3.15.3 ML_MODEL_IMPORT

Run the ML_MODEL_IMPORT routine to import a pre-trained model into the model catalog. MySQL 8.1.0 supports the import of HeatWave AutoML format models. MySQL 8.0.31 supports the import of ONNX, Open Neural Network Exchange, format models. After import, all the HeatWave AutoML routines can be used with the ONNX model.

Models in ONNX format, .onnx. cannot be loaded directly into a MySQL table. They require string serialization and conversion to Base64 binary encoding. Before running ML_MODEL_IMPORT, follow the instructions in Section 3.13.2, “ONNX Model Import” to carry out the required pre-processing and then load the model into a temporary table for import to MySQL HeatWave.

ML_MODEL_IMPORT Syntax

mysql> CALL sys.ML_MODEL_IMPORT (model_object, model_metadata, model_handle);

ML_MODEL_IMPORT parameters:

• model_object: The preprocessed ONNX model object, which must be string serialized and BASE64 encoded. See Section 3.13.2, “ONNX Model Import”.

• model_metadata: An optional JSON object that contains key-value pairs with model metadata. See Section 3.15.12, “Model Metadata”.

• model_handle: The model handle for the model. The model is stored in the model catalog under this name and accessed using it. Specify a model handle that does not already exist in the model catalog.

Syntax Examples

• An example that imports a HeatWave AutoML model with metadata:

  mysql> SET @hwml_model = "hwml_model";
  Query OK, 0 rows affected (0.00 sec)
  
  mysql> CALL sys.ML_TRAIN('mlcorpus.input_train', 'target', NULL, @hwml_model);
  Query OK, 0 rows affected (23.36 sec)
  
  mysql> SET @hwml_object = (SELECT model_object FROM ML_SCHEMA_root.MODEL_CATALOG 
            WHERE model_handle=@hwml_model);
  Query OK, 0 rows affected (0.00 sec)
  
  mysql> SET @hwml_metadata = (SELECT model_metadata FROM ML_SCHEMA_root.MODEL_CATALOG 
            WHERE model_handle=@hwml_model);
  Query OK, 0 rows affected (0.00 sec)
  
  mysql> CALL sys.ML_MODEL_IMPORT(@hwml_object, @hwml_metadata, @imported_model);
  Query OK, 0 rows affected (0.01 sec)
  
  mysql> model_metadata->>'$.task' AS task, model_owner, 
            model_metadata->>'$.build_timestamp' AS build_timestamp, 
            model_metadata->>'$.target_column_name' AS target_column_name, 
            model_metadata->>'$.train_table_name' AS train_table_name, model_object_size, 
            model_metadata->>'$.model_explanation' AS model_explanation 
            FROM ML_SCHEMA_root.MODEL_CATALOG;
  +----------------+----------------+-----------------+--------------------+----------------------+-------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
  | task           | model_owner    | build_timestamp | target_column_name | train_table_name     | model_object_size | model_explanation                                                                                                                                                                          |
  +----------------+----------------+-----------------+--------------------+----------------------+-------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
  | classification | root           |      1679202888 | target             | mlcorpus.input_train |              4748 | {"permutation_importance": {"C0": 0.0, "C1": 0.0, "C2": 0.0, "C3": 0.0, "C4": 0.0, "C5": 0.0, "C6": 0.0, "C7": 0.0, "C8": 0.0, "C9": 0.0, "C10": 0.0, "C11": 0.0, "C12": 0.0, "C13": 0.0}} |
  | classification | root           |      1679202888 | target             | mlcorpus.input_train |              4748 | {"permutation_importance": {"C0": 0.0, "C1": 0.0, "C2": 0.0, "C3": 0.0, "C4": 0.0, "C5": 0.0, "C6": 0.0, "C7": 0.0, "C8": 0.0, "C9": 0.0, "C10": 0.0, "C11": 0.0, "C12": 0.0, "C13": 0.0}} |
  +----------------+----------------+-----------------+--------------------+----------------------+-------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
  2 rows in set (0.00 sec)
  
  
  mysql> SELECT model_metadata->>'$.column_names' AS column_names 
            FROM ML_SCHEMA_root.MODEL_CATALOG
            WHERE model_handle LIKE 'hwml%';
  +------------------------------------------------------------------------------------------+
  | column_names                                                                             |
  +------------------------------------------------------------------------------------------+
  | ["C0", "C1", "C2", "C3", "C4", "C5", "C6", "C7", "C8", "C9", "C10", "C11", "C12", "C13"] |
  | ["C0", "C1", "C2", "C3", "C4", "C5", "C6", "C7", "C8", "C9", "C10", "C11", "C12", "C13"] |
  +------------------------------------------------------------------------------------------+
  2 rows in set (0.00 sec)

• An example that imports an ONNX model without specifying metadata:

  mysql> CALL sys.ML_MODEL_IMPORT(@onnx_encode, NULL, 'onnx_test');