---
title: TensorBoard | Databricks on AWS
source: https://docs.databricks.com/aws/en/machine-learning/train-model/tensorboard
ingestedAt: "2026-06-18T08:13:40.185Z"
---

[TensorBoard](https://www.tensorflow.org/tensorboard) is a suite of visualization tools for debugging, optimizing, and understanding TensorFlow, PyTorch, Hugging Face Transformers, and other machine learning programs.

## Use TensorBoard[​](#use-tensorboard "Direct link to Use TensorBoard")

Starting TensorBoard in Databricks is no different than starting it on a Jupyter notebook on your local computer.

1.  Load the `%tensorboard` magic command and define your log directory.
    
        %load_ext tensorboardexperiment_log_dir = <log-directory>
    
2.  Invoke the `%tensorboard` magic command.
    
        %tensorboard --logdir $experiment_log_dir
    
    The TensorBoard server starts and displays the user interface inline in the notebook. It also provides a link to open TensorBoard in a new tab.
    
    The following screenshot shows the TensorBoard UI started in a populated log directory.
    
    ![TensorBoard UI started in populated log directory](https://docs.databricks.com/aws/en/assets/images/tensorboard-46f0c70b910199be6cbe8e068646b712.png)
    

You can also start TensorBoard by using TensorBoard's notebook module directly.

Python

    from tensorboard import notebooknotebook.start("--logdir {}".format(experiment_log_dir))

## TensorBoard logs and directories[​](#tensorboard-logs-and-directories "Direct link to TensorBoard logs and directories")

TensorBoard visualizes your machine learning programs by reading logs generated by TensorBoard callbacks and functions in [TensorBoard](https://www.tensorflow.org/tensorboard/get_started) or [PyTorch](https://pytorch.org/docs/stable/tensorboard.html). To generate logs for other machine learning libraries, you can directly write logs using TensorFlow file writers (see [Module: tf.summary](https://www.tensorflow.org/api_docs/python/tf/summary) for TensorFlow 2.x and see [Module: tf.compat.v1.summary](https://www.tensorflow.org/api_docs/python/tf/compat/v1/summary) for the older API in TensorFlow 1.x ).

To make sure that your experiment logs are reliably stored, Databricks recommends writing logs to cloud storage rather than on the ephemeral cluster file system. For each experiment, start TensorBoard in a unique directory. For each run of your machine learning code in the experiment that generates logs, set the TensorBoard callback or file writer to write to a subdirectory of the experiment directory. That way, the data in the TensorBoard UI is separated into runs.

Read the official [TensorBoard documentation](https://www.tensorflow.org/tensorboard/get_started) to get started using TensorBoard to log information for your machine learning program.

## Manage TensorBoard processes[​](#manage-tensorboard-processes "Direct link to Manage TensorBoard processes")

The TensorBoard processes started within Databricks notebook are not terminated when the notebook is detached or the REPL is restarted (for example, when you clear the state of the notebook). To manually kill a TensorBoard process, send it a termination signal using `%sh kill -15 pid`. Improperly killed TensorBoard processes might corrupt `notebook.list()`.

To list the TensorBoard servers currently running on your cluster, with their corresponding log directories and process IDs, run `notebook.list()` from the TensorBoard notebook module.

## Known issues[​](#known-issues "Direct link to Known issues")

*   The inline TensorBoard UI is inside an iframe. Browser security features prevent external links within the UI from working unless you open the link in a new tab.
*   The `--window_title` option of TensorBoard is overridden on Databricks.
*   By default, TensorBoard scans a port range for selecting a port to listen to. If there are too many TensorBoard processes running on the cluster, all ports in the port range might be unavailable. You can work around this limitation by specifying a port number with the `--port` argument. The specified port should be between 6006 and 6106.
*   For download links to work, you must open TensorBoard in a tab.
*   When using TensorBoard 1.15.0, the Projector tab is blank. As a workaround, to visit the projector page directly, you can replace `#projector` in the URL by `data/plugin/projector/projector_binary.html`.
*   TensorBoard 2.4.0 has a [known issue](https://github.com/tensorflow/tensorboard/issues/4421) that might affect TensorBoard rendering if upgraded.
*   If you're logging TensorBoard-related data to DBFS or UC Volumes, you may get an error like `No dashboards are active for the current data set`. To overcome this error, it's advisable to call `writer.flush()` and `writer.close()` after using the `writer` to log data. This ensures that all logged data is properly written and available for TensorBoard to render.
