TensorBoard: Visualization Toolkit for ML

When to Use This Skill

Use TensorBoard when you need to:

• Visualize training metrics like loss and accuracy over time
• Debug models with histograms and distributions
• Compare experiments across multiple runs
• Visualize model graphs and architecture
• Project embeddings to lower dimensions (t-SNE, PCA)
• Track hyperparameter experiments
• Profile performance and identify bottlenecks
• Visualize images and text during training

Users: 20M+ downloads/year | GitHub Stars: 27k+ | License: Apache 2.0

Installation

# Install TensorBoard
pip install tensorboard

# PyTorch integration
pip install torch torchvision tensorboard

# TensorFlow integration (TensorBoard included)
pip install tensorflow

# Launch TensorBoard
tensorboard --logdir=runs
# Access at http://localhost:6006

Quick Start

PyTorch

from torch.utils.tensorboard import SummaryWriter

# Create writer
writer = SummaryWriter('runs/experiment_1')

# Training loop
for epoch in range(10):
    train_loss = train_epoch()
    val_acc = validate()

    # Log metrics
    writer.add_scalar('Loss/train', train_loss, epoch)
    writer.add_scalar('Accuracy/val', val_acc, epoch)

# Close writer
writer.close()

# Launch: tensorboard --logdir=runs

TensorFlow/Keras

import tensorflow as tf

# Create callback
tensorboard_callback = tf.keras.callbacks.TensorBoard(
    log_dir='logs/fit',
    histogram_freq=1
)

# Train model
model.fit(
    x_train, y_train,
    epochs=10,
    validation_data=(x_val, y_val),
    callbacks=[tensorboard_callback]
)

# Launch: tensorboard --logdir=logs

Core Concepts

1. SummaryWriter (PyTorch)

from torch.utils.tensorboard import SummaryWriter

# Default directory: runs/CURRENT_DATETIME
writer = SummaryWriter()

# Custom directory
writer = SummaryWriter('runs/experiment_1')