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LLVM-based compiler for LightGBM models

Project description

lleaves 🍃

CI Documentation Status

A LLVM-based compiler for LightGBM decision trees.

lleaves converts trained LightGBM models to optimized machine code, speeding-up inference by up to 10x.

Example

lgbm_model = lightgbm.Model(model_file="NYC_taxi/model.txt")
%timeit lgbm_model.predict(df)
# 11.6 s ± 442 ms

llvm_model = lleaves.Model(model_file="NYC_taxi/model.txt")
llvm_model.compile()
%timeit llvm_model.predict(df)
# 1.84 s ± 68.7 ms

Why lleaves?

  • Speed: Both low-latency single-row prediction and high-throughput batch-prediction.
  • Drop-in replacement: The interface of lleaves.Model is a subset of LightGBM.Booster.
  • Dependencies: llvmlite and numpy. LLVM comes statically linked.

Some LightGBM features are not yet implemented: multiclass prediction, linear models.

Benchmarks

Ran on Intel Xeon Haswell, 8vCPUs. Some of the variance is due to performance interference.

Datasets: NYC-taxi (mostly numerical features), Airlines (categorical features with high cardinality)

Small batches (single-threaded)

benchmark small batches

Large batches (multi-threaded)

benchmark large batches

Development

conda env create
conda activate lleaves
pip install -e .
pre-commit install
pytest

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