PyTorch Integration

Tracelet provides seamless integration with PyTorch through automatic TensorBoard metric capture.

Overview

The PyTorch integration works by automatically patching TensorBoard's SummaryWriter to capture all logged metrics and route them to your configured backends.

Supported Features

• Scalar Metrics - Training loss, validation accuracy, learning rates
• Histograms - Weight distributions, gradient histograms
• Images - Sample predictions, model visualizations
• Text - Training logs, model summaries
• Audio - Audio samples and generated content

Basic Usage

import tracelet
import torch
from torch.utils.tensorboard import SummaryWriter

# Start tracking
tracelet.start_logging(
    exp_name="pytorch_experiment",
    project="my_project",
    backend="mlflow"
)

# Use TensorBoard normally
writer = SummaryWriter()

# Your training loop
for epoch in range(num_epochs):
    for batch_idx, (data, target) in enumerate(train_loader):
        # Training code...
        loss = train_step(model, data, target)

        # Log metrics - automatically captured by Tracelet
        writer.add_scalar('Loss/Train', loss.item(), epoch * len(train_loader) + batch_idx)

        if batch_idx % 100 == 0:
            # Log histograms
            for name, param in model.named_parameters():
                writer.add_histogram(f'Parameters/{name}', param, epoch)

# Stop tracking
tracelet.stop_logging()

Advanced Configuration

# Configure specific PyTorch tracking options
tracelet.start_logging(
    exp_name="advanced_pytorch",
    backend="wandb",
    config={
        "track_tensorboard": True,      # Enable TensorBoard capture
        "track_system": True,           # Monitor system metrics
        "metrics_interval": 5.0,        # System metrics every 5 seconds
    }
)

Best Practices

Metric Organization

Organize your metrics with clear hierarchies:

# Good: Hierarchical naming
writer.add_scalar('Loss/Train', train_loss, step)
writer.add_scalar('Loss/Validation', val_loss, step)
writer.add_scalar('Accuracy/Train', train_acc, step)
writer.add_scalar('Accuracy/Validation', val_acc, step)

# Avoid: Flat naming
writer.add_scalar('train_loss', train_loss, step)
writer.add_scalar('val_loss', val_loss, step)

Performance Optimization

For high-frequency logging:

# Log less frequently for expensive operations
if step % 100 == 0:
    writer.add_histogram('gradients', model.parameters(), step)

if step % 1000 == 0:
    writer.add_image('predictions', sample_predictions, step)

Troubleshooting

Common Issues

TensorBoard metrics not appearing: Ensure you're using SummaryWriter after calling tracelet.start_logging().

Memory issues: Reduce logging frequency for large tensors like histograms and images.

Performance impact: Use metrics_interval to control system metrics collection frequency.