我剛剛寫了一個純Python腳本，它完成了您似乎需要的一些事情（我從pystream項目中獲取了大部分內容）。它基本上只是CUDA運行時庫（它使用ctypes）中的一些函數的包裝。查看main（）函數以查看示例用法。另外，請注意，我只是寫了它，所以它可能包含錯誤。謹慎使用。
#!/bin/bash
import sys
import platform
import ctypes
"""
cudart.py: used to access pars of the CUDA runtime library.
Most of this code was lifted from the pystream project (it's BSD licensed):
http://code.google.com/p/pystream
Note that this is likely to only work with CUDA 2.3
To extend to other versions, you may need to edit the DeviceProp Class
"""
cudaSuccess = 0
errorDict = {
1: 'MissingConfigurationError',
2: 'MemoryAllocationError',
3: 'InitializationError',
4: 'LaunchFailureError',
5: 'PriorLaunchFailureError',
6: 'LaunchTimeoutError',
7: 'LaunchOutOfResourcesError',
8: 'InvalidDeviceFunctionError',
9: 'InvalidConfigurationError',
10: 'InvalidDeviceError',
11: 'InvalidValueError',
12: 'InvalidPitchValueError',
13: 'InvalidSymbolError',
14: 'MapBufferObjectFailedError',
15: 'UnmapBufferObjectFailedError',
16: 'InvalidHostPointerError',
17: 'InvalidDevicePointerError',
18: 'InvalidTextureError',
19: 'InvalidTextureBindingError',
20: 'InvalidChannelDescriptorError',
21: 'InvalidMemcpyDirectionError',
22: 'AddressOfConstantError',
23: 'TextureFetchFailedError',
24: 'TextureNotBoundError',
25: 'SynchronizationError',
26: 'InvalidFilterSettingError',
27: 'InvalidNormSettingError',
28: 'MixedDeviceExecutionError',
29: 'CudartUnloadingError',
30: 'UnknownError',
31: 'NotYetImplementedError',
32: 'MemoryValueTooLargeError',
33: 'InvalidResourceHandleError',
34: 'NotReadyError',
0x7f: 'StartupFailureError',
10000: 'ApiFailureBaseError'}
try:
if platform.system() == "Microsoft":
_libcudart = ctypes.windll.LoadLibrary('cudart.dll')
elif platform.system()=="Darwin":
_libcudart = ctypes.cdll.LoadLibrary('libcudart.dylib')
else:
_libcudart = ctypes.cdll.LoadLibrary('libcudart.so')
_libcudart_error = None
except OSError, e:
_libcudart_error = e
_libcudart = None
def _checkCudaStatus(status):
if status != cudaSuccess:
eClassString = errorDict[status]
# Get the class by name from the top level of this module
eClass = globals()[eClassString]
raise eClass()
def _checkDeviceNumber(device):
assert isinstance(device, int), "device number must be an int"
assert device >= 0, "device number must be greater than 0"
assert device < 2**8-1, "device number must be < 255"
# cudaDeviceProp
class DeviceProp(ctypes.Structure):
_fields_ = [
("name", 256*ctypes.c_char), # < ASCII string identifying device
("totalGlobalMem", ctypes.c_size_t), # < Global memory available on device in bytes
("sharedMemPerBlock", ctypes.c_size_t), # < Shared memory available per block in bytes
("regsPerBlock", ctypes.c_int), # < 32-bit registers available per block
("warpSize", ctypes.c_int), # < Warp size in threads
("memPitch", ctypes.c_size_t), # < Maximum pitch in bytes allowed by memory copies
("maxThreadsPerBlock", ctypes.c_int), # < Maximum number of threads per block
("maxThreadsDim", 3*ctypes.c_int), # < Maximum size of each dimension of a block
("maxGridSize", 3*ctypes.c_int), # < Maximum size of each dimension of a grid
("clockRate", ctypes.c_int), # < Clock frequency in kilohertz
("totalConstMem", ctypes.c_size_t), # < Constant memory available on device in bytes
("major", ctypes.c_int), # < Major compute capability
("minor", ctypes.c_int), # < Minor compute capability
("textureAlignment", ctypes.c_size_t), # < Alignment requirement for textures
("deviceOverlap", ctypes.c_int), # < Device can concurrently copy memory and execute a kernel
("multiProcessorCount", ctypes.c_int), # < Number of multiprocessors on device
("kernelExecTimeoutEnabled", ctypes.c_int), # < Specified whether there is a run time limit on kernels
("integrated", ctypes.c_int), # < Device is integrated as opposed to discrete
("canMapHostMemory", ctypes.c_int), # < Device can map host memory with cudaHostAlloc/cudaHostGetDevicePointer
("computeMode", ctypes.c_int), # < Compute mode (See ::cudaComputeMode)
("__cudaReserved", 36*ctypes.c_int),
]
def __str__(self):
return """NVidia GPU Specifications:
Name: %s
Total global mem: %i
Shared mem per block: %i
Registers per block: %i
Warp size: %i
Mem pitch: %i
Max threads per block: %i
Max treads dim: (%i, %i, %i)
Max grid size: (%i, %i, %i)
Total const mem: %i
Compute capability: %i.%i
Clock Rate (GHz): %f
Texture alignment: %i
""" % (self.name, self.totalGlobalMem, self.sharedMemPerBlock,
self.regsPerBlock, self.warpSize, self.memPitch,
self.maxThreadsPerBlock,
self.maxThreadsDim[0], self.maxThreadsDim[1], self.maxThreadsDim[2],
self.maxGridSize[0], self.maxGridSize[1], self.maxGridSize[2],
self.totalConstMem, self.major, self.minor,
float(self.clockRate)/1.0e6, self.textureAlignment)
def cudaGetDeviceCount():
if _libcudart is None: return 0
deviceCount = ctypes.c_int()
status = _libcudart.cudaGetDeviceCount(ctypes.byref(deviceCount))
_checkCudaStatus(status)
return deviceCount.value
def getDeviceProperties(device):
if _libcudart is None: return None
_checkDeviceNumber(device)
props = DeviceProp()
status = _libcudart.cudaGetDeviceProperties(ctypes.byref(props), device)
_checkCudaStatus(status)
return props
def getDriverVersion():
if _libcudart is None: return None
version = ctypes.c_int()
_libcudart.cudaDriverGetVersion(ctypes.byref(version))
v = "%d.%d" % (version.value//1000,
version.value%100)
return v
def getRuntimeVersion():
if _libcudart is None: return None
version = ctypes.c_int()
_libcudart.cudaRuntimeGetVersion(ctypes.byref(version))
v = "%d.%d" % (version.value//1000,
version.value%100)
return v
def getGpuCount():
count=0
for ii in range(cudaGetDeviceCount()):
props = getDeviceProperties(ii)
if props.major!=9999: count+=1
return count
def getLoadError():
return _libcudart_error
version = getDriverVersion()
if version is not None and not version.startswith('2.3'):
sys.stdout.write("WARNING: Driver version %s may not work with %s\n" %
(version, sys.argv[0]))
version = getRuntimeVersion()
if version is not None and not version.startswith('2.3'):
sys.stdout.write("WARNING: Runtime version %s may not work with %s\n" %
(version, sys.argv[0]))
def main():
sys.stdout.write("Driver version: %s\n" % getDriverVersion())
sys.stdout.write("Runtime version: %s\n" % getRuntimeVersion())
nn = cudaGetDeviceCount()
sys.stdout.write("Device count: %s\n" % nn)
for ii in range(nn):
props = getDeviceProperties(ii)
sys.stdout.write("\nDevice %d:\n" % ii)
#sys.stdout.write("%s" % props)
for f_name, f_type in props._fields_:
attr = props.__getattribute__(f_name)
sys.stdout.write(" %s: %s\n" % (f_name, attr))
gpuCount = getGpuCount()
if gpuCount > 0:
sys.stdout.write("\n")
sys.stdout.write("GPU count: %d\n" % getGpuCount())
e = getLoadError()
if e is not None:
sys.stdout.write("There was an error loading a library:\n%s\n\n" % e)
if __name__=="__main__":
main()
可以避免使用CMake來運行與CUDA運行時一起安裝的工具，如nvidia-smi，從而避免維護和編譯單獨的程序。看到我的答案。 – mabraham 2017-01-10 16:27:05