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Cookiecutter MLHOps

A Cookiecutter Template for Building Production-ready ML Components in Healthcare

License

This repository provides a Cookiecutter template for developing ML-based software components to be integrated into digital health solutions.

The template aims to:

  • promote MLOps best practices in the development of ML models for healthcare applications;
  • establish a standardized repository structure, ensuring consistency across similar projects.

The template includes setup scripts and configuration files for a range of MLOps tools. For instance, it enables robust experiment traceability and reproducibility by:

  • integrating git and DVC for code and data version control, respectively;
  • supporting experiment tracking and model management through MLFlow.

Additionally, the template incorporates a suite of quality assurance tools to ensure high code standards. These include static analyzers such as ruff, pynblint, bandit, and mypy.

Table of Contents

Requirements

To use this template, ensure the following tools are available on your system:

  • Python
  • Make
  • Cookiecutter

Python

This template is compatible with Python versions 3.8 to 3.11.

To check your Python version, use the following command:

python --version

Make

If you do not have Make already available on your system, install it using the following commands:

Cookiecutter

This template is compatible with Cookiecutter version 2.5.0 or later.

Use pipx to install Cookiecutter on your system:

pipx install cookiecutter

The use of pipx is strongly recommended. If you prefer using pip instead, then run:

python -m pip install --user cookiecutter

Usage

To create a new MLOps project using this template, use Cookiecutter by running:

cookiecutter https://github.com/collab-uniba/Cookiecutter-MLHOps

or

python3 -m cookiecutter https://github.com/collab-uniba/Cookiecutter-MLHOps

Once the template download is complete, Cookiecutter will request input to create the project directory. Ensure you respond to all prompts and do not leave any fields blank.

Step 1 - Create of a virtual environment and install the pre-defined project requirements

Once the project directory is created, you can set up a virtual environment and install the pre-defined project requirements. These include the MLOps tools and quality assurance tools mentioned above. To do so, follow these steps:

  1. open the terminal in the directory created by Cookiecutter (hereafter referred to as the 'root directory');

  2. run the Make commands:

    make virtualenv
make install

Step 2 - Configure Git and DVC [Optional]

To initialize the Git repository, run the following make command:

make initialize_git

To initialize the DVC repository, run the following make command:

make initialize_dvc

Note: If you are using Windows, use Git Bash to execute this command.

At this point, you can push the repo to the code hosting platform of your choice (e.g., GithHub). Likewise, you can assign a remote storage location for DVC.

Step 3 - Start the development environment

MLFlow

The template includes a set of scripts to start the MLFlow server and track your ML experiments.

To start the local MLflow server, run:

make start_tracking

Once the server starts, open your web browser and visit http://localhost:8080.

To stop the MLflow server, press CTRL + C in the terminal window where it was started.

Note: If you encounter issues with the port used by the local MLFlow UI server, you can change it in the Makefile.

Step 4 – Quality Assurance

Static Analysis

This template provides quality assurance tools for evaluating Python code, including static analyzers such as ruff, pynblint, bandit, and mypy.

To perform static analysis using these tools, run the following command in the terminal:

make start_QA

Testing

To run your test suite using Pytest, run the following command:

make start_testing

Repository structure

The repository structure promoted by this Cookiecutter template is freely inspired by the one offered in Cookiecutter Data Science.

├── {{cookiecutter.project_name}}    <- Main project directory.
│  ├── 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               <- Directory for documentation.
│  ├── example            <- Directory for test scripts and src.
│  ├── models             <- Trained and serialized models, model predictions, or model summaries.
│  ├── notebooks          <- Directory with Jupyter notebooks.
│  ├── references         <- Directory containing project references.
│  ├── reports            <- Generated HTML, PDF, LaTeX, etc. reports.
│  │   └── figures        <- Graphs and figures generated to use in reports.
│  ├── src                <- Project source code.
│  │   ├── data           <- Scripts to download or generate data.
│  │   ├── features       <- Scripts to turn raw data into features.
│  │   ├── models         <- Scripts to train and use models.
│  │   └── visualization  <- Scripts to create visualizations.
│  ├── test               <- Project test code.
│  │   ├── data
│  │   ├── features
│  │   ├── models
│  │   └── visualization
│  ├── Makefile           <- Makefile with `make install_requirements` command.
│  ├── README.md          <- Project markdown file created.
│  ├── requirements.txt   <- Txt file containing all requirements to install in venv.
│  ├── setup.sh           <- Allows you to configure git and DVC.
├── cookiecutter.json     <- Cookiecutter configuration file.
├── README.md             <- Markdown file for developers utilizing the template.