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Makefile.config
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Makefile.config
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## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!
# cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1
# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1
# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++
# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr
# CUDA architecture setting: going with all of them (up to CUDA 5.5 compatible).
# For the latest architecture, you need to install CUDA >= 6.0 and uncomment
# the *_50 lines below.
CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
-gencode arch=compute_20,code=sm_21 \
-gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_35,code=sm_35 \
#-gencode arch=compute_50,code=sm_50 \
#-gencode arch=compute_50,code=compute_50
# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := open
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
BLAS_INCLUDE := /opt/OpenBLAS/include
BLAS_LIB := /opt/OpenBLAS/lib
# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
MATLAB_DIR := /afs/cs/software
# MATLAB_DIR := /Applications/MATLAB_R2012b.app
# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
#PYTHON_INCLUDE := /usr/include/python2.7 \
# /usr/lib/python2.7/dist-packages/numpy/core/include
PYTHON_INCLUDE := /scr/r6/vigneshr/tibetpy/include/python2.7 \
/scr/r6/vigneshr/tibetpy/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# PYTHON_INCLUDE := $(HOME)/anaconda/include \
# $(HOME)/anaconda/include/python2.7 \
# $(HOME)/anaconda/lib/python2.7/site-packages/numpy/core/include
# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /data2/vigneshr/tibetpy/lib
# PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(HOME)/anaconda/lib
# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /tibet/stanford/cudnn-6.5-linux-R1
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /opt/intel/composer_xe_2013_sp1.0.080/mkl/lib/intel64 /tibet/stanford/cudnn-6.5-linux-R1
#/tibet/stanford/cudnn-6.5-linux-R1
BUILD_DIR := build
DISTRIBUTE_DIR := distribute
# Uncomment for debugging.
DEBUG := 1
# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0