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Enhancing Machine Learning Decision Trees with Elixir
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Source: dockyard.com
The article by Sean Moriarity talks about a new Elixir library called Mockingjay, created by Andres Alejos, that aims to improve the performance of tree-based machine learning algorithms by compiling them into native Nx functions. Traditional Gradient Boosting algorithms, like the ones used in EXGBoost, rely heavily on NIFs which can lead to limitations within the Elixir ecosystem. Mockingjay allows for the transformation of trained XGBoost models into a format that integrates well with Nx code, enabling JIT compilation or the ability to use models as an Nx serving. This facilitates a more streamlined machine learning stack, offering compatibility with GPU and TPU accelerators without the need for custom interfaces. Moreover, early benchmarks have demonstrated that Mockingjay compiled decision trees can be up to 70% faster on CPUs when used with Nx and EXLA. The adoption of Mockingjay can greatly simplify machine learning infrastructure and results in performance enhancements, while maintaining a minimal reliance on NIFs.
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