vAIn: A Decentralized AGI System

Overview

vAIn (Virtual Artificial Intelligence Network) is a decentralized Artificial General Intell

from vain.node import NodeConfiguration, SecurityConfig, FederatedConfig, VAInNode

class ComputeNode:
    def __init__(self, node_id, node_type, max_memory, max_cpu_cores, gpu_available, network_bandwidth, storage_capacity):
        self.node_config = NodeConfiguration(
            node_id=node_id,
            node_type=node_type,
            max_memory=max_memory,
            max_cpu_cores=max_cpu_cores,
            gpu_available=gpu_available,
            network_bandwidth=network_bandwidth,
            storage_capacity=storage_capacity
        )
        self.security_config = SecurityConfig()
        self.federated_config = FederatedConfig()

    def start(self):
        node = VAInNode(
            node_config=self.node_config,
            security_config=self.security_config,
            federated_config=self.federated_config
        )
        node.start()

# Configure and start a compute node
compute_node = ComputeNode(
    node_id="compute-01",
    node_type="COMPUTE",
    max_memory=16384,  # 16GB
    max_cpu_cores=8,
    gpu_available=True,
    network_bandwidth=1000,  # 1Gbps
    storage_capacity=1000    # 1TB
)
compute_node.start()
```igence (AGI) system that operates across a network of peer nodes. Each node contributes to the collective intelligence through federated learning and shared computation while maintaining privacy and security.

## Key Features
- **Decentralized Architecture**: P2P network using libp2p for distributed computation
- **Federated Learning**: Privacy-preserving collaborative model training with secure aggregation
- **Meta-Learning System**: Cross-domain knowledge transfer and adaptive learning
- **Neural Architecture Search**: Evolutionary strategies for architecture optimization
- **Security & Privacy**: Homomorphic encryption and differential privacy (ε=0.1)

## System Components

### Core Layer (80% Complete)
- Federated Learning Engine with secure aggregation
- Meta-Learning System (MAML implementation)
- Neural Architecture Search with evolutionary strategies
- Privacy-preserving computation with noise injection

### Network Layer
- P2P communication using libp2p
- Secure message passing
- Node discovery and trust scoring
- Multi-transport support

### Client Layer
- React-based frontend dashboard
- Real-time performance monitoring
- Node management interface
- Training visualization

### Storage Layer
- Distributed storage using IPFS
- Model weight distribution
- Secure data management
- Performance metrics tracking

## Getting Started

### Prerequisites
- Python 3.8+
- Node.js 16+
- GPU recommended for compute nodes

### Basic Node Setup
```python
from vain.node import NodeConfiguration, SecurityConfig, FederatedConfig, VAInNode

# Configure a compute node
node_config = NodeConfiguration(
    node_id="compute-01",
    node_type="COMPUTE",
    max_memory=16384,  # 16GB
    max_cpu_cores=8,
    gpu_available=True,
    network_bandwidth=1000,  # 1Gbps
    storage_capacity=1000    # 1TB
)

# Start the node
node = VAInNode(
    node_config=node_config,
    security_config=SecurityConfig(),
    federated_config=FederatedConfig()
)
node.start()

For detailed setup instructions and examples, see User Guide.

Technical Status

Core System: 80% complete
Test Coverage: 65%
Documentation: 80% complete
Active Development: Meta-learning and security features

See Technical Status for detailed implementation status.

Documentation

Project Structure

vAIn/
├── docs/         # Comprehensive documentation
└── src/          # Source code
    ├── client/   # Frontend components
    ├── communication/  # P2P network layer
    ├── core/     # AGI components
    └── storage/  # Distributed storage

Contributing

Contributions are welcome! Please see our contributing guidelines and review the Future Roadmap for planned features.

Creator

Vincent Janse van Rensburg

License

[License details to be added]

VAIn

Install / Use

README