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.
- Core System: 80% complete
- Test Coverage: 65%
- Documentation: 80% complete
- Active Development: Meta-learning and security features
See Technical Status for detailed implementation status.
vAIn/
├── docs/ # Comprehensive documentation
└── src/ # Source code
├── client/ # Frontend components
├── communication/ # P2P network layer
├── core/ # AGI components
└── storage/ # Distributed storage
Contributions are welcome! Please see our contributing guidelines and review the Future Roadmap for planned features.
Vincent Janse van Rensburg
[License details to be added]