SkLearn Model Serving

Hopsworks supports serving of SkLearn models using Flask servers. The Flask servers can be put behind a load-balancer for scaling up and down dynamically based on load.

Export your model

The first step to serving your model is to export it as a servable model. This is typically done using the joblib library

from sklearn.neighbors import KNeighborsClassifier
from sklearn.externals import joblib
iris_knn = KNeighborsClassifier(), y)
joblib.dump(iris_knn, "iris_knn.pkl")
hdfs.copy_to_hdfs("iris_knn.pkl", "Resources", overwrite=True)

Serving SkLearn Models in Hopsworks

Step 1.

The first step is to train and export a servable SkLearn model to your Hopsworks project.

To demonstrate this we provide an example notebook which is also included in the Deep Learning Tour on Hopsworks (see here.)

In order to serve a SkLearn model on Hopsworks, the .pkl model file a python script that handles requests should be placed in the Models dataset in your Hopsworks project. The python script should implement the Predict class and the methods predict, classify and regress, like this:

from sklearn.externals import joblib
from hops import hdfs
import os

class Predict(object):

    def __init__(self):
        """ Initializes the serving state, reads a trained model from HDFS"""
        self.model_path = "Models/iris_knn.pkl"
        print("Copying SKLearn model from HDFS to local directory")
        print("Reading local SkLearn model for serving")
        self.model = joblib.load("./iris_knn.pkl")
        print("Initialization Complete")

    def predict(self, inputs):
        """ Serves a prediction request usign a trained model"""
        return self.model.predict(inputs).tolist() # Numpy Arrays are not JSON serializable

    def classify(self, inputs):
        """ Serves a classification request using a trained model"""
        return "not implemented"

    def regress(self, inputs):
        """ Serves a regression request using a trained model"""
        return "not implemented"

Step 2.

To start serving your model, create a serving definition in the Hopsworks Model Serving service or using the Python API.

For using the Model Serving service, select the Model Serving service on the left panel (1) and then select on Create new serving (2).

New serving definition

Next, select “SkLearn serving” and click on the “Python Script” button to select a python script from your project that you want to serve. It is a best practice that his script is put inside the “Models” directory.

Create serving

This will open a popup window that will allow you to browse your project and select the script file that you want to serve.

By clicking on Advanced you can access the advanced configuration for your serving instance. In particular you can configure the Kafka topic on which the inference requests will be logged into (see the inference for more information). By default a new Kafka topic is created for each new serving (CREATE). You can avoid logging your inference requests by selecting NONE from the dropdown menu. You can also re-use an existing Kafka topic as long as its schema meets the requirement of the inference logger.

Advanced configuration

Finally click on Create Serving to create the serving instance.

For using the python API, import the serving module from the hops library (API-Docs-Python) and use the helper functions.

from hops import serving
from hops import model

# Resources/iris path containing .pkl and .py script to export as a model
model_path = "Resources/iris"

model.export(model_path, "IrisFlowerClassifier", model_version=1, overwrite=True)

script_path = "Models/IrisFlowerClassifier/1/"
if serving.exists("IrisFlowerClassifier"):
serving.create_or_update_serving(script_path, "IrisFlowerClassifier", serving_type="SKLEARN", model_version=1)

Step 3.

After having created the serving instance, a new entry is added to the list.

Start the serving

Click on the Run button to start the serving instance. After a few seconds the instance will be up and running, ready to start processing incoming inference requests.

You can check the logs of the SkLearn Serving instance by clicking on the logs button. This will bring you to the Kibana UI, from which you will be able to see if the the serving instance managed to load the model correctly.

Start the serving

Log button

View the logs

Kibana UI

Step 4.

To edit your serving, click on the edit button.

Update the serving instance

Update the serving instance

Where do I go from here?

Take a look at the Inference documentation to see how you can send inference requests to the serving server serving your model.