Docker for AI
Containers make "works on my machine" a thing of the past. Build a GPU-enabled Docker image with CUDA, PyTorch, and AI libraries from a Dockerfile. Mount host directories as volumes to persist models, datasets, and code across container rebuilds. Configure the NVIDIA Container Toolkit to expose GPUs inside containers. Orchestrate multi-service AI applications (inference server + vector database) using Docker Compose. You trained a model on your laptop with PyTorch 2.3, CUDA 12.4, and Python 3.12. Your colleague has PyTorch 2.1, CUDA 11.8, and Python 3.10. Your model crashes on their machine. Your Dockerfile works on both. AI projects are dependency nightmares. A typical stack includes Python, PyTorch, CUDA drivers, cuDNN, system-level C libraries, and specialized packages like flash-attn that need exact compiler versions. Docker packages all of this into a single image that runs identically everywhere. Docker wraps your code, runtime, libraries, and system tools into an isolated unit called a container. Think of it as a lightweight virtual machine, except it shares the host OS kernel instead of running its own, so it starts in seconds instead of minutes. GPU drivers are fragile. CUDA 12.4 code does not run on CUDA 11.8. Docker isolates the CUDA toolkit inside the container while sharing the host GPU driver through the NVIDIA Container Toolkit. Model weights are large. A 7B parameter…
Docker for AI: Containers make "works on my machine" a thing of the past. Build a GPU-enabled Docker image with CUDA, PyTorch, and AI libraries from a…
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
Browse the complete course catalog or open this lesson on GitHub.