Description
Distributed gradient boosting workloads can be run with decision-tree-based machine-learning tools.
This package is useful for developers and researchers who need the command-line components of a distributed LightGBM-style setup. It does not make data science automatic; users still prepare data, configure training, and validate models.
Distributed training can move private datasets across machines. Confirm network trust, storage paths, and experiment reproducibility.