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Demo: Using SQLFlow from Command Line

Besides running SQLFlow from Notebook, we could also run it from command line. If you are using Docker for Linux, please change host.docker.internal:3306 to localhost:3306.

docker run -it --rm --net=host sqlflow/sqlflow:latest demo \
--datasource="mysql://root:root@tcp(host.docker.internal:3306)/?maxAllowedPacket=0"

You should be able to see the following:

sqlflow>

Training a DNNClassifier and run prediction

  • Step One: Let's see some training data from Iris database
sqlflow> SELECT * from iris.train limit 2;
-----------------------------
+--------------+-------------+--------------+-------------+-------+
| SEPAL LENGTH | SEPAL WIDTH | PETAL LENGTH | PETAL WIDTH | CLASS |
+--------------+-------------+--------------+-------------+-------+
|          6.4 |         2.8 |          5.6 |         2.2 |     2 |
|            5 |         2.3 |          3.3 |           1 |     1 |
+--------------+-------------+--------------+-------------+-------+
sqlflow> SELECT *
FROM iris.train
TRAIN DNNClassifier
WITH n_classes = 3, hidden_units = [10, 20]
COLUMN sepal_length, sepal_width, petal_length, petal_width
LABEL class
INTO sqlflow_models.my_dnn_model;

...
Training set accuracy: 0.96721
Done training
  • Step Three: Run prediction using the trained model produced from step two. Note my_dnn_model is not a table, therefore it's not visually displayable from Select statement.
sqlflow> SELECT *
FROM iris.test
PREDICT iris.predict.class
USING sqlflow_models.my_dnn_model;

...
Done predicting. Predict table : iris.predict
  • Step Four: Checkout the prediction result
sqlflow>
SELECT * from iris.predict limit 3;

...
+--------------+-------------+--------------+-------------+-------+
| SEPAL LENGTH | SEPAL WIDTH | PETAL LENGTH | PETAL WIDTH | CLASS |
+--------------+-------------+--------------+-------------+-------+
|          6.3 |         2.7 |          4.9 |         1.8 |     2 |
|          5.7 |         2.8 |          4.1 |         1.3 |     1 |
|            5 |           3 |          1.6 |         0.2 |     0 |
+--------------+-------------+--------------+-------------+-------+

Congratulations! Now you have successfully completed a demo using SQLFlow syntax to train model using DNNClassifier and make a quick prediction. More demos are on the road, please stay tuned.