You have found the XGBoost JVM Package!
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
Spark 2.0 Required
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
)
<dependency>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost4j</artifactId>
<version>latest_version_num</version>
</dependency>
"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
.
You need to add GitHub as repo:
<repository>
<id>GitHub Repo</id>
<name>GitHub Repo</name>
<url>https://raw.githubusercontent.com/CodingCat/xgboost/maven-repo/</url>
</repository>
resolvers += "GitHub Repo" at "https://raw.githubusercontent.com/CodingCat/xgboost/maven-repo/"
Then add dependency as following:
<dependency>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost4j</artifactId>
<version>latest_version_num</version>
</dependency>
"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.
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.