# TensorBoard ![](https://2327526407-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LvBP1svpACTB1R1x_U4%2F-LvI8vNq_N7u3RWVAPLk%2F-LvIs_Lpza8ips01HNef%2Fimage.png?alt=media\&token=0700a505-909a-451b-91af-ec02ff836f44) TensorBoard is a powerful open source toolkit for tracking and visualizing [metrics](https://machine-learning.paperspace.com/wiki/metrics-in-machine-learning) within individual models or for comparing performance between multiple models. Also included are some powerful debugging options that help you visually explore the model. TensorBoard was initially built for TensorFlow but is now supported by other frameworks such as PyTorch. [TensorboardX](https://github.com/lanpa/tensorboardX) is a project that extends TensorBoard to other frameworks such as Chainer, MXnet, etc. ## TensorBoard + Gradient TensorBoards are first-class citizens in Gradient. A single Experiment or multiple Experiments can easily be added to TensorBoard without any setup or management. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://machine-learning.paperspace.com/wiki/tensorboard.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.