feat: support gpu

research/models/lightgbm_model.py

@@ -20,6 +20,12 @@ class LightGBMModel(TraditionalModel):
     def build_model(self) -> BaseEstimator:
         params = self.config.model_params
 
+        # Optional GPU acceleration
+        use_gpu = bool(params.get("use_gpu", False))
+        device = params.get("device", "gpu" if use_gpu else "cpu")
+        gpu_platform_id = params.get("gpu_platform_id", None)
+        gpu_device_id = params.get("gpu_device_id", None)
+
         # Leaf-wise boosted trees excel on sparse/categorical mixes; binary objective
         # and parallelism improve training speed for this task.
         return lgb.LGBMClassifier(
@@ -33,6 +39,9 @@ class LightGBMModel(TraditionalModel):
             objective=params.get("objective", "binary"),
             n_jobs=params.get("n_jobs", -1),
             verbose=2,
+            device=device,
+            gpu_platform_id=gpu_platform_id,
+            gpu_device_id=gpu_device_id,
         )
 
     def prepare_features(self, X: pd.DataFrame) -> np.ndarray: