Getting Started with Clara Parabricks
Hardware Requirements
Any NVIDIA GPU that supports CUDA architecture 60, 70, 75, or 80 and has at least 16GB of GPU RAM. Parabricks has been tested on the following NVIDIA GPUs:
V100
T4
A10, A30, A40, A100, A6000
System Requirements:
A 2 GPU server should have at least 100GB CPU RAM and at least 24 CPU threads.
A 4 GPU server should have at least 196GB CPU RAM and at least 32 CPU threads.
A 8 GPU server should have at least 392GB CPU RAM and at least 48 CPU threads.
Clara Parabricks is not supported on virtual (vGPU) or Multi-Instance (MIG) GPUs.
The Clara Parabricks deepvariant and deepvariant_germline tools ship with support for T4, V100, and A100 GPUs. See the Models for additional GPUs section for more details on downloading model files for A10, A30, A40, A100, and A6000 GPUs for the deepvariant and deepvariant_germline tools.
Software Requirements
The following are software requirements for running Clara Parabricks.
An NVIDIA driver greater than version 465.32.* .
Any Linux Operating System that supports nvidia-docker2 Docker version 20.10 (or higher)
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 515.65.01 Driver Version: 515.65.01 CUDA Version: 11.7 |
|-------------------------------+----------------------+----------------------+
| 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 515.65.01.
The supported CUDA driver API is 11.7.
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:11.0-base 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 Clara Parabricks Docker image can be obtained by running the following command:
$ docker pull nvcr.io/nvidia/clara/clara-parabricks:4.0.0-1
At this point the software is ready to use.
From the Command Line
Clara 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, to run the Clara Parabricks fq2bam tool using the Docker container, use the following command:
$ docker run \
--gpus all \
--rm \
--volume /host/data:/input_data \
--volume /host/results:/outputdir \
--workdir /image/input_data \
nvcr.io/nvidia/clara/clara-parabricks:4.0.0-1 \
pbrun fq2bam \
--ref /input_data/Homo_sapiens_assembly38.fasta \
--in-fq /input_data/fastq1.gz /input_data/fastq2.gz \
--out-bam /image/outputdir/fq2bam_output.bam
For details of the above example see the FQ2BAM Tutorial in the Tutorials for a step-by-step guide.
Some useful Docker options to consider:
--gpus alllets the Docker container use all the GPUs on the system. The GPUs available to Clara 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.-v /host/data:/image/datamounts your/host/data(a path on the server) on the Docker container in the/image/datadirectory (a path inside the Docker container).-wtells Docker what working directory to execute the commands from.The rest is the Clara Parabricks tool you want to run, followed by its arguments. For those already familiar with Clara Parabricks and its
pbruncommand, this Docker invocation takes the place ofpbrun.
Using an AWS AMI
You can also use an AWS AMI from the AWS Marketplace, which already has Clara Parabricks loaded on it.
The goal of Parabricks software is to get the highest performance for bioinformatics and genomic analysis. There are a few key system options that you can tune to achieve maximum performance.
Use a Fast SSD
Parabricks software operates with two kinds of files:
Input/output files specified by the user
Temporary files created during execution and deleted at the end
The best performance is achieved when both kinds of files are on a fast, local SSD. If this is not possible, you can place the input/output files on a fast network storage device and the temporary files on a local SSD using the --tmp-dir option.
Tests have shown that you can use up to 4 GPUs and still get good performance with the Lustre network for Input/Output files. If you plan to use more than 4 GPUs, we highly recommend using local SSDs for all kinds of files.
DGX Users
The DGX comes with a SSD, usually mounted on /raid. Use this disk, and use a directory on this disk as the --tmp-dir. For initial testing, you can even copy the input files to this disk to eliminate variability in performance.
Specifying which GPUs to use
You can choose the number of GPUs to run using the command line option --num-gpus N for tools and pipelines that use GPUs. With this command, the GPUs used will be limited to the first N GPUs listed in the output of the nvidia-smi command.
To select specific GPUs, set the environment variable NVIDIA_VISIBLE_DEVICES. GPUs are numbered starting with zero. For example, this command will use only the second (GPU #1) and fourth (GPU #3) GPUs:
$ NVIDIA_VISIBLE_DEVICES="1,3" pbrun fq2bam --num-gpus 2 --ref Ref.fa --in-fq S1_1.fastq.gz --in-fq S1_2.fastq.gz
Uninstalling Clara Parabricks is as simple as removing the Docker image.
$ docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
nvcr.io/nv-parabricks-dev/release 4.0.0-1 6850d3d937d7 2 months ago 4.17GB
$ docker rmi 6850d3d937d7
The exact value of the "IMAGE ID" will vary depending on your installation.
User guides and Reference manuals can be found on the Parabricks documentation page.
Answers to many other FAQs can be found on the developer forum.