TensorBoard is a visualization toolkit originally developed alongside TensorFlow to track, inspect, and debug machine learning experiments. It reads event logs (tfevents) and presents interactive dashboards for scalars, histograms, distributions, images, audio, embeddings, graphs, and profiling data, enabling real-time monitoring of training and model behavior. TensorBoard runs as a local or hosted web server (commonly on port 6006), supports modular plugins, and integrates with other platforms (e.g., Hugging Face Hub, PyTorch via tensorboardX or torch.utils.tensorboard) to visualize traces from multiple frameworks and share results. Its ability to compare runs, visualize model graphs and embeddings, and surface performance bottlenecks makes it valuable for experiment iteration and model debugging.