LabVIEW in Docker Containers

Docker · LabVIEW · 29 May 2025

Have you ever had code that works perfectly on one machine but fails on another? The classic "it works on my machine" problem is a daily reality in software development – and that is exactly the problem Docker solves. In May 2025 I gave a talk about Docker containers and LabVIEW for NI's LabVIEW user community. This post summarises the key takeaways.

What is Docker?

Docker is a platform for packaging and running applications in isolated environments called containers. A container holds everything an application needs – code, runtime, libraries, and configuration – and runs identically regardless of which machine it is started on.

Unlike virtual machines, containers share the host operating system's kernel. That makes them much lighter and faster to start: where a VM can take minutes to boot, a container starts in seconds. At the same time, containers are fully isolated from each other and from the host system.

Four core concepts

Docker is built around four fundamental concepts:

• Image – A read-only snapshot of a filesystem with all software installed. Images are what you share and version-control.
• Container – A running instance of an image. You can start many containers from the same image.
• Dockerfile – A text file with step-by-step instructions for building an image.
• Registry – A repository for images, such as Docker Hub. This is where you pull and publish images.

The typical workflow is straightforward: write a Dockerfile → build an image (docker build) → run a container (docker run).

LabVIEW in Docker – NI's official images

NI publishes official LabVIEW container images on Docker Hub under nationalinstruments/labview. These are pre-built images with a complete, headless LabVIEW installation ready for use in CI/CD pipelines.

What can you do with them? Everything LabVIEWCLI supports – without opening a GUI:

• MassCompile – compile an entire LabVIEW project automatically
• VI Analyzer – static code analysis across all VIs
• RunVI – run a single VI headless
• Build specs – build executables and packages
• VIDiff – compare VI versions

Images are available for both Linux and Windows, named after the LabVIEW release and platform, e.g. nationalinstruments/labview:2025q3-linux.

Getting started with ni/labview-for-containers

NI has collected everything you need in the GitHub repository ni/labview-for-containers: ready-made Dockerfiles, example GitHub Actions and GitLab CI/CD workflows, and documentation to get you started quickly.

In my talk I forked the repository and showed how quickly you can actually get going. The process is surprisingly simple:

1. Fork ni/labview-for-containers on GitHub
2. Clone your fork locally
3. Pull the image you want: docker pull nationalinstruments/labview:2025q3-linux
4. Use the included workflow examples as a starting point for your own pipeline

The repository includes ready-to-use GitHub Actions workflows that run MassCompile and VI Analyzer on your codebase on every push – without having to set up a LabVIEW installation on your build server.

VIPM in the pipeline

One of the practical challenges of running LabVIEW headless is handling package dependencies. Getting VIPM to behave properly in an automated pipeline is – to put it mildly – not always straightforward. But it can be done:

• Activate VIPM via CLI – no GUI required
• Use a .vipc file to lock all dependencies to specific versions
• Let the CI pipeline install the packages automatically on every build

This gives you reproducible builds: the same .vipc file ensures that all developers and build servers use exactly the same package versions – with no manual installation steps.

Why this matters

Docker containers solve a handful of concrete problems at once. Builds are reproducible – the same Dockerfile produces the same result on your laptop, on the build server, and three years from now. Onboarding is fast: a new colleague clones the repository and pulls an image instead of spending days manually installing and configuring LabVIEW. And because containers are lightweight, you can run many parallel builds on the same server.

For LabVIEW projects of any significant size – with multiple developers, automated builds, and requirements around reproducibility – Docker is a natural next step.

The repository ni/labview-for-containers is a great place to start. Fork it, look at the examples, and try running a MassCompile in a container – it is easier than it sounds.