How to beginner · 3 min read

How to use BentoML for model serving

Q: How to use BentoML for model serving

Use BentoML to package your ML model into a service by defining a BentoService class, then serve it locally or deploy it to production with built-in APIs. BentoML simplifies model serving by handling REST API creation, containerization, and scaling.

Quick answer

Use BentoML to package your ML model into a service by defining a BentoService class, then serve it locally or deploy it to production with built-in APIs. BentoML simplifies model serving by handling REST API creation, containerization, and scaling.

PREREQUISITES

Python 3.8+
pip install bentoml>=1.0
A trained ML model (e.g., scikit-learn, PyTorch, TensorFlow)
Basic knowledge of Python and REST APIs

Setup

Install BentoML via pip and prepare your environment variables if deploying to cloud or container platforms.

bash

pip install bentoml

Step by step

Define a BentoService class to wrap your model, implement a prediction API, save the service, and serve it locally.

python

import bentoml
from bentoml.io import JSON
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris

# Train a simple model
iris = load_iris()
model = RandomForestClassifier()
model.fit(iris.data, iris.target)

# Define BentoService
@bentoml.env(infer_pip_packages=True)
@bentoml.artifacts([bentoml.artifacts.PickleArtifact('model')])
class IrisClassifier(bentoml.BentoService):
    @bentoml.api(input=JSON(), output=JSON())
    def predict(self, parsed_json):
        data = parsed_json["data"]
        prediction = self.artifacts.model.predict(data)
        return {"prediction": prediction.tolist()}

# Create service instance and pack model
svc = IrisClassifier()
svc.pack('model', model)

# Save the service
saved_path = svc.save()
print(f"Service saved to: {saved_path}")

# Serve the model locally
# Run this in terminal: bentoml serve IrisClassifier:latest

output

Service saved to: IrisClassifier/20260401_123456_ABCD1234

Common variations

Use bentoml serve CLI to serve the model locally with REST API.
Deploy to cloud platforms using bentoml containerize to build Docker images.
Support async APIs by defining async methods in BentoService.
Use different input/output adapters like Image, Text, or Dataframe for varied data types.

python

import bentoml
from bentoml.io import JSON

class AsyncService(bentoml.BentoService):
    @bentoml.api(input=JSON(), output=JSON())
    async def predict(self, parsed_json):
        # async prediction logic here
        return {"result": "async response"}

Troubleshooting

If bentoml serve fails, check if the service is saved correctly and the model artifact is packed.
For dependency issues, use @bentoml.env(infer_pip_packages=True) to auto-detect packages.
When deploying Docker images, ensure Docker daemon is running and you have permission.

✅

Key Takeaways

Use BentoService to wrap and serve ML models with REST APIs effortlessly.
BentoML automates packaging, dependency management, and deployment workflows.
Local serving and containerization are built-in, enabling easy transition from dev to production.
Support for async APIs and multiple input/output adapters makes BentoML flexible for various use cases.

Verified 2026-04

Verify ↗