Hardware Requirements
Any NVIDIA GPU that supports CUDA architecture 70, 75, 80, 86, 89 or 90 and has at least 16GB of GPU RAM. NVIDIA Parabricks has been tested on the following NVIDIA GPUs:
V100
T4
A10, A30, A40, A100, A6000
L4, L40
H100
The fq2bam tool requires at least 24 GB of GPU memory by default; the
--low-memoryoption will reduce this to 16 GB of GPU memory at the cost of slower processing. All other tools require at least 16 GB of GPU memory per GPU.System Requirements:
A 2 GPU system should have at least 100GB CPU RAM and at least 24 CPU threads.
A 4 GPU system should have at least 196GB CPU RAM and at least 32 CPU threads.
A 8 GPU system should have at least 392GB CPU RAM and at least 48 CPU threads.
Parabricks is not supported on virtual (vGPU) or Multi-Instance (MIG) GPUs.
Software Requirements
The following are software requirements for running Parabricks.
An NVIDIA driver with version 525.60.13 or greater .
Any Linux Operating System that supports nvidia-docker2 Docker version 20.10 (or higher)
Parabricks is available as Docker image. For Singularity users, please check here about importing a Docker image into a Singularity Image,
Verifying Hardware and Software Requirements
Checking available NVIDIA hardware and driver
To check your NVIDIA hardware and driver version, use the nvidia-smi command:
$ nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI |minimum_driver_version| Driver Version: |minimum_driver_version| CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla V100-DGXS... On | 00000000:07:00.0 Off | 0 |
| N/A 44C P0 38W / 300W | 74MiB / 16155MiB | 0% Default |
| | | N/A |
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 3019 G /usr/lib/xorg/Xorg 56MiB |
+-----------------------------------------------------------------------------+
This shows the following important information:
The NVIDIA driver version is 525.60.13.
The supported CUDA driver API is 12.0.
The GPU has 16 GB of memory.
Checking available CPU RAM and threads
To see how much RAM and CPU threads in your machine, you can run the following:
# To check available memory
$ cat /proc/meminfo | grep MemTotal
# To check available number of threads
$ cat /proc/cpuinfo | grep processor | wc -l
Checking nvidia-docker2 installation
To make sure you have nvidia-docker2 installed, run this command:
$ docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu20.04 nvidia-smi
When it finishes downloading the container, it will run the nvidia-smi command and show you the same output as above.
Checking python version
To see which version of Python you have, enter the following command:
$ python3 --version
Make sure it's at least version 3 (3.6.9, 3.7, etc).
The NVIDIA Parabricks Docker image can be obtained by running the following command:
$ docker pull nvcr.io/nvidia/clara/clara-parabricks:4.3.0-1
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
dockercommand before the name of the container. For example, the user should mount their data directories within the Docker container by passing the-voption 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 (FQ2BAM + BWA-MEM) tool using a Docker container. See the tutorial for further details on how this command works.
$ docker run \
--gpus all \
--rm \
--volume $(pwd):/workdir \
--volume $(pwd):/outputdir \
nvcr.io/nvidia/clara/clara-parabricks:4.3.0-1 \
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 alllets 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. Usenvidia-smito see how many GPUs you have, and which one is which.--rmtells Docker to terminate the image once the command has finished.--volume $(pwd):/image/datamounts your current directory (a path on the server) on the Docker container in the/image/datadirectory (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.--workdirtells 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
pbruncommand, this Docker invocation takes the place ofpbrun.
Running Parabricks Using the Base Command Platform
An example command to launch a BaseCommand container on a single-GPU instance is:
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 "nvidia/clara/clara-parabricks:4.3.0-1"
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.
Uninstalling NVIDIA Parabricks is as simple as removing the Docker image.
$ docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
nvcr.io/nv-parabricks-dev/release 4.3.0-1 516740210042 2 months ago 2.82GB
$ docker rmi 516740210042
The exact value of the "IMAGE ID" will vary depending on your installation.
User guides and Reference manuals can be found on the NVIDIA Parabricks documentation page.
Answers to many other FAQs can be found on the developer forum.