Distributed XGBoost with PySpark

Starting from version 1.7.0, xgboost supports pyspark estimator APIs.

Note

The feature is still experimental and not yet ready for production use.

XGBoost PySpark Estimator

SparkXGBRegressor

SparkXGBRegressor is a PySpark ML estimator. It implements the XGBoost classification algorithm based on XGBoost python library, and it can be used in PySpark Pipeline and PySpark ML meta algorithms like CrossValidator/TrainValidationSplit/OneVsRest.

We can create a SparkXGBRegressor estimator like:

from xgboost.spark import SparkXGBRegressor
spark_reg_estimator = SparkXGBRegressor(
  features_col="features",
  label_col="label",
  num_workers=2,
)

The above snippet creates a spark estimator which can fit on a spark dataset, and return a spark model that can transform a spark dataset and generate dataset with prediction column. We can set almost all of xgboost sklearn estimator parameters as SparkXGBRegressor parameters, but some parameter such as nthread is forbidden in spark estimator, and some parameters are replaced with pyspark specific parameters such as weight_col, validation_indicator_col, use_gpu, for details please see SparkXGBRegressor doc.

The following code snippet shows how to train a spark xgboost regressor model, first we need to prepare a training dataset as a spark dataframe contains “label” column and “features” column(s), the “features” column(s) must be pyspark.ml.linalg.Vector type or spark array type or a list of feature column names.

xgb_regressor_model = xgb_regressor.fit(train_spark_dataframe)

The following code snippet shows how to predict test data using a spark xgboost regressor model, first we need to prepare a test dataset as a spark dataframe contains “features” and “label” column, the “features” column must be pyspark.ml.linalg.Vector type or spark array type.

transformed_test_spark_dataframe = xgb_regressor.predict(test_spark_dataframe)

The above snippet code returns a transformed_test_spark_dataframe that contains the input dataset columns and an appended column “prediction” representing the prediction results.

SparkXGBClassifier

SparkXGBClassifier estimator has similar API with SparkXGBRegressor, but it has some pyspark classifier specific params, e.g. raw_prediction_col and probability_col parameters. Correspondingly, by default, SparkXGBClassifierModel transforming test dataset will generate result dataset with 3 new columns:

  • “prediction”: represents the predicted label.

  • “raw_prediction”: represents the output margin values.

  • “probability”: represents the prediction probability on each label.

XGBoost PySpark GPU support

XGBoost PySpark fully supports GPU acceleration. Users are not only able to enable efficient training but also utilize their GPUs for the whole PySpark pipeline including ETL and inference. In below sections, we will walk through an example of training on a PySpark standalone GPU cluster. To get started, first we need to install some additional packages, then we can set the use_gpu parameter to True.

Prepare the necessary packages

Aside from the PySpark and XGBoost modules, we also need the cuDF package for handling Spark dataframe. We recommend using either Conda or Virtualenv to manage python dependencies for PySpark jobs. Please refer to How to Manage Python Dependencies in PySpark for more details on PySpark dependency management.

In short, to create a Python environment that can be sent to a remote cluster using virtualenv and pip:

python -m venv xgboost_env
source xgboost_env/bin/activate
pip install pyarrow pandas venv-pack xgboost
# https://rapids.ai/pip.html#install
pip install cudf-cu11 --extra-index-url=https://pypi.ngc.nvidia.com
venv-pack -o xgboost_env.tar.gz

With Conda:

conda create -y -n xgboost_env -c conda-forge conda-pack python=3.9
conda activate xgboost_env
# use conda when the supported version of xgboost (1.7) is released on conda-forge
pip install xgboost
conda install cudf pyarrow pandas -c rapids -c nvidia -c conda-forge
conda pack -f -o xgboost_env.tar.gz

Write your PySpark application

Below snippet is a small example for training xgboost model with PySpark. Notice that we are using a list of feature names and the additional parameter use_gpu:

from xgboost.spark import SparkXGBRegressor
spark = SparkSession.builder.getOrCreate()

# read data into spark dataframe
train_data_path = "xxxx/train"
train_df = spark.read.parquet(data_path)

test_data_path = "xxxx/test"
test_df = spark.read.parquet(test_data_path)

# assume the label column is named "class"
label_name = "class"

# get a list with feature column names
feature_names = [x.name for x in train_df.schema if x.name != label]

# create a xgboost pyspark regressor estimator and set use_gpu=True
regressor = SparkXGBRegressor(
  features_col=feature_names,
  label_col=label_name,
  num_workers=2,
  use_gpu=True,
)

# train and return the model
model = regressor.fit(train_df)

# predict on test data
predict_df = model.transform(test_df)
predict_df.show()

Submit the PySpark application

Assuming you have configured your Spark cluster with GPU support. Otherwise, please refer to spark standalone configuration with GPU support.

export PYSPARK_DRIVER_PYTHON=python
export PYSPARK_PYTHON=./environment/bin/python

spark-submit \
  --master spark://<master-ip>:7077 \
  --conf spark.executor.resource.gpu.amount=1 \
  --conf spark.task.resource.gpu.amount=1 \
  --archives xgboost_env.tar.gz#environment \
  xgboost_app.py

The submit command sends the Python environment created by pip or conda along with the specification of GPU allocation. We will revisit this command later on.

Model Persistence

Similar to standard PySpark ml estimators, one can persist and reuse the model with save` and ``load methods:

regressor = SparkXGBRegressor()
model = regressor.fit(train_df)
# save the model
model.save("/tmp/xgboost-pyspark-model")
# load the model
model2 = SparkXGBRankerModel.load("/tmp/xgboost-pyspark-model")

To export the underlying booster model used by XGBoost:

regressor = SparkXGBRegressor()
model = regressor.fit(train_df)
# the same booster object returned by xgboost.train
booster: xgb.Booster = model.get_booster()
booster.predict(...)
booster.save_model("model.json") # or model.ubj, depending on your choice of format.

This booster is not only shared by other Python interfaces but also used by all the XGBoost bindings including the C, Java, and the R package. Lastly, one can extract the booster file directly from a saved spark estimator without going through the getter:

import xgboost as xgb
bst = xgb.Booster()
# Loading the model saved in previous snippet
bst.load_model("/tmp/xgboost-pyspark-model/model/part-00000")

Accelerate the whole pipeline for xgboost pyspark

With RAPIDS Accelerator for Apache Spark, you can leverage GPUs to accelerate the whole pipeline (ETL, Train, Transform) for xgboost pyspark without any Python code change. An example submit command is shown below with additional spark configurations and dependencies:

export PYSPARK_DRIVER_PYTHON=python
export PYSPARK_PYTHON=./environment/bin/python

spark-submit \
  --master spark://<master-ip>:7077 \
  --conf spark.executor.resource.gpu.amount=1 \
  --conf spark.task.resource.gpu.amount=1 \
  --packages com.nvidia:rapids-4-spark_2.12:22.08.0 \
  --conf spark.plugins=com.nvidia.spark.SQLPlugin \
  --conf spark.sql.execution.arrow.maxRecordsPerBatch=1000000 \
  --archives xgboost_env.tar.gz#environment \
  xgboost_app.py

When rapids plugin is enabled, both of the JVM rapids plugin and the cuDF Python package are required. More configuration options can be found in the RAPIDS link above along with details on the plugin.