Containerization
Definition
Containerization packages an AI application with all its dependencies into a portable, isolated unit that runs consistently across different computing environments.
Why It Matters
Containerization solves the “works on my machine” problem that plagues AI deployment. Your model might run perfectly on your development laptop with specific Python versions, CUDA drivers, and library combinations, but fail completely when moved to a production server with different configurations.
Containers capture your entire runtime environment: Python version, installed packages, system libraries, and configuration files. When you deploy a containerized AI application, you’re deploying the exact same environment you tested locally. This reproducibility is essential for production ML systems where subtle dependency differences can cause prediction drift or outright failures.
For AI engineers, containerization enables consistent development workflows. Your team can share identical development environments, your CI/CD pipeline tests against the same configuration, and your production deployment matches what you validated during development.
Implementation Basics
Docker is the most common containerization platform. You define your environment in a Dockerfile, a text file specifying the base image, installed packages, copied files, and startup commands. Building this file produces an image that can run anywhere Docker is installed.
For AI applications, you typically start from a base image that includes Python and GPU support (like nvidia/cuda for NVIDIA GPUs). Then you install your ML frameworks, copy your model and code, and specify how to run your application.
Key containerization concepts for AI:
- Images are read-only templates containing your application and dependencies
- Containers are running instances of images
- Volumes persist data outside the container (useful for model checkpoints and logs)
- GPU passthrough requires nvidia-container-toolkit for CUDA-accelerated workloads
Start with single-container deployments. Add orchestration with Kubernetes only when you need multi-container scaling, self-healing, or complex networking. Keep your images small by using multi-stage builds and only including production dependencies.
Source
A container is a standard unit of software that packages up code and all its dependencies so the application runs quickly and reliably from one computing environment to another.
https://docs.docker.com/get-started/overview/