ML Production Template

This codebase is a starting point to get your Machine Learning project into Production.

This codebase is base on Full Stack Deep Learning Course.

Codebase

notebooks: Explore and visualize your data

tasks : Convenience scripts for running frequent tests and training commands

training: Logic for the training itself

• model_core: the core code of were the model lives (p.e. cat_recognizer, text_classifier, tumor detector, etc)
  • datasets: Logic for downloading, preprocessing, augmenting, and loading data
  • models: Models wrap networks and add functionality like loss functions. saving, loading, and training
  • networks : Code for constructing neural networks (dumb input | output mappings)
  • tests: Regression tests for the models code. Make sure a trained model performs well on important examples.
  • weights : Weights of the production model
  • predictor.py: wrapper for model that allows you to do inference
  • utils.py

api: Web server serving predictions. DockerFiles, Unit Tests, Flask, etc.

evaluation: Run the validation tests

experiment_manager: Settings of your experiment manager (p.e. wandb, tensorboard)

data: use it for data versioning, storing data examples and metadata of your datasets. During training use it to store your raw and processed data but don't push or save the datasets into the repo.

Note

I Recommend you to use it as a github template. Fork the repo, go to settings and the make it a template.

This ML Project Template might help you managing your project.