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[BLOCKING] [jvm-packages] add gpu_hist and enable gpu scheduling (#5171)

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* [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>
This commit is contained in:
Bobby Wang
2020-07-27 12:53:24 +08:00
committed by GitHub
parent 6347fa1c2e
commit 8943eb4314
18 changed files with 543 additions and 123 deletions
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12
jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkMLlibPipeline.scala
View File

@@ -31,8 +31,9 @@ object SparkMLlibPipeline {
def main(args: Array[String]): Unit = {
if (args.length != 3) {
println("Usage: SparkMLlibPipeline input_path native_model_path pipeline_model_path")
if (args.length != 3 && args.length != 4) {
println("Usage: SparkMLlibPipeline input_path native_model_path pipeline_model_path " +
"[cpu|gpu]")
sys.exit(1)
}
@@ -40,6 +41,10 @@ object SparkMLlibPipeline {
val nativeModelPath = args(1)
val pipelineModelPath = args(2)
val (treeMethod, numWorkers) = if (args.length == 4 && args(3) == "gpu") {
("gpu_hist", 1)
} else ("auto", 2)
val spark = SparkSession
.builder()
.appName("XGBoost4J-Spark Pipeline Example")
@@ -76,7 +81,8 @@ object SparkMLlibPipeline {
"objective" -> "multi:softprob",
"num_class" -> 3,
"num_round" -> 100,
"num_workers" -> 2
"num_workers" -> numWorkers,
"tree_method" -> treeMethod
)
)
booster.setFeaturesCol("features")

10
jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkTraining.scala
View File

@@ -28,9 +28,14 @@ object SparkTraining {
def main(args: Array[String]): Unit = {
if (args.length < 1) {
// scalastyle:off
println("Usage: program input_path")
println("Usage: program input_path [cpu|gpu]")
sys.exit(1)
}
val (treeMethod, numWorkers) = if (args.length == 2 && args(1) == "gpu") {
("gpu_hist", 1)
} else ("auto", 2)
val spark = SparkSession.builder().getOrCreate()
val inputPath = args(0)
val schema = new StructType(Array(
@@ -68,7 +73,8 @@ object SparkTraining {
"objective" -> "multi:softprob",
"num_class" -> 3,
"num_round" -> 100,
"num_workers" -> 2,
"num_workers" -> numWorkers,
"tree_method" -> treeMethod,
"eval_sets" -> Map("eval1" -> eval1, "eval2" -> eval2))
val xgbClassifier = new XGBoostClassifier(xgbParam).
setFeaturesCol("features").