Hi there, I've finally managed to implement most of the things I wanted for numpy.scons, hence a first alpha. Compared to the 2d milestone from a few days ago, a few optimized libraries are supported (ATLAS, Sun sunperf, Apple Accelerate and vecLib, Intel MKL). Who === Outside people interested in numpy.scons, people who have trouble building numpy may want to try this. In particular, using MKL or sunperf should be easier (for sunperf, it should work out of the box if sun compilers are used). Also, compiling with intel compilers on linux should be easier. DO NOT USE THIS FOR PRODUCTION USE ! I am interested in success (that is build and all the test pass) as well as failure. Numpy developers: I would be interested in comments about the code: - the package are built with the setupscons.py/SConstruct - most of the new code is in numpy/distutils/scons How to test =========== Grab the sources: svn co http://svn.scipy.org/svn/numpy/branches/numpy.scons And then just do: python setup.py scons; python setup.py install. If you have multiple CPU/Cores, you may want to use the parallel build options: python setup.py scons --jobs=N with N an integer (number of CPU/CORES + 1 is a rule of thumb). For non default compilers, use the --compiler and --fcompiler options (distutils names). For the MKL, you should add a mkl option in the site.cfg. For ATLAS or sunperf, if it is not in standard places, it won't work (yet). What works: =========== The tested platforms have been tested: - linux (ubuntu x86) with gcc, g77 and ATLAS. - linux (x86 and x64) with gcc, without BLAS or LAPACK. - linux (ubuntu x86) with gcc, g77, and mkl (9.1.023). - linux (ubuntu x86) with intel compilers. - windows (32 bits) with mingw. - windows (32 bits) with VS 2003. - solaris (solaris studio express 12) with sunperf on x86. - Mac Os X Intel with Accelerate framework. - People reported basic success on IRIX, too. What does NOT work ================== One important target missing is windows 64, but this should not be too difficult to solve. There are still many corner cases not yet solved (in particular some windows things, most libraries path cannot yet be overriden in site.cfg); also, I do not attempt to be 100 % backward compatible with the current build system (that for a given environment, you won't necessarily have the same build options). Some things are not yet supported either (overriding libraries with env variables, for example, is not supported). There is no real doc yet (I will do this once the basic public API is stabilized). Details ======= Library checkers ---------------- The main difference compared to the second milestone is the support for libraries checkers (think BLAS, Atlas, etc...). There is now support to check for different libraries, with different compilers. This effectively replaces numpy.distutils.system_info. The way I've implemented those checks are fundamentally different compared to the distutils way: instead of looking for files, I look for capabilities at runtime. For example, for mkl, instead of looking for libmkl.so, etc... I try to compile, link and run a small mkl program: if any of the step failed, then the mkl is not used. This means that I can detect config errors much sooner; also, since I use rpath, there is no need to set things like LD_LIBRARY_PATH and so on. If the path is set right in site.cfg, then everything is taken care of. Also, as before, a general library checker for new libraries is available: look at basic examples in numpy/scons_fake/. In particular, there is a NumpyCheckLib which can be used to check a library with some symbols, whose paths can be overriden with a site.cfg. Fortran support --------------- The other big work has been on fortran support. Here too, the way I implemented it is different than numpy.distutils. Instead of harcoding flags depending on versions, I use code snipets, to get link options, mangling, and so on. For example, if you call the following in a Sconscript: config.CheckF77Mangling() This will try to get the mangling of the F77 compiler, and if sucessfull, will set a python function mangle, which returns the fortran name from the 'common' name. For example, for g77, mangle('dgemm') will returns dgemm_. Since the mangling is found at runtime, this can be used for not yet known compilers. Next ==== This was quite a lot of work, and I wanted to get something working as fast as possible. As such, the code is not always clean. Once I am satisfied with the global 'feel' of the new build, I will sanitize the API and implement the doc. Still, I believe the code to be much easier, simpler and robust than distutils (everything, from fortran support to libraries checkers is less than 2000 lines of python code, including tests; of course, this is largely thanks to scons). The thing I am the less sure about is how to combine checkers in meta-checkers: for example, I have checker for mkl, ATLAS, sunperf, and we sometimes want a BLAS checker, which would check all those as long as nothing is found. API-wise, I am not yet satisfied with the current way. I also want to play nice with f2py and scipy (totally untested for now).