{"library":"xgboost-ray","type":"library","category":null,"description":"XGBoost-Ray provides a Ray backend for distributed XGBoost, enabling training and prediction on Ray clusters with minimal code changes. It extends the core XGBoost API to leverage distributed data representations and integrates seamlessly with other Ray libraries like Ray Tune for hyperparameter optimization and Ray Train for scalable ML workloads. The library is actively maintained, with frequent updates to ensure compatibility with recent XGBoost and Ray versions.","language":"python","status":"active","version":"0.1.19","tags":["machine learning","distributed computing","xgboost","ray","gradient boosting","scalability"],"last_verified":"Fri May 22","install":[{"cmd":"pip install xgboost-ray","imports":["from xgboost_ray import RayDMatrix","from xgboost_ray import RayParams","from xgboost_ray import train","from xgboost_ray import predict"]}],"homepage":null,"github":"https://github.com/ray-project/xgboost_ray","docs":null,"changelog":null,"pypi":"https://pypi.org/project/xgboost-ray/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":50,"avg_install_s":28.6,"avg_import_s":null,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/xgboost-ray/compatibility"}}