6-python-jupyter

Python + Jupyter Lab

Four notebooks in one image:

• notebook_analysis.ipynb — tiny pandas/matplotlib data analysis on data/sales.csv.
• notebook_ml_classification.ipynb — scikit-learn classifier on the digits dataset (handwritten digit recognition).
• notebook_sentiment.ipynb — HuggingFace transformers sentiment analysis with a pre-baked DistilBERT model.
• notebook_semantic_search.ipynb — sentence-transformers embeddings + cosine similarity (the foundation of RAG).

Setting this up on your laptop is the classic Python PITA: wrong Python version, broken pip install, native build failures on numpy/pandas/scikit-learn, torch wheels that come bundled with 600 MB of CUDA you don't want, conflicts with another project's virtualenv... Containerising it pins everything once and for all — and lets you bake the ML model weights into the image so it works offline.

TASK 1: Containerize this notebook environment

Make a Dockerfile that:

1. Starts from a pinned Python base image (e.g. python:3.12-slim).
2. Installs the dependencies listed in requirements.txt.
3. Copies the notebook(s) and the data/sales.csv file into the image.
4. Exposes the Jupyter port and starts Jupyter Lab when the container runs.

Then build and run it:

docker build . --tag python-jupyter
docker run --rm -p 127.0.0.1:8888:8888 python-jupyter

Open http://127.0.0.1:8888/lab?token=workshop in your browser and run data_analysis.ipynb end-to-end.

First use a single stage, then split it into a build and runtime stage.

A multi-stage build can install packages into a --user site or a virtualenv in the build stage, then copy only the site-packages into a slim runtime image.

TASK 2: Edit the notebook from your host

Mount the folder into the container so changes saved in the browser appear on your host (and vice versa):

docker run --rm -p 127.0.0.1:8888:8888 \
  -v "./notebooks:/app" \
  python-jupyter

Edit a notebook, save, and confirm the file on host disk has changed.

TASK 3: Swap the dataset at runtime

Volume-mount your own CSV over /data/sales.csv and re-run the notebook against it — no rebuild required.

BONUS 1: Shrink the image

python:3.12-slim is already small, but pandas+numpy+torch+transformers pull in a lot. Inspect the image size with docker images and see how small you can get it (e.g. multi-stage, --no-cache-dir, CPU-only torch wheels, removing build tools after install).

BONUS 2: Bake the ML model weights into the image

The sentiment and semantic-search notebooks need model weights from HuggingFace. By default they download on first run, which is slow and breaks if the container has no internet. Pre-fetch them during docker build and set HF_HOME so the notebooks find the cached weights — the image then starts instantly and works fully offline.

This is not easy stuff, so look at the Dockerfile.solution if stuck.