pyvirtualcam VS LightGBM

There is this project. You could probably also use openCV.

The parent it seems you’re missing right now is the virtual cam. This is the repo that the OP linked. There’s another package too, pyvirtualcam. Both require you to install the actual virtual camera binary to your computer where the package simply routes your edited frames to the camera.

Just like we have pyfakecam do we have a similar library for real-time audio? Actually, I need to manipulate the audio input from the microphone and send it to the webrtc in form of microphone input. pyfakecam works seamlessly with opencv and I can easily manipulate live camera input and create a virtual camera for it. But same is not possible for audio input.

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.