microsoft/hummingbird

Health v2 · maintenance only

Adoption not part of the score

3544 stars · 290 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Hummingbird is a Microsoft library that compiles trained traditional ML models (decision trees, random forests, XGBoost, LightGBM) into tensor computations for PyTorch, TorchScript, ONNX, and TVM. It lets users accelerate inference with GPU and neural network framework optimizations without re-engineering their models.

Use cases

• speed up scikit-learn model inference on GPU
• convert XGBoost or LightGBM models to PyTorch
• export decision trees to ONNX for deployment
• serve sklearn models with TorchServe
• unify traditional and neural network model inference
• accelerate random forest predictions with tensor ops

When to choose

• you need faster inference for tree-based models on GPU
• you want a single serving platform for both sklearn and neural network models
• you want to export traditional ML models to TorchScript or ONNX

When to avoid

• your models are already neural networks
• you need CPU-only inference with no performance bottleneck
• you use ML operators not in Hummingbird's supported list

Facets

library · maturity active

machine-learning compiler llm-inference machine-learning deep-learning developer-tools python cross-platform model-compilation scikit-learn pytorch onnx tvm inference-optimization tree-models xgboost lightgbm gpu

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markdown · JSON · MCP: product_card(name="microsoft/hummingbird")