The easiest way to build Machine Learning APIs
BentoML makes moving trained ML models to production easy:
- Package models trained with any ML framework and reproduce them for model serving in production
- Deploy anywhere for online API serving or offline batch serving
- High-Performance API model server with adaptive micro-batching support
- Central hub for managing models and deployment process via Web UI and APIs
- Modular and flexible design making it adaptable to your infrastrcuture
BentoML is a framework for serving, managing, and deploying machine learning models. It is aiming to bridge the gap between Data Science and DevOps, and enable teams to deliver prediction services in a fast, repeatable, and scalable way.
Documentation
BentoML documentation: https://docs.bentoml.org/
- Quickstart Guide, try it out on Google Colab
- Core Concepts
- API References
- FAQ
- Example projects: bentoml/Gallery
Key Features
Production-ready online serving:
- Support multiple ML frameworks including PyTorch, TensorFlow, Scikit-Learn, XGBoost, and many more
- Containerized model server for production deployment with Docker, Kubernetes, OpenShift, AWS ECS, Azure, GCP GKE, etc
- Adaptive micro-batching for optimal online serving performance
- Discover and package all dependencies automatically, including PyPI, conda packages and local python modules
- Serve compositions of multiple models
- Serve multiple endpoints in one model server
- Serve any Python code along with trained models
- Automatically generate REST API spec in Swagger/OpenAPI format
- Prediction logging and feedback logging endpoint
- Health check endpoint and Prometheus
/metricsendpoint for monitoring
Standardize model serving and deployment workflow for teams:
- Central repository for managing all your team's prediction services via Web UI and API
- Launch offline batch inference job from CLI or Python
- One-click deployment to cloud platforms including AWS EC2, AWS Lambda, AWS SageMaker, and Azure Functions
- Distributed batch or streaming serving with Apache Spark
- Utilities that simplify CI/CD pipelines for ML
- Automated offline batch inference job with Dask (roadmap)
- Advanced model deployment for Kubernetes ecosystem (roadmap)
- Integration with training and experimentation management products including MLFlow, Kubeflow (roadmap)
ML Frameworks
- Scikit-Learn - Docs | Examples
- PyTorch - Docs | Examples
- Tensorflow 2 - Docs | Examples
- Tensorflow Keras - Docs | Examples
- XGBoost - Docs | Examples
- LightGBM - Docs | Examples
- FastText - Docs | Examples
- FastAI - Docs | Examples
- H2O - Docs | Examples
- ONNX - Docs | Examples
- Spacy - Docs | Examples
- Statsmodels - Docs | Examples
- CoreML - Docs
- Transformers - Docs
- Gluon - Docs
- Detectron - Docs
- PaddlePaddle - Docs | Example
- EvalML - Docs
- EasyOCR -Docs
- ONNX-MLIR - Docs
Deployment Options
Be sure to check out deployment overview doc to understand which deployment option is best suited for your use case.
- One-click deployment with BentoML:
- AWS Lambda
- AWS SageMaker
- AWS EC2
- Azure Functions
- Deploy with open-source platforms:
- Docker
- Kubernetes
- Knative
- Kubeflow
- KFServing
- Clipper
- Manual cloud deployment guides:
- AWS ECS
- Google Cloud Run
- Google Cloud AI Platform Unified
- Azure container instance
- Heroku
Introduction
BentoML provides APIs for defining a prediction service, a servable model so to speak, which includes the trained ML model itself, plus its pre-processing, post-processing code, input/output specifications and dependencies. Here's what a simple prediction service look like in BentoML:
import pandas as pd
from bentoml import env, artifacts, api, BentoService
from bentoml.adapters import DataframeInput, JsonOutput
from bentoml.frameworks.sklearn import SklearnModelArtifact
# BentoML packages local python modules automatically for deployment
from my_ml_utils import my_encoder
@env(infer_pip_packages=True)
@artifacts([SklearnModelArtifact('my_model')])
class MyPredictionService(BentoService):
"""
A simple prediction service exposing a Scikit-learn model
"""
@api(input=DataframeInput(), output=JsonOutput(), batch=True)
def predict(self, df: pd.DataFrame):
"""
An inference API named `predict` that takes tabular data in pandas.DataFrame
format as input, and returns Json Serializable value as output.
A batch API is expect to receive a list of inference input and should returns
a list of prediction results.
"""
model_input_df = my_encoder.fit_transform(df)
predictions = self.artifacts.my_model.predict(model_input_df)
return list(predictions)
This can be easily plugged into your model training process: import your bentoml prediction service class, pack it with your trained model, and call save to persist the entire prediction service at the end, which creates a BentoML bundle:
from my_prediction_service import MyPredictionService
svc = MyPredictionService()
svc.pack('my_model', my_sklearn_model)
svc.save() # saves to $HOME/bentoml/repository/MyPredictionService/{version}/
The generated BentoML bundle is a file directory that contains all the code files, serialized models, and configs required for reproducing this prediction service for inference. BentoML automatically captures all the python dependencies information and have everything versioned and managed together in one place.
BentoML automatically generates a version ID for this bundle, and keeps track of all bundles created under the $HOME/bentoml directory. With a BentoML bundle, user can start a local API server hosting it, either by its file path or its name and version:
bentoml serve MyPredictionService:latest
# alternatively
bentoml serve $HOME/bentoml/repository/MyPredictionService/{version}/
A docker container image that's ready for production deployment can be created now with just one command:
bentoml containerize MyPredictionService:latest -t my_prediction_service:v3
docker run -p 5000:5000 my_prediction_service:v3 --workers 2
The container image produced will have all the required dependencies installed. Besides the model inference API, the containerized BentoML model server also comes with Prometheus metrics, health check endpoint, prediction logging, and tracing support out-of-the-box. This makes it super easy for your DevOps team to incorporate your models into production systems.
BentoML's model management component is called Yatai, it means food cart in Japanese, and you can think of it as where you'd store your bentos . Yatai provides CLI, Web UI, and Python API for accessing BentoML bundles you have created, and you can start a Yatai server for your team to manage all models on cloud storage(S3, GCS, MinIO etc) and build CI/CD workflow around it. Learn more about it here.
Read the Quickstart Guide to learn more about the basic functionalities of BentoML. You can also try it out here on Google Colab.
Why BentoML
Moving trained Machine Learning models to serving applications in production is hard. It is a sequential process across data science, engineering and DevOps teams: after a model is trained by the data science team, they hand it over to the engineering team to refine and optimize code and creates an API, before DevOps can deploy.
And most importantly, Data Science teams want to continuously repeat this process, monitor the models deployed in production and ship new models quickly. It often takes months for an engineering team to build a model serving & deployment solution that allow data science teams to ship new models in a repeatable and reliable way.
BentoML is a framework designed to solve this problem. It provides high-level APIs for Data Science team to create prediction services, abstract away DevOps' infrastructure needs and performance optimizations in the process. This allows DevOps team to seamlessly work with data science side-by-side, deploy and operate their models packaged in BentoML format in production.
Check out Frequently Asked Questions page on how does BentoML compares to Tensorflow-serving, Clipper, AWS SageMaker, MLFlow, etc.
GitHub
bentoml
