ROCm XGBoost documentation#
2026-02-05
1 min read time
XGBoost on ROCm provides GPU‑accelerated gradient boosting on AMD hardware, enabling scalable, high‑performance machine learning for financial risk modeling and data‑intensive workloads. This implementation utilizes optimized kernels, enhanced memory management, and multi‑GPU scaling to accelerate performance compared to CPU‑only baselines.
XGBoost excels in financial applications by using level-wise tree growth to generate balanced, accurate models. It ensures robustness against noisy data and outliers through strong L1 and L2 regularization and automatically manages missing values. Tunable hyperparameters provide the high precision required for tasks like loan default prediction, while interpretability tools such as feature importance aid in regulatory compliance. While GPU acceleration speeds up processing on large datasets by parallelizing split computations, memory requirements increase with tree depth.
XGBoost is part of the ROCm-Finance toolkit.
The ROCm-Finance XGBoost source code is hosted on GitHub at ROCm/XGBoost.
ROCm-Finance XGBoost documentation is organized into the following categories: