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0060c88 | 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 | #!/usr/bin/env python3
import re
import matplotlib.pyplot as plt
import numpy as np
import argparse
from pathlib import Path
def parse_training_log(log_file_path):
"""Parse training log file and extract metrics"""
step_data = []
iter_data = []
with open(log_file_path, "r") as f:
for line in f:
line = line.strip()
# Parse step lines (training and validation loss)
step_match = re.match(
r"step (\d+): train loss ([\d.]+), val loss ([\d.]+)", line
)
if step_match:
step_num = int(step_match.group(1))
train_loss = float(step_match.group(2))
val_loss = float(step_match.group(3))
step_data.append(
{"step": step_num, "train_loss": train_loss, "val_loss": val_loss}
)
# Parse iteration lines (loss, time, mfu)
iter_match = re.match(
r"iter (\d+): loss ([\d.]+), time ([\d.]+)ms, mfu ([\d.-]+)%", line
)
if iter_match:
iter_num = int(iter_match.group(1))
loss = float(iter_match.group(2))
time_ms = float(iter_match.group(3))
mfu = float(iter_match.group(4))
iter_data.append(
{"iter": iter_num, "loss": loss, "time_ms": time_ms, "mfu": mfu}
)
return step_data, iter_data
def create_visualizations(step_data, iter_data):
"""Create matplotlib visualizations of training metrics"""
# Set up the plotting style
plt.style.use("default")
plt.rcParams["figure.figsize"] = (12, 8)
plt.rcParams["font.size"] = 10
# Create subplots
fig, axes = plt.subplots(2, 2, figsize=(15, 10))
fig.suptitle("nanoGPT Training Metrics - RTX 4070", fontsize=16, fontweight="bold")
# Plot 1: Training and Validation Loss over Steps
if step_data:
steps = [d["step"] for d in step_data]
train_losses = [d["train_loss"] for d in step_data]
val_losses = [d["val_loss"] for d in step_data]
axes[0, 0].plot(steps, train_losses, "b-", label="Training Loss", linewidth=2)
axes[0, 0].plot(steps, val_losses, "r-", label="Validation Loss", linewidth=2)
axes[0, 0].set_xlabel("Training Step")
axes[0, 0].set_ylabel("Loss")
axes[0, 0].set_title("Training vs Validation Loss")
axes[0, 0].legend()
axes[0, 0].grid(True, alpha=0.3)
# Plot 2: Iteration Loss over Iterations
if iter_data:
iters = [d["iter"] for d in iter_data]
iter_losses = [d["loss"] for d in iter_data]
axes[0, 1].plot(iters, iter_losses, "g-", linewidth=1.5)
axes[0, 1].set_xlabel("Iteration")
axes[0, 1].set_ylabel("Loss")
axes[0, 1].set_title("Loss per Iteration")
axes[0, 1].grid(True, alpha=0.3)
# Plot 3: Training Time per Iteration
if iter_data:
iters = [d["iter"] for d in iter_data]
times = [d["time_ms"] for d in iter_data]
axes[1, 0].plot(iters, times, "orange", linewidth=1.5)
axes[1, 0].set_xlabel("Iteration")
axes[1, 0].set_ylabel("Time (ms)")
axes[1, 0].set_title("Training Time per Iteration")
axes[1, 0].grid(True, alpha=0.3)
# Plot 4: Model FLOP Utilization (MFU)
if iter_data:
iters = [d["iter"] for d in iter_data]
mfus = [d["mfu"] for d in iter_data]
axes[1, 1].plot(iters, mfus, "purple", linewidth=1.5)
axes[1, 1].set_xlabel("Iteration")
axes[1, 1].set_ylabel("MFU (%)")
axes[1, 1].set_title("Model FLOP Utilization (MFU)")
axes[1, 1].grid(True, alpha=0.3)
plt.tight_layout()
# Only show plot if not in headless environment
import os
if os.environ.get("DISPLAY") is not None and os.environ.get("DISPLAY") != "":
plt.show()
return fig
def print_statistics(step_data, iter_data):
"""Print basic statistics about the training"""
print("=" * 50)
print("TRAINING STATISTICS")
print("=" * 50)
if step_data:
final_step = step_data[-1]
print(f"Total Steps Completed: {final_step['step']}")
print(f"Final Training Loss: {final_step['train_loss']:.4f}")
print(f"Final Validation Loss: {final_step['val_loss']:.4f}")
# Calculate loss improvement
initial_loss = step_data[0]["train_loss"]
final_loss = final_step["train_loss"]
improvement = ((initial_loss - final_loss) / initial_loss) * 100
print(f"Training Loss Improvement: {improvement:.1f}%")
if iter_data:
final_iter = iter_data[-1]
print(f"\nTotal Iterations: {final_iter['iter']}")
# Calculate average time
avg_time = np.mean([d["time_ms"] for d in iter_data])
print(f"Average Time per Iteration: {avg_time:.1f}ms")
# Calculate average MFU
valid_mfus = [
d["mfu"] for d in iter_data if d["mfu"] > -50
] # Filter out invalid values
if valid_mfus:
avg_mfu = np.mean(valid_mfus)
print(f"Average MFU: {avg_mfu:.1f}%")
def main():
# Parse command line arguments
parser = argparse.ArgumentParser(
description="Parse and visualize nanoGPT training logs"
)
parser.add_argument("--file", "-f", type=str, help="Path to training log file")
args = parser.parse_args()
# File paths
if args.file:
log_file = Path(args.file)
else:
log_file = Path(__file__).parent / "train_log_openweb.txt"
if not log_file.exists():
print(f"Error: Log file {log_file} not found!")
return
print(f"Parsing training log: {log_file}")
# Parse the log data
step_data, iter_data = parse_training_log(log_file)
print(f"Found {len(step_data)} step records and {len(iter_data)} iteration records")
# Print statistics
print_statistics(step_data, iter_data)
# Create visualizations
fig = create_visualizations(step_data, iter_data)
# Save the plot
output_path = Path(__file__).parent / "training_metrics.png"
fig.savefig(output_path, dpi=300, bbox_inches="tight")
print(f"\nVisualization saved to: {output_path}")
# Also save as PDF for high quality
pdf_path = Path(__file__).parent / "training_metrics.pdf"
fig.savefig(pdf_path, bbox_inches="tight")
print(f"High-quality PDF saved to: {pdf_path}")
plt.show()
if __name__ == "__main__":
main()
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