Drivers and Container Orchestration Tools

Once you have provisioned a deployment environment with a Linux operating system installed, we need to configure it.

While some cloud providers will automatically install NVIDIA drivers for use with NVIDIA GPUs, many do not, so we will walk through how to install NVIDIA drivers for the GPUs and expose them for our use.

We will also step through installing a containerization platform. We highly recommend Docker, but you may also use Podman if you are using Red Hat Enterprise Linux (RHEL) version 8 or higher, or another similar distribution that does not ship or support Docker.

Prerequisites

Make sure you have completed the steps in one of the following platform guides:

• Amazon Web Services
• Google Cloud Platform
• Oracle Cloud Infrastructure
• Microsoft Azure
• Bare-Metal Servers

Note on Different Linux Distributions

Various Linux distributions have a default or preferred package manager for the installation and management of system packages. For example, apt is associated with Ubuntu and dnf is associated with RHEL and Oracle Linux.

This guide will contain instructions that should be adaptable for many Linux distributions, but are specific to one of our recommended distributions. You will see comments above the commands and sections when there is a distribution-specific action. If there are no comments or headers above a set of instructions, it should work cross-platform.

Update System Package Manager

Update your server’s operating system package manager to get information on updated versions of packages and their dependencies, and upgrade these packages as needed.

$
# Ubuntu
>
sudo apt update
>
sudo apt upgrade -y
>
# RHEL or Oracle Linux
>
sudo dnf update -y

Install GNU Toolchain Components

Install the GNU Compiler Collection (gcc) , GNU Make (make), and GNU Web Get (wget) tool:

$
# Ubuntu
>
sudo apt install -y gcc make wget
>
# RHEL or Oracle Linux
>
sudo dnf install -y gcc make wget

Install NVIDIA Drivers

Remove Nouveau Drivers

The Nouveau kernel driver is incompatible with NVIDIA drivers, so you will need to disable it before installing any NVIDIA drivers.

1. In your terminal, create a new configuration file at /etc/modprobe.d/blacklist-nouveau.conf to blacklist the Nouveau drivers.

   Shell

   $
   sudo sh -c 'printf "blacklist nouveau\noptions nouveau modeset=0\n" > /etc/modprobe.d/blacklist-nouveau.conf'

2. Regenerate the kernel with the new conf file added:

   Shell

   $
   # Ubuntu
   >
   sudo update-initramfs -u
   >
   # RHEL or Oracle Linux
   >
   sudo dracut --force

3. Unload the Nouveau drivers:

   Shell

   $
   sudo rmmod nouveau

4. Verify that Nouveau has been removed:

   Shell

   $
   lsmod | grep nouveau

   If you see no output, Nouveau was successfully removed.

Install Kernel Development Tools

Many Linux distributions require Linux kernel development tools to be installed to support installing the NVIDIA drivers.

$
# Ubuntu
>
sudo apt-get install -y linux-headers-`uname -r`
>
# RHEL
>
sudo dnf -y install kernel-devel-`uname -r` kernel-headers-`uname -r`
>
sudo dnf -y install https://dl.fedoraproject.org/pub/epel/epel-release-latest-`rpm -q --queryformat '%{VERSION}' redhat-release | cut -d. -f1`.noarch.rpm
>
sudo dnf -y install dkms
>
# Oracle Linux
>
sudo dnf -y install kernel-devel kernel-headers

Download and install the official drivers

1. We are going to identify the latest compatible driver for the GPU you are using and retrieve its download URL by going to the NVIDIA Official Drivers.

2. Select the product category. For cloud instances, this will often be Data Center/Tesla.

3. Select the product series and product. You should know the exact GPU you are using if you provisioned it yourself in your own data-center. If you are using a cloud instance, you can lookup the VM instance type on your cloud console, and use your cloud provider’s documentation to find the corresponding GPU for that instance type.

   1. The product series will the first letter of the GPU name. For example, the T4 is part of the T-series, and the A10 is part of the A-series.

4. Select your operating system. For most users, like those on Ubuntu, this will be Linux 64-bit. If you are on RHEL or a compatible distribution like Oracle Linux, select the appropriate RHEL version instead.

   For Ubuntu, make sure to select Linux 64-bit, which will eventually deliver a .run file. Do not select an Ubuntu option for the operating system, as this will deliver a .deb file that frequently fails to properly install the drivers.

5. Finally, choose the Download Type (Production Branch), and choose a CUDA toolkit with version between 12.2<=version<=12.6.

   Selecting CUDA toolkit latest or 12.7 will provide a driver on major version 565. This version has a known issue where the Deepgram Engine container will not be able to detect or use the GPU. Ensure you are using drivers with maximum version 560.

6. Select Search and check that the correct driver is displayed, then select View.

7. Right-click Download, then copy the link to save the download URL to your clipboard.

8. Download the latest driver for your GPU on your deployment environment:

   Shell

   $
   wget LINK_TO_LATEST_NVIDIA_GPU_DRIVER

   Be sure to replace the LINK_TO_LATEST_NVIDIA_GPU_DRIVER placeholder value with the URL to the latest driver for the GPU you are using.

9. Install the drivers:

   Shell

   $
   # Ubuntu
   >
   chmod +x ./{DOWNLOADED_FILE_NAME}
   >
   sudo ./{DOWNLOADED_FILE_NAME} --silent
   >
   # RHEL
   >
   sudo rpm -i DOWNLOADED_FILE_NAME
   >
   sudo dnf clean all
   >
   sudo dnf -y module install nvidia-driver:latest-dkms
   >
   # Oracle Linux
   >
   sudo rpm -i DOWNLOADED_FILE_NAME
   >
   sudo dnf install \
   >
       https://dl.fedoraproject.org/pub/epel/epel-release-latest-`grep -oP '(?<=release )\d+' /etc/redhat-release`.noarch.rpm \
   >
       https://dl.fedoraproject.org/pub/epel/epel-next-release-latest-`grep -oP '(?<=release )\d+' /etc/redhat-release`.noarch.rpm
   >
   sudo dnf clean all
   >
   sudo dnf -y module install nvidia-driver:latest-dkms

   With the --silent install on Ubuntu and other non-RHEL distros, you will see warnings that are similar to the following (they can be ignored):

   WARNING: Ignoring CC version mismatch:

   The kernel was built with gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0, GNU ld (GNU Binutils for Ubuntu) 2.34, but the current compiler version is cc (Ubuntu 9.4.0-1ubuntu1~20.04) 9.4.0.

   WARNING: nvidia-installer was forced to guess the X library path ‘/usr/lib64’ and X module path ‘/usr/lib64/xorg/modules’; these paths were not queryable from the system. If X fails to find the NVIDIA X driver module, please install the `pkg-config` utility and the X.Org SDK/development package for your distribution and reinstall the driver

10. Test that the NVIDIA drivers are installed. The following command should produce output describing the available GPU:

    Shell

    $
    nvidia-smi

Install Container Runtime

For ease of use, Deepgram provides its products in container images, so you must make sure that you have installed the latest version of Docker (or an alternative such as Podman) on all hosts.

1. Install the container runtime.

   1. To install Docker, read Install Using the Repository in Docker’s documentation.

   2. To install Podman, use your distribution’s native package list. For more details, read their installation instructions.

      Shell

      $
      # Ubuntu
      >
      sudo apt install podman
      >
      # RHEL or Oracle Linux
      >
      sudo dnf install podman

      1. If you are using Podman, other guides in the self-hosted documentation will contain commands using docker. Change all of these to use podman.

2. It’s possible to grant your user (e.g. ubuntu, ec2-user, ocp) sufficient permissions to run container runtime commands without elevated privileges (without sudo).

   1. For Docker, see Manage Docker as a Non-Root User in Docker’s optional post-installation documentation.

   2. For Podman, the process to run commands without elevated privileges is somewhat more involved. See this tutorial for basic setup and use of Podman in a rootless environment.

   If you do not follow step 2, you cannot run container runtime commands without elevated privileges. You must run any docker, docker-compose, podman, or podman-compose commands with sudo.

Install Container Composition Tools

Container Composition tools allow users to define and manage multi-container applications using simple YAML configuration files that can be checked into source control. It enables the orchestration and coordination of services, automating the deployment, scaling, and management of containerized applications.

Docker

Docker Compose V2 is now included with Docker. The plugin for CLI use should be installed with the Install Container Runtime steps. If not, you can install it independently:

$
# Ubuntu
>
sudo apt install -y docker-compose-plugin

Test the installation:

$
docker compose version

You should expect the command output to return version 2.X.X.

Podman

The open source community maintains a podman-compose tool that seeks to be compatible with Docker Compose. You can install this with their instructions on GitHub, and test your installation:

$
podman-compose version

Install the NVIDIA Container Toolkit

CUDA is NVIDIA’s library for interacting with its GPU. CUDA support is made available to containers using the NVIDIA container runtime, which is provided by the NVIDIA container toolkit.

Docker

nvidia-docker exposes the NVIDIA container toolkit for the Docker runtime. Follow the Docker instructions from NVIDIA to setup this runtime.

After you’ve setup the NVIDIA Docker runtime, you can test it with the following command:

$
docker run --runtime=nvidia --rm --gpus all ubuntu nvidia-smi

Podman

Podman has implemented support for the Container Device Interface (CDI) standard in its container runtime, which allows for direct use of the NVIDIA container toolkit. Follow the CDI Support instructions from NVIDIA to install and configure the toolkit.

After you’ve setup the NVIDIA container toolkit with CDI, you can test it with the following command:

$
 podman run --rm --device nvidia.com/gpu=all ubuntu nvidia-smi

Summary

This guide walked you through installing the NVIDIA drivers to interact with our GPU that will run inference, as well as the containerization platform that we will use to run Deepgram services.

As a reminder, many of our guides assume use of Docker. If you are on Red Hat Enterprise Linux or have another reason to use Podman instead of Docker, keep in mind the commands and configuration may be slightly different.

What’s Next

Now we head to Deepgram Console to generate needed credentials for our deployment.

• Self Service Licensing & Credentials