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+# Procedure for using db2 as a backend for model hosting
+
+DB2's Integrated Analytics System allows for an impressive amount of user defined extensions, which can be leveraged to allow db2 to serve as a model management and scoring engine. 
+We can store, manage, update and score models trained in scikit-learn and other python frameworks in DB2. We can also 
+run models trained in Lua, C, R, or C++. This document will demonstrate a python based example. 
+
+# DB2 Configuration
+
+We will need to store a python class for scoring a generic model in our DB2 server, and make some minor configuration changes. 
+
+Log in to the db2 server. In this case the user is `db2inst1`
+
+`source ~/.bashrc; sudo su - db2inst1`
+
+Make sure there is a python3 installation in your server.
+
+`sudo yum install python3`
+
+Install scikit-learn and joblib
+
+`pip3 install scikit-learn joblib`
+
+Update db2 to use the correct python path
+
+`db2 update dbm cfg using PYTHON_PATH $(which python)`
+
+Restart the Db2 instance because PYTHON_PATH is not dynamically configurable.
+
+`db2 connect reset;`
+`db2stop`
+`db2start`
+
+Add the python class for scoring the model by writing the following python class to $DB2_HOME/function/routine/score.py
+
+
+```
+import nzae
+from joblib import load
+from io import BytesIO
+import base64
+
+class predict(nzae.Ae):
+    def _setup(self):
+        self.model = None
+
+    def predict(self,data):
+        model = data[0]
+        if not self.model:
+            self.model = load(BytesIO(base64.b64decode(model)))
+
+        data = data[1:]
+        result = self.model.predict([data])
+        return float(result[0])
+
+    def _getFunctionResult(self,row):
+        price = self.predict(row)
+        return price
+
+predict.run()
+```
+
+## Model Training
+
+We will first train a simple logistic regression model on the Iris data set. We will use joblib to pickle the
+resulting model, and write it as a b64encoded string
+
+```
+from sklearn.linear_model import LogisticRegression
+from sklearn import datasets
+import joblib
+import base64
+train = datasets.load_iris().get("data")
+target = datasets.load_iris().get("target")
+lr = LogisticRegression(max_iter=1000)
+lr = lr.fit(train, target)
+
+joblib.dump(lr, "lr.joblib")
+
+with open("lr.joblib", "rb") as file:
+    file =  file.read()
+    b64model = base64.b64encode(file)
+    print(b64model)
+
+with open("model.b64", "wb") as file:
+    file.write(b64model)
+
+```
+
+## Model Deployment
+
+We need a table to store the model.
+
+```
+create table models(id integer not null generated always as identity, name varchar(30), model clob, primary key(id))
+```
+
+We can now store our model as a b64encoded string in the models table.
+
+
+`db2 "insert into models (name, model) values('iris_lr', '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')"`
+ 
+Then we register a UDF (User defined function) that references the python class.
+ 
+```
+CREATE FUNCTION iris_score(varchar(1356), float, float, float, float) \
+returns float LANGUAGE PYTHON  parameter style \
+NPSGENERIC  FENCED  NOT THREADSAFE  NO FINAL CALL  ALLOW PARALLEL  NO DBINFO  DETERMINISTIC  NO EXTERNAL ACTION \
+RETURNS NULL ON NULL INPUT  NO SQL \
+external name '/database/config/db2inst1/sqllib/function/routine/score.py'
+```
+Lets create a table for testing. 
+
+
+```
+CREATE TABLE iris(
+sepal_length float
+,sepal_width float
+,petal_length float
+,petal_width float
+,iris varchar(255)
+);
+
+INSERT INTO iris
+VALUES
+(5.1,3.5,1.4,0.2,'setosa'),
+(4.9,3,1.4,0.2,'setosa'),
+(4.7,3.2,1.3,0.2,'setosa'),
+(4.6,3.1,1.5,0.2,'setosa'),
+(5,3.6,1.4,0.2,'setosa'),
+(5.4,3.9,1.7,0.4,'setosa'),
+(4.6,3.4,1.4,0.3,'setosa'),
+(5,3.4,1.5,0.2,'setosa'),
+(4.4,2.9,1.4,0.2,'setosa'),
+(4.9,3.1,1.5,0.1,'setosa'),
+(5.4,3.7,1.5,0.2,'setosa'),
+(4.8,3.4,1.6,0.2,'setosa'),
+(4.8,3,1.4,0.1,'setosa'),
+(4.3,3,1.1,0.1,'setosa'),
+(5.8,4,1.2,0.2,'setosa'),
+(5.7,4.4,1.5,0.4,'setosa'),
+(5.4,3.9,1.3,0.4,'setosa'),
+(5.1,3.5,1.4,0.3,'setosa'),
+(5.7,3.8,1.7,0.3,'setosa'),
+(5.1,3.8,1.5,0.3,'setosa'),
+(5.4,3.4,1.7,0.2,'setosa'),
+(5.1,3.7,1.5,0.4,'setosa'),
+(4.6,3.6,1,0.2,'setosa'),
+(5.1,3.3,1.7,0.5,'setosa'),
+(4.8,3.4,1.9,0.2,'setosa'),
+(5,3,1.6,0.2,'setosa'),
+(5,3.4,1.6,0.4,'setosa'),
+(5.2,3.5,1.5,0.2,'setosa'),
+(5.2,3.4,1.4,0.2,'setosa'),
+(4.7,3.2,1.6,0.2,'setosa'),
+(4.8,3.1,1.6,0.2,'setosa'),
+(5.4,3.4,1.5,0.4,'setosa'),
+(5.2,4.1,1.5,0.1,'setosa'),
+(5.5,4.2,1.4,0.2,'setosa'),
+(4.9,3.1,1.5,0.1,'setosa'),
+(5,3.2,1.2,0.2,'setosa'),
+(5.5,3.5,1.3,0.2,'setosa'),
+(4.9,3.1,1.5,0.1,'setosa'),
+(4.4,3,1.3,0.2,'setosa'),
+(5.1,3.4,1.5,0.2,'setosa'),
+(5,3.5,1.3,0.3,'setosa'),
+(4.5,2.3,1.3,0.3,'setosa'),
+(4.4,3.2,1.3,0.2,'setosa'),
+(5,3.5,1.6,0.6,'setosa'),
+(5.1,3.8,1.9,0.4,'setosa'),
+(4.8,3,1.4,0.3,'setosa'),
+(5.1,3.8,1.6,0.2,'setosa'),
+(4.6,3.2,1.4,0.2,'setosa'),
+(5.3,3.7,1.5,0.2,'setosa'),
+(5,3.3,1.4,0.2,'setosa'),
+(7,3.2,4.7,1.4,'versicolor'),
+(6.4,3.2,4.5,1.5,'versicolor'),
+(6.9,3.1,4.9,1.5,'versicolor'),
+(5.5,2.3,4,1.3,'versicolor'),
+(6.5,2.8,4.6,1.5,'versicolor'),
+(5.7,2.8,4.5,1.3,'versicolor'),
+(6.3,3.3,4.7,1.6,'versicolor'),
+(4.9,2.4,3.3,1,'versicolor'),
+(6.6,2.9,4.6,1.3,'versicolor'),
+(5.2,2.7,3.9,1.4,'versicolor'),
+(5,2,3.5,1,'versicolor'),
+(5.9,3,4.2,1.5,'versicolor'),
+(6,2.2,4,1,'versicolor'),
+(6.1,2.9,4.7,1.4,'versicolor'),
+(5.6,2.9,3.6,1.3,'versicolor'),
+(6.7,3.1,4.4,1.4,'versicolor'),
+(5.6,3,4.5,1.5,'versicolor'),
+(5.8,2.7,4.1,1,'versicolor'),
+(6.2,2.2,4.5,1.5,'versicolor'),
+(5.6,2.5,3.9,1.1,'versicolor'),
+(5.9,3.2,4.8,1.8,'versicolor'),
+(6.1,2.8,4,1.3,'versicolor'),
+(6.3,2.5,4.9,1.5,'versicolor'),
+(6.1,2.8,4.7,1.2,'versicolor'),
+(6.4,2.9,4.3,1.3,'versicolor'),
+(6.6,3,4.4,1.4,'versicolor'),
+(6.8,2.8,4.8,1.4,'versicolor'),
+(6.7,3,5,1.7,'versicolor'),
+(6,2.9,4.5,1.5,'versicolor'),
+(5.7,2.6,3.5,1,'versicolor'),
+(5.5,2.4,3.8,1.1,'versicolor'),
+(5.5,2.4,3.7,1,'versicolor'),
+(5.8,2.7,3.9,1.2,'versicolor'),
+(6,2.7,5.1,1.6,'versicolor'),
+(5.4,3,4.5,1.5,'versicolor'),
+(6,3.4,4.5,1.6,'versicolor'),
+(6.7,3.1,4.7,1.5,'versicolor'),
+(6.3,2.3,4.4,1.3,'versicolor'),
+(5.6,3,4.1,1.3,'versicolor'),
+(5.5,2.5,4,1.3,'versicolor'),
+(5.5,2.6,4.4,1.2,'versicolor'),
+(6.1,3,4.6,1.4,'versicolor'),
+(5.8,2.6,4,1.2,'versicolor'),
+(5,2.3,3.3,1,'versicolor'),
+(5.6,2.7,4.2,1.3,'versicolor'),
+(5.7,3,4.2,1.2,'versicolor'),
+(5.7,2.9,4.2,1.3,'versicolor'),
+(6.2,2.9,4.3,1.3,'versicolor'),
+(5.1,2.5,3,1.1,'versicolor'),
+(5.7,2.8,4.1,1.3,'versicolor'),
+(6.3,3.3,6,2.5,'virginica'),
+(5.8,2.7,5.1,1.9,'virginica'),
+(7.1,3,5.9,2.1,'virginica'),
+(6.3,2.9,5.6,1.8,'virginica'),
+(6.5,3,5.8,2.2,'virginica'),
+(7.6,3,6.6,2.1,'virginica'),
+(4.9,2.5,4.5,1.7,'virginica'),
+(7.3,2.9,6.3,1.8,'virginica'),
+(6.7,2.5,5.8,1.8,'virginica'),
+(7.2,3.6,6.1,2.5,'virginica'),
+(6.5,3.2,5.1,2,'virginica'),
+(6.4,2.7,5.3,1.9,'virginica'),
+(6.8,3,5.5,2.1,'virginica'),
+(5.7,2.5,5,2,'virginica'),
+(5.8,2.8,5.1,2.4,'virginica'),
+(6.4,3.2,5.3,2.3,'virginica'),
+(6.5,3,5.5,1.8,'virginica'),
+(7.7,3.8,6.7,2.2,'virginica'),
+(7.7,2.6,6.9,2.3,'virginica'),
+(6,2.2,5,1.5,'virginica'),
+(6.9,3.2,5.7,2.3,'virginica'),
+(5.6,2.8,4.9,2,'virginica'),
+(7.7,2.8,6.7,2,'virginica'),
+(6.3,2.7,4.9,1.8,'virginica'),
+(6.7,3.3,5.7,2.1,'virginica'),
+(7.2,3.2,6,1.8,'virginica'),
+(6.2,2.8,4.8,1.8,'virginica'),
+(6.1,3,4.9,1.8,'virginica'),
+(6.4,2.8,5.6,2.1,'virginica'),
+(7.2,3,5.8,1.6,'virginica'),
+(7.4,2.8,6.1,1.9,'virginica'),
+(7.9,3.8,6.4,2,'virginica'),
+(6.4,2.8,5.6,2.2,'virginica'),
+(6.3,2.8,5.1,1.5,'virginica'),
+(6.1,2.6,5.6,1.4,'virginica'),
+(7.7,3,6.1,2.3,'virginica'),
+(6.3,3.4,5.6,2.4,'virginica'),
+(6.4,3.1,5.5,1.8,'virginica'),
+(6,3,4.8,1.8,'virginica'),
+(6.9,3.1,5.4,2.1,'virginica'),
+(6.7,3.1,5.6,2.4,'virginica'),
+(6.9,3.1,5.1,2.3,'virginica'),
+(5.8,2.7,5.1,1.9,'virginica'),
+(6.8,3.2,5.9,2.3,'virginica'),
+(6.7,3.3,5.7,2.5,'virginica'),
+(6.7,3,5.2,2.3,'virginica'),
+(6.3,2.5,5,1.9,'virginica'),
+(6.5,3,5.2,2,'virginica'),
+(6.2,3.4,5.4,2.3,'virginica'),
+(5.9,3,5.1,1.8,'virginica')
+
+```
+
+Now score the model. 
+
+```
+with input (model, sepal_length, sepal_width, petal_length, petal_width) as
+(select varchar(models.model), iris.sepal_length, iris.sepal_width, iris.petal_length, iris.petal_width
+from models, iris
+where models.id =1)
+select iris_score(varchar(model), float(sepal_length), float(sepal_width), float(petal_length), float(petal_width) ) from input
+```
+
+![output](docs/images/output.png)
+
+Lets now add a second model.
+
+```
+from sklearn.linear_model import LinearRegression
+from sklearn import datasets
+import joblib
+import base64
+train = datasets.load_iris().get("data")
+target = datasets.load_iris().get("target")
+lr = LinearRegression()
+lr = lr.fit(train, target)
+
+joblib.dump(lr, "linear.joblib")
+
+with open("linear.joblib", "rb") as file:
+    file =  file.read()
+    b64model = base64.b64encode(file)
+    print(b64model)
+
+with open("linearmodel.b64", "wb") as file:
+    file.write(b64model)
+```
+
+`db2 "insert into models (name, model) values('iris_linear', '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')`
+If the model we were registering required a different number of inputs, we would need to register a new udf. Lets do that for the sake of demonstration, even though it is not technically necessary.
+
+```
+CREATE FUNCTION iris_linear(varchar(1356), float, float, float, float) \
+returns float LANGUAGE PYTHON  parameter style \
+NPSGENERIC  FENCED  NOT THREADSAFE  NO FINAL CALL  ALLOW PARALLEL  NO DBINFO  DETERMINISTIC  NO EXTERNAL ACTION \
+RETURNS NULL ON NULL INPUT  NO SQL \
+external name '/database/config/db2inst1/sqllib/function/routine/score.py'
+```
+
+
+And score our model, this time as a linear regression model.
+
+
+```
+with input (model, sepal_length, sepal_width, petal_length, petal_width) as
+(select varchar(models.model), iris.sepal_length, iris.sepal_width, iris.petal_length, iris.petal_width
+from models, iris
+where models.id =(select id from models where name='iris_linear'))
+select iris_linear(varchar(model), float(sepal_length), float(sepal_width), float(petal_length), float(petal_width) ) from input
+
+```
+![output](docs/images/linear_output.png)
+
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