blob: fea4e7777c2269dafc8d992c1a41707c7babc2ca [file] [log] [blame]
#-------------------------------------------------------------------------------
# Template configuration for compiling mxnet
#
# If you want to change the configuration, please use the following
# steps. Assume you are on the root directory of mxnet. First copy the this
# file so that any local changes will be ignored by git
#
# $ cp make/config.mk .
#
# Next modify the according entries, and then compile by
#
# $ make
#
# or build in parallel with 8 threads
#
# $ make -j8
#-------------------------------------------------------------------------------
#---------------------
# We do not assign compilers here. Often when cross-compiling these will already
# be set correctly.
#--------------------
export NVCC = nvcc
# whether compile with options for MXNet developer
DEV = 0
# whether compile with debug
DEBUG = 0
# whether compiler with profiler
USE_PROFILER =
# the additional link flags you want to add
# TODO: Move flags here
ADD_LDFLAGS=-static-libstdc++
# the additional compile flags you want to add
ADD_CFLAGS =
#---------------------------------------------
# matrix computation libraries for CPU/GPU
#---------------------------------------------
# whether use CUDA during compile
USE_CUDA = 0
# add the path to CUDA library to link and compile flag
# if you have already add them to environment variable, leave it as NONE
# USE_CUDA_PATH = /usr/local/cuda
USE_CUDA_PATH = NONE
# whether use CuDNN R3 library
USE_CUDNN = 0
# whether use cuda runtime compiling for writing kernels in native language (i.e. Python)
USE_NVRTC = 0
# whether use opencv during compilation
# you can disable it, however, you will not able to use
# imbin iterator
USE_OPENCV = 0
# use openmp for parallelization
USE_OPENMP = 1
# MKL ML Library for Intel CPU/Xeon Phi
# Please refer to MKL_README.md for details
# MKL ML Library folder, need to be root for /usr/local
# Change to User Home directory for standard user
# For USE_BLAS!=mkl only
MKLML_ROOT=/usr/local
# whether use MKL2017 library
USE_MKL2017 = 0
# whether use MKL2017 experimental feature for high performance
# Prerequisite USE_MKL2017=1
USE_MKL2017_EXPERIMENTAL = 0
# whether use NNPACK library
USE_NNPACK = 0
# For arm builds we're using openblas
USE_BLAS = openblas
# whether use lapack during compilation
# only effective when compiled with blas versions openblas/apple/atlas/mkl
USE_LAPACK = 1
# path to lapack library in case of a non-standard installation
USE_LAPACK_PATH =
# add path to intel library, you may need it for MKL, if you did not add the path
# to environment variable
USE_INTEL_PATH = NONE
# If use MKL only for BLAS, choose static link automatically to allow python wrapper
ifeq ($(USE_MKL2017), 0)
ifeq ($(USE_BLAS), mkl)
USE_STATIC_MKL = 1
endif
else
USE_STATIC_MKL = NONE
endif
#----------------------------
# distributed computing
#----------------------------
# whether or not to enable multi-machine supporting
USE_DIST_KVSTORE = 0
# whether or not allow to read and write HDFS directly. If yes, then hadoop is
# required
USE_HDFS = 0
# path to libjvm.so. required if USE_HDFS=1
LIBJVM=$(JAVA_HOME)/jre/lib/amd64/server
# whether or not allow to read and write AWS S3 directly. If yes, then
# libcurl4-openssl-dev is required, it can be installed on Ubuntu by
# sudo apt-get install -y libcurl4-openssl-dev
USE_S3 = 0
#----------------------------
# additional operators
#----------------------------
# path to folders containing projects specific operators that you don't want to put in src/operators
EXTRA_OPERATORS =
#----------------------------
# other features
#----------------------------
# Create C++ interface package
USE_CPP_PACKAGE = 0
#----------------------------
# plugins
#----------------------------
# whether to use caffe integration. This requires installing caffe.
# You also need to add CAFFE_PATH/build/lib to your LD_LIBRARY_PATH
# CAFFE_PATH = $(HOME)/caffe
# MXNET_PLUGINS += plugin/caffe/caffe.mk
# whether to use torch integration. This requires installing torch.
# You also need to add TORCH_PATH/install/lib to your LD_LIBRARY_PATH
# TORCH_PATH = $(HOME)/torch
# MXNET_PLUGINS += plugin/torch/torch.mk
# WARPCTC_PATH = $(HOME)/warp-ctc
# MXNET_PLUGINS += plugin/warpctc/warpctc.mk
# whether to use sframe integration. This requires build sframe
# git@github.com:dato-code/SFrame.git
# SFRAME_PATH = $(HOME)/SFrame
# MXNET_PLUGINS += plugin/sframe/plugin.mk