解决方法
distutils默认情况下无法编译CUDA,因为它不支持同时使用多个编译器.默认情况下,它仅根据您的平台设置为编译器,而不是基于您拥有的源代码类型.
我在github上有一个示例项目,其中包含一些针对distutils的猴子补丁,以支持这一点.示例项目是一个C类,它管理一些GPU内存和一个CUDA内核,包含在swig中,并且只使用python setup.py install进行编译.重点是阵列操作,所以我们也使用numpy.对于此示例项目,所有内核都会将数组中的每个元素递增1.
代码在这里：https://github.com/rmcgibbo/npcuda-example.这是setup.py脚本.整个代码的关键是customize_compiler_for_nvcc().
import os from os.path import join as pjoin from setuptools import setup from distutils.extension import Extension from distutils.command.build_ext import build_ext import subprocess import numpy def find_in_path(name,path): "Find a file in a search path" #adapted fom http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/ for dir in path.split(os.pathsep): binpath = pjoin(dir,name) if os.path.exists(binpath): return os.path.abspath(binpath) return None def locate_cuda(): """Locate the CUDA environment on the system Returns a dict with keys 'home','nvcc','include',and 'lib64' and values giving the absolute path to each directory. Starts by looking for the CUDAHOME env variable. If not found,everything is based on finding 'nvcc' in the PATH. """ # first check if the CUDAHOME env variable is in use if 'CUDAHOME' in os.environ: home = os.environ['CUDAHOME'] nvcc = pjoin(home,'bin','nvcc') else: # otherwise,search the PATH for NVCC nvcc = find_in_path('nvcc',os.environ['PATH']) if nvcc is None: raise EnvironmentError('The nvcc binary Could not be ' 'located in your $PATH. Either add it to your path,or set $CUDAHOME') home = os.path.dirname(os.path.dirname(nvcc)) cudaconfig = {'home':home,'nvcc':nvcc,'include': pjoin(home,'include'),'lib64': pjoin(home,'lib64')} for k,v in cudaconfig.iteritems(): if not os.path.exists(v): raise EnvironmentError('The CUDA %s path Could not be located in %s' % (k,v)) return cudaconfig CUDA = locate_cuda() # Obtain the numpy include directory. This logic works across numpy versions. try: numpy_include = numpy.get_include() except AttributeError: numpy_include = numpy.get_numpy_include() ext = Extension('_gpuadder',sources=['src/swig_wrap.cpp','src/manager.cu'],library_dirs=[CUDA['lib64']],libraries=['cudart'],runtime_library_dirs=[CUDA['lib64']],# this Syntax is specific to this build system # we're only going to use certain compiler args with nvcc and not with gcc # the implementation of this trick is in customize_compiler() below extra_compile_args={'gcc': [],'nvcc': ['-arch=sm_20','--ptxas-options=-v','-c','--compiler-options',"'-fPIC'"]},include_dirs = [numpy_include,CUDA['include'],'src']) # check for swig if find_in_path('swig',os.environ['PATH']): subprocess.check_call('swig -python -c++ -o src/swig_wrap.cpp src/swig.i',shell=True) else: raise EnvironmentError('the swig executable was not found in your PATH') def customize_compiler_for_nvcc(self): """inject deep into distutils to customize how the dispatch to gcc/nvcc works. If you subclass UnixCCompiler,it's not trivial to get your subclass injected in,and still have the right customizations (i.e. distutils.sysconfig.customize_compiler) run on it. So instead of going the OO route,I have this. Note,it's kindof like a wierd functional subclassing going on.""" # tell the compiler it can processes .cu self.src_extensions.append('.cu') # save references to the default compiler_so and _comple methods default_compiler_so = self.compiler_so super = self._compile # Now redefine the _compile method. This gets executed for each # object but distutils doesn't have the ability to change compilers # based on source extension: we add it. def _compile(obj,src,ext,cc_args,extra_postargs,pp_opts): if os.path.splitext(src)[1] == '.cu': # use the cuda for .cu files self.set_executable('compiler_so',CUDA['nvcc']) # use only a subset of the extra_postargs,which are 1-1 translated # from the extra_compile_args in the Extension class postargs = extra_postargs['nvcc'] else: postargs = extra_postargs['gcc'] super(obj,postargs,pp_opts) # reset the default compiler_so,which we might have changed for cuda self.compiler_so = default_compiler_so # inject our redefined _compile method into the class self._compile = _compile # run the customize_compiler class custom_build_ext(build_ext): def build_extensions(self): customize_compiler_for_nvcc(self.compiler) build_ext.build_extensions(self) setup(name='gpuadder',# random Metadata. there's more you can supploy author='Robert McGibbon',version='0.1',# this is necessary so that the swigged python file gets picked up py_modules=['gpuadder'],package_dir={'': 'src'},ext_modules = [ext],# inject our custom trigger cmdclass={'build_ext': custom_build_ext},# since the package has c code,the egg cannot be zipped zip_safe=False)
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