Getting Started with Clara Parabricks¶
Installation Requirements¶
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 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.
Note
Clara Parabricks is not supported on virtual (vGPU) or Multi-Instance (MIG) GPUs.
Note
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 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).
Getting the Software¶
The Clara Parabricks Docker image can be obtained by running the following command:
$ docker pull nvcr.io/nvidia/clara/clara-parabricks:v4.0.1-1
At this point the software is ready to use.
Running Clara Parabricks¶
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
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, 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 /input_data \
nvcr.io/nvidia/clara/clara-parabricks:v4.0.1-1 \
pbrun fq2bam \
--ref /input_data/Homo_sapiens_assembly38.fasta \
--in-fq /input_data/fastq1.gz /input_data/fastq2.gz \
--out-bam /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 all
lets 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-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 /host/data:/image/data
mounts your/host/data
(a path on the server) on the Docker container in the/image/data
directory (a path inside the Docker container).
--workdir
tells 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
pbrun
command, 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.
Performance Tuning¶
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.
Note
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 those
tools 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 the software¶
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 4.0.1-1 516740210042 2 months ago 2.82GB
$ docker rmi 516740210042
The exact value of the "IMAGE ID" will vary depending on your installation.
Note
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.