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_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 https://s3-us-west-2.amazonaws.com/xgboost-nightly-builds/xgboost-[version]+[commit hash]-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. 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

  • Building on Ubuntu/Debian

  • Building on OSX

  • Building on Windows

  • Building with GPU support

  • Customized Building

• Python Package Installation

• R Package Installation

• Trouble Shooting

• Building the documentation

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++-5.0 or higher)

• CMake 3.3 or higher (3.12 for building with CUDA)

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

Building on Ubuntu/Debian

On Ubuntu, one builds XGBoost by running CMake:

git clone --recursive https://github.com/dmlc/xgboost
cd xgboost
mkdir build
cd build
cmake ..
make -j4

Building on OSX

Install with pip: simple method

First, obtain the OpenMP library (libomp) with Homebrew (https://brew.sh/) 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 https://github.com/dmlc/xgboost

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"

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.) Open the .sln file in the build directory and build with Visual Studio.

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

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

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

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

Note

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.)

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:

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.

Customized Building

We recommend the use of CMake for most use cases. See the full range of building options in CMakeLists.txt.

Alternatively, you may use Makefile. The Makefile uses a configuration file config.mk, which lets you modify 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.

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. 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

3. Install only for the current user.

cd python-package; python setup.py develop --user

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 setup.py 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:

install.packages("xgboost")

Note

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 (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.

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
mkdir build
cd build
cmake .. -DR_LIB=ON
make -j4
make install

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
   

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

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.