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Setup Guide

This document contains the instructions for setting up the development environment.

Prerequisites

  • Python (version specified in environment.yml)
    • Specific version is 3.11.14
    • environment.yml says 3.11, because .14 caused CI issues
    • This does not affect the code
  • Conda (recommended)

OR

  • Docker

Installation

No Docker

  1. Clone the repository
git clone https://github.com/Sebastijan-Dominis/hotel_management
cd hotel_management
  1. Create and activate the Conda environment:
conda env create -f environment.yml -n hotel_management # default name
conda activate hotel_management # if you used the default name
  1. Add dev packages:
conda env update -n hotel_management -f environment.dev.yml
  1. Install the rest of the packages:
pip install -r requirements.txt # for packages that can't be installed with conda-forge
  1. Set up environment variables

In {repo_root}/.env. Click here

Docker

  1. Pull the required image
docker pull pytorch/pytorch:2.10.0-cuda12.8-cudnn9-runtime
  1. Build the image
docker compose build --no-cache
  1. Set up environment variables

In {repo_root}/.env. Click here

Environment Variables

ML_SERVICE_BACKEND_URL = URL in which you want to run the FastAPI backend from ml_service ML_SERVICE_FRONTEND_URL = URL in which you want to run the Dash frontend from ml_service

Notes:

  • keep default frontend urls unless you change them in the frontend as well, or want to add more
  • frontend port currently explicitly defined in the frontend code -> change if security becomes a concern

Defaults

ML_SERVICE_BACKEND_URL=http://localhost:8000

ML_SERVICE_FRONTEND_URL=http://localhost:8050

Fake data generation

Fake data can be generated using a script found in scripts/generators/generate_fake_data.py.

If you want to do so, make sure to either:

  1. uncomment and install the two commented-out packages in requirements.txt

  2. uncomment sdv in requirements.txt and the relevant parts of the Dockerfile (if using Docker)

Note: the code in that script is not modularized, as this repo does not focus on fake data generation, and instead uses fake data generation only as a utility for quick and easy simulation and testing.

  • The repo should include some fake data at any given point, along with a trained fake data generation model.
  • Only use this feature if necessary - the packages (sdv + torch) can act oddly depending on hardware, and tend to take a while to install.

Post-installation

You can now operate the ml workflow in following ways

  • using ml_service in browser
    • backend on localhost:8000 (default)
    • frontend on localhost:8050
  • manually
    • cli for pipelines
    • manual writing of configs

Additional Notes

  • Some of the packages had to be moved from environment.yml to requirements.txt, because the CI was failing
    • Only frontend/backend packages; should not impact anything
  • The inclusion of specific package versions ensures consistency
  • Author tested the code on Windows, while CI ensures UNIX compatibility (uses Ubuntu, includes tests)
  • CI currently does not include testing on Windows and MacOS, due to higher costs of those services

Expectations

  • environment.dev.yml already includes pytest and pytest-cov packages for testing and coverage generation
  • environment.dev.yml already includes ruff, isort, mypy and pre-commit packages for quality checks
  • environment.dev.yml already includes pdoc package for API doc generation
  • GPU hyperparameter searching and training will only work on your machine as expected if you use Nvidia GPU(s); otherwise please use CPU