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Ii) Install/update cuda sudo apt-get update deb from Nvidia (version 8.0 worked for me, and is required in some cases?). I) Installing/updating the Cuda Toolkit by downloading the cuda toolkit. These are some things that caused some nvcc errors for me: Vi) In the virtualenv (if desired) in the same directory run pip install -e python-package/

build.sh, it should say completed in multi-threaded mode or the NVCC build probably failed (or another error, look above for the error) Iv) Edit the makefile Makefile, on the NVCC section to use the flag -gpu-architecture=sm_xx for GPU version (5.2 for GTX 960) on line 101 #CODE = $(foreach ver,$(COMPUTE),-gencode arch=compute_$(ver),code=sm_$(ver)) TO Iii) to make GPU, edit make/config.mk to use PLUGIN_UPDATER_GPU Ii) cd insto xgboost and make -j4 to create multi-threaded, if no GPU is desired If anyone is interested in the process to fix the build with the GPU flag, here is the process that I went through on Ubuntu 14.04. To use the Scikit learn API (using either grow_gpu or grow_hist_gpu): import xgboost as xgb After it built, I installed with pip install -e python-package/ within the repo directory. This required me to also change some NVCC flags to work on my GTX960 that was causing some build errors, then some runtime errors due to architecture mismatch. I couldn't get this working from the pip installed XGBoost, but I pulled the most recent XGBoost from GitHub ( git clone -recursive ) and compiled it with the PLUGIN_UPDATER_GPU flag which allowed me to use the GPU with the sklearn API.