Building Docker images for C++ applications with GPU acceleration

01 Oct 2023

Docker has become a popular tool for packaging and distributing applications, as it provides a lightweight and consistent environment for running software. While Docker is commonly used for web applications or server-side software, it can also be a great option for building and deploying GPU-accelerated applications written in C++.

In this blog post, we will explore the steps to build Docker images that include GPU support for C++ applications. This will allow the applications to take advantage of the computational power of GPUs, which can significantly speed up certain types of calculations.

Prerequisites

Before we dive into the steps, make sure you have the following prerequisites set up on your system:

Docker: Install Docker on your development machine. You can find installation instructions for your specific operating system on the Docker website.
NVIDIA Container Toolkit: GPU support in Docker requires the NVIDIA Container Toolkit. Install it by following the installation instructions provided in the NVIDIA documentation.

Steps to Build Docker Images for GPU-Accelerated C++ Applications

Create a Dockerfile: Start by creating a Dockerfile in your project directory. This file will define the steps to build your Docker image. Open the Dockerfile using a text editor.
Specify the base image: In the Dockerfile, specify the base image as one that supports GPU acceleration. For example, you can use the nvidia/cuda base image, which provides the CUDA runtime and libraries for GPU computing. Add the following line to the Dockerfile:
```
 FROM nvidia/cuda:latest
```
Install dependencies: If your C++ application has any specific dependencies, such as libraries or packages, you will need to install them inside the Docker image. Use the appropriate package manager, such as apt-get for Ubuntu-based images or yum for CentOS-based images, to install the required dependencies. For example, to install the CUDA toolkit, add the following line to the Dockerfile:
```
 RUN apt-get update && apt-get install -y cuda
```
Copy the application code: Copy your C++ application code into the Docker image by adding the following line to the Dockerfile:
```
 COPY ./app /app
```
Make sure your application files are located in a directory named app in the same directory as the Dockerfile.
Build the Docker image: Open a terminal or command prompt, navigate to your project directory containing the Dockerfile, and run the following command to build the Docker image:
```
 docker build -t your-image-name .
```
Replace your-image-name with a suitable name for your Docker image.
Run the Docker image: Once the Docker image is built, you can run it with GPU support enabled. Use the following command to run the image:
```
 docker run --gpus all -it your-image-name
```
This command ensures that the GPU is accessible inside the container.
Test your GPU-accelerated C++ application: Inside the running Docker container, navigate to the location where your application files are copied (/app in this example) and compile and run your C++ application using the appropriate commands. You should see your application utilizing the GPU for accelerated computations.

Conclusion

By following these steps, you can build Docker images for C++ applications with GPU acceleration. Docker provides an efficient and portable way to package and deploy GPU-accelerated applications, making it easier to distribute and run your software on different systems.

Please note that GPU support in Docker is dependent on the hardware and drivers installed on your host machine. Ensure that you have compatible hardware and the necessary drivers installed to fully leverage the GPU capabilities in your Docker containers.

#docker #cpp #gpu #acceleration