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site.cfg.example
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site.cfg.example
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# This file provides configuration information about non-Python dependencies for
# numpy.distutils-using packages. Create a file like this called "site.cfg" next
# to your package's setup.py file and fill in the appropriate sections. Not all
# packages will use all sections so you should leave out sections that your
# package does not use.
# To assist automatic installation like pip, the user's home directory
# will also be checked for the file ~/.numpy-site.cfg .
# The format of the file is that of the standard library's ConfigParser module.
# No interpolation is allowed; the RawConfigParser class is being used to load it.
#
# https://docs.python.org/library/configparser.html
#
# Each section defines settings that apply to one particular dependency. Some of
# the settings are general and apply to nearly any section and are defined here.
# Settings specific to a particular section will be defined near their section.
#
# libraries
# Comma-separated list of library names to add to compile the extension
# with. Note that these should be just the names, not the filenames. For
# example, the file "libfoo.so" would become simply "foo".
# libraries = lapack,f77blas,cblas,atlas
# This setting is available for *all* sections.
#
# library_dirs
# List of directories to add to the library search path when compiling
# extensions with this dependency. Use the character given by os.pathsep
# to separate the items in the list. Note that this character is known to
# vary on some unix-like systems; if a colon does not work, try a comma.
# This also applies to include_dirs.
# On UN*X-type systems (OS X, most BSD and Linux systems):
# library_dirs = /usr/lib:/usr/local/lib
# On Windows:
# library_dirs = c:\mingw\lib,c:\atlas\lib
# On some BSD and Linux systems:
# library_dirs = /usr/lib,/usr/local/lib
#
# include_dirs
# List of directories to add to the header file search path.
# include_dirs = /usr/include:/usr/local/include
#
# search_static_first
# Boolean (one of (0, false, no, off) for False or (1, true, yes, on) for
# True) to tell numpy.distutils to prefer static libraries (.a) over
# shared libraries (.so). It is turned off by default.
# search_static_first = false
#
# runtime_library_dirs/rpath
# List of directories that contains the libraries that should be
# used at runtime, thereby disregarding the LD_LIBRARY_PATH variable.
# See 'library_dirs' for formatting on different platforms.
# runtime_library_dirs = /opt/blas/lib:/opt/lapack/lib
# or equivalently
# rpath = /opt/blas/lib:/opt/lapack/lib
#
# extra_compile_args
# Add additional arguments to the compilation of sources.
# Split into arguments in a platform-appropriate way.
# Provide a single line with all complete flags.
# extra_compile_args = -g -ftree-vectorize
#
# extra_link_args
# Add additional arguments when libraries/executables
# are linked.
# Split into arguments in a platform-appropriate way.
# Provide a single line with all complete flags.
# extra_link_args = -lgfortran
#
# Defaults
# ========
# The settings here will apply to all sections as general defaults
# This is a good place to add general library and include directories like
# /usr/local/{lib,include}
# These settings apply when they are not overridden in the sections below.
# Note that the standard paths (e.g. `/usr/lib`) are not searched if you
# override these settings, unless they are explicitly included.
# The ``:`` is os.pathsep, which is ``;`` on windows
#[DEFAULT]
#library_dirs = /usr/local/lib64:/usr/local/lib:/usr/lib64:/usr/lib
#include_dirs = /usr/local/include:/usr/include
# ATLAS
# -----
# ATLAS is an open source optimized implementation of the BLAS and LAPACK
# routines. NumPy will try to build against ATLAS when available in
# the system library dirs (and OpenBLAS, MKL and BLIS are not installed). To
# build NumPy against a custom installation of ATLAS you can add an explicit
# section such as the following. Here we assume that ATLAS was configured with
# ``prefix=/opt/atlas``.
#
# [atlas]
# library_dirs = /opt/atlas/lib
# include_dirs = /opt/atlas/include
# OpenBLAS
# --------
# OpenBLAS is an open source optimized implementation of BLAS and LAPACK
# and is the default choice for NumPy itself (CI, wheels). OpenBLAS will be
# selected above ATLAS and Netlib BLAS/LAPACK. OpenBLAS is generically
# installed as a shared library, to force the OpenBLAS library linked to also
# be used at runtime you can utilize the runtime_library_dirs variable.
#
# [openblas]
# libraries = openblas
# library_dirs = /opt/OpenBLAS/lib
# include_dirs = /opt/OpenBLAS/include
# runtime_library_dirs = /opt/OpenBLAS/lib
# OpenBLAS (64-bit with suffix)
# -----------------------------
# OpenBLAS can be compiled with 64-bit integer size and symbol suffix '64_'
# (INTERFACE64=1 SYMBOLSUFFIX=64_). OpenBLAS built with this setting are also
# provided by some Linux distributions (e.g. Fedora's 64-bit openblas packages).
# This is an emerging "standard" for 64-bit BLAS/LAPACK, avoiding symbol clashes
# with 32-bit BLAS/LAPACK.
#
# To build Numpy with such 64-bit BLAS/LAPACK, set environment
# variables NPY_USE_BLAS_ILP64=1, NPY_BLAS_ILP64_ORDER=openblas64_,
# NPY_LAPACK_ILP64_ORDER=openblas64_ at build time.
#
# See:
# https://github.com/xianyi/OpenBLAS/issues/646
#
# [openblas64_]
# libraries = openblas64_
# library_dirs = /opt/OpenBLAS/lib
# include_dirs = /opt/OpenBLAS/include
# runtime_library_dirs = /opt/OpenBLAS/lib
# OpenBLAS (64-bit ILP64)
# -----------------------
# It is possible to also use OpenBLAS compiled with 64-bit integer
# size (ILP64) but no symbol name changes. To do that, set the
# environment variables NPY_USE_BLAS_ILP64=1,
# NPY_BLAS_ILP64_ORDER=openblas_ilp64,
# NPY_LAPACK_ILP64_ORDER=openblas_ilp64 at build time.
#
# Note that mixing both 64-bit and 32-bit BLAS without symbol suffixes
# in the same application may cause problems due to symbol name
# clashes, especially with embedded Python interpreters.
#
# The name of the library file may vary on different systems, so you
# may need to check your specific OpenBLAS installation and
# uncomment and e.g. set ``libraries = openblas`` below.
#
# [openblas_ilp64]
# libraries = openblas64
# library_dirs = /opt/OpenBLAS/lib
# include_dirs = /opt/OpenBLAS/include
# runtime_library_dirs = /opt/OpenBLAS/lib
# symbol_prefix =
# symbol_suffix =
# BLIS
# ----
# BLIS (https://github.com/flame/blis) also provides a BLAS interface. It's a
# relatively new library, its performance in some cases seems to match that of
# MKL and OpenBLAS, but it hasn't been benchmarked with NumPy or SciPy yet.
#
# Notes on compiling BLIS itself:
# - the CBLAS interface (needed by NumPy) isn't built by default; define
# BLIS_ENABLE_CBLAS to build it.
# - ``./configure auto`` doesn't support 32-bit builds, see gh-7294 for
# details.
# Notes on compiling NumPy against BLIS:
# - ``include_dirs`` below should be the directory where the BLIS cblas.h
# header is installed.
#
# [blis]
# libraries = blis
# library_dirs = /home/username/blis/lib
# include_dirs = /home/username/blis/include/blis
# runtime_library_dirs = /home/username/blis/lib
# libFLAME
# --------
# libFLAME (https://www.cs.utexas.edu/~flame/web/libFLAME.html) provides a
# LAPACK interface. It's a relatively new library, its performance in some
# cases seems to match that of MKL and OpenBLAS.
# It hasn't been benchmarked with NumPy or SciPy yet.
#
# Notes on compiling libFLAME itself:
# - the LAPACK interface (needed by NumPy) isn't built by default; please
# configure with ``./configure --enable-lapack2flame``.
#
# [flame]
# libraries = flame
# library_dirs = /home/username/flame/lib
# runtime_library_dirs = /home/username/flame/lib
# MKL
#----
# Intel MKL is Intel's very optimized yet proprietary implementation of BLAS and
# LAPACK. Find the latest info on building NumPy with Intel MKL in this article:
# https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl
# Assuming you installed the mkl in /opt/intel/compilers_and_libraries_2018/linux/mkl,
# for 64 bits code at Linux:
# [mkl]
# library_dirs = /opt/intel/compilers_and_libraries_2018/linux/mkl/lib/intel64
# include_dirs = /opt/intel/compilers_and_libraries_2018/linux/mkl/include
# libraries = mkl_rt
#
# For 32 bit code at Linux:
# [mkl]
# library_dirs = /opt/intel/compilers_and_libraries_2018/linux/mkl/lib/ia32
# include_dirs = /opt/intel/compilers_and_libraries_2018/linux/mkl/include
# libraries = mkl_rt
#
# On win-64, the following options compiles NumPy with the MKL library
# dynamically linked.
# [mkl]
# include_dirs = C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2018\windows\mkl\include
# library_dirs = C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2018\windows\mkl\lib\intel64
# libraries = mkl_rt
# UMFPACK
# -------
# The UMFPACK library is used in scikits.umfpack to factor large sparse matrices.
# It, in turn, depends on the AMD library for reordering the matrices for
# better performance. Note that the AMD library has nothing to do with AMD
# (Advanced Micro Devices), the CPU company.
#
# UMFPACK is not used by NumPy.
#
# https://www.cise.ufl.edu/research/sparse/umfpack/
# https://www.cise.ufl.edu/research/sparse/amd/
# https://scikit-umfpack.github.io/scikit-umfpack/
#
#[amd]
#libraries = amd
#
#[umfpack]
#libraries = umfpack
# FFT libraries
# -------------
# There are two FFT libraries that we can configure here: FFTW (2 and 3) and djbfft.
# Note that these libraries are not used by NumPy or SciPy.
#
# http://fftw.org/
# https://cr.yp.to/djbfft.html
#
# Given only this section, numpy.distutils will try to figure out which version
# of FFTW you are using.
#[fftw]
#libraries = fftw3
#
# For djbfft, numpy.distutils will look for either djbfft.a or libdjbfft.a .
#[djbfft]
#include_dirs = /usr/local/djbfft/include
#library_dirs = /usr/local/djbfft/lib
# Fujitsu SSL2
# ----
#[ssl2]
#library_dirs = /opt/FJSVstclanga/v1.1.0/lib64
#include_dirs = /opt/FJSVstclanga/v1.1.0/clang-comp/include
# Single-threaded version.
#libraies = fjlapacksve
# Multi-threaded version. Python itself must be built by Fujitsu compiler.
#libraies = fjlapackexsve