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dependabot[bot] 2462e22cd4
Bump com.nvidia:rapids-4-spark_2.12 in /jvm-packages (#9517) 
Bumps com.nvidia:rapids-4-spark_2.12 from 23.08.0 to 23.08.1.

---
updated-dependencies:
- dependency-name: com.nvidia:rapids-4-spark_2.12
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2023-08-29 15:10:16 +08:00
..
xgboost4j
[R] Support multi-class custom objective. (#9526)
2023-08-29 08:27:13 +08:00
xgboost4j-example
Bump version to 2.1. (#9498)
2023-08-18 01:04:04 +08:00
xgboost4j-flink
Bump version to 2.1. (#9498)
2023-08-18 01:04:04 +08:00
xgboost4j-gpu
Bump org.apache.commons:commons-lang3 in /jvm-packages (#9489)
2023-08-20 00:37:15 +08:00
xgboost4j-spark
Bump version to 2.1. (#9498)
2023-08-18 01:04:04 +08:00
xgboost4j-spark-gpu
Bump version to 2.1. (#9498)
2023-08-18 01:04:04 +08:00
xgboost4j-tester
Scala 2.13 support. (#9099)
2023-05-27 19:34:02 +08:00
.gitignore
Scala 2.13 support. (#9099)
2023-05-27 19:34:02 +08:00
checkstyle-suppressions.xml
[jvm-packages] Fixed checkstyle excludes on Windows (#2370)
2017-06-02 10:14:13 -07:00
checkstyle.xml
[jvm-packages] update checkstyle (#7335)
2021-10-18 18:42:01 +08:00
CMakeLists.txt
[jvm-packages][xgboost4j-gpu] Support GPU dataframe and DeviceQuantileDMatrix (#7195)
2021-09-24 14:25:00 +08:00
create_jni.py
[jvm-packages] Fix for space in sys.executable path in create_jni.py (#7358)
2021-10-25 13:45:11 +08:00
pom.xml
Bump com.nvidia:rapids-4-spark_2.12 in /jvm-packages (#9517)
2023-08-29 15:10:16 +08:00
README.md
Remove unmaintained jvm readme and dev scripts. (#9395)
2023-07-18 18:23:43 +08:00
scalastyle-config.xml
[jvm-packages] update checkstyle (#7335)
2021-10-18 18:42:01 +08:00

README.md

XGBoost4J: Distributed XGBoost for Scala/Java

Build Status Documentation Status GitHub license

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 customization.
  • 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.