Open source TensorBoard (TB) is a Google open source project for machine learning experiment visualization. Vertex AI TensorBoard is an enterprise-ready managed version of TensorBoard.
Vertex AI TensorBoard provides various detailed visualizations, that includes:
- Tracking and visualizing metrics such as loss and accuracy over time
- Visualizing model computational graphs (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
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.
A Vertex AI TensorBoard instance, which is a regionalized resource storing your Vertex AI TensorBoard experiments, must be created before the experiments can be visualized. You can create multiple instances in a project.
Create a Vertex AI TensorBoard instance
Google Cloud console
Follow these steps to create a Vertex AI TensorBoard instance using the Google Cloud console.
- If you're new to Vertex AI or starting a new project, set up your project and development environment.
- In the Vertex AI section of the Google Cloud console, go to the Experiments page.
Go to the Experiments page.
- Navigate to the TensorBoard Instances tab.
- Click Create at the top of the page.
- Select a region from the Region drop-down list.
- Add a description. (optional)
- Click Create to create your TensorBoard instance
gcloud CLIUse Google Cloud CLI to create a Vertex AI TensorBoard instance.
- Install the gcloud CLI
- Initialize the Google Cloud CLI by running
- To confirm installation, explore the commands.
gcloud ai tensorboards --help
The commands include
delete. If needed, you can follow these steps to set default values for your project and region before proceeding.
Now, you can create a Vertex AI TensorBoard instance.
- Authenticate to the gcloud CLI.
gcloud auth application-default login
- Create a Vertex AI 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 Vertex AI TensorBoard instance
name (for example:
projects/123/locations/us-central1/tensorboards/456) that is printed at the end of the following command. You will need it in the later steps.
gcloud ai tensorboards create --display-name DISPLAY_NAME \ --project PROJECT_NAME
Replace the following:
PROJECT_NAME: the project that you want to create the TensorBoard instance in
DISPLAY_NAME: a descriptive name for the TensorBoard instance
Vertex AI SDK for Python
Create Vertex AI TensorBoard instance using the Vertex AI SDK for Python.
def create_tensorboard_sample( project: str, display_name: str, location: str, ): aiplatform.init(project=project, location=location) tensorboard = aiplatform.tensorboard.Tensorboard.create( display_name=display_name, project=project, location=location, ) print(tensorboard.display_name) print(tensorboard.resource_name) return tensorboard
Uploading TensorBoard logs with the Vertex AI TensorBoard Uploader
Vertex AI TensorBoard offers a Python CLI for uploading TensorBoard logs. You can upload logs from any environment which can connect to Google Cloud.
Alternatively, you can upload TensorBoard logs using the Vertex AI TensorBoard REST APIs.
Creating a virtual environment (optional)
Optional, but recommended first step: create a dedicated virtual environment to install the Vertex AI TensorBoard Uploader Python CLI.
python3 -m venv PATH/TO/VIRTUAL/ENVIRONMENT source PATH/TO/VIRTUAL/ENVIRONMENT/bin/activate
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 Vertex AI TensorBoard logs
tb-gcp-uploader --tensorboard_resource_name \ TENSORBOARD_INSTANCE_NAME \ --logdir=LOG_DIR \ --experiment_name=TB_EXPERIMENT_NAME --one_shot=True
TENSORBOARD_INSTANCE_NAME: there are two ways to identify the instance name:
- The full name is printed at the end of the
gcloud ai tensorboards createcommand that you used previously.
If the TensorBoard Instance was created using the Google Cloud console, the TENSORBOARD_INSTANCE_NAME is
- To find the
TENSORBOARD_INSTANCE_ID, go to the Experiments page Vertex AI section of the Google Cloud console, and then select the TensorBoard Instances tab.
Go to the Experiments page
- To find the
- The full name is printed at the end of the
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
tb-gcp-uploader --help for more information.
View a Vertex AI TensorBoard experiment
In order to access the Vertex AI TensorBoard page in the Google Cloud console, you'll need the IAM role "Vertex AI TensorBoard Web App User". The IAM Administrator of the project can grant such access. Users with the Vertex AI Administrator role also have access.
Use the Google Cloud console
You can view your Vertex AI TensorBoard experiment from the Google Cloud console with the following steps.
In the Vertex AI section of the Google Cloud console, go to the Experiments page.
From the experiments tab, scroll or filter the experiments list to find your experiment.
To open the Vertex AI TensorBoard page, click Open TensorBoard next to your experiment.
Alternatively, if you use Vertex AI TensorBoard with custom training, select the training job from the Training page. An Open TensorBoard button appears at the top of the Training Job details page.
Use the link from CLI output
In addition, when using the TensorBoard Uploader, the CLI outputs a link to
the Vertex AI TensorBoard instance in the
first few lines of the log where you can view your experiment. For example:
View your TensorBoard at