The ML.TRIAL_INFO function

The ML.TRIAL_INFO function is used to display information regarding trials from a hyperparameter tuning model.

For information about supported model types of each SQL statement and function, and all supported SQL statements and functions for each model type, read End-to-end user journey for each model.




model_name is the name of the model you're evaluating. If you do not have a default project configured, prepend the project ID to the model name in following format: \`[PROJECT_ID].[DATASET].[MODEL]\` (including the backticks); for example, `myproject.mydataset.mymodel`.

This statement returns in one row per trial with the following columns:

  • trial_id (int64_value): 1-based ID assigned to each trial in the approximate order of trial execution.
  • hyperparameters (STRUCT): The hyperparameters used in each trial contained in a STRUCT.
  • hparam_tuning_evaluation_metrics (STRUCT): The evaluation metrics specified by hparam_tuning_objectives. Metrics are calculated from the evaluation data. See data split section for a comparison of evaluation and test data.
  • training_loss (float64_value): The loss of the trial during the last iteration calculated using the training data.
  • eval_loss (float64_value): The loss of the trial during the last iteration calculated using the evaluation data.
  • status (string_value): The final status of the trial:
    • SUCCEEDED: The trial succeeded.
    • FAILED: The trial failed.
    • INFEASIBLE: The trial was not run due to an invalid combination of hyperparameters.
  • error_message (string_value): The error message if the trial did not succeed. See hyperparameter tuning error handling for more information.
  • is_optimal (bool_value): Whether the trial had the best objective value. If multiple trials are marked as optimal, then the trial with the smallest trial_id as the default trial during model serving is used.