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lleaves reviews and mentions
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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
For a few months now I've been working on a library (as a weekend project) for speeding up inference of LightGBM gradient boosted trees. It's ~30x faster than LightGBM and ~3x faster than other tree compilers. The interface is stable now and it's well tested. I'm looking for a few more users to figure out in which direction I should develop this further and which other libraries (apart from LightGBM) I should incorporate. Link: https://github.com/siboehm/lleaves
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.
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Stats
siboehm/lleaves is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of lleaves is Python.