467 lines
19 KiB
Python
467 lines
19 KiB
Python
#!/usr/bin/env python3
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"""
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Checkpoint Management System for W&B Training
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"""
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import os
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import json
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import logging
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from datetime import datetime, timedelta
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple, Any
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from dataclasses import dataclass, asdict
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from collections import defaultdict
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import torch
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import random
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try:
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import wandb
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WANDB_AVAILABLE = True
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except ImportError:
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WANDB_AVAILABLE = False
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logger = logging.getLogger(__name__)
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@dataclass
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class CheckpointMetadata:
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checkpoint_id: str
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model_name: str
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model_type: str
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file_path: str
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created_at: datetime
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file_size_mb: float
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performance_score: float
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accuracy: Optional[float] = None
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loss: Optional[float] = None
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val_accuracy: Optional[float] = None
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val_loss: Optional[float] = None
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reward: Optional[float] = None
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pnl: Optional[float] = None
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epoch: Optional[int] = None
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training_time_hours: Optional[float] = None
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total_parameters: Optional[int] = None
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wandb_run_id: Optional[str] = None
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wandb_artifact_name: Optional[str] = None
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def to_dict(self) -> Dict[str, Any]:
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data = asdict(self)
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data['created_at'] = self.created_at.isoformat()
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return data
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> 'CheckpointMetadata':
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data['created_at'] = datetime.fromisoformat(data['created_at'])
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return cls(**data)
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class CheckpointManager:
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def __init__(self,
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base_checkpoint_dir: str = "NN/models/saved",
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max_checkpoints_per_model: int = 5,
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metadata_file: str = "checkpoint_metadata.json",
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enable_wandb: bool = True):
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self.base_dir = Path(base_checkpoint_dir)
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self.base_dir.mkdir(parents=True, exist_ok=True)
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self.max_checkpoints = max_checkpoints_per_model
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self.metadata_file = self.base_dir / metadata_file
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self.enable_wandb = enable_wandb and WANDB_AVAILABLE
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self.checkpoints: Dict[str, List[CheckpointMetadata]] = defaultdict(list)
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self._load_metadata()
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logger.info(f"Checkpoint Manager initialized - Max checkpoints per model: {self.max_checkpoints}")
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def save_checkpoint(self, model, model_name: str, model_type: str,
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performance_metrics: Dict[str, float],
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training_metadata: Optional[Dict[str, Any]] = None,
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force_save: bool = False) -> Optional[CheckpointMetadata]:
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try:
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timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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checkpoint_id = f"{model_name}_{timestamp}"
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model_dir = self.base_dir / model_name
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model_dir.mkdir(exist_ok=True)
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checkpoint_path = model_dir / f"{checkpoint_id}.pt"
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performance_score = self._calculate_performance_score(performance_metrics)
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if not force_save and not self._should_save_checkpoint(model_name, performance_score):
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logger.debug(f"Skipping checkpoint save for {model_name} - performance not improved")
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return None
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success = self._save_model_file(model, checkpoint_path, model_type)
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if not success:
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return None
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file_size_mb = checkpoint_path.stat().st_size / (1024 * 1024)
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metadata = CheckpointMetadata(
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checkpoint_id=checkpoint_id,
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model_name=model_name,
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model_type=model_type,
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file_path=str(checkpoint_path),
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created_at=datetime.now(),
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file_size_mb=file_size_mb,
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performance_score=performance_score,
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accuracy=performance_metrics.get('accuracy'),
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loss=performance_metrics.get('loss'),
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val_accuracy=performance_metrics.get('val_accuracy'),
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val_loss=performance_metrics.get('val_loss'),
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reward=performance_metrics.get('reward'),
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pnl=performance_metrics.get('pnl'),
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epoch=training_metadata.get('epoch') if training_metadata else None,
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training_time_hours=training_metadata.get('training_time_hours') if training_metadata else None,
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total_parameters=training_metadata.get('total_parameters') if training_metadata else None
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)
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if self.enable_wandb and wandb.run is not None:
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artifact_name = self._upload_to_wandb(checkpoint_path, metadata)
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metadata.wandb_run_id = wandb.run.id
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metadata.wandb_artifact_name = artifact_name
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self.checkpoints[model_name].append(metadata)
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self._rotate_checkpoints(model_name)
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self._save_metadata()
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logger.info(f"Saved checkpoint: {checkpoint_id} (score: {performance_score:.4f})")
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return metadata
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except Exception as e:
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logger.error(f"Error saving checkpoint for {model_name}: {e}")
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return None
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def load_best_checkpoint(self, model_name: str) -> Optional[Tuple[str, CheckpointMetadata]]:
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try:
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# First, try the standard checkpoint system
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if model_name in self.checkpoints and self.checkpoints[model_name]:
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# Filter out checkpoints with non-existent files
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valid_checkpoints = [
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cp for cp in self.checkpoints[model_name]
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if Path(cp.file_path).exists()
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]
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if valid_checkpoints:
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best_checkpoint = max(valid_checkpoints, key=lambda x: x.performance_score)
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logger.debug(f"Loading best checkpoint for {model_name}: {best_checkpoint.checkpoint_id}")
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return best_checkpoint.file_path, best_checkpoint
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else:
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# Clean up invalid metadata entries
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invalid_count = len(self.checkpoints[model_name])
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logger.warning(f"Found {invalid_count} invalid checkpoint entries for {model_name}, cleaning up metadata")
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self.checkpoints[model_name] = []
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self._save_metadata()
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# Fallback: Look for existing saved models in the legacy format
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logger.debug(f"No valid checkpoints found for model: {model_name}, attempting to find legacy saved models")
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legacy_model_path = self._find_legacy_model(model_name)
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if legacy_model_path:
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# Create checkpoint metadata for the legacy model using actual file data
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legacy_metadata = self._create_legacy_metadata(model_name, legacy_model_path)
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logger.debug(f"Found legacy model for {model_name}: {legacy_model_path}")
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return str(legacy_model_path), legacy_metadata
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logger.warning(f"No checkpoints or legacy models found for: {model_name}")
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return None
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except Exception as e:
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logger.error(f"Error loading best checkpoint for {model_name}: {e}")
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return None
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def _calculate_performance_score(self, metrics: Dict[str, float]) -> float:
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"""Calculate performance score with improved sensitivity for training models"""
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score = 0.0
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# Prioritize loss reduction for active training models
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if 'loss' in metrics:
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# Invert loss so lower loss = higher score, with better scaling
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loss_value = metrics['loss']
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if loss_value > 0:
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score += max(0, 100 / (1 + loss_value)) # More sensitive to loss changes
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else:
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score += 100 # Perfect loss
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# Add other metrics with appropriate weights
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if 'accuracy' in metrics:
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score += metrics['accuracy'] * 50 # Reduced weight to balance with loss
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if 'val_accuracy' in metrics:
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score += metrics['val_accuracy'] * 50
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if 'val_loss' in metrics:
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val_loss = metrics['val_loss']
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if val_loss > 0:
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score += max(0, 50 / (1 + val_loss))
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if 'reward' in metrics:
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score += metrics['reward'] * 10
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if 'pnl' in metrics:
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score += metrics['pnl'] * 5
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if 'training_samples' in metrics:
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# Bonus for processing more training samples
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score += min(10, metrics['training_samples'] / 10)
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# Return actual calculated score - NO SYNTHETIC MINIMUM
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return score
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def _should_save_checkpoint(self, model_name: str, performance_score: float) -> bool:
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"""Improved checkpoint saving logic with more frequent saves during training"""
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if model_name not in self.checkpoints or not self.checkpoints[model_name]:
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return True # Always save first checkpoint
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# Allow more checkpoints during active training
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if len(self.checkpoints[model_name]) < self.max_checkpoints:
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return True
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# Get current best and worst scores
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scores = [cp.performance_score for cp in self.checkpoints[model_name]]
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best_score = max(scores)
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worst_score = min(scores)
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# Save if better than worst (more frequent saves)
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if performance_score > worst_score:
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return True
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# For high-performing models (score > 100), be more sensitive to small improvements
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if best_score > 100:
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# Save if within 0.1% of best score (very sensitive for converged models)
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if performance_score >= best_score * 0.999:
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return True
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else:
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# Also save if we're within 10% of best score (capture near-optimal models)
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if performance_score >= best_score * 0.9:
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return True
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# Save more frequently during active training (every 5th attempt instead of 10th)
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if random.random() < 0.2: # 20% chance to save anyway
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logger.info(f"Saving checkpoint for {model_name} - periodic save during active training")
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return True
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return False
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def _save_model_file(self, model, file_path: Path, model_type: str) -> bool:
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try:
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if hasattr(model, 'state_dict'):
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torch.save({
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'model_state_dict': model.state_dict(),
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'model_type': model_type,
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'saved_at': datetime.now().isoformat()
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}, file_path)
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else:
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torch.save(model, file_path)
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return True
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except Exception as e:
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logger.error(f"Error saving model file {file_path}: {e}")
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return False
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def _rotate_checkpoints(self, model_name: str):
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checkpoint_list = self.checkpoints[model_name]
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if len(checkpoint_list) <= self.max_checkpoints:
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return
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checkpoint_list.sort(key=lambda x: x.performance_score, reverse=True)
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to_remove = checkpoint_list[self.max_checkpoints:]
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self.checkpoints[model_name] = checkpoint_list[:self.max_checkpoints]
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for checkpoint in to_remove:
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try:
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file_path = Path(checkpoint.file_path)
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if file_path.exists():
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file_path.unlink()
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logger.info(f"Rotated out checkpoint: {checkpoint.checkpoint_id}")
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except Exception as e:
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logger.error(f"Error removing rotated checkpoint {checkpoint.checkpoint_id}: {e}")
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def _upload_to_wandb(self, file_path: Path, metadata: CheckpointMetadata) -> Optional[str]:
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try:
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if not self.enable_wandb or wandb.run is None:
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return None
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artifact_name = f"{metadata.model_name}_checkpoint"
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artifact = wandb.Artifact(artifact_name, type="model")
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artifact.add_file(str(file_path))
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wandb.log_artifact(artifact)
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return artifact_name
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except Exception as e:
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logger.error(f"Error uploading to W&B: {e}")
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return None
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def _load_metadata(self):
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try:
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if self.metadata_file.exists():
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with open(self.metadata_file, 'r') as f:
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data = json.load(f)
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for model_name, checkpoint_list in data.items():
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self.checkpoints[model_name] = [
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CheckpointMetadata.from_dict(cp_data)
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for cp_data in checkpoint_list
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]
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logger.info(f"Loaded metadata for {len(self.checkpoints)} models")
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except Exception as e:
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logger.error(f"Error loading checkpoint metadata: {e}")
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def _save_metadata(self):
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try:
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data = {}
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for model_name, checkpoint_list in self.checkpoints.items():
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data[model_name] = [cp.to_dict() for cp in checkpoint_list]
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with open(self.metadata_file, 'w') as f:
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json.dump(data, f, indent=2)
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except Exception as e:
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logger.error(f"Error saving checkpoint metadata: {e}")
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def get_checkpoint_stats(self):
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"""Get statistics about managed checkpoints"""
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stats = {
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'total_models': len(self.checkpoints),
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'total_checkpoints': sum(len(checkpoints) for checkpoints in self.checkpoints.values()),
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'total_size_mb': 0.0,
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'models': {}
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}
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for model_name, checkpoint_list in self.checkpoints.items():
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if not checkpoint_list:
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continue
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model_size = sum(cp.file_size_mb for cp in checkpoint_list)
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best_checkpoint = max(checkpoint_list, key=lambda x: x.performance_score)
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stats['models'][model_name] = {
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'checkpoint_count': len(checkpoint_list),
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'total_size_mb': model_size,
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'best_performance': best_checkpoint.performance_score,
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'best_checkpoint_id': best_checkpoint.checkpoint_id,
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'latest_checkpoint': max(checkpoint_list, key=lambda x: x.created_at).checkpoint_id
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}
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stats['total_size_mb'] += model_size
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return stats
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def _find_legacy_model(self, model_name: str) -> Optional[Path]:
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"""Find legacy saved models based on model name patterns"""
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base_dir = Path(self.base_dir)
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# Define model name mappings and patterns for legacy files
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legacy_patterns = {
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'dqn_agent': [
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'dqn_agent_best_policy.pt',
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'enhanced_dqn_best_policy.pt',
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'improved_dqn_agent_best_policy.pt',
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'dqn_agent_final_policy.pt'
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],
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'enhanced_cnn': [
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'cnn_model_best.pt',
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'optimized_short_term_model_best.pt',
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'optimized_short_term_model_realtime_best.pt',
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'optimized_short_term_model_ticks_best.pt'
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],
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'extrema_trainer': [
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'supervised_model_best.pt'
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],
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'cob_rl': [
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'best_rl_model.pth_policy.pt',
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'rl_agent_best_policy.pt'
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],
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'decision': [
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# Decision models might be in subdirectories, but let's check main dir too
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'decision_best.pt',
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'decision_model_best.pt',
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# Check for transformer models which might be used as decision models
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'enhanced_dqn_best_policy.pt',
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'improved_dqn_agent_best_policy.pt'
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]
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}
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# Get patterns for this model name
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patterns = legacy_patterns.get(model_name, [])
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# Also try generic patterns based on model name
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patterns.extend([
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f'{model_name}_best.pt',
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f'{model_name}_best_policy.pt',
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f'{model_name}_final.pt',
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f'{model_name}_final_policy.pt'
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])
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# Search for the model files
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for pattern in patterns:
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candidate_path = base_dir / pattern
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if candidate_path.exists():
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logger.debug(f"Found legacy model file: {candidate_path}")
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return candidate_path
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# Also check subdirectories
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for subdir in base_dir.iterdir():
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if subdir.is_dir() and subdir.name == model_name:
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for pattern in patterns:
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candidate_path = subdir / pattern
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if candidate_path.exists():
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logger.debug(f"Found legacy model file in subdirectory: {candidate_path}")
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return candidate_path
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return None
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def _create_legacy_metadata(self, model_name: str, file_path: Path) -> CheckpointMetadata:
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"""Create metadata for legacy model files using only actual file information"""
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try:
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file_size_mb = file_path.stat().st_size / (1024 * 1024)
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created_time = datetime.fromtimestamp(file_path.stat().st_mtime)
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# NO SYNTHETIC DATA - use only actual file information
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return CheckpointMetadata(
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checkpoint_id=f"legacy_{model_name}_{int(created_time.timestamp())}",
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model_name=model_name,
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model_type=model_name,
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file_path=str(file_path),
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created_at=created_time,
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file_size_mb=file_size_mb,
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performance_score=0.0, # Unknown performance - use 0, not synthetic values
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accuracy=None,
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loss=None,
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val_accuracy=None,
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val_loss=None,
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reward=None,
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pnl=None,
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epoch=None,
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training_time_hours=None,
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total_parameters=None,
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wandb_run_id=None,
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wandb_artifact_name=None
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)
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except Exception as e:
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logger.error(f"Error creating legacy metadata for {model_name}: {e}")
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# Return a basic metadata with minimal info - NO SYNTHETIC VALUES
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return CheckpointMetadata(
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checkpoint_id=f"legacy_{model_name}",
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model_name=model_name,
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model_type=model_name,
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file_path=str(file_path),
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created_at=datetime.now(),
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file_size_mb=0.0,
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performance_score=0.0 # Unknown - use 0, not synthetic
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)
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_checkpoint_manager = None
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def get_checkpoint_manager() -> CheckpointManager:
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global _checkpoint_manager
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if _checkpoint_manager is None:
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_checkpoint_manager = CheckpointManager()
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return _checkpoint_manager
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def save_checkpoint(model, model_name: str, model_type: str,
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performance_metrics: Dict[str, float],
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training_metadata: Optional[Dict[str, Any]] = None,
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force_save: bool = False) -> Optional[CheckpointMetadata]:
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return get_checkpoint_manager().save_checkpoint(
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model, model_name, model_type, performance_metrics, training_metadata, force_save
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)
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def load_best_checkpoint(model_name: str) -> Optional[Tuple[str, CheckpointMetadata]]:
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return get_checkpoint_manager().load_best_checkpoint(model_name)
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