tensorboard

To visualize PyTorch training metrics in TensorBoard, you need to use framework-specific writers or callbacks to log scalars like loss and accuracy to a structured run directory, which TensorBoard then reads to generate interactive charts.

Yes, you can compare multiple experiment runs side by side in TensorBoard by pointing it at an organized parent log directory containing multiple run subdirectories, allowing you to evaluate changes in architecture or hyperparameters.

TensorBoard supports performance profiling by logging profiler traces from PyTorch or TensorFlow workflows, which helps identify slow operations, diagnose GPU underuse, and pinpoint memory issues during model training.

You can log histograms, embeddings, images, text, and PR curves for model debugging using TensorBoard across PyTorch, TensorFlow, Keras, Lightning, Transformers, Fast.ai, JAX, and scikit-learn workflows.

Structured event logging and organized run directories are required for experiment tracking because TensorBoard relies on structured log files to parse scalars, images, and profiler traces into visual dashboards for monitoring and comparison.

The best way to inspect model behavior and activations during training is to log distributions, histograms, and embeddings using framework-specific callbacks, then launch TensorBoard on your experiment log directory to visually diagnose the model.