# XGBoost JVM Package¶

You have found the XGBoost JVM Package!

## Installation¶

### 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:

<dependency>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost4j</artifactId>
<version>latest_source_version_num</version>
</dependency>


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"


If you want to use XGBoost4J-Spark, replace xgboost4j with xgboost4j-spark.

Note

XGBoost4J-Spark requires Apache Spark 2.3+

XGBoost4J-Spark now requires Apache Spark 2.3+. Latest versions of XGBoost4J-Spark uses facilities of org.apache.spark.ml.param.shared extensively to provide for a tight integration with Spark MLLIB framework, and these facilities are not fully available on earlier versions of Spark.

Also, make sure to install Spark directly from Apache website. Upstream XGBoost is not guaranteed to work with third-party distributions of Spark, such as Cloudera Spark. Consult appropriate third parties to obtain their distribution of XGBoost.

### Installation from maven repo¶

#### Access release version¶

maven
<dependency>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost4j</artifactId>
<version>latest_version_num</version>
</dependency>

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


This will checkout the latest stable version from the Maven Central.

For the latest release version number, please check here.

if you want to use XGBoost4J-Spark, replace xgboost4j with xgboost4j-spark.

#### Access SNAPSHOT version¶

You need to add GitHub as repo:

maven
<repository>
<id>GitHub Repo</id>
<name>GitHub Repo</name>
<url>https://raw.githubusercontent.com/CodingCat/xgboost/maven-repo/</url>
</repository>

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


maven
<dependency>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost4j</artifactId>
<version>latest_version_num</version>
</dependency>

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


For the latest release version number, please check here.

Note

Windows not supported by published JARs

The published JARs from the Maven Central and GitHub currently only supports Linux and MacOS. Windows users should consider building XGBoost4J / XGBoost4J-Spark from the source. Alternatively, checkout pre-built JARs from criteo-forks/xgboost-jars.

#### 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

CONFIG["USE_OPENMP"] = "OFF"


in order to get the benefit of multi-threading.