COMP3610-Assignment4

Prerequisites

1. Python 3.11+ installed
2. Docker Desktop installed and running
3. Trained baseline regression and tuned regression models from (COMP3610-Assignment2) downloaded, as well, the scaler and feature columns from the same assignment (in models/ folder)
4. A working Jupyter notebook environment

Setup Instructions

1. Clone and run (COMP3610-Assignment2). .pkl files for the required models, scaler and feature columns will be generated in the models/ folder
2. Clone this repository
3. Place the generated .pkl files from COMP3610-Assignment2 in a models folder in this project

Running the Project

1. Run the command pip install -r requirements.txt
2. Run the cells in the Prerequisite section
3. Run the command mlflow ui --port 5000 and open http://localhost:5000 to view the MLflow dashboard
4. Run the cells in Part 1: Model Tracking with MLflow
5. For Part 2: Model Serving with FastAPI run the app using uvicorn app:app --reload --port 8000. Visit http://localhost:8000 to see the JSON response. Visit http://localhost:8000/docs to see the auto-generated Swagger UI
6. To run the docker container (ensuring you have Docker Desktop installed and running, the models/ folder, and port 8001 free), run the command docker compose up -d --build
7. To stop the container, run the command docker compose down