# Installation Guide¶

Note

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_exact, gpu_hist) on machines with NVIDIA GPUs. However, it will not support multi-GPU training; only single GPU will be used. To enable multi-GPU training, download and install the binary wheel from this page.
• Currently, we provide binary wheels for 64-bit Linux and Windows.

## Building XGBoost from source¶

This page gives instructions on how to build and install XGBoost 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). (For R-package installation, please directly refer to R Package Installation.)
2. Then install the language packages (e.g. Python Package).

Note

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 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 have any problem during installation. If the instructions do not work for you, please feel free to ask questions at the user forum.

Contents

## Building 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 running

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


### Building on OSX¶

#### 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 gcc@5


Then install XGBoost with pip:

pip3 install xgboost


You might need to run the command with sudo if you run into permission errors.

#### Build from the source code - advanced method¶

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

brew install gcc@7


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 as follows: (specify the GCC version)

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


Now, you may build XGBoost using the following command:

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 using MinGW¶

After installing Git for Windows, you should have a shortcut named Git Bash. You should run all subsequent steps in 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'


(On 64-bit Windows, you should get MinGW64 instead.) Make sure that the path to MinGW is in the system PATH.

To build with MinGW, type:

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


#### 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 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.

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.

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

### 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


Note

Enabling multi-GPU training

By default, multi-GPU training is disabled and only a single GPU will be used. To enable multi-GPU training, set the option USE_NCCL=ON. Multi-GPU training depends on NCCL2, available at this link. Since NCCL2 is only available for Linux machines, multi-GPU training is available only for Linux.

mkdir build
cd build
cmake .. -DUSE_CUDA=ON -DUSE_NCCL=ON
make -j


On Windows, see what options for generators you have for CMake, and choose one with [arch] replaced with Win64:

cmake -help


Then run CMake as follows:

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


Note

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:

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

### Customized Building¶

The configuration file config.mk modifies several compilation flags: - Whether to enable support for various distributed filesystems such as HDFS and Amazon S3 - Which compiler to use - And some more

To customize, first copy make/config.mk to the project root and then modify the copy.

### Python Package Installation¶

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

1. Install system-wide, 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

Re-compiling XGBoost

If you recompiled XGBoost, then you need to reinstall it again to make the new library take effect.

1. 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. This option is recommended for developers who change the code frequently. 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

1. Install only for the current user.
cd python-package; python setup.py develop --user

1. 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")
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")
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 the error 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 type:

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

3. Makefile: dmlc-core/make/dmlc.mk: No such file or directory

We need to recursively clone the submodule:

git submodule init
git submodule update


Alternatively, do another clone

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