aihubcli 1.0.1

A simple cli utility to generate ML project structure for quickly starting ML projects

Project Structure for MLflow integrated ML Projects

This cli tool generates the following directory structure for quickstart ML projects

installaton:

pip install aihubcli

example use:
aihubcli create myProject

myProject/
│
├── 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