8943eb4314
* [jvm-packages] add gpu_hist tree method * change updater hist to grow_quantile_histmaker * add gpu scheduling * pass correct parameters to xgboost library * remove debug info * add use.cuda for pom * add CI for gpu_hist for jvm * add gpu unit tests * use gpu node to build jvm * use nvidia-docker * Add CLI interface to create_jni.py using argparse Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
41 lines
1.2 KiB
Bash
Executable File
41 lines
1.2 KiB
Bash
Executable File
#!/bin/bash
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set -e
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set -x
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nvidia-smi
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ls /usr/local/
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# Initialize local Maven repository
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./tests/ci_build/initialize_maven.sh
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# Get version number of XGBoost4J and other auxiliary information
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cd jvm-packages
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xgboost4j_version=$(mvn help:evaluate -Dexpression=project.version -q -DforceStdout)
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scala_binary_version=$(mvn help:evaluate -Dexpression=scala.binary.version -q -DforceStdout)
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python3 xgboost4j-tester/get_iris.py
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xgb_jars="./xgboost4j/target/xgboost4j_${scala_binary_version}-${xgboost4j_version}.jar,./xgboost4j-spark/target/xgboost4j-spark_${scala_binary_version}-${xgboost4j_version}.jar"
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example_jar="./xgboost4j-example/target/xgboost4j-example_${scala_binary_version}-${xgboost4j_version}.jar"
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echo "Run SparkTraining locally ... "
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spark-submit \
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--master 'local[1]' \
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--class ml.dmlc.xgboost4j.scala.example.spark.SparkTraining \
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--jars $xgb_jars \
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$example_jar \
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${PWD}/iris.csv gpu \
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echo "Run SparkMLlibPipeline locally ... "
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spark-submit \
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--master 'local[1]' \
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--class ml.dmlc.xgboost4j.scala.example.spark.SparkMLlibPipeline \
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--jars $xgb_jars \
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$example_jar \
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${PWD}/iris.csv ${PWD}/native_model ${PWD}/pipeline_model gpu \
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set +x
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set +e
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