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test_gpu.py

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+import numpy as np
+from xgboost import XGBClassifier
+from sklearn.datasets import make_classification
+from sklearn.metrics import accuracy_score
+
+print("🚀 Testing XGBoost with CUDA GPU...")
+try:
+    X, y = make_classification(n_samples=10000, n_features=20, n_classes=2, random_state=42)
+    model = XGBClassifier(
+        n_estimators=100,
+        max_depth=10,
+        tree_method="hist",
+        device="cuda",
+        random_state=42,
+        verbosity=1
+    )
+    print("Training model...")
+    model.fit(X, y)
+    y_pred = model.predict(X)
+    acc = accuracy_score(y, y_pred)
+    print(f"✅ Training successful! Accuracy: {acc*100:.2f}%")
+except Exception as e:
+    print(f"❌ Error during training: {e}")