The ML.TRIAL_INFO function

This document describes the ML.TRIAL_INFO function, which lets you display information about trials from a model that uses hyperparameter tuning .

You can use this function with models that support hyperparameter tuning . For more information, see End-to-end user journeys for ML models .

Syntax

ML.TRIAL_INFO(MODEL ` PROJECT_ID 
. DATASET 
. MODEL_NAME 
`)

Arguments

ML.TRIAL_INFO takes the following arguments:

• PROJECT_ID : your project ID.
• DATASET : the BigQuery dataset that contains the model.
• MODEL_NAME : The name of the model.

Output

ML.TRIAL_INFO returns one row per trial with the following columns:

• trial_id : an INT64 value that contains the ID assigned to each trial in the approximate order of trial execution. trial_id values start from 1 .
• hyperparameters : a STRUCT value that contains the hyperparameters used in the trial.
• hparam_tuning_evaluation_metrics : a STRUCT value that contains the evaluation metrics appropriate to the hyperparameter tuning objective specified by the hparam_tuning_objectives argument in the CREATE MODEL statement. Metrics are calculated from the evaluation data. For more information about the datasets used in hyperparameter tuning, see Data split .
• training_loss : a FLOAT64 value that contains the loss of the trial during the last iteration, calculated using the training data.
• eval_loss : a FLOAT64 value that contains the loss of the trial during the last iteration, calculated using the evaluation data.

• status : a STRING value that contains the final status of the trial. Possible values include the following:

  • SUCCEEDED : the trial succeeded.
  • FAILED : the trial failed.
  • INFEASIBLE : the trial was not run due to an invalid combination of hyperparameters.

• error_message : a STRING value that contains the error message that is returned if the trial didn't succeed. For more information, see Error handling .

• is_optimal : a BOOL value that indicates whether the trial had the best objective value. If multiple trials are marked as optimal, then the trial with the smallest trial_id value is used as the default trial during model serving.

Example

The following query retrieves information of all trials for the model mydataset.mymodel in your default project:

 SELECT 
  
 * 
 FROM 
  
 ML 
 . 
 TRIAL_INFO 
 ( 
 MODEL 
  
 ` 
 mydataset 
 . 
 mymodel 
 ` 
 ) 

What's next

• For information about hyperparameter tuning, see Hyperparameter tuning overview .