thundergbm
ThunderGBM: Fast GBDTs and Random Forests on GPUs
AI summary
Gpu-accelerated algo
Accelerates machine learning algorithms on GPUs to improve performance and efficiency
- stars
- 695
- forks
- 88
- watching
- 26
- awesome lists
- 2
Similar projects
Found by comparing what the projects do, not just their names.
GPU-based ML/Stats library
Enables Bayesian data analysis and machine learning on graphics processing units (GPUs) to accelerate computational tasks.
AutoML pipeline tool
Automated machine learning tool for tabular data pipelines
Gradient boosting library
An optimized distributed gradient boosting library for machine learning
Gradient Boosting Library
High-performance machine learning library with a Ruby interface to XGBoost gradient boosting algorithm
GPU language binding
Enables efficient data exchange and invocation of existing GPU kernels from host languages in the GraalVM
sotrh/learn-wgpu1.5K
GPU access library
A tutorial and guide to using the WGPU library in Rust for access to GPU functions
GPU architecture
A high-performance deep learning architecture designed to balance accuracy and efficiency on GPUs.
Memory-efficient algorithms
A C++ library for compact data structures and algorithms optimized for memory efficiency and high performance
jgbit/vuda869
GPU acceleration framework
Provides a Vulkan-based interface to CUDA's runtime API for GPU-accelerated applications
ML model
Provides XGBoost models for machine learning tasks in Clojure
XGBoost runner
An interface to run XGBoost models in Node.js
Shader optimizer
Optimized shader libraries for fast operations on graphics processing units.
boostorg/compute1.6K
GPU computing library
A C++ library providing a thin wrapper over the OpenCL API for GPU computing.
Geo processor
A GPU-accelerated geospatial data analysis platform
Time series forecasting model
A PyTorch implementation of an optimized deep learning model for time series forecasting on GPUs.
Featured in 2 awesome lists
Each link jumps to the spot where the list mentions thundergbm.
