bentoml 0.0.9

BentoML: Package and Deploy Your Machine Learning Models

BentoML

From a model in ipython notebook to production API service in 5 minutes.

BentoML is a python library for packaging and deploying machine learning models. It provides high-level APIs for defining a ML service and bundling its artifacts, source code, dependencies, and configurations into a production-system-friendly format that are ready for deployment.

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• Feature Highlights
• Installation
• Getting Started
• Examples
• More About BentoML
• Releases and Contributing
• License

Feature Highlights

• Multiple Distribution Format - Easily bundle your Machine Learning models into format that works best with your inference scenario:

  • Docker Image - include built-in REST API Server
  • PyPI Package - integrate with your python applications seamlessly
  • CLI tool - put your model into Airflow DAG or CI/CD pipeline
  • Spark UDF - run batch serving on large dataset with Spark
  • Serverless Function - host your model with serverless cloud platforms

• Multiple Framework Support - BentoML supports a wide range of ML frameworks out-of-the-box including Tensorflow, PyTorch, Scikit-Learn, xgboost and can be easily extended to work with new or custom frameworks.

• Deploy Anywhere - BentoML bundled ML service can be easily deploy with platforms such as Docker, Kubernetes, Serverless, Airflow and Clipper, on cloud platforms including AWS Lambda/ECS/SageMaker, Gogole Cloud Functions, and Azure ML.

• Custom Runtime Backend - Easily integrate your python preprocessing code with high-performance deep learning model runtime backend (such as tensorflow-serving) to deploy low-latancy serving endpoint.

Installation

pip install bentoml

Verify installation:

bentoml --version

Getting Started

BentoML does not change your training workflow. Let's train a simple scikit-learn model as example:

from sklearn import svm
from sklearn import datasets

clf = svm.SVC(gamma='scale')
iris = datasets.load_iris()
X, y = iris.data, iris.target
clf.fit(X, y)

To package this model with BentoML, you will need to create a new BentoService by subclassing it, and provides artifacts and env definition for it:

%%writefile iris_classifier.py
from bentoml import BentoService, api, env, artifacts
from bentoml.artifact import PickleArtifact
from bentoml.handlers import DataframeHandler

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

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

Now, to save your trained model for prodcution use, simply import your BentoService class and pack it with required artifacts:

from iris_classifier import IrisClassifier

svc = IrisClassifier.pack(model=clf)

svc.save('./saved_bento', version='v0.0.1') # Saving archive to ./saved_bento/IrisClassifier/v0.0.1/

That's it. Now you have created your first BentoArchive. It's a directory containing all the source code, data and configurations files required to run this model in production. There are a few ways you could use this archive:

Serving a BentoArchive via REST API

For exposing your model as a HTTP API endpoint, you can simply use the bentoml serve command:

bentoml serve ./saved_bento/IrisClassifier/v0.0.1/

Note: you must ensure the pip and conda dependencies are available in your python environment when using bentoml serve command. More commonly we recommand using BentoML API server with Docker(see below).

Build API server Docker Image from BentoArchive

You can build a Docker Image for running API server hosting your BentoML archive by using the archive folder as docker build context:

cd ./saved_bento/IrisClassifier/v0.0.1/

docker build -t myorg/iris-classifier .

Next, you can docker push the image to your choice of registry for deployment, or run it locally for development and testing:

docker run -p 5000:5000 myorg/iris-classifier

Loading BentoArchive in Python

import bentoml

bento_svc = bentoml.load('./saved_bento/IrisClassifier/v0.0.1/')
bento_svc.predict(X[0])

BentoML also supports loading an archive from s3 location directly:

bento_svc = bentoml.load('s3://my-bento-svc/iris_classifier/')

Install BentoArchive as PyPI package

First install your exported bentoml service with pip:

pip install ./saved_bento/IrisClassifier/v0.0.1/

Now you can import it and used it as a python module:

import IrisClassifier

installed_svc = IrisClassifier.load()
installed_svc.predict(X[0])

Note that you could also publish your exported BentoService as a PyPI package as a public python package on pypi.org or upload to your organization's private PyPI index:

cd ./saved_bento/IrisClassifier/v0.0.1/

python setup.py sdist upload

Loading BentoArchive from CLI

When pip install a BentoML archive, it also provides you with a CLI tool for accsing your BentoService's apis from command line:

pip install ./saved_bento/IrisClassifier/v0.0.1/

IrisClassifier info

IrisClassifier predict --input='./test.csv'

Alternatively, you can also use the bentoml cli to load and run a BentoArchive directly:

bentoml info ./saved_bento/IrisClassifier/v0.0.1/

bentoml predict ./saved_bento/IrisClassifier/v0.0.1/ --input='./test.csv'

CLI access made it very easy to put your saved BentoArchive into an Airflow DAG, integrate your packaged ML model into testing environment or use it in combination with other shell tools.

Examples

All examples can be found in the BentoML/examples directory.

• Quick Start with sklearn
• Sentiment Analysis with Scikit-Learn
• Text Classification with Tensorflow Keras
• Fashion MNIST classification with Pytorch
• Fashion MNIST classification with Tensorflow Keras
• More examples coming soon!

More About BentoML

We build BentoML because we think there should be a much simpler way for machine learning teams to ship models for production. They should not wait for engineering teams to re-implement their models for production environment or build complex feature pipelines for experimental models.

Our vision is to empower Machine Learning scientists to build and ship their own models end-to-end as production services, just like software engineers do. BentoML is enssentially this missing 'build tool' for Machine Learing projects.

Releases and Contributing

BentoML is under active development. Current version is a beta release, we may change APIs in future releases.

Want to help build BentoML? Check out our contributing documentation.

License

BentoML is GPL-3.0 licensed, as found in the COPYING file.