View model architecture

This page provides information about how to use Cloud Logging to view details about a Vertex AI model. Using Logging, you see:

• The hyperparameters of the final model as key-value pairs.
• The hyperparameters and object values used during model training and tuning, as well as an objective value.

By default, logs are deleted after 30 days.

The following topics are covered:

1. Viewing training logs.
2. Log fields.

Note: Model architecture logs are provided as part of the Cloud Logging service. For general information about Cloud Logging, see the Cloud Logging documentation.

Before you begin

Before you can view the hyperparameter logs for your model, you must train it.

To perform this task, you must have the following permissions:

• logging.logServiceIndexes.list on the project
• logging.logServices.list on the project

Viewing training logs

You can use the Google Cloud console to access the hyperparameter logs of the final model and the hyperparameter logs of the tuning trials.

1. In the Google Cloud console, go to the Vertex AI Models page.

   Go to Models page

2. In the Region drop-down, select the region where your model is located.

3. From the list of models, select your model.

4. Select your model's version number.

5. Open the Version Details tab.

6. To see the hyperparameter log of the final model, go to the Model hyperparameters row and click Model.

   1. There is just one log entry. Expand the payload as shown below. For details, see Log fields.

      

7. To see the hyperparameter log of the tuning trials, go to the Model hyperparameters row and click Trials.

   1. There is one entry for each of the tuning trials. Expand the payload as shown below. For details, see Log fields.

      

Log fields

Activity logs are structured as described in the LogEntry type documentation.

Vertex AI model logs have, among other fields:

• labels: The log_type field is set to automl_tables.
• jsonPayload: The specific details of the log entry, provided in JSON object format. For details, see Payload contents for the hyperparameter log of the final model or Payload contents for the hyperparameter log of a tuning trial.
• timestamp: The date and time when the model was created or the trial was run.

Payload contents for the hyperparameter log of the final model

The jsonPayload field for the hyperparameter log of the final model contains a modelParameters field. This field contains one entry for each model that contributes to the final ensemble model. Each entry has a hyperparameters field, whose contents depend on the model type. For details, see List of hyperparameters.

Payload contents for the hyperparameter log of a tuning trial

The jsonPayload field for the hyperparameter log of a tuning trial contains the following fields:

Field	Type	Description
modelStructure	JSON	A description of the Vertex AI model structure. This field contains a modelParameters field. The modelParameters field has a hyperparameters field, whose contents depend on the model type. For details, see List of hyperparameters.
trainingObjectivePoint	JSON	The optimization objective used for model training. This entry includes a timestamp and an objective value at the time the log entry was recorded.

List of hyperparameters

The hyperparameter data provided in the logs differ for each type of model. The following sections describe the hyperparameters for each model type.

Gradient boosted decision tree models

• Tree L1 regularization
• Tree L2 regularization
• Max tree depth
• Model type: GBDT
• Number of trees
• Tree complexity

Feedforward neural network models

• Dropout rate
• Enable batchNorm (True or False)
• Enable embedding L1 (True or False)
• Enable embedding L2 (True or False)
• Enable L1 (True or False)
• Enable L2 (True or False)
• Enable layerNorm (True or False)
• Enable numerical embedding (True or False)
• Hidden layer size
• Model type: nn
• Normalize numerical column (True or False)
• Number of cross layers
• Number of hidden layers
• Skip connections type (dense, disable, concat, or slice_or_padding)

What's next

Once you're ready to make predictions with your classification or regression model, you have two options:

• Make online (real-time) predictions using your model.
• Get batch predictions directly from your model.

Additionally, you can:

• Evaluate your model.
• Review general information about Cloud Logging.
• You can export your logs to BigQuery, Cloud Storage, or Pub/Sub. Read Route logs to supported destinations in the Logging documentation to learn how to export activity logs.