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🍱 An open framework for building, shipping and operating machine learning services
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* Include handler's dependencies to the archived env yml

* Add tests for handler dependencies
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README.md

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From a model in jupyter notebook to production API service in 5 minutes

BentoML

Installation | Getting Started | Documentation | Examples | Contributing | License

BentoML is a python framework for building, shipping and running machine learning services. It provides high-level APIs for defining an ML service and packaging its artifacts, source code, dependencies, and configurations into a production-system-friendly format that is ready for deployment.

Use BentoML if you need to:

  • Turn your ML model into REST API server, Serverless endpoint, PyPI package, or CLI tool

  • Manage the workflow of creating and deploying a ML service


Installation

pypi status

pip install bentoml

Getting Started

Defining a machine learning service with BentoML is as simple as a few lines of code:

@artifacts([PickleArtifact('model')])
@env(conda_pip_dependencies=["scikit-learn"])
class IrisClassifier(BentoService):

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

Google Colab Badge - Try out our 5-mins getting started guide, using BentoML to productionize a scikit-learn model and deploy it to AWS Lambda.

Feature Highlights

  • Multiple Distribution Format - Easily package your Machine Learning models and preprocessing code into a format that works best with your inference scenario:

    • Docker Image - deploy as containers running REST API Server
    • PyPI Package - integrate into your python applications seamlessly
    • CLI tool - put your model into Airflow DAG or CI/CD pipeline
    • Spark UDF - run batch serving on a large dataset with Spark
    • Serverless Function - host your model on serverless platforms such as AWS Lambda
  • Multiple Framework Support - BentoML supports a wide range of ML frameworks out-of-the-box including Tensorflow, PyTorch, Scikit-Learn, xgboost, H2O, FastAI and can be easily extended to work with new or custom frameworks.

  • Deploy Anywhere - BentoML bundled ML service can be easily deployed with platforms such as Docker, Kubernetes, Serverless, Airflow and Clipper, on cloud platforms including AWS, Google Cloud, and Azure.

  • Custom Runtime Backend - Easily integrate your python pre-processing code with high-performance deep learning runtime backend, such as tensorflow-serving.

Documentation

Full documentation and API references can be found at bentoml.readthedocs.io

Examples

All examples can be found under the BentoML/examples directory. More tutorials and examples coming soon!

Deployment guides:

We collect example notebook page views to help us improve this project. To opt-out of tracking, delete the [Impression] line in the first markdown cell of any example notebook: ![Impression](http...

Contributing

Have questions or feedback? Post a new github issue or join our Slack chat room: join BentoML Slack

Want to help build BentoML? Check out our contributing guide and the development guide.

To make sure you have a pleasant experience, please read the code of conduct. It outlines core values and beliefs and will make working together a happier experience.

Happy hacking!

Releases

BentoML is under active development and is evolving rapidly. Currently it is a Beta release, we may change APIs in future releases.

Read more about the latest features and changes in BentoML from the releases page. and follow the BentoML Community Calendar.

License

Apache License 2.0

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