我们从Python开源项目中，提取了以下14个代码示例，用于说明如何使用setuptools.command.build_ext()。
def run(self): # cwrap depends on pyyaml, so we can't import it earlier from tools.cwrap import cwrap from tools.cwrap.plugins.THPPlugin import THPPlugin from tools.cwrap.plugins.ArgcountSortPlugin import ArgcountSortPlugin from tools.cwrap.plugins.AutoGPU import AutoGPU from tools.cwrap.plugins.BoolOption import BoolOption from tools.cwrap.plugins.KwargsPlugin import KwargsPlugin from tools.cwrap.plugins.NullableArguments import NullableArguments from tools.cwrap.plugins.CuDNNPlugin import CuDNNPlugin cwrap('torch/csrc/generic/TensorMethods.cwrap', plugins=[ BoolOption(), THPPlugin(), AutoGPU(condition='IS_CUDA'), ArgcountSortPlugin(), KwargsPlugin(), ]) cwrap('torch/csrc/cudnn/cuDNN.cwrap', plugins=[ CuDNNPlugin(), NullableArguments() ]) # It's an old-style class in Python 2.7... setuptools.command.build_ext.build_ext.run(self)
def run(self): self.run_command('build_py') self.run_command('build_ext')
def run(self): # Print build options if WITH_NUMPY: print('-- Building with NumPy bindings') else: print('-- NumPy not found') if WITH_CUDNN: print('-- Detected cuDNN at ' + CUDNN_LIB_DIR + ', ' + CUDNN_INCLUDE_DIR) else: print('-- Not using cuDNN') if WITH_CUDA: print('-- Detected CUDA at ' + CUDA_HOME) else: print('-- Not using CUDA') if WITH_NCCL and SYSTEM_NCCL: print('-- Using system provided NCCL library') elif WITH_NCCL: print('-- Building NCCL library') else: print('-- Not using NCCL') # cwrap depends on pyyaml, so we can't import it earlier from tools.cwrap import cwrap from tools.cwrap.plugins.THPPlugin import THPPlugin from tools.cwrap.plugins.ArgcountSortPlugin import ArgcountSortPlugin from tools.cwrap.plugins.AutoGPU import AutoGPU from tools.cwrap.plugins.BoolOption import BoolOption from tools.cwrap.plugins.KwargsPlugin import KwargsPlugin from tools.cwrap.plugins.NullableArguments import NullableArguments from tools.cwrap.plugins.CuDNNPlugin import CuDNNPlugin from tools.cwrap.plugins.WrapDim import WrapDim thp_plugin = THPPlugin() cwrap('torch/csrc/generic/TensorMethods.cwrap', plugins=[ BoolOption(), thp_plugin, AutoGPU(condition='IS_CUDA'), ArgcountSortPlugin(), KwargsPlugin(), WrapDim() ]) cwrap('torch/csrc/cudnn/cuDNN.cwrap', plugins=[ CuDNNPlugin(), NullableArguments() ]) # It's an old-style class in Python 2.7... setuptools.command.build_ext.build_ext.run(self)
def install(self): self.build_dir = 'build/lib' setuptools.command.install_lib.install_lib.install(self) ################################################################################ # Setup pykaldi ################################################################################ # We add a 'dummy' extension so that setuptools runs the build_ext step.
def __init__(self, *args, **kwargs): setuptools.command.build_ext.build_ext.__init__(self, *args, **kwargs) self.journal_file = os.environ.get(JOURNAL_ENV) self.commands = []
def run(self): self.set_f90_compiler() self.start_journaling() self.compile_fortran_obj_files() result = setuptools.command.build_ext.build_ext.run(self) self.save_journal() self.cleanup() return result
def __init__(self, libdest, *args, **kwargs): self.libdest = libdest setuptools.Extension.__init__(self, *args, **kwargs) # Subclass setuptools build_ext to put the compiled shared library in the # appropriate place in the source tree.
def run(self): # Print build options if WITH_NUMPY: print('-- Building with NumPy bindings') else: print('-- NumPy not found') if WITH_CUDNN: print('-- Detected cuDNN at ' + CUDNN_LIB_DIR + ', ' + CUDNN_INCLUDE_DIR) else: print('-- Not using cuDNN') if WITH_CUDA: print('-- Detected CUDA at ' + CUDA_HOME) else: print('-- Not using CUDA') if WITH_NCCL and WITH_SYSTEM_NCCL: print('-- Using system provided NCCL library at ' + NCCL_LIB_DIR + ', ' + NCCL_INCLUDE_DIR) elif WITH_NCCL: print('-- Building NCCL library') else: print('-- Not using NCCL') if WITH_DISTRIBUTED: print('-- Building with distributed package ') monkey_patch_THD_link_flags() else: print('-- Building without distributed package') # cwrap depends on pyyaml, so we can't import it earlier from tools.cwrap import cwrap from tools.cwrap.plugins.THPPlugin import THPPlugin from tools.cwrap.plugins.ArgcountSortPlugin import ArgcountSortPlugin from tools.cwrap.plugins.AutoGPU import AutoGPU from tools.cwrap.plugins.BoolOption import BoolOption from tools.cwrap.plugins.KwargsPlugin import KwargsPlugin from tools.cwrap.plugins.NullableArguments import NullableArguments from tools.cwrap.plugins.CuDNNPlugin import CuDNNPlugin from tools.cwrap.plugins.WrapDim import WrapDim from tools.cwrap.plugins.AssertNDim import AssertNDim from tools.cwrap.plugins.Broadcast import Broadcast from tools.cwrap.plugins.ProcessorSpecificPlugin import ProcessorSpecificPlugin from tools.autograd.gen_variable_type import gen_variable_type thp_plugin = THPPlugin() cwrap('torch/csrc/generic/TensorMethods.cwrap', plugins=[ ProcessorSpecificPlugin(), BoolOption(), thp_plugin, AutoGPU(condition='IS_CUDA'), ArgcountSortPlugin(), KwargsPlugin(), AssertNDim(), WrapDim(), Broadcast() ]) cwrap('torch/csrc/cudnn/cuDNN.cwrap', plugins=[ CuDNNPlugin(), NullableArguments() ]) # Build ATen based Variable classes autograd_gen_dir = 'torch/csrc/autograd/generated' if not os.path.exists(autograd_gen_dir): os.mkdir(autograd_gen_dir) gen_variable_type( 'torch/lib/build/ATen/ATen/Declarations.yaml', autograd_gen_dir) # It's an old-style class in Python 2.7... setuptools.command.build_ext.build_ext.run(self)
def run(self): # Print build options if WITH_NUMPY: print('-- Building with NumPy bindings') else: print('-- NumPy not found') if WITH_CUDNN: print('-- Detected cuDNN at ' + CUDNN_LIB_DIR + ', ' + CUDNN_INCLUDE_DIR) else: print('-- Not using cuDNN') if WITH_CUDA: print('-- Detected CUDA at ' + CUDA_HOME) else: print('-- Not using CUDA') if WITH_NCCL and WITH_SYSTEM_NCCL: print('-- Using system provided NCCL library at ' + NCCL_SYSTEM_LIB + ', ' + NCCL_INCLUDE_DIR) elif WITH_NCCL: print('-- Building NCCL library') else: print('-- Not using NCCL') if WITH_DISTRIBUTED: print('-- Building with distributed package ') monkey_patch_THD_link_flags() else: print('-- Building without distributed package') generate_code(ninja_global) if IS_WINDOWS: build_temp = self.build_temp build_dir = 'torch/csrc' ext_filename = self.get_ext_filename('_C') lib_filename = '.'.join(ext_filename.split('.')[:-1]) + '.lib' _C_LIB = os.path.join(build_temp, build_dir, lib_filename).replace('\\', '/') THNN.extra_link_args += [_C_LIB] if WITH_CUDA: THCUNN.extra_link_args += [_C_LIB] else: # To generate .obj files for AutoGPU for the export class # a header file cannot build, so it has to be copied to someplace as a source file if os.path.exists("torch/csrc/generated/AutoGPU_cpu_win.cpp"): os.remove("torch/csrc/generated/AutoGPU_cpu_win.cpp") shutil.copyfile("torch/csrc/cuda/AutoGPU.h", "torch/csrc/generated/AutoGPU_cpu_win.cpp") if WITH_NINJA: # before we start the normal build make sure all generated code # gets built ninja_global.run() # It's an old-style class in Python 2.7... setuptools.command.build_ext.build_ext.run(self)
