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README.md

Predict on an InferenceService using BentoML

This guide demonstrates how to serve a scikit-learn based iris classifier model with BentoML and deploying the BentoML model server with KFServing. The same deployment steps are also applicable for models trained with other machine learning frameworks, see more BentoML examples here.

BentoML is an open-source platform for high-performance ML model serving. It makes building production API endpoint for your ML model easy and supports all major machine learning training frameworks, including Tensorflow, Keras, PyTorch, XGBoost, scikit-learn and etc.

BentoML comes with a high-performance API model server with adaptive micro-batching support, which achieves the advantage of batch processing in online serving. It also provides model management and model deployment functionality, giving ML teams an end-to-end model serving workflow, with DevOps best practices baked in.

Deploy a custom InferenceService

Setup

Before starting this guide, make sure you have the following:

  • Your ~/.kube/config should point to a cluster with KFServing installed.
  • Your cluster's Istio Ingress gateway must be network accessible.
  • Docker and Docker hub must be properly configured on your local system
  • Python 3.6 or above

    • Install required packages bentoml and scikit-learn on your local system:

      pip install bentoml scikit-learn

Build API model server using BentoML

The following code defines a BentoML prediction service that requires a scikit-learn model, and asks BentoML to figure out the required PyPI pip packages automatically. It also defines an API, which is the entry point for accessing this prediction service. And the API is expecting a pandas.DataFrame object as its input data.

# iris_classifier.py
from bentoml import env, artifacts, api, BentoService
from bentoml.handlers import DataframeHandler
from bentoml.artifact import SklearnModelArtifact

@env(auto_pip_dependencies=True)
@artifacts([SklearnModelArtifact('model')])
class IrisClassifier(BentoService):

    @api(DataframeHandler)
    def predict(self, df):
        return self.artifacts.model.predict(df)

The following code trains a classifier model and serve it with the IrisClassifier defined above:

# main.py
from sklearn import svm
from sklearn import datasets

from iris_classifier import IrisClassifier

if __name__ == "__main__":
    # Load training data
    iris = datasets.load_iris()
    X, y = iris.data, iris.target

    # Model Training
    clf = svm.SVC(gamma='scale')
    clf.fit(X, y)

    # Create a iris classifier service instance
    iris_classifier_service = IrisClassifier()

    # Pack the newly trained model artifact
    iris_classifier_service.pack('model', clf)

    # Save the prediction service to disk for model serving
    saved_path = iris_classifier_service.save()

The sample code above can be found in the BentoML repository, run them directly with the following command:

git clone [email protected]:bentoml/BentoML.git
python ./bentoml/guides/quick-start/main.py

After saving the BentoService instance, you can now start a REST API server with the model trained and test the API server locally:

# Start BentoML API server:
bentoml serve IrisClassifier:latest
# Send test request:
curl -i \
  --header "Content-Type: application/json" \
  --request POST \
  --data '[[5.1, 3.5, 1.4, 0.2]]' \
  http://localhost:5000/predict

Deploy InferenceService

BentoML provides a convenient way of containerizing the model API server with Docker. To create a docker container image for the sample model above:

  1. Find the file directory of the SavedBundle with bentoml get command, which is directory structured as a docker build context.
  2. Running docker build with this directory produces a docker image containing the API model server.
model_path=$(bentoml get IrisClassifier:latest -q | jq -r ".uri.uri")

# Replace {docker_username} with your Docker Hub username
docker build -t {docker_username}/iris-classifier $model_path
docker push {docker_username}/iris-classifier

Note: BentoML's REST interface is different than the Tensorflow V1 HTTP API that KFServing expects. Requests will send directly to the prediction service and bypass the top-level InferenceService.

Support for KFServing V2 prediction protocol with BentoML is coming soon.

The following is an example YAML file for specifying the resources required to run an InferenceService in KFServing. Replace {docker_username} with your Docker Hub username and save it to bentoml.yaml file:

apiVersion: serving.kubeflow.org/v1alpha2
kind: InferenceService
metadata:
  labels:
    controller-tools.k8s.io: "1.0"
  name: iris-classifier
spec:
  default:
    predictor:
      custom:
        container:
          image: {docker_username}/iris-classifier
          ports:
            - containerPort: 5000

Use kubectl apply command to deploy the InferenceService:

kubectl apply -f bentoml.yaml

Run prediction

The first step is to determine the ingress IP and ports and set INGRESS_HOST and INGRESS_PORT

MODEL_NAME=iris-classifier
SERVICE_HOSTNAME=$(kubectl get inferenceservice ${MODEL_NAME} -o jsonpath='{.status.url}' | cut -d "/" -f 3)

curl -v -H "Host: ${SERVICE_HOSTNAME}" \
  --header "Content-Type: application/json" \
  --request POST \
  --data '[[5.1, 3.5, 1.4, 0.2]]' \
  http://${INGRESS_HOST}:${INGRESS_PORT}/predict

Delete deployment

kubectl delete -f bentoml.yaml

Additional Resources