Hi all!

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I want to wish you a good evening and would like to share with you some tool I developed for my work in the particle physics community.

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Probably we are not the only ones who like to prototype and train ML models with xgboost in Python and then have to deploy them to our high performance multithreading C++ production environment. This is exactly the step that my tool which I call “FastForest” wants to make easy like a breeze!

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The mission of the library is to be:

• Easy: deploying your xgboost model should be as painless as it can be
• Fast: thanks to efficient structure-of-array data structures for storing the trees, this library goes very easy on your CPU and memory
• Safe: the FastForest objects are immutable, and therefore they are an excellent choice in multithreading environments
• Portable: FastForest has no dependency other than the C++ standard library

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I hope that this might be of use for someone else too 🙂 I think it might, because so far, the only solutions to this C++ deployment problem that I found on the web are either to use the sparsely documented xgboost C API or to transform your models into hardcoded C++ (which is cool as well of course). What I tried to write here is a very lightweight and clean C++ solution which can load models dynamically.

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Check it out here: https://github.com/guitargeek/XGBoost-FastForest