restore use_gpu.

python-package/xgboost/sklearn.py

@@ -1004,13 +1004,17 @@ def fit(
             Validation metrics will help us track the performance of the model.
 
         eval_metric : str, list of str, or callable, optional
+
             .. deprecated:: 1.6.0
-                Use `eval_metric` in :py:meth:`__init__` or :py:meth:`set_params` instead.
+
+            Use `eval_metric` in :py:meth:`__init__` or :py:meth:`set_params` instead.
 
         early_stopping_rounds : int
+
             .. deprecated:: 1.6.0
-                Use `early_stopping_rounds` in :py:meth:`__init__` or
-                :py:meth:`set_params` instead.
+
+            Use `early_stopping_rounds` in :py:meth:`__init__` or :py:meth:`set_params`
+            instead.
         verbose :
             If `verbose` is True and an evaluation set is used, the evaluation metric
             measured on the validation set is printed to stdout at each boosting stage.

python-package/xgboost/spark/core.py

@@ -340,8 +340,18 @@ def _validate_params(self) -> None:
                 f"It cannot be less than 1 [Default is 1]"
             )
 
+        tree_method = self.getOrDefault(self.getParam("tree_method"))
+        if (
+            self.getOrDefault(self.use_gpu) or use_cuda(self.getOrDefault(self.device))
+        ) and not _can_use_qdm(tree_method):
+            raise ValueError(
+                f"The `{tree_method}` tree method is not supported on GPU."
+            )
+
         if self.getOrDefault(self.features_cols):
-            if not use_cuda(self.getOrDefault(self.device)):
+            if not use_cuda(self.getOrDefault(self.device)) and not self.getOrDefault(
+                self.use_gpu
+            ):
                 raise ValueError(
                     "features_col param with list value requires `device=cuda`."
                 )
@@ -396,7 +406,7 @@ def _validate_params(self) -> None:
                     "`pyspark.ml.linalg.Vector` type."
                 )
 
-        if use_cuda(self.getOrDefault(self.device)):
+        if use_cuda(self.getOrDefault(self.device)) or self.getOrDefault(self.use_gpu):
             gpu_per_task = (
                 _get_spark_session()
                 .sparkContext.getConf()
@@ -553,6 +563,7 @@ def __init__(self) -> None:
         self._setDefault(
             num_workers=1,
             device="cpu",
+            use_gpu=False,
             force_repartition=False,
             repartition_random_shuffle=False,
             feature_names=None,
@@ -874,20 +885,9 @@ def _fit(self, dataset: DataFrame) -> "_SparkXGBModel":
             dmatrix_kwargs,
         ) = self._get_xgb_parameters(dataset)
 
-        run_on_gpu = use_cuda(self.getOrDefault(self.device))
-        tree_method = self.getParam("tree_method")
-        # Validation before submitting function to worker.
-        if (
-            run_on_gpu
-            and self.getOrDefault(tree_method)
-            and self.getOrDefault(tree_method) != "hist"
-        ):
-            raise ValueError(
-                f"The `{self.getOrDefault(tree_method)}` tree method is"
-                " not supported"
-                " on GPU."
-            )
-
+        run_on_gpu = use_cuda(self.getOrDefault(self.device)) or self.getOrDefault(
+            self.use_gpu
+        )
         is_local = _is_local(_get_spark_session().sparkContext)
 
         num_workers = self.getOrDefault(self.num_workers)

python-package/xgboost/spark/estimator.py

@@ -3,7 +3,7 @@
 # pylint: disable=fixme, too-many-ancestors, protected-access, no-member, invalid-name
 # pylint: disable=unused-argument, too-many-locals
 
-
+import warnings
 from typing import Any, List, Optional, Type, Union
 
 import numpy as np
@@ -134,6 +134,10 @@ class SparkXGBRegressor(_SparkXGBEstimator):
     num_workers:
         How many XGBoost workers to be used to train.
         Each XGBoost worker corresponds to one spark task.
+    use_gpu:
+        .. deprecated:: 2.0.0
+
+        Use `device` instead.
     device:
         Device for XGBoost workers, available options are `cpu`, `cuda`, and `gpu`.
     force_repartition:
@@ -194,6 +198,7 @@ def __init__(
         weight_col: Optional[str] = None,
         base_margin_col: Optional[str] = None,
         num_workers: int = 1,
+        use_gpu: Optional[bool] = None,
         device: Optional[str] = None,
         force_repartition: bool = False,
         repartition_random_shuffle: bool = False,
@@ -202,6 +207,10 @@ def __init__(
     ) -> None:
         super().__init__()
         input_kwargs = self._input_kwargs
+        if use_gpu:
+            warnings.warn(
+                "`use_gpu` is deprecated, use `device` instead", FutureWarning
+            )
         self.setParams(**input_kwargs)
 
     @classmethod
@@ -302,6 +311,10 @@ class SparkXGBClassifier(_SparkXGBEstimator, HasProbabilityCol, HasRawPrediction
     num_workers:
         How many XGBoost workers to be used to train.
         Each XGBoost worker corresponds to one spark task.
+    use_gpu:
+        .. deprecated:: 2.0.0
+
+        Use `device` instead.
     device:
         Device for XGBoost workers, available options are `cpu`, `cuda`, and `gpu`.
     force_repartition:
@@ -362,6 +375,7 @@ def __init__(
         weight_col: Optional[str] = None,
         base_margin_col: Optional[str] = None,
         num_workers: int = 1,
+        use_gpu: Optional[bool] = None,
         device: Optional[str] = None,
         force_repartition: bool = False,
         repartition_random_shuffle: bool = False,
@@ -374,6 +388,10 @@ def __init__(
         # binary or multinomial input dataset, and we need to remove the fixed default
         # param value as well to avoid causing ambiguity.
         input_kwargs = self._input_kwargs
+        if use_gpu:
+            warnings.warn(
+                "`use_gpu` is deprecated, use `device` instead", FutureWarning
+            )
         self.setParams(**input_kwargs)
         self._setDefault(objective=None)
 
@@ -473,6 +491,10 @@ class SparkXGBRanker(_SparkXGBEstimator):
     num_workers:
         How many XGBoost workers to be used to train.
         Each XGBoost worker corresponds to one spark task.
+    use_gpu:
+        .. deprecated:: 2.0.0
+
+        Use `device` instead.
     device:
         Device for XGBoost workers, available options are `cpu`, `cuda`, and `gpu`.
     force_repartition:
@@ -539,6 +561,7 @@ def __init__(
         base_margin_col: Optional[str] = None,
         qid_col: Optional[str] = None,
         num_workers: int = 1,
+        use_gpu: Optional[bool] = None,
         device: Optional[str] = None,
         force_repartition: bool = False,
         repartition_random_shuffle: bool = False,
@@ -547,6 +570,10 @@ def __init__(
     ) -> None:
         super().__init__()
         input_kwargs = self._input_kwargs
+        if use_gpu:
+            warnings.warn(
+                "`use_gpu` is deprecated, use `device` instead", FutureWarning
+            )
         self.setParams(**input_kwargs)
 
     @classmethod

tests/test_distributed/test_gpu_with_spark/test_gpu_spark.py

@@ -197,6 +197,19 @@ 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