diff options
Diffstat (limited to 'site.cfg.example')
| -rw-r--r-- | site.cfg.example | 40 |
1 files changed, 20 insertions, 20 deletions
diff --git a/site.cfg.example b/site.cfg.example index f5c9ca227..c3169f3be 100644 --- a/site.cfg.example +++ b/site.cfg.example @@ -4,11 +4,11 @@ # packages will use all sections so you should leave out sections that your # package does not use. -# To assist automatic installation like easy_install, the user's home directory +# 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, RawConfigParser class being used to load it. +# No interpolation is allowed; the RawConfigParser class is being used to load it. # # https://docs.python.org/library/configparser.html # @@ -46,7 +46,7 @@ # LAPACK libraries are one example. However, most dependencies are more # complicated and require actual installation that you need to do # yourself. -# src_dirs = /home/rkern/src/BLAS_SRC:/home/rkern/src/LAPACK_SRC +# src_dirs = /home/username/src/BLAS_SRC:/home/username/src/LAPACK_SRC # # search_static_first # Boolean (one of (0, false, no, off) for False or (1, true, yes, on) for @@ -87,13 +87,13 @@ #include_dirs = /usr/local/include # -# Atlas +# ATLAS # ----- -# Atlas is an open source optimized implementation of the BLAS and Lapack -# routines. NumPy will try to build against Atlas by default when available in -# the system library dirs. 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 is an open source optimized implementation of the BLAS and LAPACK +# routines. NumPy will try to build against ATLAS by default when available in +# the system library dirs. 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 @@ -101,9 +101,9 @@ # OpenBLAS # -------- -# OpenBLAS is another open source optimized implementation of BLAS and Lapack -# and can be seen as an alternative to Atlas. To build numpy against OpenBLAS -# instead of Atlas, use this section instead of the above, adjusting as needed +# OpenBLAS is another open source optimized implementation of BLAS and LAPACK +# and can be seen as an alternative to ATLAS. To build NumPy against OpenBLAS +# instead of ATLAS, use this section instead of the above, adjusting as needed # for your configuration (in the following example we installed OpenBLAS with # ``make install PREFIX=/opt/OpenBLAS``. # OpenBLAS is generically installed as a shared library, to force the OpenBLAS @@ -114,7 +114,7 @@ # way Python's multiprocessing is implemented, a multithreaded OpenBLAS can # cause programs using both to hang as soon as a worker process is forked on # POSIX systems (Linux, Mac). -# This is fixed in Openblas 0.2.9 for the pthread build, the OpenMP build using +# This is fixed in OpenBLAS 0.2.9 for the pthread build, the OpenMP build using # GNU openmp is as of gcc-4.9 not fixed yet. # Python 3.4 will introduce a new feature in multiprocessing, called the # "forkserver", which solves this problem. For older versions, make sure @@ -135,7 +135,7 @@ # ---- # 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. +# 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 @@ -155,7 +155,7 @@ # 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: +# 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: @@ -172,7 +172,7 @@ # mkl_libs = mkl_rt # lapack_libs = # -# On win-64, the following options compiles numpy with the MKL library +# 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 @@ -180,9 +180,9 @@ # mkl_libs = mkl_rt # lapack_libs = -# ACCELERATE +# Accelerate # ---------- -# Accelerate/vecLib is an OSX framework providing a BLAS and LAPACK implementations. +# Accelerate/vecLib is an OSX framework providing a BLAS and LAPACK implementation. # # [accelerate] # libraries = Accelerate, vecLib @@ -195,7 +195,7 @@ # 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. +# UMFPACK is not used by NumPy. # # https://www.cise.ufl.edu/research/sparse/umfpack/ # https://www.cise.ufl.edu/research/sparse/amd/ @@ -210,7 +210,7 @@ # 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 for numpy or scipy. +# Note that these libraries are not used by NumPy or SciPy. # # http://fftw.org/ # https://cr.yp.to/djbfft.html |