Project-Blueprint

This repository provides an exemplary blueprint (based on the blog post at neptune.ai for the file structure of a machine learning project, which is intended to be populated as follows but can of course be freely adapted to fit your needs:

Directory	Intended Contents
data	Datasets
models	Model parameters, checkpoints etc.
notebooks	Jupyter notebooks used e.g. for data exploration or prototyping
reports	Experimental results, training logs, data visualizations
src/data	Data handlers, -generators etc.
src/models	Model implementations
tools	Experiment scripts

To use the blueprint, perform the following steps:

1. Either download the project as a .zip file or create a fork.
2. Replace this README.md with one that describes your project.
3. Fill out the name, and version of your project in setup.py.
4. Add your Python dependencies to requirements.txt.
5. Create a Python environment for your project using conda env create -n ENVNAME --file environment.yml or alternatively use virtualenv.
6. Activate your environment using conda activate ENVNAME and start developing.