{"categories":[{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/2"}],"enrichment":{"capability":"Distributes XGBoost training and inference across multiple nodes and GPUs using Ray as the underlying compute framework.","skillfed_tags":["distributed-ml","archived"],"use_cases":["Train gradient boosting models on datasets too large for a single machine by distributing training across a Ray cluster.","Run distributed hyperparameter tuning with Ray Tune, testing multiple configurations in parallel, each parallelized internally.","Perform inference on large datasets by sharding them across Ray actors and predicting in parallel.","Leverage multi-GPU training for XGBoost workloads on clusters with GPU nodes.","Use RayDMatrix to load and shard Parquet files and other data formats across Ray's object store for distributed training."],"what_it_does":"XGBoost-Ray wraps XGBoost's training and prediction functions to run them in parallel across a Ray cluster. Instead of the standard xgb.DMatrix, you pass data via RayDMatrix, which shards it across Ray's object store, and configure distributed training with RayParams. It provides both a functional API (train/predict) and scikit-learn-compatible estimators, making it a drop-in replacement for single-machine XGBoost when you need to scale to multiple nodes or GPUs.\n\nThe package integrates with Ray Tune for distributed hyperparameter optimization and supports various data formats (Pandas DataFrames, NumPy arrays, CSV, Parquet). However, the project is archived and has not been updated since June 2024, meaning it may not work with recent versions of xgboost, ray, or Python.","worth_installing":"No. The package is archived and abandoned as of June 2024, with no maintenance for over a year. While it has low install friction and permissive licensing, the lack of updates means it will likely fail with current versions of xgboost, ray, and modern Python releases. Use only if you are locked into an older, pinned environment. For new projects, consider Ray's native XGBoost integrations or maintained alternatives."},"id":"xgboost-ray","links":{"html":"https://skillfed.io/packages/xgboost-ray","md":"https://skillfed.io/packages/xgboost-ray.md","pypi":"https://pypi.org/project/xgboost-ray/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2023-09-20","license_spdx":null,"license_treatment":"permissive","name":"xgboost-ray","python_support":"unspecified","summary":"A Ray backend for distributed XGBoost"},"popularity":{"monthly_downloads":462218,"position":6528,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.19"}