ml-final-project-team2

Final project for MSSE Machine Learning course.
This repository contains src, data, and Docker configuration for building reproducible ML pipelines.

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Setup, Build, Run, and Clean Instructions

Prerequisites:
You will need Linux or Windows Subsystem for Linux (WSL2 recommended) and Docker installed and running. On Ubuntu/Debian you can install Docker with:

sudo apt update
sudo apt install docker.io
sudo systemctl enable --now docker

On WSL, install Docker Desktop for Windows and enable WSL integration. Verify installation with:

docker --version

Setup Docker user

sudo groupadd docker        # creates the group if it doesn’t exist
sudo usermod -aG docker $USER
newgrp docker               # so the group membership takes effect

Test your configuration:

docker run hello-world

To run and build the image, from the project root (ml-final-project-team2) run:

make 

and point your browser to http://127.0.0.1:8888/lab after the images builds and runs.

Explore our notebooks in

• src/jarvis/notebooks: ML pipeline for jarvis dataset
• src/nomad/notebooks: ML pipeline for nomad datasets

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Addition Docker build instructons.

To build the image, from the project root (ml-final-project-team2) run:

make build

This delegates into docker/Makefile and builds the image defined in docker/Dockerfile. The image includes CUDA 12.8 runtime, JupyterLab (Notebook 7), and Python dependencies from docker/requirements.txt.

To run the container and launch JupyterLab:

make run

This will start the container with GPU support (--gpus all) CPU by default (run_gpu requires docker host support), mount your project root into /workspace inside the container, expose JupyterLab on port 8888, disable authentication for local development (--ServerApp.token=''), and start JupyterLab at http://127.0.0.1:8888/lab. Stop the container with Ctrl+C.

To clean up stopped containers and dangling images:

make clean

Project layout:

• src/: ML pipeline code and notebooks for jarvis and nomad datasets
• data/: Raw and preprocessed datasets
• docker/: Container build files (Dockerfile, requirements.txt, helper scripts)
• docs/: Project proposal, ideas, and user guide

Quick test:
After make run, open your browser at:

http://127.0.0.1:8888/lab

Navigate to /workspace/src/jarvis/notebooks/discovery.ipynb or /workspace/src/nomad/notebooks/eda.ipynb to start exploring the ML workflows.