How to Contribute SQLFLow Models

This guide will introduce how to contribute to SQLFlow models. You can find design doc: Define SQLFLow Models, and feel free to check it out.

Develop an SQLFlow Model

Open the SQLFlow models repo on your web browser, and fork the official repo to your account.

Clone the forked repo on your hosts:

 > git clone https://github.com/<Your Github ID>/models.git

Set up your local python environment by make setup && source venv/bin/activate. If you are using PyCharm, you can simply make setup and then import the models folder as a new project.
You can add a new mode definition Python script under the folder sqlflow_models. For example, adding a new Python script mydnnclassfier.py:
```
 `-sqlflow_models
     |- dnnclassifier.py
     `- mydnnclassifier.py
```

You can choose whatever name you like for your model. Your model definition should be a keras subclass model

 import tensorflow as tf

 class MyDNNClassifier(tf.keras.Model):
     def __init__(self, feature_columns, hidden_units=[10,10], n_classes=2):
         ...
         ...

Import MyDNNClassfier in sqlflow_models/__init__.py:

 ...
 from .mydnnclassfier import MyDNNClassifier

You can test your MyDNNClassifier by adding a new Python unit test script tests/test_mydnnclassifier.py and run the test as: python tests/test_mydnnclassifier.py:

 from  sqlflow_models import MyDNNClassifier
 from tests.base import BaseTestCases

 import tensorflow as tf
 import unittest


 class TestMyDNNClassifier(BaseTestCases.BaseTest):
     def setUp(self):
         self.features = {...}
         self.label = [...]
         feature_columns = [...]
         self.model = MyDNNClassifier(feature_columns=feature_columns)

 if __name__ == '__main__':
     unittest.main()

Test and Debug Your Model With SQLFlow

If you have developed a new model, please perform the integration test with the SQLFlow gRPC server to make sure it works well with SQLFlow.

Launch an SQLFlow all-in-one Docker container

 cd ./models
 > docker run --rm -it -v $PWD:/models -e PYTHONPATH=/models -p 8888:8888 sqlflow/sqlflow

Open a web browser and go to localhost:8888 to access the Jupyter Notebook. Using your custom model by modifying the TRAIN parameter of the SQLFlow extend SQL: TRAIN sqlflow_models.MyDNNClassifier:

SELECT * from iris.train
TRAIN sqlflow_models.MyDNNClassifier
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;

When you need to update the model and test a gain, just modify the mode Python file on your host then run the SQL statement in the notebook one more time.

Publish your model in the SQLFlow all-in-one Docker image

If you have already tested your code, please create a pull request and invite other develops to review it. If one of the develops approve your pull request, then you can merge it to the develop branch. The travis-ci would build the SQLFlow all-in-one Docker image with the latest models code every night and push it to the Docker hub with tag: sqlflow/sqlflow:nightly, you can find the latest models in it the second day.