chyunsu3/nvforest
Highly optimized and lightweight library for decision tree inference on NVIDIA GPUs and CPUs
Lead maintainer of XGBoost. Area of interest: Machine learning systems & algorithms.
Highly optimized and lightweight library for decision tree inference on NVIDIA GPUs and CPUs
cuDF - GPU DataFrame Library
cuML - RAPIDS Machine Learning Library
Memory-efficient divide-and-conquer with automatic profiling
Rapids Analytics Framework Toolset to share building blocks between cuGraph and cuML
cuVS - a library for vector search and clustering on the GPU
🎡 Build Python wheels for all the platforms with minimal configuration.
A next generation Python CMake adaptor and Python API for plugins
RAPIDS Documentation Site
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Use GH Action to create tag for every commit
DevOps / Continuous Integration tools for XGBoost project
Modern, extensible Python project management
The Python package installer
"(Pre-)Commit to Better Code" workshop
Visualize TVM Relay program graph
A parallel (CUDA) implementation of skiplist
Host custom actions; keep track of manual approval requests for CI jobs.
PoC for next generation of XGBoost CI
hotomoe flavored Misskey(.io)