xgboost/doc/build.md

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 libxgboost.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.

Contents

• Build the Shared Library
  • Building on Ubuntu/Debian
  • Building on OSX
  • Building on Windows
  • Building with GPU support
  • Windows Binaries
  • Customized Building
• Python Package Installation
• R Package Installation
• Trouble Shooting

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 libxgboost.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 OSX

On OSX, one builds xgboost by

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

This builds xgboost without multi-threading, because by default clang in OSX does not come with open-mp. See the following paragraph for OpenMP enabled xgboost.

Here is the complete solution to use OpenMP-enabled compilers to install XGBoost. Obtain gcc-6.x.x with openmp support by brew install gcc --without-multilib. (brew is the de facto standard of apt-get on OS X. So installing HPC separately is not recommended, but it should work.). Installation of gcc can take a while (~ 30 minutes)

Now, clone the repository

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

and build using the following commands

cd xgboost; cp make/config.mk ./config.mk; make -j4

NOTE: If you use OSX El Capitan, brew installs gcc the latest version gcc-6. So you may need to modify Makefile#L46 and change gcc-5 to gcc-6. After that change gcc-5/g++-5 to gcc-6/g++-6 in make/config.mk then build using the following commands

cd xgboost; cp make/config.mk ./config.mk; make -j4

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.

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 libxgboost.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. The R package is not yet supported.

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

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

Cmake will create an xgboost.sln solution file in the build directory. Build this solution in release mode as a x64 build.

Windows Binaries

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")

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

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
   

3. 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