thomaspyrou / project-mlops

Course Machine Learning Opeations @DTU

project_mlops

DTU MLOps Project

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

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Project based on the cookiecutter data science project template. #cookiecutterdatascience

Project Description

This repository contains the project work carried out in the MLOps course taught at DTU, in a group of 5 consisting of:

Name	Student number
Alexandra Polymenopoulou	s212558
Bogdan Capsa	s210172
Jakob Fahl	s184419
Melina Siskou	s213158
Thomas Spyrou	s213161

Goal

The purpose of this project is to build a movie Recommendation System. In this project we will mainly focus on the pipeline of the system rather than the model itself.

Framework

We will be using Pytorch Geometric a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. Inference is performed using ONNX Runtime and fastai. Utilities are provided by Hydra.

Data

The Data is obtained using ArangoDB, containing 45463 records and 24 features. We are going to use the sampled version of The Movies Dataset. The dataset contains 3 csv files:

• movies_metadata.csv: Contains information on 45,000 movies featured in the Full MovieLens dataset. Features include posters, backdrops, budget, revenue, release dates, languages, production countries and companies.
• links_small.csv: Contains the TMDB and IMDB IDs of a small subset of 9,000 movies of the Full Dataset.
• ratings_small.csv: subset of 100,000 ratings from 700 users on 9,000 movies.

Model

This Graph Neural Network model (with CNNs) is designed to predict the movies that a user has not watched yet, based on the links between the user and other movies.

Credits: https://medium.com/arangodb/integrate-arangodb-with-pytorch-geometric-to-build-recommendation-systems-dd69db688465