Installation Requirements

  • Access to the internet.

  • Any NVIDIA GPU that supports CUDA architecture 60, 70, 75 or 80 and has at least 12GB of GPU RAM. Parabricks has been tested on the following Nvidia GPUs:

    • V100

    • A100

    • T4

    • A10

    • A30

    • A40

    • 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.

Please note that Clara Parabricks is not supported on virtual (vGPU) or MIG (Multi-Instance) GPUs.

The following are software requirements for running Clara Parabricks.

  • An NVIDIA driver that supports cuda-10.1 or higher. If you're using an Ampere GPU, support for cuda-11.0 or higher is required.

  • Any Linux Operating System that supports one of the following:

    • nvidia-docker2 Docker version 19.03 (or higher)

    • singularity version 3.0 (or higher)

    • Bare metal installation is supported for Ubuntu 18.04 only

  • Python 3

Checking available Nvidia hardware and driver

To check what Nvidia hardware and driver version you have, use the nvidia-smi command:

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$ nvidia-smi +-----------------------------------------------------------------------------+ | NVIDIA-SMI 450.119.04 Driver Version: 450.119.04 CUDA Version: 11.0 | |-------------------------------+----------------------+----------------------+ | 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 | +-------------------------------+----------------------+----------------------+ | 1 Tesla V100-DGXS... On | 00000000:08:00.0 Off | 0 | | N/A 44C P0 37W / 300W | 6MiB / 16158MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ | 2 Tesla V100-DGXS... On | 00000000:0E:00.0 Off | 0 | | N/A 44C P0 39W / 300W | 6MiB / 16158MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ | 3 Tesla V100-DGXS... On | 00000000:0F:00.0 Off | 0 | | N/A 44C P0 38W / 300W | 6MiB / 16158MiB | 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 | | 0 N/A N/A 3350 G /usr/bin/gnome-shell 16MiB | | 1 N/A N/A 3019 G /usr/lib/xorg/Xorg 4MiB | | 2 N/A N/A 3019 G /usr/lib/xorg/Xorg 4MiB | | 3 N/A N/A 3019 G /usr/lib/xorg/Xorg 4MiB | +-----------------------------------------------------------------------------+

This shows the following important information:

  • The NVIDIA driver version is 450.119.04.

  • The CUDA version is 11.0.

  • There are four Tesla V100 GPUs.

  • Each 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:

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#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:

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$ 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:

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$ python3 --version

Make sure it's at least version 3 (3.6.9, 3.7, etc).

There are two types of Parabricks installation licenses:

  • Node Locked: licenses are tied to a specific set of GPUs on a server.

  • Flexera based: licenses allow for a set amount of GPUs to be used at once using a license server. This will use the Nvidia License Server. You can read more about it here (optional).

The software can be installed and run in 3 ways:

  • Docker container

  • Singularity container

  • Bare-metal Debian package (.deb)

© Copyright 2023, Nvidia. Last updated on Jun 28, 2023.