Use XGBoost on Databricks

Note

The managed MLflow integration with Databricks on Google Cloud requires Databricks Runtime for Machine Learning 9.1 LTS or above.

Learn how to train machine learning models using XGBoost in Databricks. Databricks Runtime for Machine Learning includes XGBoost libraries for both Python and Scala.

Warning

Versions of XGBoost 1.2.0 and lower have a bug that can cause the shared Spark context to be killed if XGBoost model training fails. The only way to recover is to restart the cluster. Databricks Runtime 7.5 ML and lower include a version of XGBoost that is affected by this bug. To install a different version of XGBoost, see Install XGBoost on Databricks.

Train XGBoost models on a single node

You can train models using the Python xgboost package. This package supports only single node workloads. To train a PySpark ML pipeline and take advantage of distributed training, see Distributed training of XGBoost models.

XGBoost Python notebook

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Distributed training of XGBoost models

For distributed training of XGBoost models, Databricks includes PySpark estimators based on the xgboost package. Databricks also includes the Scala package xgboost-4j. For details and example notebooks, see the following: