lleaves
awesome-cold-showers
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| lleaves | awesome-cold-showers | |
|---|---|---|
| 4 | 5 | |
| 292 | 7,242 | |
| - | - | |
| 7.0 | 3.8 | |
| 27 days ago | 4 months ago | |
| Python | ||
| MIT License | GNU General Public License v3.0 or later |
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lleaves
- LLeaves: A LLVM-based compiler for LightGBM decision trees
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Cold Showers
I built this decision tree (LightGBM) compiler last summer: https://github.com/siboehm/lleaves
It get's you ~10x speedups for batch predictions, more if your model is big. It's not complicated, it ended up being <1K lines of Python code. I heard a couple of stories like yours, where people had multi-node spark clusters running LightGBM, and it always amused me because by if you compiled the trees instead you could get rid of the whole cluster.
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Tree compiler that speeds up LightGBM model inference by ~30x
In a near-future version I'll expose some of the compilation parameters, I was somewhat afraid of having an API that's too complicated deterring people who just want a no-fuzz drop-in replacement for LightGBM. But as long as I keep sane defaults and have the parameters optional it should be fine. Relevant parameters are definitely block size (needs to adjust to L1i size and tree size) as well as the LLVM codemodel (a smaller adress space increases single-batch prediction speeds but doesn't work for large models). The thread-size specific compilation I'm still looking into, it makes the API more complicated and so might not be worth it.
awesome-cold-showers
- Cold Showers
- 寒冷的阵雨 (Cold Showers)
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What are some general consensus ideas that do not align with the data?
In a similar fashion there is the Awesome Cold Showers list:
What are some alternatives?
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
p1xt-guides - Programming curricula
ngboost - Natural Gradient Boosting for Probabilistic Prediction
m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
miceforest - Multiple Imputation with LightGBM in Python
catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.