Metadata-Version: 2.1
Name: aihubcli
Version: 1.0.1
Summary: A simple cli utility to generate ML project structure for quickly starting ML projects
Home-page: UNKNOWN
Author: Vikram Soni
Author-email: vikram9880@gmail.com
License: MIT
Description: ### 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
         
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=2.7
Description-Content-Type: text/markdown
