Modern development environments for Pythonistas
summary
Shows how to build bit‑for‑bit reproducible dev and prod images using Docker and a simple Dockerfile, run and link services with docker compose, and run tests inside the same container to get true integration tests (database, Redis). Uses pip-tools to pip-compile pinned and hashed requirements, hooks Dependabot to auto‑open upgrade PRs, wraps commands in a Makefile for one‑line onboarding, reformats with black, and deploys identical images to Cloud Run so local equals production.
AI-written summary · about
chapters (21)
- 0:00Intro
- 1:29Local development environment
- 3:40Virtual environments
- 8:39Docker Compose
- 10:09Testing
- 17:04Pipe
- 17:29Hashes
- 18:53Required Hashes
- 19:16Dependencies
- 19:52Services
- 20:25Auto formatting
- 21:10Black
- 21:56reformat
- 22:53Heroku
- 23:04CloudRun
- 23:17Runtimes
- 23:43Deployment
- 24:32Running the same application
- 24:56Use Docker
- 25:55Use Black
- 26:20Questions
related talks
30mHow to Create a Great Local Python Development Environment with Docker
23mCleaning Up Your Python Environment Superfund Site
28mTesting Complex Python Dependencies With Docker Containers
42mMaking Docker & Python Play Nicely and Securely for Machine Learning & Data Science
01h 02mDocker for Data Science: Reproducibility and Deployment
29mBest Practices for Compose-managed Python Apps
32mMigrating to Python 3 using Docker for your CI/CD pipeline (even if you can't use it in production)
26mHow to containerize Python applications with Docker