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[doc] update the doc for jvm model compatibility (#7907)

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Bobby Wang 2022-05-16 14:05:26 +08:00 committed by GitHub
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doc/jvm/xgboost4j_spark_tutorial.rst
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@ -349,7 +349,23 @@ With regards to ML pipeline save and load, please refer the next section.
Interact with Other Bindings of XGBoost Interact with Other Bindings of XGBoost
--------------------------------------- ---------------------------------------
After we train a model with XGBoost4j-Spark on massive dataset, sometimes we want to do model serving in single machine or integrate it with other single node libraries for further processing. XGBoost4j-Spark supports export model to local by: After we train a model with XGBoost4j-Spark on massive dataset, sometimes we want to do model serving
in single machine or integrate it with other single node libraries for further processing.
After saving the model, we can load this model with single node Python XGBoost directly from ``version 2.0.0+``.
.. code-block:: scala
val xgbClassificationModelPath = "/tmp/xgbClassificationModel"
xgbClassificationModel.write.overwrite().save(xgbClassificationModelPath)
.. code-block:: python
import xgboost as xgb
bst = xgb.Booster({'nthread': 4})
bst.load_model("/tmp/xgbClassificationModel/data/XGBoostClassificationModel")
Before ``version 2.0.0``, XGBoost4j-Spark needs to export model to local manually by:
.. code-block:: scala .. code-block:: scala