What is tensorboard?

TensorBoard is a web-based dashboard for watching and understanding what happens when you train a machine learning model. When you run a training process, it generates a stream of data: how the error rate is changing, what the model's internal values look like over time, any images or audio the model produces. TensorBoard reads that data and turns it into charts and visualizations you can explore in a browser. The main views it provides include a scalar dashboard for tracking numbers over time (like how fast a model is learning or how its error rate is declining), a histogram dashboard for seeing the distribution of values inside the model's layers, an image viewer, an audio viewer, and a graph visualizer that draws the model's structure as a diagram. You can open multiple training runs side by side to compare, say, whether changing a setting made the model learn faster. To use it, your training code writes logs to a folder as it runs. You then point TensorBoard at that folder with a single command and open a browser to localhost. It works entirely offline with no internet connection required, which matters for research done behind firewalls or in data centers. TensorBoard is the official visualization tool bundled with TensorFlow, Google's machine learning framework, but it can also be used with other frameworks. It is installed automatically when you install TensorFlow via pip. The README is detailed on concepts and configuration options.