notebook.fix_packages

Goal

• When creating Docker images for projects and tutorials, we want to
  • Always use the same Docker template image from class_project to avoid replicating Docker containers
  • Install the needed packages in the notebooks to customize the container

Inputs

• The user passes:
  • A target dir <TARGET_DIR> (e.g., msml610/tutorials/L03_knowledge_representation) cloned from class_project/project_template
  • (Optional) pointers to Jupyter notebooks

Workflow

Use the Docker Container From the Project Teamplate

• In the project dir (e.g., msml610/tutorials/L03_knowledge_representation), use the same Dockerfile and requirements.txt as the matching files in class_project/project_template, so the project container matches the class project template
• If the image needs something that pip cannot install, add it to Dockerfile with a clear comment on why it is needed

Discover All Packages Needed by the Notebook

• A notebook's own import lines are not the full picture. Each notebook pairs with a *_utils.py file (e.g., L03_06_logic_solvers.ipynb -> L03_06_logic_solvers_utils.py), imported with a line like:

  import L03_06_logic_solvers_utils as utils
  

• Read the *_utils.py file and any local module it imports (transitively). Collect every third-party import / from ... import statement, not just those in the notebook

• Map import names to pip names where they differ (e.g., cv2 -> opencv-python, sklearn -> scikit-learn, PIL -> Pillow, yaml -> PyYAML)

• Diff this full list against requirements.txt and Dockerfile. A package used by the notebook or its utils that is missing from both is a bug, even if it is already installed in the container used to author the notebook

• Add every missing package to requirements.txt, and to Dockerfile only if it cannot be installed with pip (per ## Use the Docker Container from the Project Teamplate above)

Add Packages to Notebooks