How should I organize my project files for a devcontainer and production container in a Python project?

Below are some best practices based on Docker’s guidance for containerizing Python applications:

1. Placement of Production Dockerfile and Packaging Scripts

• Separate Them from Dev Assets:
  Keep your production assets out of the .devcontainer directory. Since the .devcontainer folder is used for the development environment configuration, place your production Dockerfile and related packaging scripts in a separate directory.

• Suggested Folder Structure:
  One common approach is to create a dedicated folder (for example, named deploy/ or production/) at the root of your project. For example:

  PLAINTEXTCopied
  ├── .devcontainer/
  │   ├── .devcontainer.json
  │   ├── Dockerfile          # for development
  │   └── requirements.txt
  ├── deploy/
  │   ├── Dockerfile          # for production
  │   └── packaging-scripts/  # any additional scripts
  ├── src/
  │   ├── file1.py
  │   └── file2.py
  └── README.md

• Alternative Naming:
  You could also name the production Dockerfile something like Dockerfile.production and keep it in the repository root. The key is to clearly differentiate it from the development Dockerfile.

2. Where to Build the Production Container

• Not Necessarily Inside the Dev Container:
  Although you could initiate a docker build from inside your dev container if it has the necessary Docker CLI access, production builds are often executed outside of the dev environment. Ideally, production images should be built in an environment that mirrors your deployment infrastructure or in your CI/CD pipeline.

• Recommended Approach:
  Run the production build on your host machine—or better yet, let your automated CI/CD environment handle the production build. This helps decouple your development environment from the production build process.

3. When to Run docker build for the Production Container

• After Code Stabilization:
  Build the production image when your code has passed final tests and is ready for release. This is typically done:

  • Manually, after verifying that all tests and checks pass.
  • Automatically, as part of your continuous integration and deployment (CI/CD) pipeline once code is merged to the main branch.

• Production Build Process:
  Running docker build for production should be part of your release process—whether manually triggered or automated—ensuring that the build steps (including any optimizations or production-specific configurations) are applied at that time.

By keeping your production assets separate and integrating the production build into your release process, you maintain a clear workflow and reduce potential mix-ups between development and production configurations.