How to containerize GPU-accelerated applications with Docker-Nvidia

The role of GPU accelerators in high performing applications and step-by-step guide to Docker-Nvidia set-up

DevOpsUPDATED ON December 2, 2021

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In this instalment of our DevOps consulting series, we look at how to build and run Docker containers using high-powered NVIDIA GPUs, providing a step-by-step tutorial. GPU-accelerated computing is the use of a graphics processing unit to accelerate deep learning, analytics, and engineering applications. First introduced in 2007 by NVIDIA, today GPU accelerators power energy-efficient data-centres worldwide and play a key role in applications’ acceleration.

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Containerization of GPU applications with Docker-Nvidia

Containerizing GPU applications leads to a number of benefits including ease of deployment, streamlined collaboration, isolation of individual devices and many more. However, Docker® containers are most commonly used to easily deploy CPU-based applications on several machines, where containers are both hardware- and platform-agnostic. The Docker engine doesn’t natively support NVIDIA GPUs as it uses specialized hardware that requires the NVIDIA driver to be installed.

This is our experience of using a graphics processing unit to build and run Docker containers and a step-by-step description of how this was achieved.

Step-by-step Nvidia-Docker set-up to unleash GPU-accelerated computing

To start, we’re going to need a server with NVIDIA GPU. Hetzner has a server with GeForce® GTX 1080


CentOS 7.3 

Docker version 19.03.15 

NVIDIA Drivers

Let’s download and install the necessary drivers for this graphic card:

After downloading, we need to install the driver, performing all the steps


How Nvidia and Docker work together

We will need to install nvidia-docker and the nvidia-docker-plugin. You can learn more about how to do that on nvidia github

1 wget -P /tmp 
2 sudo rpm -i /tmp/nvidia-docker*.rpm && rm /tmp/nvidia-docker*.rpm

Launching service:

1  sudo systemctl start nvidia-docker 


1  nvidia-docker run --rm nvidia/cuda nvidia-smi 

Should get the following result:

1 Thu Jul 27 13:44:07 2017 
2 +-------------------------------------------------------------+
3 | NVIDIA-SMI 375.20 Driver Version: 375.20 |
4 |--------------------+----------------+-----------+
5 | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
6 | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
7 |==================+=====================+======================|
8 | 0 GeForce GTX 1080 Off | 0000:01:00.0 Off | N/A |
9 | 33% 36C P8 11W / 180W | 0MiB / 8145MiB | 0% Default |  
10 +------------------------+---------------------+----------------------+
12 +-----------------------------------------------------------------------+
13 | Processes: GPU Memory |
14 | GPU PID Type Process name Usage |
15 |================================================================|
16 | No running processes found |
17 +-----------------------------------------------------------------------+

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Docker container with GPU support in orchestrator

Docker Swarm is not an option here as it is not possible to access the inside of the device in docker-compose V3.

From the official website:

We can now use the resources of the graphics card, but if we need to use orchestration tools, nvidia-docker will not be able to start, since it is an add-on over the Docker.

We’ve just launched a container in the Rancher cluster.

What exactly is Nvidia-Docker?

Now let’s dive into the details of what Nvidia-docker actually is. Basically, this is a service that creates a Docker volume and mounts the devices into a container.

To find out what was created and mounted, we will need to run the following command:

1  curl -s http://localhost:3476/docker/cli 

Here’s the result:


For mathematical calculations, we use a Python library – tensorflow-gpu (TensorFlow)

Let’s write a Dockerfile where the base image is taken from Docker Hub Nvidia/CUDA

1FROM nvidia/cuda:8.0-cudnn5-runtime-centos7
3 RUN pip install tensorflow-gpu
5 ENTRYPOINT ["python", ""]

Then write docker-compose to build and run the compute container:

1 version: '2'
2 services:
3    math: 
4   build: .
5     volumes:
6       - nvidia_driver_375.20:/usr/local/nvidia:ro
7     devices:
8      - /dev/nvidiactl
9       - /dev/nvidia-uvm
10       - /dev/nvidia-uvm-tools
11       - /dev/nvidia0
14 volumes:
15 nvidia_driver_375.20:
16  river: nvidia-docker
17  external: true

Launching Docker container:

1  docker-compose up -d 

If everything is done correctly, then when you run the command:

1  nvidia-docker run --rm nvidia/cuda nvidia-smi 
You get the following result:
1 Thu Jul 27 15:12:40 2017
2 +-----------------------------------------------------------------------+
3| NVIDIA-SMI 375.20                 Driver Version: 375.20               |
5| GPU  Name     Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC|
6| Fan  Temp  Perf  Pwr:Usage/Cap|     Memory-Usage | GPU-Util  Compute M.|
8|   0  GeForce GTX 1080    Off  | 0000:01:00.0     Off |             N/A |
9| 39%   53C   P2   86W / 180W |   7813MiB /  8145MiB |    56%    Default |
10 +-----------------------+----------------------+----------------------+
12 +----------------------------------------------------------------------+
13| Processes:                                              GPU Memory |
14|  GPU       PID  Type  Process name                      Usage      |
16|    0     27798    C   python                             7803MiB |
17 +---------------------------------------------------------------------+

In the processes, you can see that Python uses 56% of the GPU

We’ve just taught Docker, the leading container platform, to work with GeForce graphics cards and it can now be used to containerize GPU-accelerated applications. This means you can easily containerize and isolate accelerated application without any modifications and deploy it on any supported GPU-enabled infrastructure.

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