---
title: "Architecture"
description: "Documentation for Architecture"
---
# ML System Architecture
## Table of Contents
- [Section 1](#section-1)
- [Section 2](#section-2)
*Last Updated: 2025-03-06*
## Overview
Anya Core's Machine Learning system provides advanced AI capabilities for the platform, including Bitcoin analytics, security monitoring, and system intelligence. The ML system follows a hexagonal architecture pattern with clearly defined inputs, outputs, and domain logic.
## System Components
### 1. ML*/Agent Checker System (AIP-002) ✅
The Agent Checker system is a critical component that monitors and verifies the health and readiness of all system components. It uses ML-based analysis to determine component status and system stage.
**Key Features:**
- System stage management (Development: 60%, Production: 90%, Release: 99%)
- Component readiness assessment with detailed metrics
- Input monitoring and analysis
- Auto-save functionality that persists state after every 20th input
- Thread-safe implementation with proper locking
**Implementation:**
- Location: `src/ml/agent_checker.rs`
- AI Label: AIP-002
- Status: ✅ Complete
- Auto-Save: Enabled (every 20th input)
**Component States:**
```rust
pub enum SystemStage {
Development, // 60% threshold
Production, // 90% threshold
Release, // 99% threshold
Unavailable, // Below threshold
}
```
**Architecture:**
```
┌────────────────────┐ ┌─────────────────────┐ ┌────────────────────┐
│ │ │ │ │ │
│ Input Sources │───▶│ Agent Checker │───▶│ System Actions │
│ │ │ │ │ │
└────────────────────┘ └─────────────────────┘ └────────────────────┘
│ ▲
│ │
▼ │
┌────────────────┐
│ │
│ In-Memory │
│ State │
│ │
└────────────────┘
```
### 2. Model Management
The Model Management component handles ML model deployment, versioning, and lifecycle. Models can be loaded, updated, and managed through a unified interface.
**Key Features:**
- Model versioning and tracking
- Model loading and initialization
- Model metadata management
- Model evaluation and performance tracking
### 3. Inference Engine
The Inference Engine executes ML models and provides prediction capabilities to the system.
**Key Features:**
- Real-time inference
- Batch processing
- Hardware acceleration (GPU/NPU)
- Model optimization
### 4. Performance Monitoring (AIR-008) ✅
The Performance Monitoring component tracks ML model and system performance metrics.
**Key Features:**
- Resource monitoring (CPU, Memory, Network, etc.)
- Performance metrics tracking (utilization, throughput, latency)
- Target-based optimization
- Auto-save functionality for configuration changes
**Implementation:**
- Location: `src/core/performance_optimization.rs`
- AI Label: AIR-008
- Status: ✅ Complete
- Auto-Save: Enabled (every 20th change)
### 5. Federated Learning
The Federated Learning component enables distributed model training across nodes.
**Key Features:**
- Local model training
- Model aggregation
- Privacy-preserving learning
- Model distribution
## Auto-Save Implementation
All ML components with state management include auto-save functionality with the following characteristics:
- Configurable auto-save frequency (default: every 20th input/change)
- In-memory state persistence without file I/O
- Thread-safe implementation with proper locking
- Input counting and tracking
- Timestamp-based save verification
```rust
// Example auto-save implementation (simplified)
fn record_input_and_check_save(&self) {
let mut counter = self.input_counter.lock().unwrap();
*counter += 1;
// Auto-save every Nth input
if *counter % self.auto_save_frequency == 0 {
self.save_state_to_memory();
}
}
fn save_state_to_memory(&self) {
// Update last_save timestamp
let mut last_save = self.last_save.lock().unwrap();
*last_save = Instant::now();
// State is kept in memory (no file I/O)
}
```
## Data Flow
```
┌─────────────┐ ┌──────────────┐ ┌─────────────┐ ┌─────────────┐
│ │ │ │ │ │ │ │
│ Data Source │───▶│ Data Pipeline│───▶│ ML Processing│───▶│ Data Sink │
│ │ │ │ │ │ │ │
└─────────────┘ └──────────────┘ └─────────────┘ └─────────────┘
│ ▲
│ │
▼ │
┌────────────────┐
│ │
│ Model Store │
│ │
└────────────────┘
```
## System Interfaces
### Input Ports
- Data ingestion endpoints
- Model registration API
- Training data interface
- System metric collectors
### Output Ports
- Prediction API
- Model performance metrics
- System health indicators
- Alerting and notification
## Implementation Details
### Core ML Components
- `MLSystem` - Main ML system manager
- `MLModel` - Model interface
- `MLService` - Service layer
- `AgentChecker` - System verification component (AIP-002)
- `PerformanceOptimizer` - Performance monitoring and optimization (AIR-008)
### Technology Stack
- TensorFlow / PyTorch for model training and inference
- ONNX for model interoperability
- Rust for system components
- Python for model development
- CUDA/ROCm for GPU acceleration
- Custom tensors for RISC-V
## Integration with Other Components
### Security Integration
The ML system integrates with the Security Architecture to ensure:
- Secure model storage and processing
- Access control for model operations
- Audit logging for ML operations
- Threat detection in ML inputs/outputs
### Performance Integration
The ML system integrates with the Performance Architecture to:
- Monitor resource usage of ML components
- Optimize ML model execution
- Control scaling of ML operations
- Ensure efficient resource utilization
### Core System Integration
The ML system integrates with the Core System to:
- Process input through the AgentChecker
- Receive global configuration from the core system
- Report system health to the core system
- Coordinate operations with other components
## Testing Strategy
The ML system includes comprehensive testing:
1. **Unit Tests**: For individual components and functions
2. **Integration Tests**: For component interaction
3. **Performance Tests**: For model performance and scalability
4. **System Tests**: For end-to-end verification
## Security Considerations
- Model input validation
- Data privacy protection
- Access control for model operations
- Secure model storage
- Attack prevention (model poisoning, adversarial examples)
## Performance Benchmarks
Performance metrics for the ML system:
| Inference Engine | 15-50 | 100-500 | 200-500 |
| Model Loading | 200-1000 | N/A | 50-200 |
| Agent Checker | 5-10 | 1000+ | 10-50 |
| Performance Monitor | 1-5 | 2000+ | 5-20 |
## Future Enhancements
1. Enhanced ML model with more sophisticated pattern recognition
2. Cloud-based metrics storage for long-term analysis
3. Predictive capabilities for proactive component management
4. Advanced anomaly detection in system behavior
5. Automated optimization of system resources
## Bitcoin-Specific ML Features
The ML system includes specialized features for Bitcoin operations that leverage our P1 components:
### 1. Transaction Analysis
- **Pattern recognition** in transaction flows
- **Anomaly detection** in blockchain data
- **Fee estimation optimization** with adaptive learning
- **Block propagation prediction** using network metrics
### 2. Agent Checker Bitcoin Integration
The Agent Checker system specifically monitors Bitcoin-related components:
- **Node Status Monitoring**: Verifies connection status to Bitcoin nodes
- **Blockchain Sync Status**: Tracks blockchain synchronization progress
- **Transaction Pool Monitoring**: Analyzes mempool health and size
- **UTXO Set Analysis**: Monitors UTXO set size and growth patterns
### 3. Security Component Integration
Our ML-based security features integrate with Bitcoin operations:
- **Fraud Detection**: ML models identify suspicious transaction patterns
- **Double-Spend Prevention**: Real-time analysis of transaction propagation
- **Network Partition Detection**: Identifies potential network splits
- **Resource Attack Prevention**: Detects and mitigates resource exhaustion attacks
### 4. Performance Optimization for Bitcoin Operations
The Performance Optimizer specifically enhances Bitcoin operations:
- **Node Performance Tuning**: Optimizes resource allocation for Bitcoin nodes
- **Transaction Validation Acceleration**: Improves transaction verification speed
- **Block Processing Optimization**: Enhances block validation and propagation
- **Network Bandwidth Management**: Optimizes P2P network communication
### 5. Layer 2 Support
The ML system now includes specialized support for Bitcoin Layer 2 solutions:
- **BOB Integration**: Support for the BOB hybrid L2 rollup
- **Bitcoin Relay Monitoring**: Tracking the health and status of BOB's Bitcoin relay
- **Smart Contract Analysis**: ML-based monitoring of Bitcoin-interacting smart contracts
- **Cross-Layer Transaction Verification**: Verifying transactions across Bitcoin and BOB layers
- **BitVM Optimization**: Enhancing BitVM verification processes through ML-driven optimizations
- **Hybrid Stack Analytics**: Analyzing transaction patterns across the hybrid stack
- **Lightning Network Analytics**: Monitoring channel health and liquidity
- **Sidechains Monitoring**: Tracking two-way peg mechanisms and validation
- **State Channel Analysis**: Optimizing state channel opening/closing efficiency
### 6. Auto-Save for Bitcoin State
The auto-save functionality preserves critical Bitcoin operation state:
- **Mempool State**: Preserves pending transaction information
- **Peer Connection Status**: Maintains network topology information
- **Validation Progress**: Saves block validation progress
- **Resource Utilization**: Tracks resource usage patterns for Bitcoin operations
- **Layer 2 State**: Preserves the state of Layer 2 networks and their interactions with the main chain
This integration ensures that our ML*/Agent Checker system provides comprehensive monitoring and optimization for Bitcoin operations while maintaining the system's security and performance across all layers of the Bitcoin ecosystem.
---
[AIR-3][AIS-3][BPC-3][RES-3]
*For more details on how AI components are labeled, see the [AI Labeling System](../standards/AI_LABELING.md).*
## See Also
- [Related Document](#related-document)