File size: 6,537 Bytes
8d18b7c | 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 | """Logging and Metrics Tracking for Training"""
import json
import logging
import os
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Optional
import numpy as np
logger = logging.getLogger(__name__)
def setup_logging(
log_dir: str = "./logs",
log_level: str = "INFO",
console: bool = True,
file: bool = True,
):
"""Setup logging configuration."""
log_dir = Path(log_dir)
log_dir.mkdir(parents=True, exist_ok=True)
# Create formatter
formatter = logging.Formatter(
fmt="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
# Configure root logger
root_logger = logging.getLogger()
root_logger.setLevel(getattr(logging, log_level.upper()))
# Clear existing handlers
root_logger.handlers.clear()
# Console handler
if console:
console_handler = logging.StreamHandler()
console_handler.setFormatter(formatter)
root_logger.addHandler(console_handler)
# File handler
if file:
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
log_file = log_dir / f"training_{timestamp}.log"
file_handler = logging.FileHandler(log_file)
file_handler.setFormatter(formatter)
root_logger.addHandler(file_handler)
logger.info(f"Logging initialized. Log file: {log_file if file else 'console only'}")
class MetricsLogger:
"""Track and log metrics during training."""
def __init__(
self,
log_dir: str = "./logs",
experiment_name: Optional[str] = None,
):
self.log_dir = Path(log_dir)
self.log_dir.mkdir(parents=True, exist_ok=True)
self.experiment_name = experiment_name or f"zenith_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
self.metrics_file = self.log_dir / f"{self.experiment_name}_metrics.jsonl"
self.tensorboard_logdir = self.log_dir / "tensorboard" / self.experiment_name
# Metrics history
self.history: List[Dict[str, Any]] = []
# TensorBoard
try:
from torch.utils.tensorboard import SummaryWriter
self.writer = SummaryWriter(log_dir=str(self.tensorboard_logdir))
self.has_tensorboard = True
except ImportError:
self.has_tensorboard = False
logger.warning("TensorBoard not available. Install with: pip install tensorboard")
def log(
self,
metrics: Dict[str, float],
step: int,
prefix: str = "train",
):
"""Log metrics."""
# Add timestamp and step
log_entry = {
"timestamp": datetime.now().isoformat(),
"step": step,
"prefix": prefix,
**{f"{prefix}/{k}" if prefix != k else k: v for k, v in metrics.items()},
}
self.history.append(log_entry)
# Write to file
with open(self.metrics_file, "a") as f:
f.write(json.dumps(log_entry) + "\n")
# TensorBoard
if self.has_tensorboard:
for key, value in metrics.items():
self.writer.add_scalar(f"{prefix}/{key}", value, step)
def log_hyperparams(self, params: Dict[str, Any]):
"""Log hyperparameters."""
if self.has_tensorboard:
from torch.utils.tensorboard import SummaryWriter
# TensorBoard expects flat dict
flat_params = self._flatten_dict(params)
self.writer.add_hparams(flat_params, {})
def _flatten_dict(self, d: Dict[str, Any], parent_key: str = "", sep: str = "/") -> Dict[str, Any]:
"""Flatten nested dictionary."""
items = []
for k, v in d.items():
new_key = f"{parent_key}{sep}{k}" if parent_key else k
if isinstance(v, dict):
items.extend(self._flatten_dict(v, new_key, sep=sep).items())
else:
items.append((new_key, v))
return dict(items)
def get_metrics(self, prefix: Optional[str] = None) -> List[Dict[str, Any]]:
"""Get metrics history, optionally filtered by prefix."""
if prefix is None:
return self.history
filtered = []
for entry in self.history:
if entry.get("prefix") == prefix:
filtered.append(entry)
return filtered
def close(self):
"""Close logger."""
if self.has_tensorboard:
self.writer.close()
class ProgressLogger:
"""Simple progress tracking with ETA."""
def __init__(self, total: int, desc: str = "Progress"):
self.total = total
self.desc = desc
self.current = 0
self.start_time = datetime.now()
def update(self, n: int = 1):
"""Update progress."""
self.current += n
self._log_progress()
def _log_progress(self):
"""Log current progress."""
elapsed = (datetime.now() - self.start_time).total_seconds()
if self.current > 0:
rate = elapsed / self.current
remaining = rate * (self.total - self.current)
logger.info(
f"{self.desc}: {self.current}/{self.total} "
f"({100 * self.current / self.total:.1f}%) "
f"- ETA: {remaining / 60:.1f}m"
)
def log_metrics_summary(metrics: Dict[str, float], step: int, logger_obj: Optional[logging.Logger] = None):
"""Log a summary of metrics in a nice format."""
if logger_obj is None:
logger_obj = logger
lines = [f"Step {step} - Metrics Summary:"]
for key, value in sorted(metrics.items()):
if isinstance(value, float):
lines.append(f" {key}: {value:.4f}")
else:
lines.append(f" {key}: {value}")
logger_obj.info("\n".join(lines))
def save_metrics_to_csv(metrics_history: List[Dict[str, Any]], filepath: str):
"""Save metrics history to CSV."""
import pandas as pd
df = pd.DataFrame(metrics_history)
df.to_csv(filepath, index=False)
logger.info(f"Metrics saved to {filepath}")
def load_metrics_from_jsonl(filepath: str) -> List[Dict[str, Any]]:
"""Load metrics from JSONL file."""
metrics = []
with open(filepath, "r") as f:
for line in f:
metrics.append(json.loads(line.strip()))
return metrics
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