Image Details
Getting Started
Known Issues
Additional Information
Earlier Versions

Compute Resources

Have questions or need help with compute, including activation or issues? Follow this link.
User Agreement

Docker Usage

The information contained on this page assumes that you have a knowledge base of using Docker to create images and push them to a repository for use. If you need to review that information, please see the links below.
Docker and the RIS Compute Service
Docker Basics: Building, Tagging, & Pushing A Custom Docker Image

Image Details

Docker image hosted at nvcr.io/nvidia/clara/clara-parabricks:4.0.0-1
Official documentation for Parabricks version 4.0.0.

Getting Started

Connect to compute client.

ssh wustlkey@compute1-client-1.ris.wustl.edu

Prepare the computing environment before submitting a job.

# Use scratch file system for temp space
export SCRATCH1=/scratch1/fs1/${COMPUTE_ALLOCATION}

# Use Active storage for input and output data
export STORAGE1=/storage1/fs1/${STORAGE_ALLOCATION}/Active

# Use host level communications for the GPUs
export LSF_DOCKER_NETWORK=host

# Use debug flag when trying to figure out why your job failed to launch on the cluster
#export LSF_DOCKER_RUN_LOGLEVEL=DEBUG

# Use entry point since the parabricks container has other entrypoints but our cluster, by default, requires /bin/sh
export LSF_DOCKER_ENTRYPOINT=/bin/sh

# Create tmp dir
export TMP_DIR=${SCRATCH1}"/parabricks-tmp"
[ ! -d $TMP_DIR ] && mkdir $TMP_DIR

Submit job. Basic commands for use:

bsub -n 16 -M 64GB -R 'gpuhost rusage[mem=64GB] span[hosts=1]' -q general -gpu "num=1:j_exclusive=yes" -a 'docker(nvcr.io/nvidia/clara/clara-parabricks:4.0.0-1)' pbrun command options

Known Issues

Parabricks relies on available GPU(s) noted with NVIDIA_VISIBLE_DEVICES which defaults to ‘all’ regardless the quantity and
device number of GPU(s) reserved at runtime. As such, there is a possibility the software will attempt to run on GPU(s) the job does not have access to. At this time it is advised to prepend pbrun with the following.
for VAR in $(printenv | grep CUDA_VISIBLE_DEVICES); do export ${VAR/CUDA/NVIDIA}; done

Additional Information

Cores (-n) and memory (-M and mem) may need to be adjusted depending on the data set used.
- 1 GPU server should have 64GB CPU RAM, at least 16 CPU threads.
- 2 GPU server should have 100GB CPU RAM, at least 24 CPU threads.
- 4 GPU server should have 196GB CPU RAM, at least 32 CPU threads.
It is suggested to keep the GPUs at 4 and RAM at 196GB unless your data set is smaller than the 5GB test data set.
There is diminishing returns on using more GPUs on small data sets.
Replace command with any of the pbrun commands such as fq2bam, bqsr, applybqsr, or haplotypecaller.
Please refer to official Parabricks documentation for additional direction.

Earlier Versions

Earlier versions are still available but no longer directly supported by RIS. Please refer to the latest version for direct support.

Parabricks Quickstart: Earlier Versions

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