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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Use TensorBoard when you need to:
Users: 20M+ downloads/year | GitHub Stars: 27k+ | License: Apache 2.0
# 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])
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:
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)
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
)
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
In TensorBoard:
# 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
references/visualization.md - Comprehensive visualization guidereferences/profiling.md - Performance profiling patternsreferences/integrations.md - Framework-specific integration examples
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).