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