimate memory usage. Determine how much memory your jobs will need by benchmarking (see point 3 below). Allocate some extra memory for your jobs as described in the resources described in point 3 below.
  3. Profile/benchmark smriprep jobs: We recommend making sure a given smriprep job can scale before launching a large number of jobs. You can do this by profiling/benchmarking small jobs (e.g., a swarm of 3 smriprep commands), then monitoring the jobs by using either the user dashboard or the commands jobhist, sjobs, squeue, and jobload (see biowulf utilities). There are resources prepared by the HPC staff that go over how to monitor your jobs for benchmarking and profiling (video, and slides). Once you have profiled a swarm with a few jobs and you have refined the memory, and walltime requirements, it is probably safe to (gradually) increase the number of smriprep jobs. For many analyses pipelines one has no way of knowing in advance how much memory will be actually required in an HPC environment. This is why it is very important to profile/benchmark.
  4. Opt out of uploading fmriprep metrics to fmriprep website. You can disable the submission with the --notrack flag.

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

[user@biowulf]$ sinteractive
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 smriprep

[user@cn3144 ~]$ smriprep -h

[user@cn3144 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$

Create a batch input file (e.g. smriprep.sh). For example (using test data below):

#!/bin/bash
#SBATCH --job-name=smriprep
#SBATCH --gres=lscratch:20
#SBATCH --exclusive
#SBATCH --mem=32g
#SBATCH --time=72:00:00

module load smriprep

smriprep /lscratch/${SLURM_JOB_ID}/input001 /lscratch/${SLURM_JOB_ID}/smriprep.out.ds001 \
         participant --participant_label sub-01 -w /lscratch/${SLURM_JOB_ID}

Submit this job using the Slurm sbatch command.

sbatch smriprep.sh

Create a swarmfile (e.g. smriprep.swarm). For example:

smriprep /data/${USER}/BIDS-dataset/input001/ /data/$USER/BIDS-dataset/smriprep.out.ds001 \
         participant --participant_label sub-01 -w /lscratch/${SLURM_JOB_ID} \
smriprep /data/${USER}/BIDS-dataset/input002/ /data/$USER/BIDS-dataset/smriprep.out.ds002 \
         participant --participant_label sub-02 -w /lscratch/${SLURM_JOB_ID} \
smriprep /data/${USER}/BIDS-dataset/input003/ /data/$USER/BIDS-dataset/smriprep.out.ds003 \
         participant --participant_label sub-03 -w /lscratch/${SLURM_JOB_ID} \

Submit this job using the swarm command.

swarm -f smriprep.swarm [--gres=lscratch:#] [-g #] -t auto --module smriprep