Get started with Vertex AI TensorBoard

Open-source TensorBoard is a Google open source project for machine learning experiment visualization. Vertex AI TensorBoard is an enterprise-ready managed version of TensorBoard.

TensorBoard provides a variety of detailed visualizations, that includes:

  • Tracking and visualizing metrics such as loss and accuracy over time
  • Visualizing a model computational graph (ops and layers)
  • Viewing histograms of weights, biases, or other tensors as they change over time
  • Projecting embeddings to a lower dimensional space
  • Displaying image, text, and audio samples
  • Profiling TensorFlow programs

In addition to the powerful visualizations from TensorBoard, Vertex AI TensorBoard provides:

  • A persistent, shareable link to your experiment's dashboard

  • A searchable list of all experiments in a project

  • Tight integrations with Vertex AI services for model training

  • Enterprise-grade security, privacy, and compliance

With Vertex AI TensorBoard, you can track, visualize, and compare ML experiments and share them with your team.

Get Started

Before using Vertex AI TensorBoard, you must create a managed TensorBoard instance. A TensorBoard instance is a regionalized resource that stores your Vertex AI TensorBoard experiments. You can create multiple TensorBoard instances in a project.

Create a Vertex AI TensorBoard instance

Follow these steps to create a TensorBoard instance using the Cloud Console

  1. If you are new to Vertex AI or starting a new project, set up your project and development environment.

  2. In the Vertex AI section of the Google Cloud Console, go to the Experiments page.

    Go to the Experiments page

  3. Navigate to the TensorBoard Instances tab.

  4. Click Create at the top of the page.

  5. Modify the TensorBoard instance name field to create a descriptive display name.

  6. Select a region from the Region drop-down list and optionally add a description.

  7. Click Create to create your TensorBoard instance

Alternatively, you can create a TensorBoard instance from the gcloud CLI.

  1. Install the gcloud CLI.

  2. Initialize the Cloud SDK by running gcloud init.

  3. Install the gcloud beta component, as Vertex AI TensorBoard is in Preview.

    gcloud components install beta
    gcloud components update
  4. Commands for Vertex AI TensorBoard are now accessible. Explore the commands to confirm installation.

    gcloud beta ai tensorboards --help

    The commands include create, describe, list, and delete. If needed, you can follow these steps to set default values for your project and region before proceeding.

    Now, you can create a TensorBoard instance.

  5. Authenticate to gcloud.

    gcloud auth application-default login
  6. Create a TensorBoard instance by providing a project name and a display name. This step might take a few minutes to complete for the first time in a project. Make note of the TensorBoard instance name (for example: projects/123/locations/us-central1/tensorboards/456) that will be printed at the end of the following command. You will need it in the later steps.

    gcloud beta ai tensorboards create --display-name DISPLAY_NAME \
      --project PROJECT_NAME

    Replace the following:

    • PROJECT_NAME: the project you wish to create the TensorBoard instance in

    • DISPLAY_NAME: a descriptive name for the TensorBoard instance

Using Vertex AI TensorBoard

TensorBoard is an open source visualization and analysis toolkit. In order to visualize experiments using Vertex AI TensorBoard you need to use TensorBoard APIs to prepare your TensorBoard logs. These logs can then be uploaded or streamed into your Vertex AI TensorBoard instance.

For more information on TensorBoard APIs, visit the TensorBoard open source docs.

Uploading TensorBoard logs with the Vertex AI TensorBoard Uploader

Vertex AI TensorBoard offers a Python CLI for uploading TensorBoard logs. You can use this to upload logs from any environment which can connect to Google Cloud.

Creating a virtual environment (optional)

Optional, but recommended first step: create a dedicated virtual environment to install the Vertex AI TensorBoard Uploader Python CLI.


Replace PATH/TO/VIRTUAL/ENVIRONMENT with the path to your dedicated virtual environment.

Install the Vertex AI TensorBoard Uploader through Vertex AI SDK

The uploader needs the latest version of pip in order to get installed properly.

pip install -U pip
pip install google-cloud-aiplatform[tensorboard]

Uploading TensorBoard logs

tb-gcp-uploader --tensorboard_resource_name \
  --logdir=LOG_DIR \
  --experiment_name=TB_EXPERIMENT_NAME --one_shot=True

Replace the following:

  • PROJECT_NUMBER: GCP project number.

  • LOCATION: the region of the previously created TensorBoard instance.

  • TENSORBOARD_INSTANCE_ID: the ID of the previously created TensorBoard resource.

  • LOG_DIR: the location of the event logs, that resides either in the local file system or Cloud Storage

  • TB_EXPERIMENT_NAME: the name of the experiment, for example test-experiment. The experiment name should be unique within a TensorBoard resource

The uploader CLI by default runs indefinitely, monitoring changes in the LOG_DIR, and uploads newly added logs. --one_shot=True disables the behavior. Run tb-gcp-uploader --help for more information.

The CLI will output a link to the Vertex AI TensorBoard in the first few lines of the log where you can view your experiment. For example: View your TensorBoard at

Viewing a Vertex AI TensorBoard experiment

You can view your Vertex AI TensorBoard experiment from the Google Cloud Console with the following steps.

  1. In the Vertex AI section of the Google Cloud Console, go to the Experiments page.

    Go to the Experiments page

  2. From the experiments tab, scroll or filter the experiments list to find your experiment.

  3. Click Open TensorBoard next to your experiment to open the Vertex AI TensorBoard UI.

If you use Vertex AI TensorBoard with custom training, an Open TensorBoard button appears on the top of the Training Job details page after selecting the training job from the Training page.

What's next