What is Docker and Why Does Everyone Use It?

Almost every modern Machine Learning project, and certainly every serious inference system, uses Docker today. But what makes it so important - and why can’t we do without it?

The Problem Docker Solves

In the development world, every computer has a slightly different environment - operating system, different Python versions, libraries, drivers, etc. When someone else tries to run your code, they find that… “It doesn’t work on my machine.”

This is where Docker comes in. It wraps the entire environment - the code, libraries, configurations - into a single “container” that can be run anywhere and produce exactly the same result.

What is a Container?

Think of a container as an “isolated box” that has its own mini-system: files, dependencies, versions, and everything the application needs to run.

Each container uses the kernel of the operating system but lives in a separate environment - like an apartment in a shared building.

Why is This So Important for AI and Inference?

In the world of models and benchmarking, it’s crucial that every experiment runs under the exact same conditions. Docker enables:

• Running a model with the same CUDA or TensorRT version on any system
• Comparing different inference engines fairly
• Deploying the model quickly and reliably on servers or in the cloud

In short - Docker not only saves developers headaches, it ensures precision, consistency, and portability at every stage: from development to production.

Conclusion

Docker is much more than just a “container technology” - it’s the foundation that enables building and deploying smart models in a controlled, stable, and reproducible environment.