> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/clara/parabricks/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/clara/parabricks/_mcp/server.

# Deploy NVIDIA Parabricks

Get started by deploying Parabricks using a Docker image. Then you can run and customize Parabricks.

## Getting the Parabricks Docker Image

Run the following command to obtain the image:

```bash
$ docker pull nvcr.io/nvidia/clara/clara-parabricks:4.7.1-1
```

## Running NVIDIA Parabricks

You can run Parabricks using either the command line or the base command platform.

### Using the Command Line to Run Parabricks

After deploying Parabricks using a Docker image, you can begin customizing it.
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.  Refer to the
  [Tutorials](/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-reference/tools/fq-2-bam-bwa-mem-gatk) tool
using a Docker container.  Refer to the [tutorial](/tutorials) for
more information on how this command works.

```bash
$ docker run \
      --gpus all \
      --rm \
      --volume $(pwd):/workdir \
      --volume $(pwd):/outputdir \
    nvcr.io/nvidia/clara/clara-parabricks:4.7.1-1 \
    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 free and available. Refer to the [Getting the Sample Data](/tutorials/step-by-step-tutorials/getting-the-sample-data) section in the
[Tutorials](/tutorials) for instructions on obtaining the sample data and a step-by-step guide to
using both [fq2bam](/tutorials/step-by-step-tutorials/fq-2-bam-tutorial) and [Haplotype Caller](/tutorials/step-by-step-tutorials/haplotype-caller-tutorial).

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](https://www.nvidia.com/en-us/data-center/base-command/)
container on a single-GPU instance is:

```bash
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:4.7.1-1"
```

For other Parabricks commands, such as fq2bam, HaplotypeCaller, and 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.