Intro

To get everything setup very easily we have created a docker container that has all the libraries installed you need for the labs. The container also contains local jupyter notebook server, so you can run all the notebooks locally.

Local setup (without Docker)

Homebrew Python blocks global pip install. Use the project virtual environment instead:

python3.12 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
jupyter lab --ip=0.0.0.0 --port=8888 --no-browser

In Cursor/VS Code, select the kernel "ML Con (.venv)" or the interpreter at .venv/bin/python.

1) Build the container

$ docker build -t aikurs-container .

2) Run the container and open interactive bash

$ docker run -it --rm -v $(pwd):/app -p 8000:8000 -p 8888:8888 aikurs-container bash

• pwd is your current working directory on the host machine
• app is the path in the container where the host directory is mapped to

3) Run jupyter notebook server within container

$ jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root

4) Run uvicorn app within container

$ uvicorn main:app --host 0.0.0.0 --port 8000