Docker and Python: making them play nicely and securely for Data Science and ML
summary
Practical checklist: use an official Python slim-buster base image rather than Alpine for scientific stacks; prefer Jupyter Docker Stacks or repo2docker instead of hand-rolling everything; split RUNs, prefer COPY, add labels and a .dockerignore, and run containers as a nonroot user. Build compiled dependencies in a builder stage and copy artifacts with multi-stage builds. Automate builds with CI (GitHub Actions) and rebuild images weekly to pick up security fixes and keep runtime images small.
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chapters (7)
- 0:00Introduction
- 1:50Why Docker
- 15:40Jupiter
- 16:49Best practices
- 21:31Security
- 27:00Automation
- 34:27Top tips
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