Our goal is to build the shared library:
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
For a list of CMake options, see #-- Options in CMakeLists.txt on top level of source tree.
On Ubuntu, one builds XGBoost by running CMake:
git clone --recursive https://github.com/dmlc/xgboost
cd xgboost
mkdir build
cd build
cmake ..
make -j$(nproc)
First, obtain the OpenMP library (libomp) with Homebrew (https://brew.sh/) to enable multi-threading (i.e. using multiple CPU threads for training):
Then install XGBoost with pip:
You might need to run the command with --user flag if you run into permission errors.
Obtain libomp from Homebrew:
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.
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.
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.
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.
It’s only used for creating shorthands for running linters, performing packaging tasks
etc. So the remaining makefiles are legacy.
The Python package is located at python-package/. There are several ways to build and
install the package from source:
Use Python setuptools directly
The XGBoost Python package supports most of the setuptools commands, here is a list of tested commands:
python setup.py install # Install the XGBoost to your current Python environment.
python setup.py build # Build the Python package.
python setup.py build_ext # Build only the C++ core.
python setup.py sdist # Create a source distribution
python setup.py bdist # Create a binary distribution
python setup.py bdist_wheel # Create a binary distribution with wheel format
Running python setup.py 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 setup.py install --use-cuda --use-nccl
Please refer to setup.py 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 setup.py 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 setup.py develop # Create a editable installation.
pip install -e . # Same as above, but carried out by pip.
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/libxgboost.so. 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 setup.py develop
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:
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.
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.
The Python interpreter will crash on exit if XGBoost was used. This is usually not a big issue.
-O3 is OK.
-mtune=native is also OK.
Don’t use -march=native gcc flag. Using it causes the Python interpreter to crash if the DLL was actually used.
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.
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
and then run install.packages("xgboost"). Without OpenMP, XGBoost will only use a single CPU core, leading to suboptimal training speed.
Make sure you have installed git and a recent C++ compiler supporting C++11 (See above
sections for requirements of building C++ core).
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 -j$(nproc)
make install
If all fails, try Building the shared library to see whether a problem is specific to R
package or not. Notice that the R package is installed by CMake directly.
On Windows, CMake with Visual C++ Build Tools (or Visual Studio) can be used to build the R package.
While not required, this build can be faster if you install the R package processx with install.packages("processx").
Note
Setting correct PATH environment variable on Windows
If you are using Windows, make sure to include the right directories in the PATH environment variable.
Open the Command Prompt and navigate to the XGBoost directory, and then run the following commands. Make sure to specify the correct R version.
cd C:\path\to\xgboost
mkdir build
cd build
cmake .. -G"Visual Studio 16 2019" -A x64 -DR_LIB=ON -DR_VERSION=4.0.0
cmake --build . --target install --config Release
The procedure and requirements are similar as in Building with GPU support, so make sure to read it first.
On Linux, starting from the XGBoost directory type:
mkdir build
cd build
cmake .. -DUSE_CUDA=ON -DR_LIB=ON
make install -j$(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 Studio has to be used to build an R package with GPU support. Rtools must also be installed.
Note
Setting correct PATH environment variable on Windows
If you are using Windows, make sure to include the right directories in the PATH environment variable.
Open the Command Prompt and navigate to the XGBoost directory, and then run the following commands. Make sure to specify the correct R version.
cd C:\path\to\xgboost
mkdir build
cd build
cmake .. -G"Visual Studio 16 2019" -A x64 -DUSE_CUDA=ON -DR_LIB=ON -DR_VERSION=4.0.0
cmake --build . --target install --config Release
If CMake can’t find your R during the configuration step, you might provide the location of R to CMake like this: -DLIBR_HOME="C:\Program Files\R\R-4.0.0".
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.
Compile failed after git pull
Please first update the submodules, clean all and recompile:
git submodule update && make clean_all && make -j4
XGBoost uses Sphinx for documentation. To build it locally, you need a installed XGBoost with all its dependencies along with:
System dependencies
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.