https://github.com/drmikecrypto/decentralized-ai-model-training
https://github.com/drmikecrypto/decentralized-ai-model-training
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/drmikecrypto/decentralized-ai-model-training
- Owner: drmikecrypto
- License: mit
- Created: 2024-11-04T19:58:54.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-04T20:00:12.000Z (over 1 year ago)
- Last Synced: 2025-02-05T03:27:25.715Z (over 1 year ago)
- Size: 3.91 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Decentralized AI Model Training
A decentralized AI training infrastructure built on the Sonic network, participating in the Sonic Boom program. This project enables distributed machine learning model training while utilizing Sonic's blockchain technology for secure and efficient AI model development.
## 🎯 Project Overview
This project aims to revolutionize AI model training by:
- Decentralizing the training process across multiple nodes
- Reducing computational costs through shared resources
- Ensuring data privacy and security
- Leveraging blockchain for model verification
- Creating an incentivized training ecosystem
## 🚀 Key Features
- Distributed AI model training across nodes
- Blockchain-based model verification
- Secure data handling and encryption
- Incentive mechanism for node operators
- Automatic resource optimization
- Gas Monetization (GasM) integration
- Real-time model progress tracking
## 🛠 Technical Stack
- Python 3.8+
- Sonic Smart Contracts
- PyTorch/TensorFlow
- Web3.py
- IPFS
- GPU Acceleration Tools
## 📋 Prerequisites
- Python 3.8 or higher
- CUDA-compatible GPU (recommended)
- Sonic network access
- IPFS node
- Required Python packages:
- torch/tensorflow
- web3
- ipfshttpclient
- numpy
- cryptography
- aiohttp
## 🔧 Installation
1. Clone the repository:
```bash
git clone https://github.com/drmikecrypto/decentralized-ai-training.git
cd decentralized-ai-training
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Configure your environment:
```json
{
"web3": {
"rpc_url": "YOUR_RPC_URL",
"private_key": "YOUR_PRIVATE_KEY",
"contract_address": "CONTRACT_ADDRESS",
"contract_abi_path": "abi.json"
},
"ipfs": {
"host": "/ip4/127.0.0.1/tcp/5001",
"cache_dir": "./ipfs_cache"
},
"training": {
"batch_size": 32,
"learning_rate": 0.001,
"epochs": 100
}
}
```
## 💡 How It Works
1. Model Distribution
- AI models are split into trainable components
- Components are distributed across network nodes
- Training tasks are assigned based on node capacity
2. Training Process
- Nodes receive training data and model components
- Local training occurs on each node
- Results are verified through blockchain consensus
3. Model Aggregation
- Trained components are collected and verified
- Results are merged into the final model
- Performance metrics are recorded on-chain
## 🔒 Security Features
- End-to-end encryption
- Secure model weight transfer
- Blockchain-based verification
- Node reputation system
- Anti-tampering mechanisms
- Secure key management
## ⚡ Performance Optimization
- Dynamic batch sizing
- GPU acceleration
- Smart resource allocation
- Efficient data handling
- Adaptive learning rates
- Performance monitoring
## 📊 Monitoring & Analytics
- Real-time training progress
- Node performance metrics
- Resource utilization stats
- Network health monitoring
- Cost tracking
- Training efficiency metrics
## 🤝 Contributing
We welcome contributions! Here's how to help:
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/YourFeature`)
3. Commit changes (`git commit -m 'Add YourFeature'`)
4. Push to branch (`git push origin feature/YourFeature`)
5. Open a Pull Request
## 🎯 Roadmap
- [x] Basic infrastructure setup
- [x] Smart contract integration
- [ ] Advanced node selection algorithm
- [ ] Enhanced security features
- [ ] Mobile node support
- [ ] Cross-chain compatibility
## 📜 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🌟 Acknowledgments
- Sonic Labs and the Sonic Boom program
- The Fantom Foundation
- Safe AA technology partnership
- Open-source AI community
## ⚠️ Disclaimer
This is an experimental project participating in the Sonic Boom program. Use at your own risk.