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Get Started with XGBoost4J-Spark with Jupyter Notebook

This is a getting started guide to XGBoost4J-Spark using an Jupyter notebook. At the end of this guide, the reader will be able to run a sample notebook that runs on NVIDIA GPUs.

Before you begin, please ensure that you have setup a Spark Standalone Cluster.

It is assumed that the SPARK_MASTER and SPARK_HOME environment variables are defined and point to the master spark URL (e.g. spark://localhost:7077), and the home directory for Apache Spark respectively.

  1. Make sure you have Jupyter notebook installed. If you install it with conda, please makes sure your Python version is consistent.

  2. Make sure you have SPARK_JARS and SPARK_PY_FILES set properly. Please note, here cudf-0.9.2-cuda10.jar is used as an example. Please choose other cudf-0.9.2 jars based on your environment. You may need to update these env variables because the working directory will be changed:

export LIBS_PATH=[full path to xgboost4j_spark/libs]
export SPARK_JARS=${LIBS_PATH}/cudf-0.9.2-cuda10.jar,${LIBS_PATH}/xgboost4j_2.x-1.0.0-Beta3.jar,${LIBS_PATH}/xgboost4j-spark_2.x-1.0.0-Beta3.jar
export SPARK_PY_FILES=${LIBS_PATH}/xgboost4j-spark_2.x-1.0.0-Beta3.jar,${LIBS_PATH}/samples.zip
  1. Go to the project root directory and launch the notebook:
PYSPARK_DRIVER_PYTHON=jupyter       \
PYSPARK_DRIVER_PYTHON_OPTS=notebook \
pyspark                             \
--master ${SPARK_MASTER}            \
--jars ${SPARK_JARS}                \
--py-files ${SPARK_PY_FILES}

Then you start your notebook and open mortgage-gpu.ipynb to explore.