kodi-standalone-service
LightGBM
kodi-standalone-service | LightGBM | |
---|---|---|
2 | 11 | |
159 | 16,057 | |
- | 0.6% | |
4.6 | 9.1 | |
4 months ago | 5 days ago | |
Roff | C++ | |
- | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
kodi-standalone-service
-
Telemetry paradox
Setup Kodi with kodi-standalone-service (note: read the readme) so it always runs. Plug your pi into the TV. You can probably use your existing remotes to control Kodi using CEC.
-
Running Kodi of a Raspberry Pi w/o desktop environment?
Did you try something like this: https://github.com/graysky2/kodi-standalone-service ?
LightGBM
-
SIRUS.jl: Interpretable Machine Learning via Rule Extraction
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.
-
[D] RAM speeds for tabular machine learning algorithms
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.
-
[P] LightGBM but lighter in another language?
LightBGM seems to have C API support, and C++ example in the main repo
-
Use whatever is best for the problem, but still
LGBM doesn't do RF well, but it's easy to manually bag single LGBM trees.
-
What's New with AWS: Amazon SageMaker built-in algorithms now provides four new Tabular Data Modeling Algorithms
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.
-
Search YouTube from the terminal written in python
Microsoft lightGBM. https://github.com/microsoft/LightGBM
-
LightGBM VS CXXGraph - a user suggested alternative
2 projects | 28 Feb 2022
-
Writing the fastest GBDT libary in Rust
Here are our benchmarks on training time comparing Tangram's Gradient Boosted Decision Tree Library to LightGBM, XGBoost, CatBoost, and sklearn.
-
Workstation Management With Nix Flakes: Build a Cmake C++ Package
{ 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 ]; }; } ); }
-
Is it possible to clean memory after using a package that has a memory leak in my python script?
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.
What are some alternatives?
LibreELEC.tv - Just enough OS for KODI
tensorflow - An Open Source Machine Learning Framework for Everyone
glmark2 - glmark2 is an OpenGL 2.0 and ES 2.0 benchmark
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Kodi Home Theater Software - Kodi is an award-winning free and open source home theater/media center software and entertainment hub for digital media. With its beautiful interface and powerful skinning engine, it's available for Android, BSD, Linux, macOS, iOS, tvOS and Windows.
GPBoost - Combining tree-boosting with Gaussian process and mixed effects models
catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
amazon-sagemaker-examples - Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
pijarr - A shell script to automate the installation and configuration of Jackett, Sonarr, Radarr, Lidarr, Readarr, Prowlarr, and Bazarr on Debian based distros.
yggdrasil-decision-forests - A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
hosts - 🔒 Consolidating and extending hosts files from several well-curated sources. Optionally pick extensions for porn, social media, and other categories.
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation