/boilerplate

🍪 ML application template to create API services around your ML code.

Primary LanguagePythonMIT LicenseMIT

ML Application Template

An application template to wrap your machine learning code as a FAST RESTful API, complete with Dockerfiles, TensorBoard, etc. Check out these simple projects for examples of how this template can be leveraged. → TensorFlow or PyTorch

Set Up

pip install cookiecutter invoke requests
cookiecutter gh:madewithml/ml-app-template

Steps

  1. Add ML functionality inside {{ cookiecutter.package_name }}.
  2. Add unit tests in tests as you develop.
  3. Add API functionality inside app.py.
  4. Complete README.md with the appropriate details.
  5. Ensure that the Dockerfile is updated.
  6. Update .gitignore before committing to git.