hummingbird-ml 0.0.5

Convert trained traditional machine learning models into tensor computations

Introduction

Hummingbird is a library for compiling trained traditional ML models into tensor computations. Hummingbird allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models. Thanks to Hummingbird, users can benefit from: (1) all the current and future optimizations implemented in neural network frameworks; (2) native hardware acceleration; (3) having a unique platform to support for both traditional and neural network models; and have all of this (4) without having to re-engineer their models.

Currently, you can use Hummingbird to convert your trained traditional ML models into PyTorch, TorchScript, and ONNX. Hummingbird supports a variety of ML models and featurizers. These models include scikit-learn Decision Trees and Random Forest, and also LightGBM and XGBoost Classifiers/Regressors. Support for other neural network backends (e.g., TVM) and models is on our roadmap.

Hummingbird also provides a convenient uniform "inference" API following the Sklearn API. This allows swapping Sklearn models with Hummingbird-generated ones without having to change the inference code.

Installation

Hummingbird was tested on Python >= 3.5 on Linux, Windows and MacOS machines. It is recommended to use a virtual environment (See: python3 venv doc or Using Python environments in VS Code.)

Hummingbird requires PyTorch >= 1.4.0. Please go here for instructions on how to install PyTorch based on your platform and hardware.

Once PyTorch is installed, you can get Hummingbird from pip with:

pip install hummingbird-ml

If you require the optional dependencies lightgbm and xgboost, you can use:

pip install hummingbird-ml[extra]

See also Troubleshooting for common problems.

Examples

See the notebooks section for examples that demonstrate use and speedups.

In general, Hummingbird syntax is very intuitive and minimal. To run your traditional ML model on DNN frameworks, you only need to import hummingbird.ml and add convert(model, 'dnn_framework') to your code. Below is an example using a scikit-learn random forest model and PyTorch as target framework.

import numpy as np
from sklearn.ensemble import RandomForestClassifier
from hummingbird.ml import convert

# Create some random data for binary classification
num_classes = 2
X = np.random.rand(100000, 28)
y = np.random.randint(num_classes, size=100000)

# Create and train a model (scikit-learn RandomForestClassifier in this case)
skl_model = RandomForestClassifier(n_estimators=10, max_depth=10)
skl_model.fit(X, y)

# Use Hummingbird to convert the model to PyTorch
model = convert(skl_model, 'pytorch')

# Run predictions on CPU
model.predict(X)

# Run predictions on GPU
model.to('cuda')
model.predict(X)

Documentation

The API documentation is here.

You can also read about Hummingbird in our blog post here.

For more details on the vision and on the technical details related to Hummingbird, please check our papers:

• A Tensor-based Approach for One-size-fits-all ML Prediction Serving. Supun Nakandalam, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi. To appear at OSDI 2020.
• Compiling Classical ML Pipelines into Tensor Computations for One-size-fits-all Prediction Serving. Supun Nakandala, Gyeong-In Yu, Markus Weimer, Matteo Interlandi. System for ML Workshop. NeurIPS 2019

Contributing

We welcome contributions! Please see the guide on Contributing.

Also, see our roadmap of planned features.

Community

Join our community!

For more formal enquiries, you can contact us.

Authors

• Supun Nakandala
• Matteo Interlandi
• Karla Saur

License

MIT License