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Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit
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Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit 📦 Ships with claude-code-templates
</> SKILL.md
mlops-tensorboard.SKILL.md
---
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: 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
```bash
Flowy AI Flows that just work. Hand-picked Claude Code plugins that fire the right skill as you prompt.
# 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
```python
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
```python
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)
```python
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
```python
# 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()
```
```python
# 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
```python
# 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
```python
# 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)
```
```python
# 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
```python
# 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)
```
```python
# 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
```python
# PyTorch
import torch
model = MyModel()
dummy_input = torch.randn(1, 3, 224, 224)
writer.add_graph(model, dummy_input)
writer.close()
```
```python
# 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.
```python
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
```python
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
```python
# 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.
```python
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
```python
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
```python
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
```bash
# 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
```python
# 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
```python
# ✅ 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
```python
# ✅ 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
```python
# ✅ 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
```python
# ✅ 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
```python
# ✅ 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
```python
# 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
```python
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)
## See Also
- `references/visualization.md` - Comprehensive visualization guide
- `references/profiling.md` - Performance profiling patterns
- `references/integrations.md` - Framework-specific integration examples
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