def get_used(self):
""" Return the vram in use """
self.initialize()
if IS_MACOS:
vram = [pynvx.cudaGetMemUsed(handle, ignore=True) / (1024 * 1024)
for handle in self.handles]
else:
vram = [pynvml.nvmlDeviceGetMemoryInfo(handle).used / (1024 * 1024)
for handle in self.handles]
self.shutdown()
if self.logger:
self.logger.verbose("GPU VRAM used: %s", vram)
return vram
def get_used(self):
""" Return the vram in use """
self.initialize()
if IS_MACOS:
vram = [pynvx.cudaGetMemUsed(handle, ignore=True) / (1024 * 1024)
for handle in self.handles]
else:
vram = [pynvml.nvmlDeviceGetMemoryInfo(handle).used / (1024 * 1024)
for handle in self.handles]
self.shutdown()
if self.logger:
self.logger.verbose("GPU VRAM used: %s", vram)
return vram
def get_used(self):
""" Return the vram in use """
self.initialize()
if is_macos:
vram = [
pynvx.cudaGetMemUsed(handle, ignore=True) / (1024 * 1024)
for handle in self.handles
]
else:
vram = [
pynvml.nvmlDeviceGetMemoryInfo(handle).used / (1024 * 1024)
for handle in self.handles
]
self.shutdown()
if self.verbose:
print("GPU VRAM used: {}".format(vram))
return vram
def get_used(self):
""" Return the vram in use """
self.initialize()
if self.plaid:
# NB There is no useful way to get allocated VRAM on PlaidML.
# OpenCL loads and unloads VRAM as required, so this returns 0
# It's not particularly useful
vram = [0 for idx in range(self.device_count)]
elif IS_MACOS:
vram = [pynvx.cudaGetMemUsed(handle, ignore=True) / (1024 * 1024)
for handle in self.handles]
else:
vram = [pynvml.nvmlDeviceGetMemoryInfo(handle).used / (1024 * 1024)
for handle in self.handles]
self.shutdown()
if self.logger:
self.logger.verbose("GPU VRAM used: %s", vram)
return vram
def print_info():
try:
pynvx.cudaInit()
except RuntimeError as e:
print(e)
return
print('================ CUDA INFO =====================')
print('Driver Version : {}'.format(pynvx.cudaSystemGetDriverVersion()))
print('Runtime Version : {}'.format(pynvx.cudaSystemGetRuntimeVersion()))
print('Device Count : {}'.format(pynvx.cudaDeviceGetCount()))
handles = pynvx.cudaDeviceGetHandles()
for handle in handles:
print('------------------------------------------------')
print('Device {}:'.format(handle))
print('Device Name : {}'.format(
pynvx.cudaGetName(handle)))
print('Device ClockRate : {} MHz'.format(
pynvx.cudaGetClockRate(handle) / 1024))
print('Device ComputeCapability : {}'.format(
pynvx.cudaGetComputeCapability(handle)))
print('Device ProcessorCount : {}'.format(
pynvx.cudaGetMultiProcessorCount(handle)))
print('Device PciBusID : {}'.format(
pynvx.cudaGetPciBusID(handle)))
print('Device PciDeviceID : {}'.format(
pynvx.cudaGetPciDeviceID(handle)))
print('Device PciDomainID : {}'.format(
pynvx.cudaGetPciDomainID(handle)))
print('Device MemTotal : {} MiB'.format(
pynvx.cudaGetMemTotal(handle) / (1024 * 1024)))
print('Device MemFree : {} MiB'.format(
pynvx.cudaGetMemFree(handle) / (1024 * 1024)))
print('Device MemUsed : {} MiB'.format(
pynvx.cudaGetMemUsed(handle) / (1024 * 1024)))
def test_memory_used():
v = m.cudaGetMemUsed(0, ignore=True)
assert type(v) in integer_types