tensorboard

Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit

Jump to install

Repository: davila7/claude-code-templates
Last source activity: January 8, 2026 at 14:24
Detected SKILL.md language: English
Stars: 30,385
Forks: 3,433

Install options

The review-first prompt is selected by default. You can switch to a direct command or download a local copy.

Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.

A direct command skips the review prompt. Inspect the source before running it.

npx skills add https://github.com/davila7/claude-code-templates --skill tensorboard

The command stays on one line. Scroll horizontally to inspect it before copying.

Prefer a local copy? Download the files currently available to SkillsMP.

Review the source files

Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.

File Explorer

4 files

SKILL.md

Source instructions · Read-only preview

Back to search Back to 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

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

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

from torch.utils.tensorboard import SummaryWriter

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

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

# Custom comment (appended to default directory)
writer = SummaryWriter(comment='baseline')

# Log data
writer.add_scalar('Loss/train', 0.5, step=0)
writer.add_scalar('Loss/train', 0.3, step=1)

# Flush and close
writer.flush()
writer.close()

# PyTorch
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter()

for epoch in range(100):
    train_loss = train()
    val_loss = validate()

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

    # Learning rate
    lr = optimizer.param_groups[0]['lr']
    writer.add_scalar('Learning_rate', lr, epoch)

writer.close()

# TensorFlow
import tensorflow as tf

train_summary_writer = tf.summary.create_file_writer('logs/train')
val_summary_writer = tf.summary.create_file_writer('logs/val')

for epoch in range(100):
    with train_summary_writer.as_default():
        tf.summary.scalar('loss', train_loss, step=epoch)
        tf.summary.scalar('accuracy', train_acc, step=epoch)

    with val_summary_writer.as_default():
        tf.summary.scalar('loss', val_loss, step=epoch)
        tf.summary.scalar('accuracy', val_acc, step=epoch)

# PyTorch: Group related metrics
writer.add_scalars('Loss', {
    'train': train_loss,
    'validation': val_loss,
    'test': test_loss
}, epoch)

writer.add_scalars('Metrics', {
    'accuracy': accuracy,
    'precision': precision,
    'recall': recall,
    'f1': f1_score
}, epoch)

# PyTorch
import torch
from torchvision.utils import make_grid

# Single image
writer.add_image('Input/sample', img_tensor, epoch)

# Multiple images as grid
img_grid = make_grid(images[:64], nrow=8)
writer.add_image('Batch/inputs', img_grid, epoch)

# Predictions visualization
pred_grid = make_grid(predictions[:16], nrow=4)
writer.add_image('Predictions', pred_grid, epoch)

# TensorFlow
import tensorflow as tf

with file_writer.as_default():
    # Encode images as PNG
    tf.summary.image('Training samples', images, step=epoch, max_outputs=25)

# PyTorch: Track weight distributions
for name, param in model.named_parameters():
    writer.add_histogram(name, param, epoch)

    # Track gradients
    if param.grad is not None:
        writer.add_histogram(f'{name}.grad', param.grad, epoch)

# Track activations
writer.add_histogram('Activations/relu1', activations, epoch)

# TensorFlow
with file_writer.as_default():
    tf.summary.histogram('weights/layer1', layer1.kernel, step=epoch)
    tf.summary.histogram('activations/relu1', activations, step=epoch)

# PyTorch
import torch

model = MyModel()
dummy_input = torch.randn(1, 3, 224, 224)

writer.add_graph(model, dummy_input)
writer.close()

# TensorFlow (automatic with Keras)
tensorboard_callback = tf.keras.callbacks.TensorBoard(
    log_dir='logs',
    write_graph=True
)

model.fit(x, y, callbacks=[tensorboard_callback])

import torch
from torch.utils.tensorboard import SummaryWriter

# Get embeddings (e.g., word embeddings, image features)
embeddings = model.get_embeddings(data)  # Shape: (N, embedding_dim)

# Metadata (labels for each point)
metadata = ['class_1', 'class_2', 'class_1', ...]

# Images (optional, for image embeddings)
label_images = torch.stack([img1, img2, img3, ...])

# Log to TensorBoard
writer.add_embedding(
    embeddings,
    metadata=metadata,
    label_img=label_images,
    global_step=epoch
)

from torch.utils.tensorboard import SummaryWriter

# Try different hyperparameters
for lr in [0.001, 0.01, 0.1]:
    for batch_size in [16, 32, 64]:
        # Create unique run directory
        writer = SummaryWriter(f'runs/lr{lr}_bs{batch_size}')

        # Log hyperparameters
        writer.add_hparams(
            {'lr': lr, 'batch_size': batch_size},
            {'hparam/accuracy': final_acc, 'hparam/loss': final_loss}
        )

        # Train and log
        for epoch in range(10):
            loss = train(lr, batch_size)
            writer.add_scalar('Loss/train', loss, epoch)

        writer.close()

# Compare in TensorBoard's "HParams" tab

# PyTorch: Log text (e.g., model predictions, summaries)
writer.add_text('Predictions', f'Epoch {epoch}: {predictions}', epoch)
writer.add_text('Config', str(config), 0)

# Log markdown tables
markdown_table = """
| Metric | Value |
|--------|-------|
| Accuracy | 0.95 |
| F1 Score | 0.93 |
"""
writer.add_text('Results', markdown_table, epoch)

from torch.utils.tensorboard import SummaryWriter

# Get predictions and labels
predictions = model(test_data)  # Shape: (N, num_classes)
labels = test_labels  # Shape: (N,)

# Log PR curve for each class
for i in range(num_classes):
    writer.add_pr_curve(
        f'PR_curve/class_{i}',
        labels == i,
        predictions[:, i],
        global_step=epoch
    )

import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter

# Setup
writer = SummaryWriter('runs/resnet_experiment')
model = ResNet50()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# Log model graph
dummy_input = torch.randn(1, 3, 224, 224)
writer.add_graph(model, dummy_input)

# Training loop
for epoch in range(50):
    model.train()
    train_loss = 0.0
    train_correct = 0

    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        pred = output.argmax(dim=1)
        train_correct += pred.eq(target).sum().item()

        # Log batch metrics (every 100 batches)
        if batch_idx % 100 == 0:
            global_step = epoch * len(train_loader) + batch_idx
            writer.add_scalar('Loss/train_batch', loss.item(), global_step)

    # Epoch metrics
    train_loss /= len(train_loader)
    train_acc = train_correct / len(train_loader.dataset)

    # Validation
    model.eval()
    val_loss = 0.0
    val_correct = 0

    with torch.no_grad():
        for data, target in val_loader:
            output = model(data)
            val_loss += criterion(output, target).item()
            pred = output.argmax(dim=1)
            val_correct += pred.eq(target).sum().item()

    val_loss /= len(val_loader)
    val_acc = val_correct / len(val_loader.dataset)

    # Log epoch metrics
    writer.add_scalars('Loss', {'train': train_loss, 'val': val_loss}, epoch)
    writer.add_scalars('Accuracy', {'train': train_acc, 'val': val_acc}, epoch)

    # Log learning rate
    writer.add_scalar('Learning_rate', optimizer.param_groups[0]['lr'], epoch)

    # Log histograms (every 5 epochs)
    if epoch % 5 == 0:
        for name, param in model.named_parameters():
            writer.add_histogram(name, param, epoch)

    # Log sample predictions
    if epoch % 10 == 0:
        sample_images = data[:8]
        writer.add_image('Sample_inputs', make_grid(sample_images), epoch)

writer.close()

import tensorflow as tf

# Define model
model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(28, 28, 1)),
    tf.keras.layers.MaxPooling2D(),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

# TensorBoard callback
tensorboard_callback = tf.keras.callbacks.TensorBoard(
    log_dir='logs/fit',
    histogram_freq=1,          # Log histograms every epoch
    write_graph=True,          # Visualize model graph
    write_images=True,         # Visualize weights as images
    update_freq='epoch',       # Log metrics every epoch
    profile_batch='500,520',   # Profile batches 500-520
    embeddings_freq=1          # Log embeddings every epoch
)

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

# Run experiments with different configs
python train.py --lr 0.001 --logdir runs/exp1
python train.py --lr 0.01 --logdir runs/exp2
python train.py --lr 0.1 --logdir runs/exp3

# View all runs together
tensorboard --logdir=runs

# Hierarchical organization
runs/
├── baseline/
│   ├── run_1/
│   └── run_2/
├── improved/
│   ├── run_1/
│   └── run_2/
└── final/
    └── run_1/

# Log with hierarchy
writer = SummaryWriter('runs/baseline/run_1')

# ✅ Good: Descriptive names
from datetime import datetime
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
writer = SummaryWriter(f'runs/resnet50_lr0.001_bs32_{timestamp}')

# ❌ Bad: Auto-generated names
writer = SummaryWriter()  # Creates runs/Jan01_12-34-56_hostname

# ✅ Good: Grouped metrics
writer.add_scalar('Loss/train', train_loss, step)
writer.add_scalar('Loss/val', val_loss, step)
writer.add_scalar('Accuracy/train', train_acc, step)
writer.add_scalar('Accuracy/val', val_acc, step)

# ❌ Bad: Flat namespace
writer.add_scalar('train_loss', train_loss, step)
writer.add_scalar('val_loss', val_loss, step)

# ✅ Good: Log epoch metrics always, batch metrics occasionally
for epoch in range(100):
    for batch_idx, (data, target) in enumerate(train_loader):
        loss = train_step(data, target)

        # Log every 100 batches
        if batch_idx % 100 == 0:
            writer.add_scalar('Loss/batch', loss, global_step)

    # Always log epoch metrics
    writer.add_scalar('Loss/epoch', epoch_loss, epoch)

# ❌ Bad: Log every batch (creates huge log files)
for batch in train_loader:
    writer.add_scalar('Loss', loss, step)  # Too frequent

# ✅ Good: Use context manager
with SummaryWriter('runs/exp1') as writer:
    for epoch in range(10):
        writer.add_scalar('Loss', loss, epoch)
# Automatically closes

# Or manually
writer = SummaryWriter('runs/exp1')
# ... logging ...
writer.close()

# ✅ Good: Separate log directories
train_writer = SummaryWriter('runs/exp1/train')
val_writer = SummaryWriter('runs/exp1/val')

train_writer.add_scalar('loss', train_loss, epoch)
val_writer.add_scalar('loss', val_loss, epoch)

# Enable profiling
tensorboard_callback = tf.keras.callbacks.TensorBoard(
    log_dir='logs',
    profile_batch='10,20'  # Profile batches 10-20
)

model.fit(x, y, callbacks=[tensorboard_callback])

# View in TensorBoard Profile tab
# Shows: GPU utilization, kernel stats, memory usage, bottlenecks

import torch.profiler as profiler

with profiler.profile(
    activities=[
        profiler.ProfilerActivity.CPU,
        profiler.ProfilerActivity.CUDA
    ],
    on_trace_ready=torch.profiler.tensorboard_trace_handler('./runs/profiler'),
    record_shapes=True,
    with_stack=True
) as prof:
    for batch in train_loader:
        loss = train_step(batch)
        prof.step()

# View in TensorBoard Profile tab

name	tensorboard
description	Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit
version	1.0.0
author	Orchestra Research
license	MIT
tags	["MLOps","TensorBoard","Visualization","Training Metrics","Model Debugging","PyTorch","TensorFlow","Experiment Tracking","Performance Profiling"]
dependencies	["tensorboard","torch","tensorflow"]

name	tensorboard
description	Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit
version	1.0.0
author	Orchestra Research
license	MIT
tags	["MLOps","TensorBoard","Visualization","Training Metrics","Model Debugging","PyTorch","TensorFlow","Experiment Tracking","Performance Profiling"]
dependencies	["tensorboard","torch","tensorflow"]

tensorboard

Install options

Review the source files

TensorBoard: Visualization Toolkit for ML

When to Use This Skill

TensorBoard: Visualization Toolkit for ML

When to Use This Skill

Installation

Quick Start

PyTorch

TensorFlow/Keras

Core Concepts

1. SummaryWriter (PyTorch)

2. Logging Scalars

3. Logging Multiple Scalars

4. Logging Images

5. Logging Histograms

6. Logging Model Graph

Advanced Features

Embedding Projector

Hyperparameter Tuning

Text Logging

PR Curves

Integration Examples

PyTorch Training Loop

TensorFlow/Keras Training

Comparing Experiments

Multiple Runs

Organizing Experiments

Best Practices

1. Use Descriptive Run Names

2. Group Related Metrics

3. Log Regularly but Not Too Often

4. Close Writer When Done

5. Use Separate Writers for Train/Val

Performance Profiling

TensorFlow Profiler

PyTorch Profiler

Resources

See Also

tensorboard

Source facts

Install options

Review the source files

More from this repository

More from this repository

TensorBoard: Visualization Toolkit for ML

When to Use This Skill

TensorBoard: Visualization Toolkit for ML

When to Use This Skill

Installation

Quick Start

PyTorch

TensorFlow/Keras

Core Concepts

1. SummaryWriter (PyTorch)

2. Logging Scalars

3. Logging Multiple Scalars

4. Logging Images

5. Logging Histograms

6. Logging Model Graph

Advanced Features

Embedding Projector

Hyperparameter Tuning

Text Logging

PR Curves

Integration Examples

PyTorch Training Loop

TensorFlow/Keras Training

Comparing Experiments

Multiple Runs

Organizing Experiments

Best Practices

1. Use Descriptive Run Names

2. Group Related Metrics

3. Log Regularly but Not Too Often

4. Close Writer When Done

5. Use Separate Writers for Train/Val

Performance Profiling

TensorFlow Profiler

PyTorch Profiler

Resources

See Also