edgebridge 0.1.0

EdgeBridge: Lightweight AI Model Conversion, Quantization, Pruning & Deployment Toolkit

EdgeBridge

EdgeBridge is a comprehensive Python library designed to streamline AI, Machine Learning, and edge computing workflows. It provides a modular framework for model handling, data processing, optimization, deployment, and automation, making it easy for developers to integrate advanced functionalities into Python projects efficiently.

Table of Contents

1. Project Overview
2. Features
3. Capabilities
4. Installation
5. Requirements
6. Setup
7. Folder Structure
8. Modules & Usage
9. Examples
10. Testing
11. GUI
12. Contributing
13. License

Project Overview

EdgeBridge bridges the gap between traditional Python scripting and scalable AI/ML solutions. With modularity at its core, developers can pick and use only the components they need. It provides tools for handling complex ML workflows, preprocessing datasets, optimizing models, converting data formats, running CLI commands, and deploying to edge devices.

Features

• AI & ML Integration: Simplified APIs to integrate pre-trained or custom ML models.
• Edge Deployment Ready: Utilities for deploying AI models on Raspberry Pi, Jetson devices, or other edge hardware.
• Data Handling: Cleaning, preprocessing, normalization, feature extraction, and transformation utilities.
• CLI Support: Command-line tools to automate tasks, run workflows, and execute scripts.
• Modular Structure: Independent modules to prevent unnecessary bloat.
• Optimizer Utilities: Hyperparameter tuning, model pruning, and performance improvement tools.
• Converters: Seamless conversion between CSV, JSON, Excel, and other formats.
• Extensible: Easily extendable architecture for adding new modules or modifying existing ones.
• Testing & GUI Support: Built-in testing framework, GUI components, and visualization utilities.

Capabilities

EdgeBridge can perform a wide range of tasks, including but not limited to:

1. Data Processing & Transformation

   • Loading datasets from multiple formats (CSV, JSON, Excel).
   • Cleaning missing or inconsistent data.
   • Normalizing, scaling, and encoding features.
   • Splitting datasets for training, validation, and testing.
   • Feature engineering and extraction for ML models.

2. Machine Learning Integration

   • Loading and training ML models with minimal setup.
   • Support for scikit-learn, PyTorch, TensorFlow, or custom models.
   • Easy model evaluation and performance tracking.
   • Saving and loading trained models.

3. Model Optimization & Automation

   • Hyperparameter tuning for better performance.
   • Automated model pruning to reduce size.
   • Batch processing and pipeline automation.
   • Multi-step task runners for complex workflows.

4. Converters & Interoperability

   • Convert datasets between CSV, JSON, Excel, and other formats.
   • Handle encoding and format inconsistencies.
   • Ready-made utilities for common data conversions.

5. CLI Tools

   • Run tasks from the command line without opening Python scripts.
   • Execute data processing, training, or optimization tasks.
   • Integrate into automation pipelines and scripts.

6. GUI & Visualization

   • GUI components to visualize datasets, model performance, and results.
   • Interactive graphs and plots for insights.
   • Simple dashboards to monitor workflows.

7. Testing & Reliability

   • Built-in unit testing framework to verify modules.
   • Ensures reliability and correctness across all components.

8. Edge Deployment

   • Tools to deploy models on low-resource devices.
   • Optimized code paths for efficient inference.
   • Ready-to-use deployment scripts for common edge devices.

Installation

Install via PyPI:

pip install edgebridge

Or clone the repository:

git clone https://github.com/<your-username>/EdgeBridge.git
cd EdgeBridge
python -m pip install .

Requirements

• Python 3.8 or higher
• Dependencies (install via pip or requirements.txt): numpy, pandas, scikit-learn, matplotlib, torch (optional for deep learning)

Setup

1. Clone repository
2. Install requirements
3. Build package (optional)
4. Install locally (optional)

Folder Structure

EdgeBridge/ ├── edgebridge/ (core package) ├── utils.py ├── try.py ├── setup.py ├── runners.py ├── pyproject.toml ├── optimizer.py ├── core.py ├── converters.py ├── cli.py ├── tests/ ├── gui/ ├── examples/ ├── .github/ ├── egg-info/ ├── README.md ├── LICENSE ├── .gitignore

Modules & Usage

Core

Handles dataset processing, ML integration, and central pipeline management.

from edgebridge.core import Core
core = Core()
core.load_data("dataset.csv")
core.process_data()

Utils

Provides helper functions for data manipulation, logging, and general utilities.

from utils import helper_function
result = helper_function(data)

Optimizer

Optimizes models, hyperparameters, and pipelines.

from optimizer import Optimizer
opt = Optimizer(model)
opt.tune_parameters()

Converters

Converts between file formats and handles data interoperability.

from converters import convert_csv_to_json
convert_csv_to_json("data.csv", "data.json")

CLI

Run scripts, automate tasks, and execute workflows from the terminal.

python cli.py --run example

Runners

Automates multi-step workflows, batch processing, and task orchestration.

from runners import Runner
runner = Runner(task="train")
runner.execute()

Examples

Run examples in examples/ folder:

python examples/data_processing_example.py
python examples/model_training_example.py
python examples/cli_example.py

Testing

python -m unittest discover -s tests
pytest tests/

GUI

Run GUI examples:

python gui/main.py

Contributing

1. Fork repository
2. Create branch
3. Commit changes
4. Push branch
5. Open Pull Request

License

MIT License

Contact

Open GitHub issues or contact author directly.