XGBoost JVM Package

Build Status GitHub license

You have found the XGBoost JVM Package!


Installation from source

Building XGBoost4J using Maven requires Maven 3 or newer, Java 7+ and CMake 3.2+ for compiling the JNI bindings.

Before you install XGBoost4J, you need to define environment variable JAVA_HOME as your JDK directory to ensure that your compiler can find jni.h correctly, since XGBoost4J relies on JNI to implement the interaction between the JVM and native libraries.

After your JAVA_HOME is defined correctly, it is as simple as run mvn package under jvm-packages directory to install XGBoost4J. You can also skip the tests by running mvn -DskipTests=true package, if you are sure about the correctness of your local setup.

To publish the artifacts to your local maven repository, run

mvn install

Or, if you would like to skip tests, run

mvn -DskipTests install

This command will publish the xgboost binaries, the compiled java classes as well as the java sources to your local repository. Then you can use XGBoost4J in your Java projects by including the following dependency in pom.xml:



For sbt, please add the repository and dependency in build.sbt as following:


resolvers += "Local Maven Repository" at "file://"+Path.userHome.absolutePath+"/.m2/repository"

"ml.dmlc" % "xgboost4j" % "latest_source_version_num"

Installation from repo

You need to add github as repo:


  <id>GitHub Repo</id>
  <name>GitHub Repo</name>


resolvers += "GitHub Repo" at "https://raw.githubusercontent.com/CodingCat/xgboost/maven-repo/"

the add dependency as following:




 "ml.dmlc" % "xgboost4j" % "latest_version_num"

For the latest release version number, please check here.

After integrating with Dataframe/Dataset APIs of Spark 2.0, XGBoost4J-Spark only supports compile with Spark 2.x. You can build XGBoost4J-Spark as a component of XGBoost4J by running mvn package, and you can specify the version of spark with mvn -Dspark.version=2.0.0 package. (To continue working with Spark 1.x, the users are supposed to update pom.xml by modifying the properties like spark.version, scala.version, and scala.binary.version. Users also need to change the implementation by replacing SparkSession with SQLContext and the type of API parameters from Dataset[_] to Dataframe)

Enabling OpenMP for Mac OS

If you are on Mac OS and using a compiler that supports OpenMP, you need to go to the file xgboost/jvm-packages/create_jni.py and comment out the line


in order to get the benefit of multi-threading.