Documentation | Resources | Release Notes

XGBoost4J is the JVM package of xgboost. It brings all the optimizations and power xgboost into JVM ecosystem.

• Train XGBoost models in scala and java with easy customizations.
• Run distributed xgboost natively on jvm frameworks such as Apache Flink and Apache Spark.

You can find more about XGBoost on Documentation and Resource Page.

XGBoost4J, XGBoost4J-Spark, etc. in maven repository is compiled with g -4.8.5.

Maven

<dependency>
    <groupId>ml.dmlc</groupId>
    <artifactId>xgboost4j_2.12</artifactId>
    <version>latest_version_num</version>
</dependency>
<dependency>
    <groupId>ml.dmlc</groupId>
    <artifactId>xgboost4j-spark_2.12</artifactId>
    <version>latest_version_num</version>
</dependency>

sbt

libraryDependencies   = Seq(
  "ml.dmlc" %% "xgboost4j" % "latest_version_num",
  "ml.dmlc" %% "xgboost4j-spark" % "latest_version_num"
)

For the latest release version number, please check here.

To enable the GPU algorithm (tree_method='gpu_hist'), use artifacts xgboost4j-gpu_2.12 and xgboost4j-spark-gpu_2.12 instead.

First add the following Maven repository hosted by the XGBoost project:

Maven:

<repository>
  <id>XGBoost4J Snapshot Repo</id>
  <name>XGBoost4J Snapshot Repo</name>
  <url>https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/snapshot/</url>
</repository>

sbt:

resolvers  = "XGBoost4J Snapshot Repo" at "https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/snapshot/"

Then add XGBoost4J as a dependency:

Maven

<dependency>
    <groupId>ml.dmlc</groupId>
    <artifactId>xgboost4j_2.12</artifactId>
    <version>latest_version_num-SNAPSHOT</version>
</dependency>
<dependency>
    <groupId>ml.dmlc</groupId>
    <artifactId>xgboost4j-spark_2.12</artifactId>
    <version>latest_version_num-SNAPSHOT</version>
</dependency>

sbt

libraryDependencies   = Seq(
  "ml.dmlc" %% "xgboost4j" % "latest_version_num-SNAPSHOT",
  "ml.dmlc" %% "xgboost4j-spark" % "latest_version_num-SNAPSHOT"
)

For the latest release version number, please check the repository listing.

To enable the GPU algorithm (tree_method='gpu_hist'), use artifacts xgboost4j-gpu_2.12 and xgboost4j-spark-gpu_2.12 instead.

Full code examples for Scala, Java, Apache Spark, and Apache Flink can be found in the examples package.

NOTE on LIBSVM Format:

There is an inconsistent issue between XGBoost4J-Spark and other language bindings of XGBoost.

When users use Spark to load trainingset/testset in LIBSVM format with the following code snippet:

spark.read.format("libsvm").load("trainingset_libsvm")

Spark assumes that the dataset is 1-based indexed. However, when you do prediction with other bindings of XGBoost (e.g. Python API of XGBoost), XGBoost assumes that the dataset is 0-based indexed. It creates a pitfall for the users who train model with Spark but predict with the dataset in the same format in other bindings of XGBoost.

You can build/package xgboost4j locally with the following steps:

Linux:

1. Ensure Docker for Linux is installed.
2. Clone this repo: git clone --recursive https://github.com/dmlc/xgboost.git
3. Run the following command:

• With Tests: ./xgboost/jvm-packages/dev/build-linux.sh
• Skip Tests: ./xgboost/jvm-packages/dev/build-linux.sh --skip-tests

Windows:

1. Ensure Docker for Windows is installed.
2. Clone this repo: git clone --recursive https://github.com/dmlc/xgboost.git
3. Run the following command:

• With Tests: .\xgboost\jvm-packages\dev\build-linux.cmd
• Skip Tests: .\xgboost\jvm-packages\dev\build-linux.cmd --skip-tests

Note: this will create jars for deployment on Linux machines.