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 :doc:`gpu/index`. Also we have both stable releases and nightly builds, see below for how to install them. For building from source, visit :doc:`this page </build>`.

Contents

Stable Release
Nightly Build
- Python
- R
- JVM

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

Parts of the Python package now require glibc 2.28+

Starting from 2.1.0, XGBoost Python package will be distributed in two variants:

manylinux_2_28: for recent Linux distros with glibc 2.28 or newer. This variant comes with all features enabled.
manylinux2014: for old Linux distros with glibc older than 2.28. This variant does not support GPU algorithms or federated learning.

The pip package manager will automatically choose the correct variant depending on your system.

Starting from May 31, 2025, we will stop distributing the manylinux2014 variant and exclusively distribute the manylinux_2_28 variant. We made this decision so that our CI/CD pipeline won't have depend on software components that reached end-of-life (such as CentOS 7). We strongly encourage everyone to migrate to recent Linux distros in order to use future versions of XGBoost.

Note. If you want to use GPU algorithms or federated learning on an older Linux distro, you have two alternatives:

Upgrade to a recent Linux distro with glibc 2.28+. OR
Build XGBoost from the source.

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	✔	✘

Minimal installation (CPU-only)

The default installation with pip will install the full XGBoost package, including the support for the GPU algorithms and federated learning.

You may choose to reduce the size of the installed package and save the disk space, by opting to install xgboost-cpu instead:

pip install xgboost-cpu

The xgboost-cpu variant will have drastically smaller disk footprint, but does not provide some features, such as the GPU algorithms and federated learning.

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

To force the installation of the GPU variant on a machine that does not have an NVIDIA GPU, use environment variable CONDA_OVERRIDE_CUDA, as described in "Managing Virtual Packages" in the conda docs.

export CONDA_OVERRIDE_CUDA="12.5"
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-Spark

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

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

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

XGBoost4j-Spark-GPU

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

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

libraryDependencies ++= Seq(
  "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-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

<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>

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

Then add XGBoost4J-Spark as a dependency:

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

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

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

XGBoost4j-Spark-GPU

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

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

libraryDependencies ++= Seq(
  "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).

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Installation Guide

Stable Release

Python

Minimal installation (CPU-only)

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R

JVM

Nightly Build

Python

R

JVM

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Installation Guide