model checkpoint manager
This commit is contained in:
Dobromir Popov
@@ -16,6 +16,9 @@ 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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@@ -68,39 +71,48 @@ class CheckpointManager:
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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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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"""
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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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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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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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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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# 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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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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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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@@ -112,9 +124,8 @@ class CheckpointManager:
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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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# W&B disabled
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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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@@ -128,14 +139,42 @@ class CheckpointManager:
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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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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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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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@@ -146,22 +185,22 @@ class CheckpointManager:
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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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446
utils/model_registry.py
Normal file
446
utils/model_registry.py
Normal file
@@ -0,0 +1,446 @@
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#!/usr/bin/env python3
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"""
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Unified Model Registry for Centralized Model Management
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This module provides a unified interface for saving, loading, and managing
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all machine learning models in the trading system. It consolidates model
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storage from multiple locations into a single, organized structure.
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"""
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import os
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import json
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import torch
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import logging
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import pickle
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from pathlib import Path
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from typing import Dict, Any, Optional, Tuple, List
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from datetime import datetime
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import hashlib
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logger = logging.getLogger(__name__)
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class ModelRegistry:
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"""
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Unified model registry for centralized model management.
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Handles saving, loading, and organization of all ML models.
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"""
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def __init__(self, base_dir: str = "models"):
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"""
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Initialize the model registry.
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Args:
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base_dir: Base directory for model storage
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"""
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self.base_dir = Path(base_dir)
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self.saved_dir = self.base_dir / "saved"
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self.checkpoint_dir = self.base_dir / "checkpoints"
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self.archive_dir = self.base_dir / "archive"
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# Model type directories
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self.model_dirs = {
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'cnn': self.base_dir / "cnn",
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'dqn': self.base_dir / "dqn",
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'transformer': self.base_dir / "transformer",
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'hybrid': self.base_dir / "hybrid"
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}
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# Ensure all directories exist
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self._ensure_directories()
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# Metadata tracking
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self.metadata_file = self.base_dir / "registry_metadata.json"
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self.metadata = self._load_metadata()
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logger.info(f"Model Registry initialized at {self.base_dir}")
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def _ensure_directories(self):
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"""Ensure all required directories exist."""
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directories = [
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self.saved_dir,
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self.checkpoint_dir,
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self.archive_dir
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]
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# Add model type directories
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for model_dir in self.model_dirs.values():
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directories.extend([
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model_dir / "saved",
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model_dir / "checkpoints",
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model_dir / "archive"
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])
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for directory in directories:
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directory.mkdir(parents=True, exist_ok=True)
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def _load_metadata(self) -> Dict[str, Any]:
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"""Load registry metadata."""
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if self.metadata_file.exists():
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try:
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with open(self.metadata_file, 'r') as f:
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return json.load(f)
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except Exception as e:
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logger.warning(f"Failed to load metadata: {e}")
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return {'models': {}, 'last_updated': datetime.now().isoformat()}
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def _save_metadata(self):
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"""Save registry metadata."""
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self.metadata['last_updated'] = datetime.now().isoformat()
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try:
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with open(self.metadata_file, 'w') as f:
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json.dump(self.metadata, f, indent=2)
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except Exception as e:
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logger.error(f"Failed to save metadata: {e}")
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def save_model(self, model: Any, model_name: str, model_type: str = 'cnn',
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metadata: Optional[Dict[str, Any]] = None) -> bool:
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"""
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Save a model to the unified storage.
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Args:
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model: The model to save
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model_name: Name of the model
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model_type: Type of model (cnn, dqn, transformer, hybrid)
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metadata: Additional metadata to save
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Returns:
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bool: True if successful, False otherwise
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"""
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try:
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model_dir = self.model_dirs.get(model_type, self.saved_dir)
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save_dir = model_dir / "saved"
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# Generate filename with timestamp
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timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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filename = f"{model_name}_{timestamp}.pt"
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filepath = save_dir / filename
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# Also save as latest
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latest_filepath = save_dir / f"{model_name}_latest.pt"
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# Save model
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save_dict = {
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'model_state_dict': model.state_dict() if hasattr(model, 'state_dict') else {},
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'model_class': model.__class__.__name__,
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'model_type': model_type,
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'timestamp': timestamp,
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'metadata': metadata or {}
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}
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torch.save(save_dict, filepath)
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torch.save(save_dict, latest_filepath)
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# Update metadata
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if model_name not in self.metadata['models']:
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self.metadata['models'][model_name] = {}
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self.metadata['models'][model_name].update({
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'type': model_type,
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'latest_path': str(latest_filepath),
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'last_saved': timestamp,
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'save_count': self.metadata['models'][model_name].get('save_count', 0) + 1
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})
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self._save_metadata()
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logger.info(f"Model {model_name} saved to {filepath}")
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return True
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except Exception as e:
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logger.error(f"Failed to save model {model_name}: {e}")
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return False
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def load_model(self, model_name: str, model_type: str = 'cnn',
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model_class: Optional[Any] = None) -> Optional[Any]:
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"""
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Load a model from the unified storage.
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Args:
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model_name: Name of the model to load
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model_type: Type of model (cnn, dqn, transformer, hybrid)
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model_class: Model class to instantiate (if needed)
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Returns:
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The loaded model or None if failed
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"""
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try:
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model_dir = self.model_dirs.get(model_type, self.saved_dir)
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save_dir = model_dir / "saved"
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latest_filepath = save_dir / f"{model_name}_latest.pt"
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if not latest_filepath.exists():
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logger.warning(f"Model {model_name} not found at {latest_filepath}")
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return None
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# Load checkpoint
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checkpoint = torch.load(latest_filepath, map_location='cpu')
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# Instantiate model if class provided
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if model_class is not None:
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model = model_class()
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model.load_state_dict(checkpoint['model_state_dict'])
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else:
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# Try to reconstruct model from state_dict
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model = type('LoadedModel', (), {})()
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model.state_dict = lambda: checkpoint['model_state_dict']
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model.load_state_dict = lambda state_dict: None
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logger.info(f"Model {model_name} loaded from {latest_filepath}")
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return model
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except Exception as e:
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logger.error(f"Failed to load model {model_name}: {e}")
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return None
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def save_checkpoint(self, model: Any, model_name: str, model_type: str = 'cnn',
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performance_score: float = 0.0,
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metadata: Optional[Dict[str, Any]] = None) -> bool:
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"""
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Save a model checkpoint.
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Args:
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model: The model to checkpoint
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model_name: Name of the model
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model_type: Type of model
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performance_score: Performance score for this checkpoint
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metadata: Additional metadata
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Returns:
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bool: True if successful, False otherwise
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"""
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try:
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model_dir = self.model_dirs.get(model_type, self.checkpoint_dir)
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checkpoint_dir = model_dir / "checkpoints"
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# Generate checkpoint ID
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timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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checkpoint_id = f"{model_name}_{timestamp}_{performance_score:.4f}"
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filepath = checkpoint_dir / f"{checkpoint_id}.pt"
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# Save checkpoint
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checkpoint_data = {
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'model_state_dict': model.state_dict() if hasattr(model, 'state_dict') else {},
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'model_class': model.__class__.__name__,
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'model_type': model_type,
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'model_name': model_name,
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'performance_score': performance_score,
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'timestamp': timestamp,
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'metadata': metadata or {}
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}
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torch.save(checkpoint_data, filepath)
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# Update metadata
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if model_name not in self.metadata['models']:
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self.metadata['models'][model_name] = {}
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if 'checkpoints' not in self.metadata['models'][model_name]:
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self.metadata['models'][model_name]['checkpoints'] = []
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checkpoint_info = {
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'id': checkpoint_id,
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'path': str(filepath),
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'performance_score': performance_score,
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'timestamp': timestamp
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}
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self.metadata['models'][model_name]['checkpoints'].append(checkpoint_info)
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# Keep only top 5 checkpoints
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checkpoints = self.metadata['models'][model_name]['checkpoints']
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if len(checkpoints) > 5:
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checkpoints.sort(key=lambda x: x['performance_score'], reverse=True)
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checkpoints_to_remove = checkpoints[5:]
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for checkpoint in checkpoints_to_remove:
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try:
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os.remove(checkpoint['path'])
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except:
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pass
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self.metadata['models'][model_name]['checkpoints'] = checkpoints[:5]
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self._save_metadata()
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logger.info(f"Checkpoint {checkpoint_id} saved with score {performance_score}")
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return True
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except Exception as e:
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logger.error(f"Failed to save checkpoint for {model_name}: {e}")
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return False
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def load_best_checkpoint(self, model_name: str, model_type: str = 'cnn') -> Optional[Tuple[str, Any]]:
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"""
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Load the best checkpoint for a model.
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Args:
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model_name: Name of the model
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model_type: Type of model
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Returns:
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Tuple of (checkpoint_path, checkpoint_data) or None
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"""
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try:
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if model_name not in self.metadata['models']:
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logger.warning(f"No metadata found for model {model_name}")
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return None
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checkpoints = self.metadata['models'][model_name].get('checkpoints', [])
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if not checkpoints:
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logger.warning(f"No checkpoints found for model {model_name}")
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return None
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# Find best checkpoint by performance score
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best_checkpoint = max(checkpoints, key=lambda x: x['performance_score'])
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checkpoint_path = best_checkpoint['path']
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if not os.path.exists(checkpoint_path):
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logger.warning(f"Checkpoint file not found: {checkpoint_path}")
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return None
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checkpoint_data = torch.load(checkpoint_path, map_location='cpu')
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logger.info(f"Best checkpoint loaded for {model_name}: {best_checkpoint['id']}")
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return checkpoint_path, checkpoint_data
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except Exception as e:
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logger.error(f"Failed to load best checkpoint for {model_name}: {e}")
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return None
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def archive_model(self, model_name: str, model_type: str = 'cnn') -> bool:
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"""
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Archive a model by moving it to archive directory.
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Args:
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model_name: Name of the model to archive
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model_type: Type of model
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||||
|
||||
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