⚡ Thunders-AI

Thunders-AI is a high-performance, modular framework designed to orchestrate and deploy artificial intelligence models at scale. Built for speed and flexibility, it allows developers to integrate complex AI workflows into their applications with minimal boilerplate.

🚀 Features

• Lightning Fast: Optimized for low-latency inference and high-throughput processing.
• Model Agnostic: Seamlessly switch between PyTorch, TensorFlow, and ONNX models.
• Modular Architecture: Plug-and-play components for data preprocessing, inference, and post-processing.
• Scalable: Designed to run from edge devices to distributed cloud clusters.
• Easy Integration: Simple Python API that fits naturally into existing codebases.

📦 Installation

Install Thunders-AI via pip for the latest stable release:

pip install thunders-ai

Or install the development version from source:

git clone https://github.com/Thunders-AI/Thunders-AI.git
cd Thunders-AI
pip install -e .

🛠️ Usage

Here is a simple example of how to initialize a model and run a prediction.

Basic Example

from thunders import Model, Pipeline

# Initialize a pre-trained model
model = Model.load("thunders/efficient-nano-v1")

# Create a processing pipeline
pipeline = Pipeline([
    model,
    # Add post-processing steps here
])

# Run inference
input_data = {"text": "Hello, Thunders-AI!"}
result = pipeline.run(input_data)

print(result)
# Output: {'prediction': 'Success', 'confidence': 0.99}

CLI Tool

Thunders-AI also comes with a built-in command-line interface for quick testing.

thunders predict --model "thunders/efficient-nano-v1" --input "data.json"

📚 Documentation

For more detailed information, API references, and tutorials, please visit our official Documentation.

• Getting Started Guide
• API Reference
• Contributing Guidelines

🗺️ Roadmap

• Core Inference Engine
• Python API Wrapper
• REST API Server
• Dashboard UI
• Support for LLMs (Large Language Models)

🤝 Contributing

We welcome contributions from the community! If you'd like to help improve Thunders-AI, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/amazing-feature).
3. Commit your changes (git commit -m 'Add some amazing feature').
4. Push to the branch (git push origin feature/amazing-feature).
5. Open a Pull Request.

Please read our Code of Conduct before interacting with our community.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Made with ❤️ by the Thunders-AI Team