Installation Guide

XGBoost provides binary packages for some language bindings. The binary packages support the GPU algorithm (device=cuda:0) on machines with NVIDIA GPUs. Please note that training with multiple GPUs is only supported for Linux platform. See XGBoost GPU Support. Also we have both stable releases and nightly builds, see below for how to install them. For building from source, visit this page.

Stable Release

Python

Pre-built binary wheels are uploaded to PyPI (Python Package Index) for each release. Supported platforms are Linux (x86_64, aarch64), Windows (x86_64) and MacOS (x86_64, Apple Silicon).

# Pip 21.3+ is required
pip install xgboost

You might need to run the command with --user flag or use virtualenv if you run into permission errors.

Note

Windows users need to install Visual C++ Redistributable

XGBoost requires DLLs from Visual C++ Redistributable in order to function, so make sure to install it. Exception: If you have Visual Studio installed, you already have access to necessary libraries and thus don’t need to install Visual C++ Redistributable.

Capabilities of binary wheels for each platform:

Platform	GPU	Multi-Node-Multi-GPU
Linux x86_64	✔	✔
Linux aarch64	✘	✘
MacOS x86_64	✘	✘
MacOS Apple Silicon	✘	✘
Windows	✔	✘

Conda

You may use the Conda packaging manager to install XGBoost:

conda install -c conda-forge py-xgboost

Conda should be able to detect the existence of a GPU on your machine and install the correct variant of XGBoost. If you run into issues, try indicating the variant explicitly:

# CPU only
conda install -c conda-forge py-xgboost-cpu
# Use NVIDIA GPU
conda install -c conda-forge py-xgboost-gpu

Visit the Miniconda website to obtain Conda.

Note

py-xgboost-gpu not available on Windows.

The py-xgboost-gpu is currently not available on Windows. If you are using Windows, please use pip to install XGBoost with GPU support.

R

• From CRAN:

  install.packages("xgboost")
  

  Note

  Using all CPU cores (threads) on Mac OSX

  If you are using Mac OSX, you should first install OpenMP library (libomp) by running

  brew install libomp
  

  and then run install.packages("xgboost"). Without OpenMP, XGBoost will only use a single CPU core, leading to suboptimal training speed.

• We also provide experimental pre-built binary with GPU support. With this binary, you will be able to use the GPU algorithm without building XGBoost from the source. Download the binary package from the Releases page. The file name will be of the form xgboost_r_gpu_[os]_[version].tar.gz, where [os] is either linux or win64. (We build the binaries for 64-bit Linux and Windows.) Then install XGBoost by running:

  # Install dependencies
  R -q -e "install.packages(c('data.table', 'jsonlite'))"
  # Install XGBoost
  R CMD INSTALL ./xgboost_r_gpu_linux.tar.gz
  

JVM

• XGBoost4j/XGBoost4j-Spark

Maven

<properties>
  ...
  <!-- Specify Scala version in package name -->
  <scala.binary.version>2.12</scala.binary.version>
</properties>

<dependencies>
  ...
  <dependency>
      <groupId>ml.dmlc</groupId>
      <artifactId>xgboost4j_${scala.binary.version}</artifactId>
      <version>latest_version_num</version>
  </dependency>
  <dependency>
      <groupId>ml.dmlc</groupId>
      <artifactId>xgboost4j-spark_${scala.binary.version}</artifactId>
      <version>latest_version_num</version>
  </dependency>
</dependencies>

sbt

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

• XGBoost4j-GPU/XGBoost4j-Spark-GPU

Maven

<properties>
  ...
  <!-- Specify Scala version in package name -->
  <scala.binary.version>2.12</scala.binary.version>
</properties>

<dependencies>
  ...
  <dependency>
      <groupId>ml.dmlc</groupId>
      <artifactId>xgboost4j-gpu_${scala.binary.version}</artifactId>
      <version>latest_version_num</version>
  </dependency>
  <dependency>
      <groupId>ml.dmlc</groupId>
      <artifactId>xgboost4j-spark-gpu_${scala.binary.version}</artifactId>
      <version>latest_version_num</version>
  </dependency>
</dependencies>

sbt

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

This will check out the latest stable version from the Maven Central.

For the latest release version number, please check release page.

To enable the GPU algorithm (device='cuda'), use artifacts xgboost4j-gpu_2.12 and xgboost4j-spark-gpu_2.12 instead (note the gpu suffix).

Note

Windows not supported in the JVM package

Currently, XGBoost4J-Spark does not support Windows platform, as the distributed training algorithm is inoperational for Windows. Please use Linux or MacOS.

Nightly Build

Python

Nightly builds are available. You can go to this page, find the wheel with the commit ID you want and install it with pip:

pip install <url to the wheel>

The capability of Python pre-built wheel is the same as stable release.

R

Other than standard CRAN installation, we also provide experimental pre-built binary on with GPU support. You can go to this page, Find the commit ID you want to install and then locate the file xgboost_r_gpu_[os]_[commit].tar.gz, where [os] is either linux or win64. (We build the binaries for 64-bit Linux and Windows.) Download it and run the following commands:

# Install dependencies
R -q -e "install.packages(c('data.table', 'jsonlite', 'remotes'))"
# Install XGBoost
R CMD INSTALL ./xgboost_r_gpu_linux.tar.gz

JVM

• XGBoost4j/XGBoost4j-Spark

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

<properties>
  ...
  <!-- Specify Scala version in package name -->
  <scala.binary.version>2.12</scala.binary.version>
</properties>

<dependencies>
  ...
  <dependency>
      <groupId>ml.dmlc</groupId>
      <artifactId>xgboost4j_${scala.binary.version}</artifactId>
      <version>latest_version_num-SNAPSHOT</version>
  </dependency>
  <dependency>
      <groupId>ml.dmlc</groupId>
      <artifactId>xgboost4j-spark_${scala.binary.version}</artifactId>
      <version>latest_version_num-SNAPSHOT</version>
  </dependency>
</dependencies>

sbt

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

• XGBoost4j-GPU/XGBoost4j-Spark-GPU

maven

<properties>
  ...
  <!-- Specify Scala version in package name -->
  <scala.binary.version>2.12</scala.binary.version>
</properties>

<dependencies>
  ...
  <dependency>
      <groupId>ml.dmlc</groupId>
      <artifactId>xgboost4j-gpu_${scala.binary.version}</artifactId>
      <version>latest_version_num-SNAPSHOT</version>
  </dependency>
  <dependency>
      <groupId>ml.dmlc</groupId>
      <artifactId>xgboost4j-spark-gpu_${scala.binary.version}</artifactId>
      <version>latest_version_num-SNAPSHOT</version>
  </dependency>
</dependencies>

sbt

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

Look up the version field in pom.xml to get the correct version number.

The SNAPSHOT JARs are hosted by the XGBoost project. Every commit in the master branch will automatically trigger generation of a new SNAPSHOT JAR. You can control how often Maven should upgrade your SNAPSHOT installation by specifying updatePolicy. See here for details.

You can browse the file listing of the Maven repository at https://s3-us-west-2.amazonaws.com/xgboost-maven-repo/list.html.

To enable the GPU algorithm (device='cuda'), use artifacts xgboost4j-gpu_2.12 and xgboost4j-spark-gpu_2.12 instead (note the gpu suffix).