v6d VS LightGBM

Brief Overview for any interested: Vineyard (v6d) is an in-memory immutable data manager that provides out-of-the-box high-level abstraction and zero-copy in-memory sharing for distributed data in big data tasks, such as graph analytics (e.g., GraphScope), numerical computing (e.g., Mars), and machine learning.

- https://github.com/v6d-io/v6d/pull/948

We propose vineyard, https://github.com/v6d-io/v6d to address such challenges, which, provides efficient zero-copy data sharing between different compute engines, without extra cost of copying and serialization, compared other similar solutions.

SIRUS.jl is a pure Julia implementation of the SIRUS algorithm by Bénard et al. (2021). The algorithm is a rule-based machine learning model meaning that it is fully interpretable. The algorithm does this by firstly fitting a random forests and then converting this forest to rules. Furthermore, the algorithm is stable and achieves a predictive performance that is comparable to LightGBM, a state-of-the-art gradient boosting model created by Microsoft. Interpretability, stability, and predictive performance are described in more detail below.

Hey, thanks everybody for your answers. I've asked around in the XGBoost and LightGBM repos and some folks there also agreed that memory speed will be a bottleneck yes.

LightBGM seems to have C API support, and C++ example in the main repo

LGBM doesn't do RF well, but it's easy to manually bag single LGBM trees.

LightGBM is a popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT). To learn how to use this algorithm, please see example notebooks for Classification and Regression.

Microsoft lightGBM. https://github.com/microsoft/LightGBM

Here are our benchmarks on training time comparing Tangram's Gradient Boosted Decision Tree Library to LightGBM, XGBoost, CatBoost, and sklearn.

{ inputs = { nixpkgs = { url = "github:nixos/nixpkgs/nixos-unstable"; }; flake-utils = { url = "github:numtide/flake-utils"; }; }; outputs = { nixpkgs, flake-utils, ... }: flake-utils.lib.eachDefaultSystem (system: let pkgs = import nixpkgs { inherit system; }; lightgbm-cli = (with pkgs; stdenv.mkDerivation { pname = "lightgbm-cli"; version = "3.3.1"; src = fetchgit { url = "https://github.com/microsoft/LightGBM"; rev = "v3.3.1"; sha256 = "pBrsey0RpxxvlwSKrOJEBQp7Hd9Yzr5w5OdUuyFpgF8="; fetchSubmodules = true; }; nativeBuildInputs = [ clang cmake ]; buildPhase = "make -j $NIX_BUILD_CORES"; installPhase = '' mkdir -p $out/bin mv $TMP/LightGBM/lightgbm $out/bin ''; } ); in rec { defaultApp = flake-utils.lib.mkApp { drv = defaultPackage; }; defaultPackage = lightgbm-cli; devShell = pkgs.mkShell { buildInputs = with pkgs; [ lightgbm-cli ]; }; } ); }

I'm working on the AutoML python package (Github repo). In my package, I'm using many different algorithms. One of the algorithms is LightGBM. The algorithm after the training doesn't release the memory, even if del is called and gc.collect() after. I created the issue on LightGBM GitHub -> link. Because of this leak, memory consumption is growing very fast during algorithm training.