@@ -924,21 +924,17 @@ def _train_booster(
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# Note: Checking `is_cudf_available` in spark worker side because
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# spark worker might has different python environment with driver side.
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use_qdm = use_qdm and is_cudf_available ()
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+ get_logger ("XGBoost-PySpark" ).info (
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+ "Leveraging %s to train with QDM: %s" ,
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+ booster_params ["device" ],
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+ "on" if use_qdm else "off" ,
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+ )
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if use_qdm and (booster_params .get ("max_bin" , None ) is not None ):
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dmatrix_kwargs ["max_bin" ] = booster_params ["max_bin" ]
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_rabit_args = {}
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if context .partitionId () == 0 :
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- get_logger ("XGBoostPySpark" ).debug (
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- "booster params: %s\n "
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- "train_call_kwargs_params: %s\n "
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- "dmatrix_kwargs: %s" ,
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- booster_params ,
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- train_call_kwargs_params ,
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- dmatrix_kwargs ,
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- )
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-
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_rabit_args = _get_rabit_args (context , num_workers )
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worker_message = {
@@ -995,7 +991,19 @@ def _run_job() -> Tuple[str, str]:
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)
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return ret [0 ], ret [1 ]
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+ get_logger ("XGBoost-PySpark" ).info (
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+ "Running xgboost-%s on %s workers with"
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+ "\n \t booster params: %s"
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+ "\n \t train_call_kwargs_params: %s"
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+ "\n \t dmatrix_kwargs: %s" ,
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+ xgboost ._py_version (),
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+ num_workers ,
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+ booster_params ,
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+ train_call_kwargs_params ,
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+ dmatrix_kwargs ,
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+ )
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(config , booster ) = _run_job ()
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+ get_logger ("XGBoost-PySpark" ).info ("Finished xgboost training!" )
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result_xgb_model = self ._convert_to_sklearn_model (
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bytearray (booster , "utf-8" ), config
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