refactoring
This commit is contained in:
Dobromir Popov
@@ -1,560 +0,0 @@
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#!/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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WANDB_AVAILABLE = False
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# Import model registry
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from utils.model_registry import get_model_registry
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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 = False):
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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 = False
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self.checkpoints: Dict[str, List[CheckpointMetadata]] = defaultdict(list)
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self._warned_models = set() # Track models we've warned about to reduce spam
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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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"""Save a model checkpoint with improved error handling and validation using unified registry"""
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try:
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from utils.model_registry import save_checkpoint as registry_save_checkpoint
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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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# Use unified registry for checkpointing
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success = registry_save_checkpoint(
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model=model,
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model_name=model_name,
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model_type=model_type,
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performance_score=performance_score,
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metadata={
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'performance_metrics': performance_metrics,
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'training_metadata': training_metadata,
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'checkpoint_manager': True
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}
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)
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if not success:
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return None
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# Get checkpoint info from registry
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registry = get_model_registry()
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checkpoint_info = registry.metadata['models'][model_name]['checkpoints'][-1]
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# Create CheckpointMetadata object
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metadata = CheckpointMetadata(
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checkpoint_id=checkpoint_info['id'],
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model_name=model_name,
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model_type=model_type,
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file_path=checkpoint_info['path'],
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created_at=datetime.fromisoformat(checkpoint_info['timestamp']),
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file_size_mb=0.0, # Will be calculated by registry
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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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# Update local checkpoint tracking
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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.debug(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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from utils.model_registry import load_best_checkpoint as registry_load_checkpoint
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# First, try the unified registry
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registry_result = registry_load_checkpoint(model_name, 'cnn') # Try CNN type first
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if registry_result is None:
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registry_result = registry_load_checkpoint(model_name, 'dqn') # Try DQN type
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if registry_result:
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checkpoint_path, checkpoint_data = registry_result
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# Create CheckpointMetadata from registry data
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metadata = CheckpointMetadata(
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checkpoint_id=f"{model_name}_registry",
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model_name=model_name,
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model_type=checkpoint_data.get('model_type', 'unknown'),
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file_path=checkpoint_path,
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created_at=datetime.fromisoformat(checkpoint_data.get('timestamp', datetime.now().isoformat())),
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file_size_mb=0.0, # Will be calculated by registry
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performance_score=checkpoint_data.get('performance_score', 0.0),
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accuracy=checkpoint_data.get('accuracy'),
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loss=checkpoint_data.get('loss'),
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reward=checkpoint_data.get('reward'),
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pnl=checkpoint_data.get('pnl')
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)
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logger.debug(f"Loading checkpoint from unified registry for {model_name}")
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return checkpoint_path, metadata
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# Fallback: 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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# Only warn once per model to avoid spam
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if model_name not in self._warned_models:
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logger.info(f"No checkpoints found for {model_name}, starting fresh")
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self._warned_models.add(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.debug(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.debug(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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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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# Additional search locations
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search_dirs = [
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base_dir,
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Path("models/saved"),
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Path("NN/models/saved"),
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Path("models"),
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Path("models/archive"),
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Path("models/backtest")
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]
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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_session_policy.pt',
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'dqn_agent_session_agent_state.pt',
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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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'trading_agent_best_pnl.pt'
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],
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'enhanced_cnn': [
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'cnn_model_session.pt',
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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 in all search directories
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for search_dir in search_dirs:
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if not search_dir.exists():
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continue
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for pattern in patterns:
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candidate_path = search_dir / pattern
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if candidate_path.exists():
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logger.info(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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# Extended search: scan common project model directories for best checkpoints
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try:
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# Attempt to infer project root from base_dir (NN/models/saved -> root)
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project_root = base_dir.resolve().parent.parent.parent
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except Exception:
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project_root = Path(".").resolve()
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additional_dirs = [
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project_root / "models",
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project_root / "models" / "archive",
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project_root / "models" / "backtest",
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]
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def _match_legacy_name(candidate: Path, model: str) -> bool:
|
||||
name = candidate.name.lower()
|
||||
model_keys = {
|
||||
'dqn_agent': ['dqn', 'agent', 'policy'],
|
||||
'enhanced_cnn': ['cnn', 'optimized_short_term'],
|
||||
'extrema_trainer': ['supervised', 'extrema'],
|
||||
'cob_rl': ['cob', 'rl', 'policy'],
|
||||
'decision': ['decision', 'transformer']
|
||||
}.get(model, [model])
|
||||
return any(k in name for k in model_keys)
|
||||
|
||||
candidates: List[Path] = []
|
||||
for adir in additional_dirs:
|
||||
if not adir.exists():
|
||||
continue
|
||||
try:
|
||||
for pt in adir.rglob('*.pt'):
|
||||
# Prefer files that indicate "best" and match model hints
|
||||
lname = pt.name.lower()
|
||||
if 'best' in lname and _match_legacy_name(pt, model_name):
|
||||
candidates.append(pt)
|
||||
# Do not add generic fallbacks to avoid mismatched model types
|
||||
except Exception:
|
||||
# Ignore directory traversal issues
|
||||
pass
|
||||
|
||||
if candidates:
|
||||
# Pick the most recently modified candidate
|
||||
try:
|
||||
best = max(candidates, key=lambda p: p.stat().st_mtime)
|
||||
logger.debug(f"Found legacy model file in project models dir: {best}")
|
||||
return best
|
||||
except Exception:
|
||||
# If stat fails, just return the first one deterministically
|
||||
candidates.sort()
|
||||
logger.debug(f"Found legacy model file in project models dir: {candidates[0]}")
|
||||
return candidates[0]
|
||||
|
||||
return None
|
||||
|
||||
def _create_legacy_metadata(self, model_name: str, file_path: Path) -> CheckpointMetadata:
|
||||
"""Create metadata for legacy model files using only actual file information"""
|
||||
try:
|
||||
file_size_mb = file_path.stat().st_size / (1024 * 1024)
|
||||
created_time = datetime.fromtimestamp(file_path.stat().st_mtime)
|
||||
|
||||
# NO SYNTHETIC DATA - use only actual file information
|
||||
return CheckpointMetadata(
|
||||
checkpoint_id=f"legacy_{model_name}_{int(created_time.timestamp())}",
|
||||
model_name=model_name,
|
||||
model_type=model_name,
|
||||
file_path=str(file_path),
|
||||
created_at=created_time,
|
||||
file_size_mb=file_size_mb,
|
||||
performance_score=0.0, # Unknown performance - use 0, not synthetic values
|
||||
accuracy=None,
|
||||
loss=None,
|
||||
val_accuracy=None,
|
||||
val_loss=None,
|
||||
reward=None,
|
||||
pnl=None,
|
||||
epoch=None,
|
||||
training_time_hours=None,
|
||||
total_parameters=None,
|
||||
wandb_run_id=None,
|
||||
wandb_artifact_name=None
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Error creating legacy metadata for {model_name}: {e}")
|
||||
# Return a basic metadata with minimal info - NO SYNTHETIC VALUES
|
||||
return CheckpointMetadata(
|
||||
checkpoint_id=f"legacy_{model_name}",
|
||||
model_name=model_name,
|
||||
model_type=model_name,
|
||||
file_path=str(file_path),
|
||||
created_at=datetime.now(),
|
||||
file_size_mb=0.0,
|
||||
performance_score=0.0 # Unknown - use 0, not synthetic
|
||||
)
|
||||
|
||||
_checkpoint_manager = None
|
||||
|
||||
def get_checkpoint_manager() -> CheckpointManager:
|
||||
global _checkpoint_manager
|
||||
if _checkpoint_manager is None:
|
||||
_checkpoint_manager = CheckpointManager()
|
||||
return _checkpoint_manager
|
||||
|
||||
def save_checkpoint(model, model_name: str, model_type: str,
|
||||
performance_metrics: Dict[str, float],
|
||||
training_metadata: Optional[Dict[str, Any]] = None,
|
||||
force_save: bool = False) -> Optional[CheckpointMetadata]:
|
||||
return get_checkpoint_manager().save_checkpoint(
|
||||
model, model_name, model_type, performance_metrics, training_metadata, force_save
|
||||
)
|
||||
|
||||
def load_best_checkpoint(model_name: str) -> Optional[Tuple[str, CheckpointMetadata]]:
|
||||
return get_checkpoint_manager().load_best_checkpoint(model_name)
|
||||
@@ -1,446 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Unified Model Registry for Centralized Model Management
|
||||
|
||||
This module provides a unified interface for saving, loading, and managing
|
||||
all machine learning models in the trading system. It consolidates model
|
||||
storage from multiple locations into a single, organized structure.
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
import torch
|
||||
import logging
|
||||
import pickle
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, Optional, Tuple, List
|
||||
from datetime import datetime
|
||||
import hashlib
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class ModelRegistry:
|
||||
"""
|
||||
Unified model registry for centralized model management.
|
||||
Handles saving, loading, and organization of all ML models.
|
||||
"""
|
||||
|
||||
def __init__(self, base_dir: str = "models"):
|
||||
"""
|
||||
Initialize the model registry.
|
||||
|
||||
Args:
|
||||
base_dir: Base directory for model storage
|
||||
"""
|
||||
self.base_dir = Path(base_dir)
|
||||
self.saved_dir = self.base_dir / "saved"
|
||||
self.checkpoint_dir = self.base_dir / "checkpoints"
|
||||
self.archive_dir = self.base_dir / "archive"
|
||||
|
||||
# Model type directories
|
||||
self.model_dirs = {
|
||||
'cnn': self.base_dir / "cnn",
|
||||
'dqn': self.base_dir / "dqn",
|
||||
'transformer': self.base_dir / "transformer",
|
||||
'hybrid': self.base_dir / "hybrid"
|
||||
}
|
||||
|
||||
# Ensure all directories exist
|
||||
self._ensure_directories()
|
||||
|
||||
# Metadata tracking
|
||||
self.metadata_file = self.base_dir / "registry_metadata.json"
|
||||
self.metadata = self._load_metadata()
|
||||
|
||||
logger.info(f"Model Registry initialized at {self.base_dir}")
|
||||
|
||||
def _ensure_directories(self):
|
||||
"""Ensure all required directories exist."""
|
||||
directories = [
|
||||
self.saved_dir,
|
||||
self.checkpoint_dir,
|
||||
self.archive_dir
|
||||
]
|
||||
|
||||
# Add model type directories
|
||||
for model_dir in self.model_dirs.values():
|
||||
directories.extend([
|
||||
model_dir / "saved",
|
||||
model_dir / "checkpoints",
|
||||
model_dir / "archive"
|
||||
])
|
||||
|
||||
for directory in directories:
|
||||
directory.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
def _load_metadata(self) -> Dict[str, Any]:
|
||||
"""Load registry metadata."""
|
||||
if self.metadata_file.exists():
|
||||
try:
|
||||
with open(self.metadata_file, 'r') as f:
|
||||
return json.load(f)
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to load metadata: {e}")
|
||||
return {'models': {}, 'last_updated': datetime.now().isoformat()}
|
||||
|
||||
def _save_metadata(self):
|
||||
"""Save registry metadata."""
|
||||
self.metadata['last_updated'] = datetime.now().isoformat()
|
||||
try:
|
||||
with open(self.metadata_file, 'w') as f:
|
||||
json.dump(self.metadata, f, indent=2)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to save metadata: {e}")
|
||||
|
||||
def save_model(self, model: Any, model_name: str, model_type: str = 'cnn',
|
||||
metadata: Optional[Dict[str, Any]] = None) -> bool:
|
||||
"""
|
||||
Save a model to the unified storage.
|
||||
|
||||
Args:
|
||||
model: The model to save
|
||||
model_name: Name of the model
|
||||
model_type: Type of model (cnn, dqn, transformer, hybrid)
|
||||
metadata: Additional metadata to save
|
||||
|
||||
Returns:
|
||||
bool: True if successful, False otherwise
|
||||
"""
|
||||
try:
|
||||
model_dir = self.model_dirs.get(model_type, self.saved_dir)
|
||||
save_dir = model_dir / "saved"
|
||||
|
||||
# Generate filename with timestamp
|
||||
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
filename = f"{model_name}_{timestamp}.pt"
|
||||
filepath = save_dir / filename
|
||||
|
||||
# Also save as latest
|
||||
latest_filepath = save_dir / f"{model_name}_latest.pt"
|
||||
|
||||
# Save model
|
||||
save_dict = {
|
||||
'model_state_dict': model.state_dict() if hasattr(model, 'state_dict') else {},
|
||||
'model_class': model.__class__.__name__,
|
||||
'model_type': model_type,
|
||||
'timestamp': timestamp,
|
||||
'metadata': metadata or {}
|
||||
}
|
||||
|
||||
torch.save(save_dict, filepath)
|
||||
torch.save(save_dict, latest_filepath)
|
||||
|
||||
# Update metadata
|
||||
if model_name not in self.metadata['models']:
|
||||
self.metadata['models'][model_name] = {}
|
||||
|
||||
self.metadata['models'][model_name].update({
|
||||
'type': model_type,
|
||||
'latest_path': str(latest_filepath),
|
||||
'last_saved': timestamp,
|
||||
'save_count': self.metadata['models'][model_name].get('save_count', 0) + 1
|
||||
})
|
||||
|
||||
self._save_metadata()
|
||||
|
||||
logger.info(f"Model {model_name} saved to {filepath}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to save model {model_name}: {e}")
|
||||
return False
|
||||
|
||||
def load_model(self, model_name: str, model_type: str = 'cnn',
|
||||
model_class: Optional[Any] = None) -> Optional[Any]:
|
||||
"""
|
||||
Load a model from the unified storage.
|
||||
|
||||
Args:
|
||||
model_name: Name of the model to load
|
||||
model_type: Type of model (cnn, dqn, transformer, hybrid)
|
||||
model_class: Model class to instantiate (if needed)
|
||||
|
||||
Returns:
|
||||
The loaded model or None if failed
|
||||
"""
|
||||
try:
|
||||
model_dir = self.model_dirs.get(model_type, self.saved_dir)
|
||||
save_dir = model_dir / "saved"
|
||||
latest_filepath = save_dir / f"{model_name}_latest.pt"
|
||||
|
||||
if not latest_filepath.exists():
|
||||
logger.warning(f"Model {model_name} not found at {latest_filepath}")
|
||||
return None
|
||||
|
||||
# Load checkpoint
|
||||
checkpoint = torch.load(latest_filepath, map_location='cpu')
|
||||
|
||||
# Instantiate model if class provided
|
||||
if model_class is not None:
|
||||
model = model_class()
|
||||
model.load_state_dict(checkpoint['model_state_dict'])
|
||||
else:
|
||||
# Try to reconstruct model from state_dict
|
||||
model = type('LoadedModel', (), {})()
|
||||
model.state_dict = lambda: checkpoint['model_state_dict']
|
||||
model.load_state_dict = lambda state_dict: None
|
||||
|
||||
logger.info(f"Model {model_name} loaded from {latest_filepath}")
|
||||
return model
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to load model {model_name}: {e}")
|
||||
return None
|
||||
|
||||
def save_checkpoint(self, model: Any, model_name: str, model_type: str = 'cnn',
|
||||
performance_score: float = 0.0,
|
||||
metadata: Optional[Dict[str, Any]] = None) -> bool:
|
||||
"""
|
||||
Save a model checkpoint.
|
||||
|
||||
Args:
|
||||
model: The model to checkpoint
|
||||
model_name: Name of the model
|
||||
model_type: Type of model
|
||||
performance_score: Performance score for this checkpoint
|
||||
metadata: Additional metadata
|
||||
|
||||
Returns:
|
||||
bool: True if successful, False otherwise
|
||||
"""
|
||||
try:
|
||||
model_dir = self.model_dirs.get(model_type, self.checkpoint_dir)
|
||||
checkpoint_dir = model_dir / "checkpoints"
|
||||
|
||||
# Generate checkpoint ID
|
||||
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
checkpoint_id = f"{model_name}_{timestamp}_{performance_score:.4f}"
|
||||
|
||||
filepath = checkpoint_dir / f"{checkpoint_id}.pt"
|
||||
|
||||
# Save checkpoint
|
||||
checkpoint_data = {
|
||||
'model_state_dict': model.state_dict() if hasattr(model, 'state_dict') else {},
|
||||
'model_class': model.__class__.__name__,
|
||||
'model_type': model_type,
|
||||
'model_name': model_name,
|
||||
'performance_score': performance_score,
|
||||
'timestamp': timestamp,
|
||||
'metadata': metadata or {}
|
||||
}
|
||||
|
||||
torch.save(checkpoint_data, filepath)
|
||||
|
||||
# Update metadata
|
||||
if model_name not in self.metadata['models']:
|
||||
self.metadata['models'][model_name] = {}
|
||||
|
||||
if 'checkpoints' not in self.metadata['models'][model_name]:
|
||||
self.metadata['models'][model_name]['checkpoints'] = []
|
||||
|
||||
checkpoint_info = {
|
||||
'id': checkpoint_id,
|
||||
'path': str(filepath),
|
||||
'performance_score': performance_score,
|
||||
'timestamp': timestamp
|
||||
}
|
||||
|
||||
self.metadata['models'][model_name]['checkpoints'].append(checkpoint_info)
|
||||
|
||||
# Keep only top 5 checkpoints
|
||||
checkpoints = self.metadata['models'][model_name]['checkpoints']
|
||||
if len(checkpoints) > 5:
|
||||
checkpoints.sort(key=lambda x: x['performance_score'], reverse=True)
|
||||
checkpoints_to_remove = checkpoints[5:]
|
||||
|
||||
for checkpoint in checkpoints_to_remove:
|
||||
try:
|
||||
os.remove(checkpoint['path'])
|
||||
except:
|
||||
pass
|
||||
|
||||
self.metadata['models'][model_name]['checkpoints'] = checkpoints[:5]
|
||||
|
||||
self._save_metadata()
|
||||
|
||||
logger.info(f"Checkpoint {checkpoint_id} saved with score {performance_score}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to save checkpoint for {model_name}: {e}")
|
||||
return False
|
||||
|
||||
def load_best_checkpoint(self, model_name: str, model_type: str = 'cnn') -> Optional[Tuple[str, Any]]:
|
||||
"""
|
||||
Load the best checkpoint for a model.
|
||||
|
||||
Args:
|
||||
model_name: Name of the model
|
||||
model_type: Type of model
|
||||
|
||||
Returns:
|
||||
Tuple of (checkpoint_path, checkpoint_data) or None
|
||||
"""
|
||||
try:
|
||||
if model_name not in self.metadata['models']:
|
||||
logger.warning(f"No metadata found for model {model_name}")
|
||||
return None
|
||||
|
||||
checkpoints = self.metadata['models'][model_name].get('checkpoints', [])
|
||||
if not checkpoints:
|
||||
logger.warning(f"No checkpoints found for model {model_name}")
|
||||
return None
|
||||
|
||||
# Find best checkpoint by performance score
|
||||
best_checkpoint = max(checkpoints, key=lambda x: x['performance_score'])
|
||||
checkpoint_path = best_checkpoint['path']
|
||||
|
||||
if not os.path.exists(checkpoint_path):
|
||||
logger.warning(f"Checkpoint file not found: {checkpoint_path}")
|
||||
return None
|
||||
|
||||
checkpoint_data = torch.load(checkpoint_path, map_location='cpu')
|
||||
|
||||
logger.info(f"Best checkpoint loaded for {model_name}: {best_checkpoint['id']}")
|
||||
return checkpoint_path, checkpoint_data
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to load best checkpoint for {model_name}: {e}")
|
||||
return None
|
||||
|
||||
def archive_model(self, model_name: str, model_type: str = 'cnn') -> bool:
|
||||
"""
|
||||
Archive a model by moving it to archive directory.
|
||||
|
||||
Args:
|
||||
model_name: Name of the model to archive
|
||||
model_type: Type of model
|
||||
|
||||
Returns:
|
||||
bool: True if successful, False otherwise
|
||||
"""
|
||||
try:
|
||||
model_dir = self.model_dirs.get(model_type, self.saved_dir)
|
||||
save_dir = model_dir / "saved"
|
||||
archive_dir = model_dir / "archive"
|
||||
|
||||
latest_filepath = save_dir / f"{model_name}_latest.pt"
|
||||
|
||||
if not latest_filepath.exists():
|
||||
logger.warning(f"Model {model_name} not found to archive")
|
||||
return False
|
||||
|
||||
# Move to archive with timestamp
|
||||
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
archive_filepath = archive_dir / f"{model_name}_archived_{timestamp}.pt"
|
||||
|
||||
os.rename(latest_filepath, archive_filepath)
|
||||
|
||||
logger.info(f"Model {model_name} archived to {archive_filepath}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to archive model {model_name}: {e}")
|
||||
return False
|
||||
|
||||
def list_models(self, model_type: Optional[str] = None) -> Dict[str, Any]:
|
||||
"""
|
||||
List all models in the registry.
|
||||
|
||||
Args:
|
||||
model_type: Filter by model type (optional)
|
||||
|
||||
Returns:
|
||||
Dictionary of model information
|
||||
"""
|
||||
models_info = {}
|
||||
|
||||
for model_name, model_data in self.metadata['models'].items():
|
||||
if model_type and model_data.get('type') != model_type:
|
||||
continue
|
||||
|
||||
models_info[model_name] = {
|
||||
'type': model_data.get('type'),
|
||||
'last_saved': model_data.get('last_saved'),
|
||||
'save_count': model_data.get('save_count', 0),
|
||||
'checkpoint_count': len(model_data.get('checkpoints', [])),
|
||||
'latest_path': model_data.get('latest_path')
|
||||
}
|
||||
|
||||
return models_info
|
||||
|
||||
def cleanup_old_checkpoints(self, model_name: str, keep_count: int = 5) -> int:
|
||||
"""
|
||||
Clean up old checkpoints, keeping only the best ones.
|
||||
|
||||
Args:
|
||||
model_name: Name of the model
|
||||
keep_count: Number of checkpoints to keep
|
||||
|
||||
Returns:
|
||||
Number of checkpoints removed
|
||||
"""
|
||||
if model_name not in self.metadata['models']:
|
||||
return 0
|
||||
|
||||
checkpoints = self.metadata['models'][model_name].get('checkpoints', [])
|
||||
if len(checkpoints) <= keep_count:
|
||||
return 0
|
||||
|
||||
# Sort by performance score (descending)
|
||||
checkpoints.sort(key=lambda x: x['performance_score'], reverse=True)
|
||||
|
||||
# Remove old checkpoints
|
||||
removed_count = 0
|
||||
for checkpoint in checkpoints[keep_count:]:
|
||||
try:
|
||||
os.remove(checkpoint['path'])
|
||||
removed_count += 1
|
||||
except:
|
||||
pass
|
||||
|
||||
# Update metadata
|
||||
self.metadata['models'][model_name]['checkpoints'] = checkpoints[:keep_count]
|
||||
self._save_metadata()
|
||||
|
||||
logger.info(f"Cleaned up {removed_count} old checkpoints for {model_name}")
|
||||
return removed_count
|
||||
|
||||
|
||||
# Global registry instance
|
||||
_registry_instance = None
|
||||
|
||||
def get_model_registry() -> ModelRegistry:
|
||||
"""Get the global model registry instance."""
|
||||
global _registry_instance
|
||||
if _registry_instance is None:
|
||||
_registry_instance = ModelRegistry()
|
||||
return _registry_instance
|
||||
|
||||
def save_model(model: Any, model_name: str, model_type: str = 'cnn',
|
||||
metadata: Optional[Dict[str, Any]] = None) -> bool:
|
||||
"""
|
||||
Convenience function to save a model using the global registry.
|
||||
"""
|
||||
return get_model_registry().save_model(model, model_name, model_type, metadata)
|
||||
|
||||
def load_model(model_name: str, model_type: str = 'cnn',
|
||||
model_class: Optional[Any] = None) -> Optional[Any]:
|
||||
"""
|
||||
Convenience function to load a model using the global registry.
|
||||
"""
|
||||
return get_model_registry().load_model(model_name, model_type, model_class)
|
||||
|
||||
def save_checkpoint(model: Any, model_name: str, model_type: str = 'cnn',
|
||||
performance_score: float = 0.0,
|
||||
metadata: Optional[Dict[str, Any]] = None) -> bool:
|
||||
"""
|
||||
Convenience function to save a checkpoint using the global registry.
|
||||
"""
|
||||
return get_model_registry().save_checkpoint(model, model_name, model_type, performance_score, metadata)
|
||||
|
||||
def load_best_checkpoint(model_name: str, model_type: str = 'cnn') -> Optional[Tuple[str, Any]]:
|
||||
"""
|
||||
Convenience function to load the best checkpoint using the global registry.
|
||||
"""
|
||||
return get_model_registry().load_best_checkpoint(model_name, model_type)
|
||||
Reference in New Issue
Block a user