Distributed training of XGBoost models using xgboost.spark


This feature is in Public Preview.

The Python package xgboost>=1.7 contains a new module xgboost.spark. This module includes the xgboost PySpark estimators xgboost.spark.SparkXGBRegressor, xgboost.spark.SparkXGBClassifier, and xgboost.spark.SparkXGBRanker. These new classes support the inclusion of XGBoost estimators in SparkML Pipelines. For API details, see the XGBoost python spark API doc.


Databricks Runtime 12.0 ML and above.

xgboost.spark parameters

The estimators defined in the xgboost.spark module support most of the same parameters and arguments used in standard XGBoost.

  • The parameters for the class constructor, fit method, and predict method are largely identical to those in the xgboost.sklearn module.

  • Naming, values, and defaults are mostly identical to those described in XGBoost parameters.

  • Exceptions are a few unsupported parameters (such as gpu_id, nthread, sample_weight, eval_set), and the pyspark estimator specific parameters that have been added (such as featuresCol, labelCol, use_gpu, validationIndicatorCol). For details, see XGBoost Python Spark API documentation.

Distributed training

PySpark estimators defined in the xgboost.spark module support distributed XGBoost training using the num_workers parameter. To use distributed training, create a classifier or regressor and set num_workers to the number of concurrent running Spark tasks during distributed training. To use the all Spark task slots, set num_workers=sc.defaultParallelism.

For example:

from xgboost.spark import SparkXGBClassifier
classifier = SparkXGBClassifier(num_workers=sc.defaultParallelism)


  • You cannot use mlflow.xgboost.autolog with distributed XGBoost. To log an xgboost Spark model using MLflow, use mlflow.spark.log_model(spark_xgb_model, artifact_path).

  • You cannot use distributed XGBoost on a cluster that has autoscaling enabled. New worker nodes that start in this elastic scaling paradigm cannot receive new sets of tasks and remain idle. For instructions to disable autoscaling, see Enable autoscaling.

Enable optimization for training on sparse features dataset

PySpark Estimators defined in xgboost.spark module support optimization for training on datasets with sparse features. To enable optimization of sparse feature sets, you need to provide a dataset to the fit method that contains a features column consisting of values of type pyspark.ml.linalg.SparseVector and set the estimator parameter enable_sparse_data_optim to True. Additionally, you need to set the missing parameter to 0.0.

For example:

from xgboost.spark import SparkXGBClassifier
classifier = SparkXGBClassifier(enable_sparse_data_optim=True, missing=0.0)

GPU training

PySpark estimators defined in the xgboost.spark module support training on GPUs. Set the parameter use_gpu to True to enable GPU training.


For each Spark task used in XGBoost distributed training, only one GPU is used in training when the use_gpu argument is set to True. Databricks recommends using the default value of 1 for the Spark cluster configuration spark.task.resource.gpu.amount. Otherwise, the additional GPUs allocated to this Spark task are idle.

For example:

from xgboost.spark import SparkXGBClassifier
classifier = SparkXGBClassifier(num_workers=sc.defaultParallelism, use_gpu=True)

Example notebook

This notebook shows the use of the Python package xgboost.spark with Spark MLlib.

PySpark-XGBoost notebook

Open notebook in new tab

Migration guide for the deprecated sparkdl.xgboost module

  • Replace from sparkdl.xgboost import XgboostRegressor with from xgboost.spark import SparkXGBRegressor and replace from sparkdl.xgboost import XgboostClassifier with from xgboost.spark import SparkXGBClassifier.

  • Change all parameter names in the estimator constructor from camelCase style to snake_case style. For example, change XgboostRegressor(featuresCol=XXX) to SparkXGBRegressor(features_col=XXX).

  • The parameters use_external_storage and external_storage_precision have been removed. xgboost.spark estimators use the DMatrix data iteration API to use memory more efficiently. There is no longer a need to use the inefficient external storage mode. For extremely large datasets, Databricks recommends that you increase the num_workers parameter, which makes each training task partition the data into smaller, more manageable data partitions. Consider setting num_workers = sc.defaultParallelism, which sets num_workers to the total number of Spark task slots in the cluster.

  • For estimators defined in xgboost.spark, setting num_workers=1 executes model training using a single Spark task. This utilizes the number of CPU cores specified by the Spark cluster configuration setting spark.task.cpus, which is 1 by default. To use more CPU cores to train the model, increase num_workers or spark.task.cpus. You cannot set the nthread or n_jobs parameter for estimators defined in xgboost.spark. This behavior is different from the previous behavior of estimators defined in the deprecated sparkdl.xgboost package.

Convert sparkdl.xgboost model into xgboost.spark model

sparkdl.xgboost models are saved in a different format than xgboost.spark models and have different parameter settings. Use the following utility function to convert the model:

def convert_sparkdl_model_to_xgboost_spark_model(
  :param xgboost_spark_estimator_cls:
      `xgboost.spark` estimator class, e.g. `xgboost.spark.SparkXGBRegressor`
  :param sparkdl_xgboost_model:
      `sparkdl.xgboost` model instance e.g. the instance of
       `sparkdl.xgboost.XgboostRegressorModel` type.

      A `xgboost.spark` model instance

  def convert_param_key(key):
    from xgboost.spark.core import _inverse_pyspark_param_alias_map
    if key == "baseMarginCol":
      return "base_margin_col"
    if key in _inverse_pyspark_param_alias_map:
      return _inverse_pyspark_param_alias_map[key]
    if key in ['use_external_storage', 'external_storage_precision', 'nthread', 'n_jobs', 'base_margin_eval_set']:
      return None
    return key

  xgboost_spark_params_dict = {}
  for param in sparkdl_xgboost_model.params:
    if param.name == "arbitraryParamsDict":
    if sparkdl_xgboost_model.isDefined(param):
      xgboost_spark_params_dict[param.name] = sparkdl_xgboost_model.getOrDefault(param)


  xgboost_spark_params_dict = {
    convert_param_key(k): v
    for k, v in xgboost_spark_params_dict.items()
    if convert_param_key(k) is not None

  booster = sparkdl_xgboost_model.get_booster()
  booster_bytes = booster.save_raw("json")
  booster_config = booster.save_config()
  estimator = xgboost_spark_estimator_cls(**xgboost_spark_params_dict)
  sklearn_model = estimator._convert_to_sklearn_model(booster_bytes, booster_config)
  return estimator._copyValues(estimator._create_pyspark_model(sklearn_model))

# Example
from xgboost.spark import SparkXGBRegressor

new_model = convert_sparkdl_model_to_xgboost_spark_model(

If you have a pyspark.ml.PipelineModel model containing a sparkdl.xgboost model as the last stage, you can replace the stage of sparkdl.xgboost model with the converted xgboost.spark model.

pipeline_model.stages[-1] = convert_sparkdl_model_to_xgboost_spark_model(