Edge Vision Framework (EVF)

A web application framework for managing and optimizing deep learning models for edge devices with GPU support and real-time monitoring capabilities.

Key Features

• 🚀 Project Management: Organize models, datasets, and experiments in isolated workspaces
• 🎯 Model Training: Train models with automatic GPU resource allocation
• 📊 Real-time Monitoring: Track training progress and system resource usage
• 🛠️ Model Optimization: Optimize models for edge deployment
• 📈 Interactive Dashboard: Monitor system resources and training metrics
• 👥 Multi-user Support: Secure authentication and workspace isolation

Installation

Prerequisites

• Python 3.8+
• CUDA-compatible GPU
• Node.js 14+ (for frontend development)

Setup

1. Clone the repository:

git clone https://github.com/yourusername/nn-comp-evf.git
cd nn-comp-evf

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows, use: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Initialize the application:

python init.sh

5. Start the server:

python app.py

The application will be available at http://localhost:5001

Basic Usage

1. Create a new project from the dashboard
2. Upload or configure your dataset
3. Define your model architecture
4. Start a training run with GPU allocation
5. Monitor training progress in real-time

License

Apache 2.0 License - see LICENSE for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgement

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2021-0-00907, Development of Adaptive and Lightweight Edge-Collaborative Analysis Technology for Enabling Proactively Immediate Response and Rapid Learning).