Skip to content

Latest commit

 

History

History
99 lines (72 loc) · 3.74 KB

build.md

File metadata and controls

99 lines (72 loc) · 3.74 KB
Raw

Build XGBoost

  • Run bash build.sh (you can also type make)
  • If you have C++11 compiler, it is recommended to type make cxx11=1
    • C++11 is not used by default
  • If your compiler does not come with OpenMP support, it will fire an warning telling you that the code will compile into single thread mode, and you will get single thread xgboost
  • You may get a error: -lgomp is not found
    • You can type make no_omp=1, this will get you single thread xgboost
    • Alternatively, you can upgrade your compiler to compile multi-thread version
  • Windows(VS 2010): see ../windows folder
    • In principle, you put all the cpp files in the Makefile to the project, and build
  • OS X with multi-threading support: see next section

Build XGBoost in OS X with OpenMP

Here is the complete solution to use OpenMp-enabled compilers to install XGBoost.

  1. Obtain gcc with openmp support by brew install gcc --without-multilib or clang with openmp by brew install clang-omp. The clang one is recommended because the first method requires us compiling gcc inside the machine (more than an hour in mine)! (BTW, brew is the de facto standard of apt-get on OS X. So installing HPC separately is not recommended, but it should work.)

  2. if you are planing to use clang-omp - in step 3 and/or 4, change line 9 in xgboost/src/utils/omp.h to

#include <libiomp/omp.h> /* instead of #include <omp.h> */`

to make it work, otherwise you might get this error

src/tree/../utils/omp.h:9:10: error: 'omp.h' file not found...

  1. Set the Makefile correctly for compiling cpp version xgboost then python version xgboost.
export CC  = gcc-4.9
export CXX = g++-4.9

Or

export CC = clang-omp
export CXX = clang-omp++

Remember to change header (mentioned in step 2) if using clang-omp.

Then cd xgboost then bash build.sh to compile XGBoost. And go to wrapper sub-folder to install python version.

  1. 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!).

So, add or change ~/.R/Makevars to the following lines:

CC=gcc-4.9
CXX=g++-4.9
SHLIB_OPENMP_CFLAGS = -fopenmp
SHLIB_OPENMP_CXXFLAGS = -fopenmp
SHLIB_OPENMP_FCFLAGS = -fopenmp
SHLIB_OPENMP_FFLAGS = -fopenmp

Or

CC=clang-omp
CXX=clang-omp++
SHLIB_OPENMP_CFLAGS = -fopenmp
SHLIB_OPENMP_CXXFLAGS = -fopenmp
SHLIB_OPENMP_FCFLAGS = -fopenmp
SHLIB_OPENMP_FFLAGS = -fopenmp

Again, remember to change header if using clang-omp.

Then inside R, run

install.packages('xgboost/R-package/', repos=NULL, type='source')

Or

devtools::install_local('xgboost/', subdir = 'R-package') # you may use devtools

Build with HDFS and S3 Support

  • To build xgboost use with HDFS/S3 support and distributed learnig. It is recommended to build with dmlc, with the following steps
    • git clone https://github.com/dmlc/dmlc-core
    • Follow instruction in dmlc-core/make/config.mk to compile libdmlc.a
    • In root folder of xgboost, type make dmlc=dmlc-core
  • This will allow xgboost to directly load data and save model from/to hdfs and s3
    • Simply replace the filename with prefix s3:// or hdfs://
  • This xgboost that can be used for distributed learning