feat: support gpu

research/models/xgboost_model.py

@@ -20,6 +20,14 @@ class XGBoostModel(TraditionalModel):
     def build_model(self) -> BaseEstimator:
         params = self.config.model_params
 
+        # Optional GPU acceleration
+        use_gpu = bool(params.get("use_gpu", False))
+        default_tree_method = "gpu_hist" if use_gpu else "hist"
+        tree_method = params.get("tree_method", default_tree_method)
+        predictor = params.get(
+            "predictor", "gpu_predictor" if tree_method.startswith("gpu") else "auto"
+        )
+
         # Histogram-based trees and parallelism provide fast training; default
         # logloss metric suits binary classification of gender.
         return xgb.XGBClassifier(
@@ -31,7 +39,8 @@ class XGBoostModel(TraditionalModel):
             random_state=self.config.random_seed,
             eval_metric="logloss",
             n_jobs=params.get("n_jobs", -1),
-            tree_method=params.get("tree_method", "hist"),
+            tree_method=tree_method,
+            predictor=predictor,
             verbosity=2,
         )