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SQLFlow

Export Trained Models

Once we train a model using SQLFlow via a statement like the following,

SELECT * FROM iris.train
TO TRAIN DNNClassifier
WITH model.n_classes=3,model.hidden_units=[10,20]
INTO sqlflow_models.my_dnn_model;

the trained model sqlflow_models.my_dnn_model is saved in the database. Anyone with the read access can write SQLFlow statement to visually explain the model or to use the model for prediction.

In some cases, you might want to export and download a trained model from the database, so you can use it out of SQLFlow, for example, load it to an online prediction service of an online advertising system. To export a model, you can use the command-line tool sqlflow. we can download the trained model (sqlflow_models.my_dnn_model), using the command:

# Configure database connection string
export SQLFLOW_DATASOURCE="mysql://root:root@tcp(127.0.0.1:3306)/?"
# Configure sqlflow server address
export SQLFLOW_SERVER="localhost:50051"
# Assume you have trained a model named sqlflow_models.my_dnn_model
./sqlflow get model "sqlflow_models.my_dnn_model"

If the model has been downloaded successfully, you can see the below output on the terminal:

model "sqlflow_models.my_dnn_model" downloaded successfully at
/your/working/directory/model_dump.tar.gz

model_dump.tar.gz contains below files/folders:

  • exported_path file contains one line indicating the path of the Tensorflow exported model (if the model is a Tensorflow model).
  • model_meta.json file is a JSON serialized file containing how the model is trained by SQLFlow.
  • model_save directory contains all the files of the trained model.