[pyspark] Handle the device parameter in pyspark. (#9390)

- Handle the new `device` parameter in PySpark.
- Deprecate the old `use_gpu` parameter.

tests/test_distributed/test_gpu_with_spark/test_gpu_spark.py

@@ -154,7 +154,7 @@ def spark_diabetes_dataset_feature_cols(spark_session_with_gpu):
 def test_sparkxgb_classifier_with_gpu(spark_iris_dataset):
     from pyspark.ml.evaluation import MulticlassClassificationEvaluator
 
-    classifier = SparkXGBClassifier(use_gpu=True, num_workers=num_workers)
+    classifier = SparkXGBClassifier(device="cuda", num_workers=num_workers)
     train_df, test_df = spark_iris_dataset
     model = classifier.fit(train_df)
     pred_result_df = model.transform(test_df)
@@ -169,7 +169,7 @@ def test_sparkxgb_classifier_feature_cols_with_gpu(spark_iris_dataset_feature_co
     train_df, test_df, feature_names = spark_iris_dataset_feature_cols
 
     classifier = SparkXGBClassifier(
-        features_col=feature_names, use_gpu=True, num_workers=num_workers
+        features_col=feature_names, device="cuda", num_workers=num_workers
     )
 
     model = classifier.fit(train_df)
@@ -185,7 +185,7 @@ def test_cv_sparkxgb_classifier_feature_cols_with_gpu(spark_iris_dataset_feature
     train_df, test_df, feature_names = spark_iris_dataset_feature_cols
 
     classifier = SparkXGBClassifier(
-        features_col=feature_names, use_gpu=True, num_workers=num_workers
+        features_col=feature_names, device="cuda", num_workers=num_workers
     )
     grid = ParamGridBuilder().addGrid(classifier.max_depth, [6, 8]).build()
     evaluator = MulticlassClassificationEvaluator(metricName="f1")
@@ -197,11 +197,24 @@ def test_cv_sparkxgb_classifier_feature_cols_with_gpu(spark_iris_dataset_feature
     f1 = evaluator.evaluate(pred_result_df)
     assert f1 >= 0.97
 
+    clf = SparkXGBClassifier(
+        features_col=feature_names, use_gpu=True, num_workers=num_workers
+    )
+    grid = ParamGridBuilder().addGrid(clf.max_depth, [6, 8]).build()
+    evaluator = MulticlassClassificationEvaluator(metricName="f1")
+    cv = CrossValidator(
+        estimator=clf, evaluator=evaluator, estimatorParamMaps=grid, numFolds=3
+    )
+    cvModel = cv.fit(train_df)
+    pred_result_df = cvModel.transform(test_df)
+    f1 = evaluator.evaluate(pred_result_df)
+    assert f1 >= 0.97
+
 
 def test_sparkxgb_regressor_with_gpu(spark_diabetes_dataset):
     from pyspark.ml.evaluation import RegressionEvaluator
 
-    regressor = SparkXGBRegressor(use_gpu=True, num_workers=num_workers)
+    regressor = SparkXGBRegressor(device="cuda", num_workers=num_workers)
     train_df, test_df = spark_diabetes_dataset
     model = regressor.fit(train_df)
     pred_result_df = model.transform(test_df)
@@ -215,7 +228,7 @@ def test_sparkxgb_regressor_feature_cols_with_gpu(spark_diabetes_dataset_feature
 
     train_df, test_df, feature_names = spark_diabetes_dataset_feature_cols
     regressor = SparkXGBRegressor(
-        features_col=feature_names, use_gpu=True, num_workers=num_workers
+        features_col=feature_names, device="cuda", num_workers=num_workers
     )
 
     model = regressor.fit(train_df)