Metadata-Version: 2.1
Name: modelstack
Version: 1.0.1
Summary: ModelStack - Lightweight Local Model Registry
Home-page: https://github.com/Aniruddh-k/modelstack
Author: Aniruddha
Author-email: aniruddhakide16@example.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: click
Requires-Dist: tabulate
Requires-Dist: joblib
Requires-Dist: torch
Requires-Dist: scikit-learn
Requires-Dist: numpy

# ModelStack

ModelStack is a lightweight, local-first model registry designed for data scientists and machine learning engineers. It allows efficient storage, versioning, and retrieval of ML models along with their associated metadata, without requiring any cloud services or external infrastructure.

## Features

- Local registry to manage models directly on your machine  
- Command Line Interface (CLI) for intuitive interaction  
- Version control for models  
- Support for PyTorch, Joblib, and Pickle formats  
- Metadata tracking (accuracy, metrics, timestamp, etc.)

## Installation

To install ModelStack locally, run:

``` pip install modelstack ```

## Usage
After installation, you can use the modelstack CLI.

To initailize a  db

``` modelstack init ```

To register a model in db

``` modelstack register "name_of_model metadata" ```

To list all models in db 

``` modelstack list ```

To delete a model 

``` modelstack delete "name_of_model" ```

To delete all 

``` modelstack delete --all ```
