aihub-cookiecutter

Machine Learning / Data Science project template based on CookieCutter.

Requirements

cookiecutter

pip install cookiecutter

How to use

use the following command to generate the project structure

cookiecutter https://github.com/vikramsoni2/aihub-cookiecutter.git

It generates the following project structure

Project Structure

project_dir/
│
├── input/
│   ├── raw/                <-- Raw data here
│   ├── interim/            <-- Any intermediate data, to pause and continue experiments
│   └── processed/          <-- Processed data ready for ML pipeline
│
├── output/
│   ├── models/             <-- Model pickle or model weights stored here
│   ├── artifacts/          <-- Serialized artifacts like LabelEncoder, Vectorizer etc
│   ├── figures/            <-- All plots and visualizations goes here
│   └── results/            <-- If the results needs to be stored for review, save here
│
├── notebooks/              <-- All notebooks and experiments resides here
│   ├── eda_plots.ipynb     <-- ┌───────────────────────────────────────────┐
│   ├── ml_rnn.ipynb        <-- │ free to name notebooks any way you prefer │
│   └── ml_seq2seq.ipynb    <-- └───────────────────────────────────────────┘
│
├── src/                    <-- Final program, with training and prediction pipeline
│   ├── __init__.py         <-- Makes src a Python module                    
│   ├── preprocess.py       <-- code related to preprocessing the data and storing it in input/processed/
│   ├── model.py            <-- model definition here, can be used in train or prediction
│   ├── train.py            <-- all code related to training model goes here
│   ├── hyperopt.py         <-- hyperparameter optimizations related code
│   ├── package.py          <-- packaging the trained model with preprocessing logic for MLflow
│   ├── predict.py          <-- prediction logic, usually loads the model from Mlflow registry and predict
│   └── server.py           <-- any API interface like Flask etc. Create as needed
│
README.md                   <-- Description and instruction about the project
MLProject                   <-- MLflow project file. If you want to use this directory as MLflow project
requirements.txt            <-- python dependencies
config.yml                  <-- configuration key value pairs in yaml format