NVIDIA Docs Hub NVIDIA Clara Clara Parabricks v4.5.0 Getting the Software

Getting the Software

The NVIDIA Parabricks Docker image can be obtained by running the following command:

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            $ docker pull nvcr.io/nvidia/clara/clara-parabricks:4.5.0-1

At this point the software is ready to use.

From the Command Line

Parabricks is deployed using a Docker image. There are two parts to customizing a Parabricks run:

Customizing Docker container specific options: These are the options that are passed to the docker command before the name of the container. For example, the user should mount their data directories within the Docker container by passing the -v option to Docker. See the Tutorials for more detailed examples.
Parabricks specific options: These options are passed to the Parabricks command line to customize the Parabricks run. For example, you can choose which tool to run and pass tool-specific options.

For example, use the following command to run the Parabricks fq2bam (BWA-MEM + GATK) tool using a Docker container. See the tutorial for further details on how this command works.

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            $ docker run \
      --gpus all \
      --rm \
      --volume $(pwd):/workdir \
      --volume $(pwd):/outputdir \
    nvcr.io/nvidia/clara/clara-parabricks:4.5.0-1 \
    pbrun fq2bam \
      --ref /workdir/parabricks_sample/Ref/Homo_sapiens_assembly38.fasta \
      --in-fq /workdir/parabricks_sample/Data/sample_1.fq.gz /workdir/parabricks_sample/Data/sample_2.fq.gz \
      --out-bam /outputdir/fq2bam_output.bam

Sample data is freely available. See the Getting The Sample Data section in the Tutorials for instructions on obtaining the sample data, and a step-by-step guide to using both fq2bam and Haplotype Caller.

Some useful Docker options to consider:

--gpus all lets the Docker container use all the GPUs on the system. The GPUs available to Parabricks container can be limited using the --gpus "device=<list of GPUs>" option. Use nvidia-smi to see how many GPUs you have, and which one is which.
--rm tells Docker to terminate the image once the command has finished.
--volume $(pwd):/image/data mounts your current directory (a path on the server) on the Docker container in the /image/data directory (a path inside the Docker container). If your data is not in the current directory use an option similar to --volume /path/to/your/data:/image/data.
--workdir tells Docker what working directory to execute the commands from (inside the container).
The rest of the command is the Parabricks tool you want to run, followed by its arguments. For those familiar with pre-v4.0 versions of Parabricks and its pbrun command, this Docker invocation takes the place of pbrun.

Running Parabricks Using the Base Command Platform

An example command to launch a BaseCommand container on a single-GPU instance is:

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            ngc batch run --name "parabricks-germline" \
    --instance dgxa100.80g.1.norm \
    --commandline "pbrun germline \
--ref /workspace/parabricks_sample/Ref/Homo_sapiens_assembly38.fasta \
--in-fq /Data/HG002-NA24385-pFDA_S2_L002_R1_001-30x.fastq.gz /Data/HG002-NA24385-pFDA_S2_L002_R2_001-30x.fastq.gz \
--knownSites /workspace/parabricks_sample/Ref/Homo_sapiens_assembly38.known_indels.vcf.gz \
--out-bam output.bam \
--out-variants output.vcf \
--out-recal-file report.txt \
--run-partition \
--no-alt-contigs" \
    --result /results \
    --image "nvcr.io/nvidia/clara/clara-parabricks:4.5.0-1"

Note that for other Parabricks commands (i.e. fq2bam, HaplotypeCaller, DeepVariant) the ngc batch run command is similar. Make sure to use the correct paths for your workplace or dataset that contains the data you intend to use.

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