add contribute models doc (#14)

* add contribute models doc

* update by comment

* update by comment

* update by comment

* update

Author: Yancey0623

doc/contribute_models.md

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+# How to Contribute SQLFLow Models
+
+This guide will introduce how to contribute to SQLFlow models. You can find design doc: [Define SQLFLow Models](/doc/customized+model.md), and feel free to check it out.
+
+## Develop an SQLFlow Model
+
+1. Open the [SQLFlow models repo](https://github.com/sql-machine-learning/models) on your web browser, and fork the official repo to your account.
+
+1. Clone the forked repo on your hosts:
+
+    ``` bash
+    > git clone https://github.com/<Your Github ID>/models.git
+    ```
+
+1. Set up your local python environment by `make setup && source venv/bin/activate`. If you are using [PyCharm](https://www.jetbrains.com/pycharm/), you can simply `make setup` and then import the `models` folder as a new project.
+
+1. You can add a new mode definition Python script under the folder [sqlflow_models](/sqlflow_models). For example, adding a new Python script `mydnnclassfier.py`:
+
+    ``` text
+    `-sqlflow_models
+        |- dnnclassifier.py
+        `- mydnnclassifier.py
+    ```
+
+1. You can choose whatever name you like for your model. Your model definition should be a [keras subclass model](https://keras.io/models/about-keras-models/#model-subclassing)
+
+    ``` python
+    import tensorflow as tf
+
+    class MyDNNClassifier(tf.keras.Model):
+        def __init__(self, feature_columns, hidden_units=[10,10], n_classes=2):
+            ...
+            ...
+    ```
+
+1. Import `MyDNNClassfier` in [sqlflow_models/\_\_init__.py](/sqlflow_models/__init__.py):
+
+    ``` python
+    ...
+    from .mydnnclassfier import MyDNNClassifier
+    ```
+
+1. 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`:
+
+    ``` python
+    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 Your SQLFlow Model
+
+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.
+
+1. Launch an SQLFlow all-in-one Docker container
+
+    ``` bash
+    cd ./models
+    > docker run --rm -it -v $PWD:/models -p 8888:8888 sqlflow/sqlflow
+    ```
+
+1. Update `sqlflow_models` in the SQLFlow all-in-one Docker container:
+
+    ``` bash
+    > docker exec -it <container-id> pip install -U /models
+    ```
+
+1. 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`:
+
+``` sql
+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;
+```
+
+## 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.