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* added docs for using DB2 as backend for model management

* fixed typos

* Update In_Db2_Machine_Learning/In DB Model Deployment and Management/README.md

Co-authored-by: Kelly Rodger <[email protected]>

* Update In_Db2_Machine_Learning/In DB Model Deployment and Management/README.md

Co-authored-by: Kelly Rodger <[email protected]>

* Update In_Db2_Machine_Learning/In DB Model Deployment and Management/README.md

Co-authored-by: Kelly Rodger <[email protected]>

* fixed typo

Co-authored-by: Kelly Rodger <[email protected]>
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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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