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139 lines
4.6 KiB
Text
Executable file
.. -*- rest -*-
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.. vim:syntax=rest
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.. NB! Keep this document a valid restructured document.
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Building and installing NumPy
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+++++++++++++++++++++++++++++
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:Authors: Numpy Developers <numpy-discussion@scipy.org>
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:Discussions to: numpy-discussion@scipy.org
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.. Contents::
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PREREQUISITES
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=============
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Building NumPy requires the following software installed:
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1) Python__ 2.4.x or newer
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On Debian and derivative (Ubuntu): python python-dev
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On Windows: the official python installer on Python__ is enough
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Make sure that the Python package distutils is installed before
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continuing. For example, in Debian GNU/Linux, distutils is included
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in the python-dev package.
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Python must also be compiled with the zlib module enabled.
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2) nose__ (pptional) 0.10.3 or later
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This is required for testing numpy, but not for using it.
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Python__ http://www.python.org
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nose__ http://somethingaboutorange.com/mrl/projects/nose/
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Fortran ABI mismatch
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====================
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The two most popular open source fortran compilers are g77 and gfortran.
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Unfortunately, they are not ABI compatible, which means that concretely you
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should avoid mixing libraries built with one with another. In particular, if
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your blas/lapack/atlas is built with g77, you *must* use g77 when building
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numpy and scipy; on the contrary, if your atlas is built with gfortran, you
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*must* build numpy/scipy with gfortran.
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Choosing the fortran compiler
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-----------------------------
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To build with g77:
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python setup.py build --fcompiler=gnu
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To build with gfortran:
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python setup.py build --fcompiler=gnu95
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How to check the ABI of blas/lapack/atlas
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-----------------------------------------
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One relatively simple and reliable way to check for the compiler used to build
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a library is to use ldd on the library. If libg2c.so is a dependency, this
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means that g77 has been used. If libgfortran.so is a a dependency, gfortran has
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been used. If both are dependencies, this means both have been used, which is
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almost always a very bad idea.
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Building with ATLAS support
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===========================
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Ubuntu 8.10 (Intrepid)
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----------------------
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You can install the necessary packages for optimized ATLAS with this command:
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sudo apt-get install libatlas-base-dev
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If you have a recent CPU with SIMD suppport (SSE, SSE2, etc...), you should
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also install the corresponding package for optimal performances. For example,
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for SSE2:
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sudo apt-get install libatlas3gf-sse2
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*NOTE*: if you build your own atlas, Intrepid changed its default fortran
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compiler to gfortran. So you should rebuild everything from scratch, including
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lapack, to use it on Intrepid.
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Ubuntu 8.04 and lower
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---------------------
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You can install the necessary packages for optimized ATLAS with this command:
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sudo apt-get install atlas3-base-dev
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If you have a recent CPU with SIMD suppport (SSE, SSE2, etc...), you should
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also install the corresponding package for optimal performances. For example,
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for SSE2:
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sudo apt-get install atlas3-sse2
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Windows 64 bits notes
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=====================
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Note: only AMD64 is supported (IA64 is not) - AMD64 is the version most people
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want.
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Free compilers (mingw-w64)
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--------------------------
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http://mingw-w64.sourceforge.net/
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To use the free compilers (mingw-w64), you need to build your own toolchain, as
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the mingw project only distribute cross-compilers (cross-compilation is not
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supported by numpy). Since this toolchain is still being worked on, serious
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compilers bugs can be expected. binutil 2.19 + gcc 4.3.3 + mingw-w64 runtime
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gives you a working C compiler (but the C++ is broken). gcc 4.4 will hopefully
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be able to run natively.
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This is the only tested way to get a numpy with a FULL blas/lapack (scipy does
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not work because of C++).
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MS compilers
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------------
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If you are familiar with MS tools, that's obviously the easiest path, and the
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compilers are hopefully more mature (although in my experience, they are quite
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fragile, and often segfault on invalid C code). The main drawback is that no
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fortran compiler + MS compiler combination has been tested - mingw-w64 gfortran
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+ MS compiler does not work at all (it is unclear whether it ever will).
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For python 2.5, you need VS 2005 (MS compiler version 14) targetting
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AMD64 bits, or the Platform SDK v6.0 or below (which gives command
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line versions of 64 bits target compilers). The PSDK is free.
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For python 2.6, you need VS 2008. The freely available version does not
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contains 64 bits compilers (you also need the PSDK, v6.1).
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It is *crucial* to use the right version: python 2.5 -> version 14, python 2.6,
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version 15. You can check the compiler version with cl.exe /?. Note also that
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for python 2.5, 64 bits and 32 bits versions use a different compiler version.
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