seldon1.py

from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets
import bentoml
from bentoml.adapters import DataframeInput
from bentoml.frameworks.sklearn import SklearnModelArtifact

def train():
    iris = datasets.load_iris()
    X = iris.data
    y = iris.target
    model = RandomForestClassifier()
    model.fit(X, y)
    return model

@bentoml.artifacts([SklearnModelArtifact('model')])
@bentoml.env(docker_base_image="bentoml/model-server:latest-slim-py37")
class IrisExampleForestService(bentoml.BentoService):
    @bentoml.api(input=DataframeInput(), batch=True)
    def predict(self, df):
        result = self.artifacts.model.predict(df)
        return result

svc = IrisExampleForestService()
svc.pack('model', train())
svc.save()