Spaces:
Runtime error
Runtime error
File size: 7,490 Bytes
c3efd49 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 |
"""Metrics tracking for training monitoring."""
import torch
import numpy as np
from typing import Dict, Any, List, Optional
from collections import defaultdict
import logging
import json
from pathlib import Path
logger = logging.getLogger(__name__)
class MetricsTracker:
"""
Tracks and aggregates training metrics.
Logs rewards, losses, learning rates, GPU memory, and custom metrics.
"""
def __init__(self, log_dir: str = "logs"):
"""
Initialize metrics tracker.
Args:
log_dir: Directory to save metric logs
"""
self.log_dir = Path(log_dir)
self.log_dir.mkdir(parents=True, exist_ok=True)
# Storage for metrics
self.metrics = defaultdict(list)
self.step_counter = 0
logger.info(f"MetricsTracker initialized: log_dir={log_dir}")
def log_metric(
self,
name: str,
value: float,
step: Optional[int] = None
) -> None:
"""
Log a single metric value.
Args:
name: Metric name
value: Metric value
step: Optional step number (uses internal counter if not provided)
"""
if step is None:
step = self.step_counter
self.metrics[name].append({
'step': step,
'value': float(value)
})
def log_metrics(
self,
metrics: Dict[str, float],
step: Optional[int] = None
) -> None:
"""
Log multiple metrics at once.
Args:
metrics: Dictionary of metric names and values
step: Optional step number
"""
if step is None:
step = self.step_counter
for name, value in metrics.items():
self.log_metric(name, value, step)
self.step_counter += 1
def log_training_metrics(
self,
episode: int,
reward: float,
loss: float,
learning_rate: float,
**kwargs
) -> None:
"""
Log standard training metrics.
Args:
episode: Episode number
reward: Episode reward
loss: Training loss
learning_rate: Current learning rate
**kwargs: Additional metrics
"""
metrics = {
'reward': reward,
'loss': loss,
'learning_rate': learning_rate,
**kwargs
}
self.log_metrics(metrics, step=episode)
def log_gpu_memory(self, step: Optional[int] = None) -> None:
"""
Log GPU memory usage.
Args:
step: Optional step number
"""
if torch.cuda.is_available():
allocated = torch.cuda.memory_allocated() / (1024 ** 2) # MB
reserved = torch.cuda.memory_reserved() / (1024 ** 2) # MB
self.log_metric('gpu_memory_allocated_mb', allocated, step)
self.log_metric('gpu_memory_reserved_mb', reserved, step)
def get_metric(self, name: str) -> List[Dict[str, Any]]:
"""
Get all values for a specific metric.
Args:
name: Metric name
Returns:
List of {step, value} dictionaries
"""
return self.metrics.get(name, [])
def get_latest_value(self, name: str) -> Optional[float]:
"""
Get the most recent value for a metric.
Args:
name: Metric name
Returns:
Latest value or None
"""
values = self.metrics.get(name, [])
if values:
return values[-1]['value']
return None
def get_metric_statistics(self, name: str) -> Dict[str, float]:
"""
Get statistics for a metric.
Args:
name: Metric name
Returns:
Dictionary with mean, std, min, max
"""
values = [entry['value'] for entry in self.metrics.get(name, [])]
if not values:
return {
'count': 0,
'mean': 0.0,
'std': 0.0,
'min': 0.0,
'max': 0.0
}
return {
'count': len(values),
'mean': float(np.mean(values)),
'std': float(np.std(values)),
'min': float(np.min(values)),
'max': float(np.max(values))
}
def get_all_metrics(self) -> Dict[str, List[Dict[str, Any]]]:
"""
Get all tracked metrics.
Returns:
Dictionary of all metrics
"""
return dict(self.metrics)
def get_metric_names(self) -> List[str]:
"""
Get names of all tracked metrics.
Returns:
List of metric names
"""
return list(self.metrics.keys())
def aggregate_metrics(
self,
window_size: int = 10
) -> Dict[str, Dict[str, float]]:
"""
Aggregate metrics over a sliding window.
Args:
window_size: Size of sliding window
Returns:
Dictionary of aggregated metrics
"""
aggregated = {}
for name, values in self.metrics.items():
if len(values) >= window_size:
recent_values = [v['value'] for v in values[-window_size:]]
aggregated[name] = {
'mean': float(np.mean(recent_values)),
'std': float(np.std(recent_values)),
'min': float(np.min(recent_values)),
'max': float(np.max(recent_values))
}
return aggregated
def save_metrics(self, filename: str = "metrics.json") -> None:
"""
Save metrics to JSON file.
Args:
filename: Output filename
"""
output_path = self.log_dir / filename
with open(output_path, 'w') as f:
json.dump(dict(self.metrics), f, indent=2)
logger.info(f"Metrics saved to {output_path}")
def load_metrics(self, filename: str = "metrics.json") -> None:
"""
Load metrics from JSON file.
Args:
filename: Input filename
"""
input_path = self.log_dir / filename
if not input_path.exists():
raise FileNotFoundError(f"Metrics file not found: {input_path}")
with open(input_path, 'r') as f:
loaded_metrics = json.load(f)
self.metrics = defaultdict(list, loaded_metrics)
logger.info(f"Metrics loaded from {input_path}")
def reset(self) -> None:
"""Reset all metrics."""
self.metrics.clear()
self.step_counter = 0
logger.info("Metrics reset")
def summary(self) -> Dict[str, Any]:
"""
Generate summary of all metrics.
Returns:
Summary dictionary
"""
summary = {
'total_steps': self.step_counter,
'num_metrics': len(self.metrics),
'metrics': {}
}
for name in self.metrics.keys():
summary['metrics'][name] = self.get_metric_statistics(name)
return summary
|