tensorboard

Q: How do I install tensorboard?

Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill tensorboard in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/tensorboard/ (personal) or .claude/skills/tensorboard/ (project).

by orchestra-researchCode Quality & Workflow

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

Installs: 973
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/orchestra-research/ai-research-skills --skill tensorboard

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads tensorboard automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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')

# 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()

2. Logging Scalars

# 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)

3. Logging Multiple Scalars

# 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)

4. Logging Images

# 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)

5. Logging Histograms

# 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)

6. Logging Model Graph

# 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])

Advanced Features

Embedding Projector

Visualize high-dimensional data (embeddings, features) in 2D/3D.

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
)

In TensorBoard:

Navigate to "Projector" tab
Choose PCA, t-SNE, or UMAP visualization
Search, filter, and explore clusters

Hyperparameter Tuning

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

Text Logging

# 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)

PR Curves

Precision-Recall curves for classification.

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
    )

Integration Examples

PyTorch Training Loop

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()

TensorFlow/Keras Training

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]
)

Comparing Experiments

Multiple Runs

# 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

In TensorBoard:

All runs appear in the same dashboard
Toggle runs on/off for comparison
Use regex to filter run names
Overlay charts to compare metrics

Organizing Experiments

# 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')

Best Practices

1. Use Descriptive Run Names

# ✅ 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

2. Group Related Metrics

# ✅ 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)

3. Log Regularly but Not Too Often

# ✅ 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

4. Close Writer When Done

# ✅ 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()

5. Use Separate Writers for Train/Val

# ✅ 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)

Performance Profiling

TensorFlow Profiler

# 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

PyTorch Profiler

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

Resources

Documentation: https://www.tensorflow.org/tensorboard
PyTorch Integration: https://pytorch.org/docs/stable/tensorboard.html
GitHub: https://github.com/tensorflow/tensorboard (27k+ stars)
TensorBoard.dev: https://tensorboard.dev (share experiments publicly)

FAQ

What does the tensorboard skill do?

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

How do I install tensorboard?

Run npx skills add https://github.com/orchestra-research/ai-research-skills --skill tensorboard in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/tensorboard/ (personal) or .claude/skills/tensorboard/ (project).

Which AI agents can use tensorboard?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is tensorboard safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains tensorboard?

It is published by orchestra-research in orchestra-research/ai-research-skills. It has 973 recorded installs.