log GPU

ANNOTATE/core/real_training_adapter.py

@@ -1800,6 +1800,14 @@ class RealTrainingAdapter:
                 logger.info(f"    GPU available: {torch.cuda.get_device_name(0)}")
                 logger.info(f"    GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f} GB")
                 logger.info(f"    Pre-moving batches to GPU for maximum efficiency")
+                
+                # Log initial GPU status
+                try:
+                    from utils.gpu_monitor import get_gpu_monitor
+                    gpu_monitor = get_gpu_monitor()
+                    gpu_monitor.log_gpu_status("Initial GPU status")
+                except Exception as e:
+                    logger.debug(f"GPU monitor not available: {e}")
             
             # Convert and move batches to GPU immediately
             cached_batches = []
@@ -1929,10 +1937,17 @@ class RealTrainingAdapter:
                 
                 # Log GPU status at start of epoch
                 if use_gpu and torch.cuda.is_available():
-                    # Use CUDA device (0) for memory stats, not the device variable
-                    mem_allocated = torch.cuda.memory_allocated(0) / 1024**3
-                    mem_reserved = torch.cuda.memory_reserved(0) / 1024**3
-                    logger.info(f"   Epoch {epoch + 1}/{session.total_epochs} - GPU Memory: {mem_allocated:.2f}GB allocated, {mem_reserved:.2f}GB reserved")
+                    # Use GPU monitor for detailed metrics
+                    try:
+                        from utils.gpu_monitor import get_gpu_monitor
+                        gpu_monitor = get_gpu_monitor()
+                        gpu_monitor.log_gpu_status(f"Epoch {epoch + 1}/{session.total_epochs}")
+                    except Exception as e:
+                        # Fallback to basic memory stats if monitor not available
+                        logger.debug(f"GPU monitor not available: {e}")
+                        mem_allocated = torch.cuda.memory_allocated(0) / 1024**3
+                        mem_reserved = torch.cuda.memory_reserved(0) / 1024**3
+                        logger.info(f"   Epoch {epoch + 1}/{session.total_epochs} - GPU Memory: {mem_allocated:.2f}GB allocated, {mem_reserved:.2f}GB reserved")
                 
                 # MEMORY FIX: Aggressive cleanup before epoch
                 gc.collect()
@@ -1980,9 +1995,21 @@ class RealTrainingAdapter:
                                     denorm_values = [f"{tf}=${loss:.2f}" for tf, loss in batch_candle_loss_denorm.items()]
                                     denorm_str = f", Real RMSE: {', '.join(denorm_values)}"
                                 
+                                # Get GPU utilization during training
+                                gpu_info = ""
+                                if use_gpu and torch.cuda.is_available():
+                                    try:
+                                        from utils.gpu_monitor import get_gpu_monitor
+                                        gpu_monitor = get_gpu_monitor()
+                                        gpu_summary = gpu_monitor.get_summary_string()
+                                        if gpu_summary != "GPU monitoring not available":
+                                            gpu_info = f" | {gpu_summary}"
+                                    except Exception:
+                                        pass  # GPU monitoring optional
+                                
                                 logger.info(f"      Batch {i + 1}/{total_batches}, Loss: {batch_loss:.6f}, "
                                           f"Candle Acc: {batch_accuracy:.1%}, Trend Acc: {batch_trend_accuracy:.1%}, "
-                                          f"Action Acc: {batch_action_accuracy:.1%}{rmse_str}{denorm_str}")
+                                          f"Action Acc: {batch_action_accuracy:.1%}{rmse_str}{denorm_str}{gpu_info}")
                         else:
                             logger.warning(f"   Batch {i + 1} returned None result - skipping")
                         
@@ -2120,6 +2147,15 @@ class RealTrainingAdapter:
                 # Check memory usage
                 log_memory_usage(f"   Epoch {epoch + 1} end - ")
                 
+                # Log GPU status at end of epoch
+                if use_gpu and torch.cuda.is_available():
+                    try:
+                        from utils.gpu_monitor import get_gpu_monitor
+                        gpu_monitor = get_gpu_monitor()
+                        gpu_monitor.log_gpu_status(f"Epoch {epoch + 1} end")
+                    except Exception:
+                        pass  # GPU monitoring optional
+                
                 logger.info(f"   Epoch {epoch + 1}/{session.total_epochs}, Loss: {avg_loss:.6f}, Accuracy: {avg_accuracy:.2%} ({num_batches} batches)")
             
             session.final_loss = session.current_loss

requirements.txt

@@ -13,7 +13,6 @@ seaborn>=0.12.0
 ta>=0.11.0
 ccxt>=4.0.0
 dash-bootstrap-components>=2.0.0
-asyncio-compat>=0.1.2
 wandb>=0.16.0
 pybit>=5.11.0
 requests>=2.31.0
@@ -38,6 +37,10 @@ duckdb>=0.9.0
 Flask>=3.0.0
 flask-cors>=4.0.0
 
+# Optional GPU monitoring (for NVIDIA GPUs - install if you want detailed GPU utilization metrics)
+pynvml>=11.5.0  # NVIDIA Management Library for GPU utilization monitoring
+# GPUtil>=1.4.0   # Alternative GPU monitoring library
+
 # NOTE: PyTorch is intentionally not pinned here to avoid pulling wrong GPU deps.
 # We only need torch (not torchvision/torchaudio) for trading systems.
 #

utils/gpu_monitor.py

@@ -0,0 +1,248 @@
+"""
+GPU Utilization Monitor
+Provides real-time GPU utilization metrics for NVIDIA and AMD GPUs
+"""
+
+import logging
+import time
+from typing import Dict, Optional, Any
+import platform
+
+logger = logging.getLogger(__name__)
+
+# Try to import GPU monitoring libraries
+try:
+    import torch
+    HAS_TORCH = True
+except ImportError:
+    HAS_TORCH = False
+    torch = None
+
+# Try NVIDIA Management Library (pynvml)
+try:
+    import pynvml
+    HAS_NVML = True
+except ImportError:
+    HAS_NVML = False
+    pynvml = None
+
+# Try GPUtil (alternative NVIDIA monitoring)
+try:
+    import GPUtil
+    HAS_GPUTIL = True
+except ImportError:
+    HAS_GPUTIL = False
+    GPUtil = None
+
+
+class GPUMonitor:
+    """Monitor GPU utilization and performance metrics"""
+    
+    def __init__(self):
+        self.monitoring_enabled = False
+        self.gpu_type = None
+        self.device_id = 0
+        
+        # Initialize monitoring based on available libraries
+        if HAS_TORCH and torch.cuda.is_available():
+            self.monitoring_enabled = True
+            self.device_id = 0
+            
+            # Try to determine GPU vendor
+            try:
+                gpu_name = torch.cuda.get_device_name(0)
+                if 'nvidia' in gpu_name.lower() or 'geforce' in gpu_name.lower() or 'rtx' in gpu_name.lower() or 'gtx' in gpu_name.lower():
+                    self.gpu_type = 'nvidia'
+                elif 'amd' in gpu_name.lower() or 'radeon' in gpu_name.lower():
+                    self.gpu_type = 'amd'
+                else:
+                    self.gpu_type = 'unknown'
+            except Exception:
+                self.gpu_type = 'unknown'
+            
+            # Initialize NVIDIA monitoring if available
+            if self.gpu_type == 'nvidia' and HAS_NVML:
+                try:
+                    pynvml.nvmlInit()
+                    self.nvml_handle = pynvml.nvmlDeviceGetHandleByIndex(self.device_id)
+                    logger.info("GPU monitoring initialized: NVIDIA GPU with NVML")
+                except Exception as e:
+                    logger.debug(f"NVML initialization failed: {e}, will use PyTorch metrics only")
+                    self.nvml_handle = None
+            else:
+                self.nvml_handle = None
+        else:
+            logger.debug("GPU monitoring disabled: No CUDA GPU available")
+    
+    def get_gpu_utilization(self) -> Optional[Dict[str, Any]]:
+        """
+        Get current GPU utilization metrics
+        
+        Returns:
+            Dictionary with GPU utilization metrics or None if not available
+        """
+        if not self.monitoring_enabled or not HAS_TORCH:
+            return None
+        
+        try:
+            metrics = {
+                'device_id': self.device_id,
+                'gpu_type': self.gpu_type,
+                'timestamp': time.time()
+            }
+            
+            # Get GPU memory usage (always available via PyTorch)
+            if torch.cuda.is_available():
+                metrics['memory_allocated_gb'] = torch.cuda.memory_allocated(self.device_id) / 1024**3
+                metrics['memory_reserved_gb'] = torch.cuda.memory_reserved(self.device_id) / 1024**3
+                
+                try:
+                    props = torch.cuda.get_device_properties(self.device_id)
+                    metrics['memory_total_gb'] = props.total_memory / 1024**3
+                    metrics['memory_usage_percent'] = (metrics['memory_allocated_gb'] / metrics['memory_total_gb']) * 100
+                except Exception:
+                    metrics['memory_total_gb'] = None
+                    metrics['memory_usage_percent'] = None
+                
+                metrics['gpu_name'] = torch.cuda.get_device_name(self.device_id)
+            
+            # Get GPU utilization percentage (NVIDIA only via NVML)
+            if self.gpu_type == 'nvidia' and self.nvml_handle is not None:
+                try:
+                    # Get utilization rates
+                    util = pynvml.nvmlDeviceGetUtilizationRates(self.nvml_handle)
+                    metrics['gpu_utilization_percent'] = util.gpu
+                    metrics['memory_utilization_percent'] = util.memory
+                    
+                    # Get power usage
+                    try:
+                        power = pynvml.nvmlDeviceGetPowerUsage(self.nvml_handle) / 1000.0  # Convert mW to W
+                        metrics['power_usage_watts'] = power
+                    except Exception:
+                        metrics['power_usage_watts'] = None
+                    
+                    # Get temperature
+                    try:
+                        temp = pynvml.nvmlDeviceGetTemperature(self.nvml_handle, pynvml.NVML_TEMPERATURE_GPU)
+                        metrics['temperature_celsius'] = temp
+                    except Exception:
+                        metrics['temperature_celsius'] = None
+                    
+                except Exception as e:
+                    logger.debug(f"Failed to get NVML metrics: {e}")
+                    metrics['gpu_utilization_percent'] = None
+                    metrics['memory_utilization_percent'] = None
+            
+            # Fallback to GPUtil if NVML not available
+            elif self.gpu_type == 'nvidia' and HAS_GPUTIL:
+                try:
+                    gpus = GPUtil.getGPUs()
+                    if gpus and len(gpus) > self.device_id:
+                        gpu = gpus[self.device_id]
+                        metrics['gpu_utilization_percent'] = gpu.load * 100
+                        metrics['memory_utilization_percent'] = (gpu.memoryUsed / gpu.memoryTotal) * 100
+                        metrics['temperature_celsius'] = gpu.temperature
+                    else:
+                        metrics['gpu_utilization_percent'] = None
+                        metrics['memory_utilization_percent'] = None
+                except Exception as e:
+                    logger.debug(f"Failed to get GPUtil metrics: {e}")
+                    metrics['gpu_utilization_percent'] = None
+            
+            # For AMD GPUs or when NVML/GPUtil not available, estimate utilization
+            # based on memory usage and activity
+            else:
+                # Estimate GPU utilization based on memory activity
+                # This is a rough estimate - actual GPU compute utilization requires vendor-specific APIs
+                if metrics.get('memory_usage_percent') is not None:
+                    # If memory is being used actively, GPU is likely active
+                    # This is a heuristic, not exact
+                    metrics['gpu_utilization_percent'] = min(metrics['memory_usage_percent'] * 1.2, 100)
+                    metrics['memory_utilization_percent'] = metrics['memory_usage_percent']
+                else:
+                    metrics['gpu_utilization_percent'] = None
+                    metrics['memory_utilization_percent'] = None
+            
+            return metrics
+            
+        except Exception as e:
+            logger.debug(f"Error getting GPU utilization: {e}")
+            return None
+    
+    def log_gpu_status(self, context: str = "") -> None:
+        """
+        Log current GPU status
+        
+        Args:
+            context: Optional context string to include in log message
+        """
+        metrics = self.get_gpu_utilization()
+        if not metrics:
+            return
+        
+        context_str = f"{context} - " if context else ""
+        
+        # Build log message
+        parts = []
+        
+        if metrics.get('gpu_name'):
+            parts.append(f"GPU: {metrics['gpu_name']}")
+        
+        if metrics.get('gpu_utilization_percent') is not None:
+            parts.append(f"Util: {metrics['gpu_utilization_percent']:.1f}%")
+        
+        if metrics.get('memory_allocated_gb') is not None:
+            mem_str = f"Mem: {metrics['memory_allocated_gb']:.2f}GB"
+            if metrics.get('memory_total_gb'):
+                mem_str += f"/{metrics['memory_total_gb']:.2f}GB"
+            if metrics.get('memory_usage_percent'):
+                mem_str += f" ({metrics['memory_usage_percent']:.1f}%)"
+            parts.append(mem_str)
+        
+        if metrics.get('temperature_celsius') is not None:
+            parts.append(f"Temp: {metrics['temperature_celsius']}C")
+        
+        if metrics.get('power_usage_watts') is not None:
+            parts.append(f"Power: {metrics['power_usage_watts']:.1f}W")
+        
+        if parts:
+            logger.info(f"{context_str}{', '.join(parts)}")
+    
+    def get_summary_string(self) -> str:
+        """
+        Get a summary string of current GPU status
+        
+        Returns:
+            Formatted string with GPU metrics
+        """
+        metrics = self.get_gpu_utilization()
+        if not metrics:
+            return "GPU monitoring not available"
+        
+        parts = []
+        
+        if metrics.get('gpu_utilization_percent') is not None:
+            parts.append(f"GPU: {metrics['gpu_utilization_percent']:.1f}%")
+        
+        if metrics.get('memory_allocated_gb') is not None:
+            mem_str = f"Mem: {metrics['memory_allocated_gb']:.2f}GB"
+            if metrics.get('memory_usage_percent'):
+                mem_str += f" ({metrics['memory_usage_percent']:.1f}%)"
+            parts.append(mem_str)
+        
+        if metrics.get('temperature_celsius') is not None:
+            parts.append(f"Temp: {metrics['temperature_celsius']}C")
+        
+        return ", ".join(parts) if parts else "No metrics available"
+
+
+# Global instance
+_gpu_monitor = None
+
+def get_gpu_monitor() -> GPUMonitor:
+    """Get or create global GPU monitor instance"""
+    global _gpu_monitor
+    if _gpu_monitor is None:
+        _gpu_monitor = GPUMonitor()
+    return _gpu_monitor
+

web/clean_dashboard.py

@@ -4533,8 +4533,8 @@ class CleanTradingDashboard:
                 
                 # Add high pivots for this level
                 if highs_x:
-                fig.add_trace(
-                    go.Scatter(
+                    fig.add_trace(
+                        go.Scatter(
                             x=highs_x, y=highs_y,
                             mode='markers',
                             name=f'L{level_num} Pivot High',
@@ -4547,14 +4547,14 @@ class CleanTradingDashboard:
                             ),
                             showlegend=(level_num == 1),  # Only show legend for Level 1
                             hovertemplate=f"Level {level_num} High: ${{y:.2f}}<extra></extra>"
-                    ),
-                    row=row, col=1
-                )
+                        ),
+                        row=row, col=1
+                    )
 
                 # Add low pivots for this level
                 if lows_x:
-                fig.add_trace(
-                    go.Scatter(
+                    fig.add_trace(
+                        go.Scatter(
                             x=lows_x, y=lows_y,
                             mode='markers',
                             name=f'L{level_num} Pivot Low',
@@ -4567,9 +4567,9 @@ class CleanTradingDashboard:
                             ),
                             showlegend=(level_num == 1),  # Only show legend for Level 1
                             hovertemplate=f"Level {level_num} Low: ${{y:.2f}}<extra></extra>"
-                    ),
-                    row=row, col=1
-                )
+                        ),
+                        row=row, col=1
+                    )
 
             # Build external legend HTML (no annotation on chart to avoid scale distortion)
             legend_children = []