# Design Document

## Overview

The UI Stability Fix implements a comprehensive solution to resolve critical stability issues between the dashboard UI and training processes. The design focuses on complete process isolation, proper async/await handling, resource conflict resolution, and robust error handling. The solution ensures that the dashboard can operate independently without affecting training system stability.

## Architecture

### High-Level Architecture

```mermaid
graph TB
    subgraph "Training Process"
        TP[Training Process]
        TM[Training Models]
        TD[Training Data]
        TL[Training Logs]
    end
    
    subgraph "Dashboard Process"
        DP[Dashboard Process]
        DU[Dashboard UI]
        DC[Dashboard Cache]
        DL[Dashboard Logs]
    end
    
    subgraph "Shared Resources"
        SF[Shared Files]
        SC[Shared Config]
        SM[Shared Models]
        SD[Shared Data]
    end
    
    TP --> SF
    DP --> SF
    TP --> SC
    DP --> SC
    TP --> SM
    DP --> SM
    TP --> SD
    DP --> SD
    
    TP -.->|No Direct Connection| DP
```

### Process Isolation Design

The system will implement complete process isolation using:

1. **Separate Python Processes**: Dashboard and training run as independent processes
2. **Inter-Process Communication**: File-based communication for status and data sharing
3. **Resource Partitioning**: Separate resource allocation for each process
4. **Independent Lifecycle Management**: Each process can start, stop, and restart independently

### Async/Await Error Resolution

The design addresses async issues through:

1. **Proper Event Loop Management**: Single event loop per process with proper lifecycle
2. **Async Context Isolation**: Separate async contexts for different components
3. **Coroutine Handling**: Proper awaiting of all async operations
4. **Exception Propagation**: Proper async exception handling and propagation

## Components and Interfaces

### 1. Process Manager

**Purpose**: Manages the lifecycle of both dashboard and training processes

**Interface**:
```python
class ProcessManager:
    def start_training_process(self) -> bool
    def start_dashboard_process(self, port: int = 8050) -> bool
    def stop_training_process(self) -> bool
    def stop_dashboard_process(self) -> bool
    def get_process_status(self) -> Dict[str, str]
    def restart_process(self, process_name: str) -> bool
```

**Implementation Details**:
- Uses subprocess.Popen for process creation
- Monitors process health with periodic checks
- Handles process output logging and error capture
- Implements graceful shutdown with timeout handling

### 2. Isolated Dashboard

**Purpose**: Provides a completely isolated dashboard that doesn't interfere with training

**Interface**:
```python
class IsolatedDashboard:
    def __init__(self, config: Dict[str, Any])
    def start_server(self, host: str, port: int) -> None
    def stop_server(self) -> None
    def update_data_from_files(self) -> None
    def get_training_status(self) -> Dict[str, Any]
```

**Implementation Details**:
- Runs in separate process with own event loop
- Reads data from shared files instead of direct memory access
- Uses file-based communication for training status
- Implements proper async/await patterns for all operations

### 3. Isolated Training Process

**Purpose**: Runs training completely isolated from UI components

**Interface**:
```python
class IsolatedTrainingProcess:
    def __init__(self, config: Dict[str, Any])
    def start_training(self) -> None
    def stop_training(self) -> None
    def get_training_metrics(self) -> Dict[str, Any]
    def save_status_to_file(self) -> None
```

**Implementation Details**:
- No UI dependencies or imports
- Writes status and metrics to shared files
- Implements proper resource cleanup
- Uses separate logging configuration

### 4. Shared Data Manager

**Purpose**: Manages data sharing between processes through files

**Interface**:
```python
class SharedDataManager:
    def write_training_status(self, status: Dict[str, Any]) -> None
    def read_training_status(self) -> Dict[str, Any]
    def write_market_data(self, data: Dict[str, Any]) -> None
    def read_market_data(self) -> Dict[str, Any]
    def write_model_metrics(self, metrics: Dict[str, Any]) -> None
    def read_model_metrics(self) -> Dict[str, Any]
```

**Implementation Details**:
- Uses JSON files for structured data
- Implements file locking to prevent corruption
- Provides atomic write operations
- Includes data validation and error handling

### 5. Resource Manager

**Purpose**: Manages resource allocation and prevents conflicts

**Interface**:
```python
class ResourceManager:
    def allocate_gpu_resources(self, process_name: str) -> bool
    def release_gpu_resources(self, process_name: str) -> None
    def check_memory_usage(self) -> Dict[str, float]
    def enforce_resource_limits(self) -> None
```

**Implementation Details**:
- Monitors GPU memory usage per process
- Implements resource quotas and limits
- Provides resource conflict detection
- Includes automatic resource cleanup

### 6. Async Handler

**Purpose**: Properly handles all async operations in the dashboard

**Interface**:
```python
class AsyncHandler:
    def __init__(self, loop: asyncio.AbstractEventLoop)
    async def handle_orchestrator_connection(self) -> None
    async def handle_cob_integration(self) -> None
    async def handle_trading_decisions(self, decision: Dict) -> None
    def run_async_safely(self, coro: Coroutine) -> Any
```

**Implementation Details**:
- Manages single event loop per process
- Provides proper exception handling for async operations
- Implements timeout handling for long-running operations
- Includes async context management

## Data Models

### Process Status Model
```python
@dataclass
class ProcessStatus:
    name: str
    pid: int
    status: str  # 'running', 'stopped', 'error'
    start_time: datetime
    last_heartbeat: datetime
    memory_usage: float
    cpu_usage: float
    error_message: Optional[str] = None
```

### Training Status Model
```python
@dataclass
class TrainingStatus:
    is_running: bool
    current_epoch: int
    total_epochs: int
    loss: float
    accuracy: float
    last_update: datetime
    model_path: str
    error_message: Optional[str] = None
```

### Dashboard State Model
```python
@dataclass
class DashboardState:
    is_connected: bool
    last_data_update: datetime
    active_connections: int
    error_count: int
    performance_metrics: Dict[str, float]
```

## Error Handling

### Exception Hierarchy
```python
class UIStabilityError(Exception):
    """Base exception for UI stability issues"""
    pass

class ProcessCommunicationError(UIStabilityError):
    """Error in inter-process communication"""
    pass

class AsyncOperationError(UIStabilityError):
    """Error in async operation handling"""
    pass

class ResourceConflictError(UIStabilityError):
    """Error due to resource conflicts"""
    pass
```

### Error Recovery Strategies

1. **Automatic Retry**: For transient network and file I/O errors
2. **Graceful Degradation**: Fallback to basic functionality when components fail
3. **Process Restart**: Automatic restart of failed processes
4. **Circuit Breaker**: Temporary disable of failing components
5. **Rollback**: Revert to last known good state

### Error Monitoring

- Centralized error logging with structured format
- Real-time error rate monitoring
- Automatic alerting for critical errors
- Error trend analysis and reporting

## Testing Strategy

### Unit Tests
- Test each component in isolation
- Mock external dependencies
- Verify error handling paths
- Test async operation handling

### Integration Tests
- Test inter-process communication
- Verify resource sharing mechanisms
- Test process lifecycle management
- Validate error recovery scenarios

### System Tests
- End-to-end stability testing
- Load testing with concurrent processes
- Failure injection testing
- Performance regression testing

### Monitoring Tests
- Health check endpoint testing
- Metrics collection validation
- Alert system testing
- Dashboard functionality testing

## Performance Considerations

### Resource Optimization
- Minimize memory footprint of each process
- Optimize file I/O operations for data sharing
- Implement efficient data serialization
- Use connection pooling for external services

### Scalability
- Support multiple dashboard instances
- Handle increased data volume gracefully
- Implement efficient caching strategies
- Optimize for high-frequency updates

### Monitoring
- Real-time performance metrics collection
- Resource usage tracking per process
- Response time monitoring
- Throughput measurement

## Security Considerations

### Process Isolation
- Separate user contexts for processes
- Limited file system access permissions
- Network access restrictions
- Resource usage limits

### Data Protection
- Secure file sharing mechanisms
- Data validation and sanitization
- Access control for shared resources
- Audit logging for sensitive operations

### Communication Security
- Encrypted inter-process communication
- Authentication for API endpoints
- Input validation for all interfaces
- Rate limiting for external requests

## Deployment Strategy

### Development Environment
- Local process management scripts
- Development-specific configuration
- Enhanced logging and debugging
- Hot-reload capabilities

### Production Environment
- Systemd service management
- Production configuration templates
- Log rotation and archiving
- Monitoring and alerting setup

### Migration Plan
1. Deploy new process management components
2. Update configuration files
3. Test process isolation functionality
4. Gradually migrate existing deployments
5. Monitor stability improvements
6. Remove legacy components