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. ```mermaid graph TD subgraph without["Without Docker"] A1["Your machine
Python 3.12
CUDA 12.4
PyTorch 2.3"] -->|crashes| X1["???"] A2["Their machine
Python 3.10
CUDA 11.8
PyTorch 2.1"] -->|crashes| X2["???"] A3["Server
Python 3.11
CUDA 12.1
PyTorch 2.2"] -->|crashes| X3["???"] end subgraph with_docker["With Docker — Same image everywhere"] B1["Your machine
Python 3.12 | CUDA 12.4
PyTorch 2.3 | Your code"] B2["Their machine
Python 3.12 | CUDA 12.4
PyTorch 2.3 | Your code"] B3["Server
Python 3.12 | CUDA 12.4
PyTorch 2.3 | Your code"] end ``` ### Why AI projects need Docker more than most 1. **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. 2. **Model weights are large.** A 7B parameter model is 14 GB in fp16. You do not want to re-download it every time you rebuild. Docker volumes let you mount a models directory from the host. 3. **Multi-service architectures are common.** A real AI application is not just a Python script. It is an inference server, a vector database for RAG, maybe a web frontend. Docker Compose orchestrates all of these with one command. ### Key vocabulary | Term | What it means | |------|---------------| | Image | A read-only template. Your recipe. Built from a Dockerfile. | | Container | A running instance of an image. Your kitchen. | | Dockerfile | Instructions to build an image. Layer by layer. | | Volume | Persistent storage that survives container restarts. | | docker-compose | A tool for defining multi-container applications in YAML. | ### Common container patterns in AI ``` Dev Container Full toolkit. Editor support. Jupyter. Debugging tools. Used during development and experimentation. Training Container Minimal. Just the training script and dependencies. Runs on GPU clusters. No editor, no Jupyter. Inference Container Optimized for serving. Small image. Fast cold start. Runs behind a load balancer in production. ```