diff --git a/python-package/xgboost/spark/core.py b/python-package/xgboost/spark/core.py
index 998afbf77..2150e5055 100644
--- a/python-package/xgboost/spark/core.py
+++ b/python-package/xgboost/spark/core.py
@@ -924,21 +924,17 @@ class _SparkXGBEstimator(Estimator, _SparkXGBParams, MLReadable, MLWritable):
                 # Note: Checking `is_cudf_available` in spark worker side because
                 # spark worker might has different python environment with driver side.
                 use_qdm = use_qdm and is_cudf_available()
+                get_logger("XGBoost-PySpark").info(
+                    "Leveraging %s to train with QDM: %s",
+                    booster_params["device"],
+                    "on" if use_qdm else "off",
+                )
 
             if use_qdm and (booster_params.get("max_bin", None) is not None):
                 dmatrix_kwargs["max_bin"] = booster_params["max_bin"]
 
             _rabit_args = {}
             if context.partitionId() == 0:
-                get_logger("XGBoostPySpark").debug(
-                    "booster params: %s\n"
-                    "train_call_kwargs_params: %s\n"
-                    "dmatrix_kwargs: %s",
-                    booster_params,
-                    train_call_kwargs_params,
-                    dmatrix_kwargs,
-                )
-
                 _rabit_args = _get_rabit_args(context, num_workers)
 
             worker_message = {
@@ -995,7 +991,19 @@ class _SparkXGBEstimator(Estimator, _SparkXGBParams, MLReadable, MLWritable):
             )
             return ret[0], ret[1]
 
+        get_logger("XGBoost-PySpark").info(
+            "Running xgboost-%s on %s workers with"
+            "\n\tbooster params: %s"
+            "\n\ttrain_call_kwargs_params: %s"
+            "\n\tdmatrix_kwargs: %s",
+            xgboost._py_version(),
+            num_workers,
+            booster_params,
+            train_call_kwargs_params,
+            dmatrix_kwargs,
+        )
         (config, booster) = _run_job()
+        get_logger("XGBoost-PySpark").info("Finished xgboost training!")
 
         result_xgb_model = self._convert_to_sklearn_model(
             bytearray(booster, "utf-8"), config
diff --git a/python-package/xgboost/spark/utils.py b/python-package/xgboost/spark/utils.py
index 5f3bb19ba..33a45a90e 100644
--- a/python-package/xgboost/spark/utils.py
+++ b/python-package/xgboost/spark/utils.py
@@ -104,6 +104,10 @@ def get_logger(name: str, level: str = "INFO") -> logging.Logger:
     # If the logger is configured, skip the configure
     if not logger.handlers and not logging.getLogger().handlers:
         handler = logging.StreamHandler(sys.stderr)
+        formatter = logging.Formatter(
+            "%(asctime)s %(levelname)s %(name)s: %(funcName)s %(message)s"
+        )
+        handler.setFormatter(formatter)
         logger.addHandler(handler)
     return logger