Deployment of the FastAPE model 🚀

This project provides all the code necessary to deploy the APE classification model using FastAPI and MLflow. This repository empowers users to expose the model as an API, making it easily accessible for various applications and services.

Prerequisites

• Python 3.12
• Python libraries: see pyproject.toml

Setup

export MLFLOW_S3_ENDPOINT_URL="https://$AWS_S3_ENDPOINT"
export MLFLOW_TRACKING_URI=***
export MLFLOW_MODEL_NAME=***
export MLFLOW_MODEL_VERSION=***

uv sync
uv run pre-commit install
uv run -m nltk.downloader stopwords
cd src
uv run uvicorn api.main:app --host 0.0.0.0 --port 5000

⚠️ The --reload flag in the last command significantly slows down the forward pass of the model, as it introduces multiprocessing, monitoring overhead, and potential thread contention — all of which degrade performance, especially for CPU-bound inference.

🏄‍♂️ For now, the API should serve a model trained on the 2025 version of the nomenclature. For the 2008 version, it needs to be adapted in a future release.

License

This project is under the Apache license to encourage collaboration and free use.