A lightweight machine learning project

This is an example project for a lightweight and ready-to-deploy machine learning application.

Installation

Install dependencies with Poetry:

$ poetry install

To enforce consistency, make sure you install the pre-commit hooks as well:

$ pre-commit install

Training

Use DVC to check the status of the model:

$ dvc status

and re-train it, if necessary:

$ dvc repro

Usage

Start the server locally:

$ gunicorn application

Alternatively, you can also start it in a Docker container. Build it first:

$ docker build -t machine-learning-application .

and then run it:

docker run -p 8000:8000 machine-learning-application

Example

You can POST requets to the /classification endpoint:

$ curl \
  --request POST \
  --data '{"text": "Die Sopranos ist eine US-amerikanische Fernsehserie"}' \
  http://0.0.0.0:8000/classification
{"label": "show", "probability": 0.8808274865150452}

or check if the server is up and healthy:

$ curl \
  --request GET \
  http://0.0.0.0:8000/health

Profiling

You can also profile the application:

$ python tools/profiling.py

and inspect the stats with SnakeViz:

$ snakeviz request.prof

License

This package is licensed under the terms of the MIT license.

Made with ♥ at snapADDY