Hummingbird v0.0.5

This release adds TorchScript as a backend, removes the problematic auto-installation of pytorch, improves syntax for the ONNX converter, adds notebook enhancements, and adds 3rd party library version upgrades. Hummingbird now provides the same Sklearn API across backends for executing inference.

Announcing: Integration with TorchScript

This release adds TorchScript as a backend (#225).

Users can convert models with:

hummingbird.ml.convert(model, "torchscript", X)

Installation changes:

After several reports from users across multiple platforms (in terms of both OS and underlying hardware), we changed the Hummingbird installer to require users to first install pytorch before installing Hummingbird (#246). This allows users to select the right pytorch version for a specific platform and should simplify the installation process and remove issues caused by having the wrong pytorch version installed.

ONNX API

For ONNX, we changed the API to have a more seamless experience, allowing users to interact with ONNX models in a consistent way with other models in Hummingbird (#231).

Instead of the user having to instanciate the ONNX runtime session:

-        session = ort.InferenceSession(onnx_model.SerializeToString())
-        onnx_pred = session.run(output_names, inputs)

The user can now just call predict, predict_proba, transform, etc. as with other Hummingbird conversions.

+        onnx_pred = onnx_model.predict(X)

New Operators

• OneHotEncoder - integers (#197)
• Support for Tweedie distribution in LGBM (#242)

Miscellaneous

• The target opset for ONNX is now 11 (#214)
• The target pytorch version is now 1.6.0, except for with Python 3.5 it remains at 1.5.1 for compatibility reasons (#213)
• Docs are now auto-generated (#223)

Credits

Thanks to @KranthiGV for the updated LGBM ONNX notebook example

Hummingbird v0.0.4

This release adds several new operators to both scikit-learn and Onnx.

New Features

• float 64 support [#186]
• Better windows installation support [#179]

New Operators - scikit-learn

• IsolationForest [#191]
• LGBMRanker [#173]
• OneHotEncoder [#193]
• Scaler [#171]
• XBGRanker [#189]

New Operators - Onnx

• ArrayFeatureExtractor [#198]
• Linear Classifier/Regressor [#190, #194]
• Normalizer [#188]
• Scaler [#196]

Credits

This release would not have been possible without the following contributors: @ahmedkrmn, @KranthiGV, @TuanNguyen27, @zhanjiezhu

Hummingbird v0.0.3

This release adds several new cool features and bug fixes to Hummingbird!

API Changes

When selecting the backend to use for conversion, we renamed pytorch into torch (to match the module name). [#142]

New Operators

• HistGradientBoostingRegressor [#135 ]
• LinearRegression [#140 ]
• LinearSVC [#140 ]
• LogisticRegression [#140 ]
• LogisticRegressionCV [#140 ]
• Normalizer [#126]

New Features

• transform method is added to the PyTorch container to match the transformer API of Sklearn. [#148 ]
• Support for ONNX models as input (at the moment this feature only works in combination with the lightgbm_converter in ONNXMLTOOLS) [#142 ]
• Generation of ONNX models as output (at the moment this feature only works when a ONNX model is passed as input) [#142]

Credits

This release would not have been possible without the following contributors: @ahmedkrmn, @jspisak, and @TuanNguyen27.

Hummingbird v0.0.2

This release adds several new operators, an updated API, and contains several documentation fixes.

New Operators

• DecisionTreeRegressor [#102 ]
• ExtraTreesRegressor [#91 ]
• GradientBoostingRegressor [#88 ]
• HistGradientBoostingClassifier [#87]

Credits

Special thanks to following contributors: @KranthiGV (DecisionTreeRegressor), @mmbhatk (ExtraTreesRegressor), @bfgray3 (GradientBoostingRegressor), and @ahmedkrmn (HistGradientBoostingClassifier)

Hummingbird v0.0.1

This is the first release for Hummingbird! In this release, Hummingbird supports conversion from scikit-learn, LightGBM and XGBoost models to PyTorch. Currently supported models are listed here.