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