Frameworks

Here are the all of the supported ML frameworks for BentoML. You can find the official BentoML example projects in the bentoml/gallery repository, group by the ML training frameworks used in the project.

You can download the examples below and run them on your computer. Links to run them on Google Colab are also available, although some of the features demoed in the notebooks does not work in the Colab environment due to its limitations, including running the BentoML API model server, building docker image or creating cloud deployment.

Scikit-Learn

Example Projects:

PyTorch

Example Projects:

Tensorflow 2.0 (Native API)

Example Projects:

Keras (Tensorflow 2.0 as the backend)

Example Projects:

Tensorflow 1.0

Example Projects:

FastAI v2

Example Projects:

XGBoost

Example Projects:

LightGBM

Example Projects:

FastText

Example Projects:

H2O

Example Projects:

CoreML

ONNX

Example Projects:

ONNX-MLIR

Spacy

Transformers

Statsmodels

For statsmodels, we recommend using PickleModel:

Example Projects:

Gluon

Pytorch Lightning

Detectron

Paddle

Example Projects:

EasyOCR

EvalML