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Get Started with XGBoost4J-Spark on Apache Hadoop YARN

This is a getting started guide to XGBoost4J-Spark on Apache Hadoop YARN. At the end of this guide, the reader will be able to run a sample Apache Spark Python application that runs on NVIDIA GPUs.

Prerequisites

  • Apache Spark 2.3+ running on YARN.

  • Hardware Requirements

    • NVIDIA Pascal™ GPU architecture or better
    • Multi-node clusters with homogenous GPU configuration

  • Software Requirements

    • Ubuntu 16.04/CentOS
    • NVIDIA driver 410.48+
    • CUDA V10.0/9.2
    • NCCL 2.4.7
    • Python 2.7/3.4/3.5/3.6/3.7
    • NumPy

  • EXCLUSIVE_PROCESS must be set for all GPUs in each NodeManager. This can be accomplished using the nvidia-smi utility:

    nvidia-smi -i [gpu index] -c EXCLUSIVE_PROCESS

    For example:

    nvidia-smi -i 0 -c EXCLUSIVE_PROCESS

    Sets EXCLUSIVE_PROCESS for GPU 0.

  • The number of GPUs per NodeManager dictates the number of Spark executors that can run in that NodeManager. Additionally, cores per Spark executor and cores per Spark task must match, such that each executor can run 1 task at any given time. For example: if each NodeManager has 4 GPUs, there should be 4 executors running on each NodeManager, and each executor should run 1 task (for a total of 4 tasks running on 4 GPUs). In order to achieve this, you may need to adjust spark.task.cpus and spark.executor.cores to match (both set to 1 by default). Additionally, we recommend adjusting executor-memory to divide host memory evenly amongst the number of GPUs in each NodeManager, such that Spark will schedule as many executors as there are GPUs in each NodeManager.

  • The SPARK_HOME environment variable is assumed to point to the cluster's Apache Spark installation.

Get Application Jar and Dataset

  1. samples.zip and main.py: Please build the files as specified in the guide
  2. Jars: Please download the following jars:
  3. Dataset: https://rapidsai-data.s3.us-east-2.amazonaws.com/spark/mortgage.zip

Place dataset and other files in a local directory. In this example the dataset was unzipped in the xgboost4j_spark_python/data directory, and all other files in the xgboost4j_spark_python/libs directory.

[xgboost4j_spark_python]$ find . -type f | sort
./data/mortgage/csv/test/mortgage_eval_merged.csv
./data/mortgage/csv/train/mortgage_train_merged.csv
./libs/cudf-0.9-cuda10.jar
./libs/main.py
./libs/samples.zip
./libs/xgboost4j_2.11-1.0.0-Beta.jar
./libs/xgboost4j-spark_2.11-1.0.0-Beta.jar

Create a directory in HDFS, and copy:

[xgboost4j_spark_python]$ hadoop fs -mkdir /tmp/xgboost4j_spark_python
[xgboost4j_spark_python]$ hadoop fs -copyFromLocal * /tmp/xgboost4j_spark_python

Verify that the jar and dataset are in HDFS:

[xgboost4j_spark_python]$ hadoop fs -find /tmp/xgboost4j_spark_python | grep "\." | sort
/tmp/xgboost4j_spark_python/data/mortgage/csv/test/mortgage_eval_merged.csv
/tmp/xgboost4j_spark_python/data/mortgage/csv/train/mortgage_train_merged.csv
/tmp/xgboost4j_spark_python/libs/cudf-0.9-cuda10.jar
/tmp/xgboost4j_spark_python/libs/main.py
/tmp/xgboost4j_spark_python/libs/samples.zip
/tmp/xgboost4j_spark_python/libs/xgboost4j_2.11-1.0.0-Beta.jar
/tmp/xgboost4j_spark_python/libs/xgboost4j-spark_2.11-1.0.0-Beta.jar

Launch GPU Mortgage Example

Variables required to run spark-submit command:

# location where data was downloaded
export DATA_PATH=hdfs:/tmp/xgboost4j_spark_python/data

# location for the required libraries
export LIBS_PATH=hdfs:/tmp/xgboost4j_spark_python/libs

# spark deploy mode (see Apache Spark documentation for more information)
export SPARK_DEPLOY_MODE=cluster

# run a single executor for this example to limit the number of spark tasks and
# partitions to 1 as currently this number must match the number of input files
export SPARK_NUM_EXECUTORS=1

# spark driver memory
export SPARK_DRIVER_MEMORY=4g

# spark executor memory
export SPARK_EXECUTOR_MEMORY=8g

# python entrypoint
export SPARK_PYTHON_ENTRYPOINT=${LIBS_PATH}/main.py

# example class to use
export EXAMPLE_CLASS=ai.rapids.spark.examples.mortgage.gpu_main

# additional jars for XGBoost4J example
export SPARK_JARS=${LIBS_PATH}/cudf-0.9-cuda10.jar,${LIBS_PATH}/xgboost4j_2.11-1.0.0-Beta.jar,${LIBS_PATH}/xgboost4j-spark_2.11-1.0.0-Beta.jar

# additional Python files for XGBoost4J example
export SPARK_PY_FILES=${LIBS_PATH}/xgboost4j-spark_2.11-1.0.0-Beta.jar,${LIBS_PATH}/samples.zip

# tree construction algorithm
export TREE_METHOD=gpu_hist

Run spark-submit:

${SPARK_HOME}/bin/spark-submit                                                  \
 --master yarn                                                                  \
 --deploy-mode ${SPARK_DEPLOY_MODE}                                             \
 --num-executors ${SPARK_NUM_EXECUTORS}                                         \
 --driver-memory ${SPARK_DRIVER_MEMORY}                                         \
 --executor-memory ${SPARK_EXECUTOR_MEMORY}                                     \
 --jars ${SPARK_JARS}                                                           \
 --py-files ${SPARK_PY_FILES}                                                   \
 ${SPARK_PYTHON_ENTRYPOINT}                                                     \
 --mainClass=${EXAMPLE_CLASS}                                                   \
 --trainDataPath=${DATA_PATH}/mortgage/csv/train/mortgage_train_merged.csv      \
 --evalDataPath=${DATA_PATH}/mortgage/csv/test/mortgage_eval_merged.csv         \
 --format=csv                                                                   \
 --numWorkers=${SPARK_NUM_EXECUTORS}                                            \
 --treeMethod=${TREE_METHOD}                                                    \
 --numRound=100                                                                 \
 --maxDepth=8

In the stdout driver log, you should see timings* (in seconds), and the RMSE accuracy metric:

----------------------------------------------------------------------------------------------------
Training takes 10.75 seconds

----------------------------------------------------------------------------------------------------
Transformation takes 4.38 seconds

----------------------------------------------------------------------------------------------------
Accuracy is 0.997544753891

Launch CPU Mortgage Example

If you are running this example after running the GPU example above, please set these variables, to set both training and testing to run on the CPU exclusively:

# example class to use
export EXAMPLE_CLASS=ai.rapids.spark.examples.mortgage.cpu_main

# tree construction algorithm
export TREE_METHOD=hist

This is the full variable listing, if you are running the CPU example from scratch:

# location where data was downloaded
export DATA_PATH=hdfs:/tmp/xgboost4j_spark_python/data

# location for the required libraries
export LIBS_PATH=hdfs:/tmp/xgboost4j_spark_python/libs

# spark deploy mode (see Apache Spark documentation for more information)
export SPARK_DEPLOY_MODE=cluster

# run a single executor for this example to limit the number of spark tasks and
# partitions to 1 as currently this number must match the number of input files
export SPARK_NUM_EXECUTORS=1

# spark driver memory
export SPARK_DRIVER_MEMORY=4g

# spark executor memory
export SPARK_EXECUTOR_MEMORY=8g

# python entrypoint
export SPARK_PYTHON_ENTRYPOINT=${LIBS_PATH}/main.py

# example class to use
export EXAMPLE_CLASS=ai.rapids.spark.examples.mortgage.cpu_main

# additional jars for XGBoost4J example
export SPARK_JARS=${LIBS_PATH}/cudf-0.9-cuda10.jar,${LIBS_PATH}/xgboost4j_2.11-1.0.0-Beta.jar,${LIBS_PATH}/xgboost4j-spark_2.11-1.0.0-Beta.jar

# additional Python files for XGBoost4J example
export SPARK_PY_FILES=${LIBS_PATH}/xgboost4j-spark_2.11-1.0.0-Beta.jar,${LIBS_PATH}/samples.zip

# tree construction algorithm
export TREE_METHOD=hist

This is the same command as for the GPU example, repeated for convenience:

${SPARK_HOME}/bin/spark-submit                                                  \
 --master yarn                                                                  \
 --deploy-mode ${SPARK_DEPLOY_MODE}                                             \
 --num-executors ${SPARK_NUM_EXECUTORS}                                         \
 --driver-memory ${SPARK_DRIVER_MEMORY}                                         \
 --executor-memory ${SPARK_EXECUTOR_MEMORY}                                     \
 --jars ${SPARK_JARS}                                                           \
 --py-files ${SPARK_PY_FILES}                                                   \
 ${SPARK_PYTHON_ENTRYPOINT}                                                     \
 --mainClass=${EXAMPLE_CLASS}                                                   \
 --trainDataPath=${DATA_PATH}/mortgage/csv/train/mortgage_train_merged.csv      \
 --evalDataPath=${DATA_PATH}/mortgage/csv/test/mortgage_eval_merged.csv         \
 --format=csv                                                                   \
 --numWorkers=${SPARK_NUM_EXECUTORS}                                            \
 --treeMethod=${TREE_METHOD}                                                    \
 --numRound=100                                                                 \
 --maxDepth=8

In the stdout driver log, you should see timings* (in seconds), and the RMSE accuracy metric:

----------------------------------------------------------------------------------------------------
Training takes 10.76 seconds

----------------------------------------------------------------------------------------------------
Transformation takes 1.25 seconds

----------------------------------------------------------------------------------------------------
Accuracy is 0.998526852335

* The timings in this Getting Started guide are only illustrative. Please see our release announcement for official benchmarks.