Docker

Why It Matters

Docker has become the standard for AI application deployment. While you can deploy Python applications directly, Docker containers provide isolation, reproducibility, and portability that direct deployment can’t match.

For AI engineers specifically, Docker solves dependency hell. AI applications often require specific versions of CUDA, cuDNN, Python, and numerous ML libraries. These requirements frequently conflict with other applications or system configurations. Docker containers isolate your AI stack completely, letting you run different CUDA versions for different projects on the same machine.

Docker also standardizes the handoff between development and deployment. You can develop locally in a Docker container that matches your production environment exactly. This eliminates surprises when deploying models to staging or production servers.

Implementation Basics

Dockerfile is a text file that defines your container image. For AI applications, you typically start from an official base image with GPU support:

FROM nvidia/cuda:12.1-runtime-ubuntu22.04

Then install Python, your dependencies, copy your code, and define the startup command. The requirements.txt file locks your Python dependencies, while the base image locks your system dependencies.

Key Docker concepts for AI:

• docker build creates an image from your Dockerfile
• docker run starts a container from an image
• docker-compose orchestrates multiple containers (API server, database, cache)
• GPU support requires nvidia-container-toolkit installed on the host

Common patterns for AI engineers:

• Multi-stage builds reduce image size by separating build-time dependencies from runtime
• Volume mounts persist model files and logs outside containers
• Environment variables configure API keys and model paths at runtime
• Health checks verify your model is responding correctly

Start simple: containerize a single FastAPI endpoint serving your model. Add complexity (multiple services, orchestration) only when you have specific scaling requirements.