Building From Source

This page gives instructions on how to build and install XGBoost from the source code on various systems. If the instructions do not work for you, please feel free to ask questions at GitHub.

Note

Pre-built binary is available: now with GPU support

Consider installing XGBoost from a pre-built binary, to avoid the trouble of building XGBoost from the source. Checkout Installation Guide.

Obtaining the Source Code

To obtain the development repository of XGBoost, one needs to use git. XGBoost uses Git submodules to manage dependencies. So when you clone the repo, remember to specify --recursive option:

git clone --recursive https://github.com/dmlc/xgboost

Building the Shared Library

This section describes the procedure to build the shared library and CLI interface independently. For building language specific package, see corresponding sections in this document.

  • On Linux and other UNIX-like systems, the target library is libxgboost.so

  • On MacOS, the target library is libxgboost.dylib

  • On Windows the target library is xgboost.dll

This shared library is used by different language bindings (with some additions depending on the binding you choose). The minimal building requirement is

  • A recent C++ compiler supporting C++17. We use gcc, clang, and MSVC for daily testing. Mingw is only used for the R package and has limited features.

  • CMake 3.18 or higher.

For a list of CMake options like GPU support, see #-- Options in CMakeLists.txt on top level of source tree. We use ninja for build in this document, specified via the CMake flag -GNinja. If you prefer other build tools like make or Visual Studio 17 2022, please change the corresponding CMake flags. Consult the CMake generator document when needed.

Running CMake and build

After obtaining the source code, one builds XGBoost by running CMake:

cd xgboost
cmake -B build -S . -DCMAKE_BUILD_TYPE=RelWithDebInfo -GNinja
cd build && ninja

The same command applies for both Unix-like systems and Windows. After running the build, one should see a shared object under the xgboost/lib directory.

  • Building on MacOS

    On MacOS, one needs to obtain libomp from Homebrew first:

    brew install libomp

  • Visual Studio

    The latest Visual Studio has builtin support for CMake projects. If you prefer using an IDE over the command line, you can use the open with visual studio option in the right-click menu under the xgboost source directory. Consult the VS document for more info.

Building with GPU support

XGBoost can be built with GPU support for both Linux and Windows using CMake. See Building R package with GPU support for special instructions for R.

An up-to-date version of the CUDA toolkit is required.

Note

Checking your compiler version

CUDA is really picky about supported compilers, a table for the compatible compilers for the latest CUDA version on Linux can be seen here.

Some distros package a compatible gcc version with CUDA. If you run into compiler errors with nvcc, try specifying the correct compiler with -DCMAKE_CXX_COMPILER=/path/to/correct/g++ -DCMAKE_C_COMPILER=/path/to/correct/gcc. On Arch Linux, for example, both binaries can be found under /opt/cuda/bin/. In addition, the CMAKE_CUDA_HOST_COMPILER parameter can be useful.

From the command line on Linux starting from the XGBoost directory, add the USE_CUDA flag:

cmake -B build -S . -DUSE_CUDA=ON -GNinja
cd build && ninja

To speed up compilation, the compute version specific to your GPU could be passed to cmake as, e.g., -DCMAKE_CUDA_ARCHITECTURES=75. A quick explanation and numbers for some architectures can be found in this page.

  • Faster distributed GPU training with NCCL

    By default, distributed GPU training is enabled with the option USE_NCCL=ON. Distributed GPU training depends on NCCL2, available at this link. Since NCCL2 is only available for Linux machines, Distributed GPU training is available only for Linux.

    cmake -B build -S . -DUSE_CUDA=ON -DUSE_NCCL=ON -DNCCL_ROOT=/path/to/nccl2 -GNinja
cd build && ninja

    Some additional flags are available for NCCL, BUILD_WITH_SHARED_NCCL enables building XGBoost with NCCL as a shared library, while USE_DLOPEN_NCCL enables XGBoost to load NCCL at runtime using dlopen.

Federated Learning

The federated learning plugin requires grpc and protobuf. To install grpc, refer to the installation guide from the gRPC website. Alternatively, one can use the libgrpc and the protobuf package from conda forge if conda is available. After obtaining the required dependencies, enable the flag: -DPLUGIN_FEDERATED=ON when running CMake. Please note that only Linux is supported for the federated plugin.

cmake -B build -S . -DPLUGIN_FEDERATED=ON -GNinja
cd build && ninja

Building Python Package from Source

The Python package is located at python-package/.

Building Python Package with Default Toolchains

There are several ways to build and install the package from source:

  1. Build C++ core with CMake first

You can first build C++ library using CMake as described in Building the Shared Library. After compilation, a shared library will appear in lib/ directory. On Linux distributions, the shared library is lib/libxgboost.so. The install script pip install . will reuse the shared library instead of compiling it from scratch, making it quite fast to run.

$ cd python-package/
$ pip install .  # Will re-use lib/libxgboost.so

  1. Install the Python package directly

If the shared object is not present, the Python project setup script will try to run the CMake build command automatically. Navigate to the python-package/ directory and install the Python package by running:

$ cd python-package/
$ pip install -v . # Builds the shared object automatically.

which will compile XGBoost’s native (C++) code using default CMake flags. To enable additional compilation options, pass corresponding --config-settings:

$ pip install -v . --config-settings use_cuda=True --config-settings use_nccl=True

Use Pip 22.1 or later to use --config-settings option.

Here are the available options for --config-settings:

@dataclasses.dataclass
class BuildConfiguration:  # pylint: disable=R0902
    """Configurations use when building libxgboost"""

    # Whether to hide C++ symbols in libxgboost.so
    hide_cxx_symbols: bool = True
    # Whether to enable OpenMP
    use_openmp: bool = True
    # Whether to enable CUDA
    use_cuda: bool = False
    # Whether to enable NCCL
    use_nccl: bool = False
    # Whether to load nccl dynamically
    use_dlopen_nccl: bool = False
    # Whether to enable federated learning
    plugin_federated: bool = False
    # Whether to enable rmm support
    plugin_rmm: bool = False
    # Special option: See explanation below
    use_system_libxgboost: bool = False

use_system_libxgboost is a special option. See Item 4 below for detailed description.

Note

Verbose flag recommended

As pip install . will build C++ code, it will take a while to complete. To ensure that the build is progressing successfully, we suggest that you add the verbose flag (-v) when invoking pip install.

  1. Editable installation

To further enable rapid development and iteration, we provide an editable installation. In an editable installation, the installed package is simply a symbolic link to your working copy of the XGBoost source code. So every changes you make to your source directory will be immediately visible to the Python interpreter. To install XGBoost as editable installation, first build the shared library as previously described in Running CMake and build, then install the Python package with the -e flag:

# Build shared library libxgboost.so
cmake -B build -S . -GNinja
cd build && ninja
# Install as editable installation
cd ../python-package
pip install -e .

  1. Reuse the libxgboost.so on system path.

This option is useful for package managers that wish to separately package libxgboost.so and the XGBoost Python package. For example, Conda publishes libxgboost (for the shared library) and py-xgboost (for the Python package).

To use this option, first make sure that libxgboost.so exists in the system library path:

import sys
import pathlib
libpath = pathlib.Path(sys.base_prefix).joinpath("lib", "libxgboost.so")
assert libpath.exists()

Then pass use_system_libxgboost=True option to pip install:

cd python-package
pip install . --config-settings use_system_libxgboost=True

Note

See Notes on packaging XGBoost’s Python package for instructions on packaging and distributing XGBoost as Python distributions.

Building R Package From Source

By default, the package installed by running install.packages is built from source using the package from CRAN. Here we list some other options for installing development version.

Installing the development version (Linux / Mac OSX)

Make sure you have installed git and a recent C++ compiler supporting C++11 (See above sections for requirements of building C++ core).

Due to the use of git-submodules, remotes::install_github() cannot be used to install the latest version of R package. Thus, one has to run git to check out the code first, see Obtaining the Source Code on how to initialize the git repository for XGBoost. The simplest way to install the R package after obtaining the source code is:

cd R-package
R CMD INSTALL .

Use the environment variable MAKEFLAGS=-j$(nproc) if you want to speedup the build. As an alternative, the package can also be loaded through devtools::load_all() from the same subfolder R-package in the repository’s root, and by extension, can be installed through RStudio’s build panel if one adds that folder R-package as an R package project in the RStudio IDE.

library(devtools)
devtools::load_all(path = "/path/to/xgboost/R-package")

On Linux, if you want to use the CMake build for greater flexibility around compile flags, the earlier snippet can be replaced by:

cmake -B build -S . -DR_LIB=ON -GNinja
cd build && ninja install

Warning

MSVC is not supported for the R package as it has difficulty handling R C headers. CMake build is not supported either.

Note in this case that cmake will not take configurations from your regular Makevars file (if you have such a file under ~/.R/Makevars) - instead, custom configurations such as compilers to use and flags need to be set through CMake variables like -DCMAKE_CXX_COMPILER.

Building R package with GPU support

The procedure and requirements are similar as in Building with GPU support, so make sure to read it first.

On Linux, starting from the XGBoost directory type:

cmake -B build -S . -DUSE_CUDA=ON -DR_LIB=ON
cmake --build build --target install -j$(nproc)

When default target is used, an R package shared library would be built in the build area. The install target, in addition, assembles the package files with this shared library under build/R-package and runs R CMD INSTALL.

Building JVM Packages

Building XGBoost4J using Maven requires Maven 3 or newer, Java 7+ and CMake 3.18+ for compiling Java code as well as the Java Native Interface (JNI) bindings. In addition, a Python script is used during configuration, make sure the command python is available on your system path (some distros use the name python3 instead of python).

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

XGBoost4J-Spark requires Apache Spark 2.3+

XGBoost4J-Spark now requires Apache Spark 3.4+. Latest versions of XGBoost4J-Spark uses facilities of org.apache.spark.ml.param.shared extensively to provide for a tight integration with Spark MLLIB framework, and these facilities are not fully available on earlier versions of Spark.

Also, make sure to install Spark directly from Apache website. Upstream XGBoost is not guaranteed to work with third-party distributions of Spark, such as Cloudera Spark. Consult appropriate third parties to obtain their distribution of XGBoost.

Additional System-dependent Features

  • OpenMP on MacOS: See Running CMake and build for installing openmp. The flag -mvn -Duse.openmp=OFF can be used to disable OpenMP support.

  • GPU support can be enabled by passing an additional flag to maven mvn -Duse.cuda=ON install. See Building with GPU support for more info.

Building the Documentation

XGBoost uses Sphinx for documentation. To build it locally, you need a installed XGBoost with all its dependencies along with:

  • System dependencies

    • git

    • graphviz

  • Python dependencies

    Checkout the requirements.txt file under doc/

Under xgboost/doc directory, run make <format> with <format> replaced by the format you want. For a list of supported formats, run make help under the same directory.