Installation Guide

This page gives instructions on how to build and install the xgboost package from scratch on various systems. It consists of two steps:

  1. First build the shared library from the C++ codes (libxgboost.so for Linux/OSX and xgboost.dll for Windows).
    • Exception: for R-package installation please directly refer to the R package section.
  2. Then install the language packages (e.g. Python Package).

Important the newest version of xgboost uses submodule to maintain packages. So when you clone the repo, remember to use the recursive option as follows.

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

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 had any problem during installation. If the instructions do not work for you, please feel free to ask questions at xgboost/issues, or even better to send pull request if you can fix the problem.

Build the Shared Library

Our goal is to build the shared library:

  • On Linux/OSX the target library is libxgboost.so
  • On Windows the target library is xgboost.dll

The minimal building requirement is

  • A recent c++ compiler supporting C++ 11 (g++-4.8 or higher)

We can edit make/config.mk to change the compile options, and then build by make. If everything goes well, we can go to the specific language installation section.

Building on Ubuntu/Debian

On Ubuntu, one builds xgboost by

git clone --recursive https://github.com/dmlc/xgboost
cd xgboost; make -j4

Building on macOS

Install with pip - simple method

First, make sure you obtained gcc-5 (newer version does not work with this method yet). Note: installation of gcc can take a while (~ 30 minutes)

brew install gcc5

You might need to run the following command with sudo if you run into some permission errors:

pip install xgboost

Build from the source code - advanced method

First, obtain gcc-7.x.x with brew (https://brew.sh/) if you want multi-threaded version, otherwise, Clang is ok if OpenMP / multi-threaded is not required. Note: installation of gcc can take a while (~ 30 minutes)

brew install gcc

Now, clone the repository

git clone --recursive https://github.com/dmlc/xgboost
cd xgboost; cp make/config.mk ./config.mk

Open config.mk and uncomment these two lines

export CC = gcc
export CXX = g++

and replace these two lines into(5 or 6 or 7; depending on your gcc-version)

export CC = gcc-7
export CXX = g++-7

To find your gcc version

gcc-version

and build using the following commands

make -j4

head over to Python Package Installation for the next steps

Building on Windows

You need to first clone the xgboost repo with recursive option clone the submodules. If you are using github tools, you can open the git-shell, and type the following command. We recommend using Git for Windows because it brings a standard bash shell. This will highly ease the installation process.

git submodule init
git submodule update

XGBoost support both build by MSVC or MinGW. Here is how you can build xgboost library using MinGW.

After installing Git for Windows, you should have a shortcut Git Bash. All the following steps are in the Git Bash.

In MinGW, make command comes with the name mingw32-make. You can add the following line into the .bashrc file.

if you don’t have a MinGW installed on your machine and you are using Windows x86_64. try to download one From
after the installation, add the path of your installation to the system env properties.
the installation path added to your env properties should be pointed to the bin folder, like:
C:\Program Files\mingw-w64\x86_64-7.2.0-posix-seh-rt_v5-rev1\mingw64\bin
for users using Windows 32, try to find a mingw32 version instead.
you are ok to follow the below instructions.
alias make='mingw32-make'

To build with MinGW

cp make/mingw64.mk config.mk; make -j4

To build with Visual Studio 2013 use cmake. Make sure you have a recent version of cmake added to your path and then from the xgboost directory:

mkdir build
cd build
cmake .. -G"Visual Studio 12 2013 Win64"

This specifies an out of source build using the MSVC 12 64 bit generator. Open the .sln file in the build directory and build with Visual Studio. To use the Python module you can copy xgboost.dll into python-package\xgboost.

Other versions of Visual Studio may work but are untested.

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 -j

Windows requirements for GPU build: only Visual C++ 2015 or 2013 with CUDA v8.0 were fully tested. Either install Visual C++ 2015 Build Tools separately, or as a part of Visual Studio 2015. If you already have Visual Studio 2017, the Visual C++ 2015 Toolchain componenet has to be installed using the VS 2017 Installer. Likely, you would need to use the VS2015 x64 Native Tools command prompt to run the cmake commands given below. In some situations, however, things run just fine from MSYS2 bash command line.

On Windows, using cmake, see what options for Generators you have for cmake, and choose one with [arch] replaced by Win64:

cmake -help

Then run cmake as:

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

To speed up compilation, 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

If build seems to use only a single process, you might try to append an option like -- /m:6 to the above command.

Windows Binaries

After the build process successfully ends, you will find a xgboost.dll library file inside ./lib/ folder, copy this file to the the API package folder like python-package/xgboost if you are using python API. And you are good to follow the below instructions.

Unofficial windows binaries and instructions on how to use them are hosted on Guido Tapia’s blog

Customized Building

The configuration of xgboost can be modified by config.mk

  • modify configuration on various distributed filesystem such as HDFS/Amazon S3/...
  • First copy make/config.mk to the project root, on which any local modification will be ignored by git, then modify the according flags.

Python Package Installation

The python package is located at python-package. There are several ways to install the package:

  1. Install system-widely, which requires root permission

    cd python-package; sudo python setup.py install
    

    You will however need Python distutils module for this to work. It is often part of the core python package or it can be installed using your package manager, e.g. in Debian use

    sudo apt-get install python-setuptools
    

    NOTE: If you recompiled xgboost, then you need to reinstall it again to make the new library take effect

  2. Only set the environment variable PYTHONPATH to tell python where to find the library. For example, assume we cloned xgboost on the home directory ~. then we can added the following line in ~/.bashrc. It is recommended for developers who may change the codes. The changes will be immediately reflected once you pulled the code and rebuild the project (no need to call setup again)

    export PYTHONPATH=~/xgboost/python-package
    
  3. Install only for the current user.

    cd python-package; python setup.py develop --user
    
  4. If you are installing the latest xgboost version which requires compilation, add MinGW to the system PATH:

    import os
    os.environ['PATH'] = os.environ['PATH'] + ';C:\\Program Files\\mingw-w64\\x86_64-5.3.0-posix-seh-rt_v4-rev0\\mingw64\\bin'
    

R Package Installation

Installing pre-packaged version

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

install.packages("xgboost")

Or you can install it from our weekly updated drat repo:

install.packages("drat", repos="https://cran.rstudio.com")
drat:::addRepo("dmlc")
install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")

For OSX users, single threaded version will be installed. To install multi-threaded version, first follow Building on OSX to get the OpenMP enabled compiler, then:

  • Set the Makevars file in highest piority for R.

    The point is, there are three Makevars : ~/.R/Makevars, xgboost/R-package/src/Makevars, and /usr/local/Cellar/r/3.2.0/R.framework/Resources/etc/Makeconf (the last one obtained by running file.path(R.home("etc"), "Makeconf") in R), and SHLIB_OPENMP_CXXFLAGS is not set by default!! After trying, it seems that the first one has highest piority (surprise!).

    Then inside R, run

    install.packages("drat", repos="https://cran.rstudio.com")
    drat:::addRepo("dmlc")
    install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
    

Installing the development version

Make sure you have installed git and a recent C++ compiler supporting C++11 (e.g., g++-4.8 or higher). On Windows, Rtools must be installed, and its bin directory has to be added to PATH during the installation. And see the previous subsection for an OSX tip.

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 https://github.com/dmlc/xgboost
cd xgboost
git submodule init
git submodule update
cd R-package
R CMD INSTALL .

If the last line fails because of “R: command not found”, it means that R was not set up to run from command line. In this case, just start R as you would normally do and run the following:

setwd('wherever/you/cloned/it/xgboost/R-package/')
install.packages('.', repos = NULL, type="source")

The package could also be built and installed with cmake (and Visual C++ 2015 on Windows) using instructions from the next section, but without GPU support (omit the -DUSE_CUDA=ON cmake parameter).

If all fails, try building the shared library to see whether a problem is specific to R package or not.

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:

mkdir build
cd build
cmake .. -DUSE_CUDA=ON -DR_LIB=ON
make install -j

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
    
  2. Compile failed after config.mk is modified

    Need to clean all first:

    make clean_all && make -j4
    
  1. Makefile: dmlc-core/make/dmlc.mk: No such file or directory

    We need to recursively clone the submodule, you can do:

    git submodule init
    git submodule update
    

    Alternatively, do another clone

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