Using NGC with Your NVIDIA TITAN or Quadro PC Setup Guide

This Setup Guide explains how to set up an NVIDIA TITAN or Quadro PC for running NGC containers.

1. Introduction

NVIDIA® GPU Cloud (NGC) containers leverage the power of GPUs based on the NVIDIA Pascal™, Volta™, or Turing™ architectures. NVIDIA DGX systems as well as NVIDIA Turing, Volta, or Pascal-based virtual machines hosted on cloud service providers utilize these GPUs.

Aside from these classes of hardware, NGC containers can also run on PCs installed with the following NVIDIA products:

NVIDIA TITAN RTX (Turing)
NVIDIA TITAN V (Volta)
NVIDIA TITAN X (Pascal)
NVIDIA TITAN Xp (Pascal)
NVIDIA Quadro GV100 (Volta)
NVIDIA Quadro GP100 (Pascal)
NVIDIA Quadro P6000 (Pascal)

The document describes how to set up an NVIDIA TITAN or Quadro PC to run NGC containers. Open the command line on the Ubuntu desktop and paste the code blocks into the command line.

Note: Unlike the Tesla GPU-based system products, these products do not support the CUDA compatibility package. Only the software and drivers obtained using the instructions in this document are supported.

Prerequisites

These instructions assume the following:

You have a single card from the supported list installed on your PC.
You have Ubuntu Desktop installed.
See the NGC support matrix for supported versions.
Ubuntu Desktop can be downloaded from https://www.ubuntu.com/download/desktop/.

2. Installing the NVIDIA Driver

NGC containers take advantage of the power of NVIDIA GPUs. Be sure your system is installed with the appropriate NVIDIA drivers.

2.1. Setting Up the Driver Repository

Install the NVIDIA Developer repository in preparation for installing the NVIDIA driver. The following code block

Installs apt-transport-https.
Installs curl.
Sets up the /etc/apt/sources.list.d/cuda.list file.
Installs the CUDA repository key.
Raises the priority for the NVIDIA repository.

For Ubuntu 16.04

sudo apt-get install -y apt-transport-https curl
cat <<EOF | sudo tee /etc/apt/sources.list.d/cuda.list > /dev/null
deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64 /
EOF

curl -s \
 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub \
 | sudo apt-key add -

cat <<EOF | sudo tee /etc/apt/preferences.d/cuda > /dev/null
Package: *
Pin: origin developer.download.nvidia.com
Pin-Priority: 600
EOF

For Ubuntu 18.04

sudo apt-get install -y apt-transport-https curl
cat <<EOF | sudo tee /etc/apt/sources.list.d/cuda.list > /dev/null
deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /
EOF

curl -s \
 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub \
 | sudo apt-key add -

cat <<EOF | sudo tee /etc/apt/preferences.d/cuda > /dev/null
Package: *
Pin: origin developer.download.nvidia.com
Pin-Priority: 600
EOF

2.2. Installing the NVIDIA Driver

Once the repository has been set up, install the NVIDIA driver packages and reboot.
```
sudo apt-get update && sudo apt-get install -y --no-install-recommends cuda-drivers
sudo reboot
```

Upon reboot, confirm that you can see your installed NVIDIA hardware in the nvidia-smi output.

nvidia-smi

Example output (TITAN card in bold):

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 384.90                 Driver Version: 384.90                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0 TITAN X (Pascal)     Off  | 00000000:02:00.0  On |                  N/A |
| 23%   30C    P8    10W / 250W |    468MiB / 12188MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

3. Installing Docker and the NVIDIA Container Runtime for Docker

The Docker runtime is required to run NGC containers. In addition, the NVIDIA Container Runtime for Docker (nvidia-docker2) ensures that the high performance power of the GPU is leveraged when running NVIDIA-optimized Docker containers.

3.1. Installing the Docker Repository

The following code block will install the "docker-ce" repository. Refer to https://docs.docker.com/engine/installation/linux/docker-ce/ubuntu/#install-docker-ce for more information.

The following code block

Installs the Docker prerequisites.
Adds the Docker official GPG key.
Adds the official stable Docker repository.

sudo apt-get install -y ca-certificates curl software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository \
 "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"

3.2. Installing the NVIDIA Container Runtime for Docker

Issue the following commands to install the NVIDIA Container Runtime for Docker (nvidia-docker2) repository, install nvidia-docker2, and then set up permissions to use Docker without sudo each time.

curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
  sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install -y nvidia-docker2
sudo usermod -aG docker $USER

For more information, see https://github.com/NVIDIA/nvidia-docker.

Reboot the system.
```
sudo reboot
```
Upon reboot, test nvidia-smi with the latest official CUDA image.
```
docker run --runtime=nvidia --rm nvcr.io/nvidia/cuda nvidia-smi
```

3.3. Enabling GPU Support for NGC Containers

To obtain the best performance when running NGC containers, three methods of providing GPU support for Docker containers have been developed:

Native GPU support (included with Docker-ce 19.03 or later)
NVIDIA Container Runtime for Docker (nvidia-docker2 package)
Docker Engine Utility for NVIDIA GPUs (nvidia-docker package)

The method implemented in your system depends on the DGX OS version installed (for DGX systems), the specific NGC Cloud Image provided by a Cloud Service Provider, or the software that you have installed in preparation for running NGC containers on TITAN PCs, Quadro PCs, or vGPUs.

Refer to the following table to assist in determining which method is implemented in your system.

GPU Support Method	When Used	How to Determine
Native GPU Support	Included with Docker-ce 19.03 or later	Run `docker version` to determine the installed Docker version.
NVIDIA Container Runtime for Docker	If the `nvidia-docker2` package is installed	Run `nvidia-docker version` and check for NVIDIA Docker version 2.0 or later
Docker Engine Utility for NVIDIA GPUs	If the `nvidia-docker` package is installed	Run `nvidia-docker version` and check for NVIDIA Docker version 1.x

Each method is invoked by using specific Docker commands, described as follows.

Using Native GPU support

Note: If Docker is updated to 19.03 on a system which already has nvidia-docker or nvidia-docker2 installed, then the corresponding methods can still be used.

To use the native support on a new installation of Docker, first enable the new GPU support in Docker.
```
$ sudo apt-get install -y docker nvidia-container-toolkit 
```
This step is not needed if you have updated Docker to 19.03 on a system with nvidia-docker2 installed. The native support will be enabled automatically.

Use docker run --gpus to run GPU-enabled containers.

Example using all GPUs
```
$ docker run --gpus all ...
```
Example using two GPUs
```
$ docker run --gpus 2 ...
```

Examples using specific GPUs

$ docker run --gpus "device=1,2" ... 
$ docker run --gpus "device=UUID-ABCDEF,1" ...

Using the NVIDIA Container Runtime for Docker

With the NVIDIA Container Runtime for Docker installed (nvidia-docker2), you can run GPU-accelerated containers in one of the following ways.

Use docker run and specify runtime=nvidia.
```
$ docker run --runtime=nvidia ...
```
Use nvidia-docker run.
```
$ nvidia-docker run ...
```
The new package provides backward compatibility, so you can still run GPU-accelerated containers by using this command, and the new runtime will be used.
Use docker run with nvidia as the default runtime.
You can set nvidia as the default runtime, for example, by adding the following line to the /etc/docker/daemon.json configuration file as the first entry.
```
"default-runtime": "nvidia",
```
The following is an example of how the added line appears in the JSON file. Do not remove any pre-existing content when making this change.
```
{
 "default-runtime": "nvidia",
  "runtimes": {
     "nvidia": {
         "path": "/usr/bin/nvidia-container-runtime",
         "runtimeArgs": []
     }
 },

}
```
You can then use docker run to run GPU-accelerated containers.
```
$ docker run ...
```
CAUTION:

If you build Docker images while nvidia is set as the default runtime, make sure the build scripts executed by the Dockerfile specify the GPU architectures that the container will need. Failure to do so may result in the container being optimized only for the GPU architecture on which it was built. Instructions for specifying the GPU architecture depend on the application and are beyond the scope of this document. Consult the specific application build process for guidance.

Using the Docker Engine Utility for NVIDIA GPUs

With the Docker Engine Utility for NVIDIA GPUs installed (nvidia-docker), run GPU-enabled containers as follows.

$ nvidia-docker run ...

4. Using NGC Containers

See the following documents for detailed instructions on using the NGC website and running NGC containers.

Notices

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Prerequisites

Notice

Trademarks

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