Installation Guide


Pre-built binary wheel for Python

If you are planning to use Python, consider installing XGBoost from a pre-built binary wheel, available from Python Package Index (PyPI). You may download and install it by running

# Ensure that you are downloading one of the following:
#   * xgboost-{version}-py2.py3-none-manylinux1_x86_64.whl
#   * xgboost-{version}-py2.py3-none-win_amd64.whl
pip3 install xgboost
  • The binary wheel will support GPU algorithms (gpu_hist) on machines with NVIDIA GPUs. Please note that training with multiple GPUs is only supported for Linux platform. See XGBoost GPU Support.

  • Currently, we provide binary wheels for 64-bit Linux and Windows.

  • Nightly builds are available. You can now run

    pip install[version]+[commithash]-py2.py3-none-manylinux1_x86_64.whl

    to install the nightly build with the given commit hash. See this page to see the list of all nightly builds.

Building XGBoost from source

This page gives instructions on how to build and install XGBoost from scratch on various systems.


Use of Git submodules

XGBoost uses Git submodules to manage dependencies. So when you clone the repo, remember to specify --recursive option:

git clone --recursive

For windows users who use github tools, you can open the git shell and type the following command:

git submodule init
git submodule update

Please refer to Trouble Shooting section first if you have any problem during installation. If the instructions do not work for you, please feel free to ask questions at the user forum.


Building the Shared Library

Our goal is to build the shared library:

  • On Linux/OSX the target library is

  • 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). For building language specific package, see corresponding sections in this document. The minimal building requirement is

  • A recent C++ compiler supporting C++11 (g++-5.0 or higher)

  • CMake 3.12 or higher.

For a list of CMake options, see #-- Options in CMakeLists.txt on top level of source tree.

Building on Linux distributions

On Ubuntu, one builds XGBoost by running CMake:

git clone --recursive
cd xgboost
mkdir build
cd build
cmake ..
make -j$(nproc)

Building on OSX

Install with pip: simple method

First, obtain the OpenMP library (libomp) with Homebrew ( to enable multi-threading (i.e. using multiple CPU threads for training):

brew install libomp

Then install XGBoost with pip:

pip3 install xgboost

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

Build from the source code - advanced method

Obtain libomp from Homebrew:

brew install libomp

Now clone the repository:

git clone --recursive

Create the build/ directory and invoke CMake. After invoking CMake, you can build XGBoost with make:

mkdir build
cd build
cmake ..
make -j4

You may now continue to Python Package Installation.

Building on Windows

You need to first clone the XGBoost repo with --recursive option, to clone the submodules. We recommend you use Git for Windows, as it comes with a standard Bash shell. This will highly ease the installation process.

git submodule init
git submodule update

XGBoost support compilation with Microsoft Visual Studio and MinGW.

Compile XGBoost with Microsoft Visual Studio

To build with Visual Studio, we will need CMake. Make sure to install a recent version of CMake. Then run the following from the root of the XGBoost directory:

mkdir build
cd build
cmake .. -G"Visual Studio 14 2015 Win64"
# for VS15: cmake .. -G"Visual Studio 15 2017" -A x64
# for VS16: cmake .. -G"Visual Studio 16 2019" -A x64
cmake --build . --config Release

This specifies an out of source build using the Visual Studio 64 bit generator. (Change the -G option appropriately if you have a different version of Visual Studio installed.)

After the build process successfully ends, you will find a xgboost.dll library file inside ./lib/ folder.

Building with GPU support

XGBoost can be built with GPU support for both Linux and Windows using CMake. GPU support works with the Python package as well as the CLI version. See Installing R package with GPU support for special instructions for R.

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

From the command line on Linux starting from the XGBoost directory:

mkdir build
cd build
cmake .. -DUSE_CUDA=ON
make -j4


Enabling distributed GPU training

By default, distributed GPU training is disabled and only a single GPU will be used. To enable distributed GPU training, set 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.

mkdir build
cd build
cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON -DNCCL_ROOT=/path/to/nccl2
make -j4

On Windows, run CMake as follows:

mkdir build
cd build
cmake .. -G"Visual Studio 14 2015 Win64" -DUSE_CUDA=ON

(Change the -G option appropriately if you have a different version of Visual Studio installed.)


Visual Studio 2017 Win64 Generator may not work

Choosing the Visual Studio 2017 generator may cause compilation failure. When it happens, specify the 2015 compiler by adding the -T option:

cmake .. -G"Visual Studio 15 2017 Win64" -T v140,cuda=8.0 -DUSE_CUDA=ON

To speed up compilation, the compute version specific to your GPU could be passed to cmake as, e.g., -DGPU_COMPUTE_VER=50. The above cmake configuration run will create an xgboost.sln solution file in the build directory. Build this solution in release mode as a x64 build, either from Visual studio or from command line:

cmake --build . --target xgboost --config Release

To speed up compilation, run multiple jobs in parallel by appending option -- /MP.


It’s only used for creating shorthands for running linters, performing packaging tasks etc. So the remaining makefiles are legacy.

Python Package Installation

The Python package is located at python-package/. There are several ways to build and install the package from source:

  1. Use Python setuptools directly

The XGBoost Python package supports most of the setuptools commands, here is a list of tested commands:

python install  # Install the XGBoost to your current Python environment.
python build    # Build the Python package.
python build_ext # Build only the C++ core.
python sdist     # Create a source distribution
python bdist     # Create a binary distribution
python bdist_wheel # Create a binary distribution with wheel format

Running python install will compile XGBoost using default CMake flags. For passing additional compilation options, append the flags to the command. For example, to enable CUDA acceleration and NCCL (distributed GPU) support:

python install --use-cuda --use-nccl

Please refer to for a complete list of avaiable options. Some other options used for development are only available for using CMake directly. See next section on how to use CMake with setuptools manually.

You can install the created distribution packages using pip. For example, after running sdist setuptools command, a tar ball similar to xgboost-1.0.0.tar.gz will be created under the dist directory. Then you can install it by invoking the following command under dist directory:

# under python-package directory
cd dist
pip install ./xgboost-1.0.0.tar.gz

For details about these commands, please refer to the official document of setuptools, or just Google “how to install Python package from source”. XGBoost Python package follows the general convention. Setuptools is usually available with your Python distribution, if not you can install it via system command. For example on Debian or Ubuntu:

sudo apt-get install python-setuptools

For cleaning up the directory after running above commands, python clean is not sufficient. After copying out the build result, simply running git clean -xdf under python-package is an efficient way to remove generated cache files. If you find weird behaviors in Python build or running linter, it might be caused by those cached files.

For using develop command (editable installation), see next section.

python develop   # Create a editable installation.
pip install -e .          # Same as above, but carried out by pip.
  1. Build C++ core with CMake first

This is mostly for C++ developers who don’t want to go through the hooks in Python setuptools. You can build C++ library directly using CMake as described in above sections. After compilation, a shared object (or called dynamic linked library, jargon depending on your platform) will appear in XGBoost’s source tree under lib/ directory. On Linux distributions it’s lib/ From there all Python setuptools commands will reuse that shared object instead of compiling it again. This is especially convenient if you are using the editable installation, where the installed package is simply a link to the source tree. We can perform rapid testing during development. Here is a simple bash script does that:

# Under xgboost source tree.
mkdir build
cd build
cmake ..
make -j$(nproc)
cd ../python-package
pip install -e .  # or equivalently python develop

Building XGBoost library for Python for Windows with MinGW-w64 (Advanced)

Windows versions of Python are built with Microsoft Visual Studio. Usually Python binary modules are built with the same compiler the interpreter is built with. However, you may not be able to use Visual Studio, for following reasons:

  1. VS is proprietary and commercial software. Microsoft provides a freeware “Community” edition, but its licensing terms impose restrictions as to where and how it can be used.

  2. Visual Studio contains telemetry, as documented in Microsoft Visual Studio Licensing Terms. Running software with telemetry may be against the policy of your organization.

So you may want to build XGBoost with GCC own your own risk. This presents some difficulties because MSVC uses Microsoft runtime and MinGW-w64 uses own runtime, and the runtimes have different incompatible memory allocators. But in fact this setup is usable if you know how to deal with it. Here is some experience.

  1. The Python interpreter will crash on exit if XGBoost was used. This is usually not a big issue.

  2. -O3 is OK.

  3. -mtune=native is also OK.

  4. Don’t use -march=native gcc flag. Using it causes the Python interpreter to crash if the DLL was actually used.

  5. You may need to provide the lib with the runtime libs. If mingw32/bin is not in PATH, build a wheel (python bdist_wheel), open it with an archiver and put the needed dlls to the directory where xgboost.dll is situated. Then you can install the wheel with pip.

R Package Installation

Installing pre-packaged version

You can install XGBoost from CRAN just like any other R package:



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.

Installing the development version

Make sure you have installed git and a recent C++ compiler supporting C++11 (See above sections for requirements of building C++ core). On Windows, Rtools must be installed, and its bin directory has to be added to PATH during the installation.

Due to the use of git-submodules, devtools::install_github can no longer be used to install the latest version of R package. Thus, one has to run git to check out the code first:

git clone --recursive
cd xgboost
git submodule init
git submodule update
mkdir build
cd build
cmake .. -DR_LIB=ON
make -j$(nproc)
make install

If all fails, try Building the shared library to see whether a problem is specific to R package or not. Notice that the R package is installed by CMake directly.

Installing 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:

mkdir build
cd build
make 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.

On Windows, CMake with Visual C++ Build Tools (or Visual Studio) has to be used to build an R package with GPU support. Rtools must also be installed (perhaps, some other MinGW distributions with gendef.exe and dlltool.exe would work, but that was not tested).

mkdir build
cd build
cmake .. -G"Visual Studio 14 2015 Win64" -DUSE_CUDA=ON -DR_LIB=ON
cmake --build . --target install --config Release

When --target xgboost is used, an R package DLL would be built under build/Release. The --target install, in addition, assembles the package files with this dll under build/R-package and runs R CMD INSTALL.

If cmake can’t find your R during the configuration step, you might provide the location of its executable to cmake like this: -DLIBR_EXECUTABLE="C:/Program Files/R/R-3.4.1/bin/x64/R.exe".

If on Windows you get a “permission denied” error when trying to write to …Program Files/R/… during the package installation, create a .Rprofile file in your personal home directory (if you don’t already have one in there), and add a line to it which specifies the location of your R packages user library, like the following:

.libPaths( unique(c("C:/Users/USERNAME/Documents/R/win-library/3.4", .libPaths())))

You might find the exact location by running .libPaths() in R GUI or RStudio.

Trouble Shooting

  1. Compile failed after git pull

    Please first update the submodules, clean all and recompile:

    git submodule update && make clean_all && make -j4

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

    • sphinx

    • breathe

    • guzzle_sphinx_theme

    • recommonmark

    • mock

    • sh

    • graphviz

    • matplotlib

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.