# Tensorboard

Visualize training metrics, debug models with histograms, compare experiments...

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Testing
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill tensorboard -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill tensorboard -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill tensorboard -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill tensorboard -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

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

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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
# 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')

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/13-mlops/tensorboard/SKILL.md