def _get_free_vram(self):
""" Obtain the amount of VRAM that is available, in Megabytes, for each connected GPU.
Returns
-------
list
List of floats containing the amount of VRAM available, in Megabytes, for each
connected GPU as corresponding to the values in :attr:`_handles
Notes
-----
There is no useful way to get free VRAM on PlaidML. OpenCL loads and unloads VRAM as
required, so this returns the total memory available per card for AMD cards, which us
not particularly useful.
"""
self._initialize()
if self._is_plaidml:
vram = self._plaid.vram
elif IS_MACOS:
vram = [
pynvx.cudaGetMemFree(handle, ignore=True) / (1024 * 1024)
for handle in self._handles
]
else:
vram = [
pynvml.nvmlDeviceGetMemoryInfo(handle).free / (1024 * 1024)
for handle in self._handles
]
self._shutdown()
self._log("debug", "GPU VRAM free: {}".format(vram))
return vram
def get_free(self):
""" Return the vram available """
self.initialize()
if IS_MACOS:
vram = [pynvx.cudaGetMemFree(handle, ignore=True) / (1024 * 1024)
for handle in self.handles]
else:
vram = [pynvml.nvmlDeviceGetMemoryInfo(handle).free / (1024 * 1024)
for handle in self.handles]
self.shutdown()
if self.logger:
self.logger.debug("GPU VRAM free: %s", vram)
return vram
def get_free(self):
""" Return the vram available """
self.initialize()
if IS_MACOS:
vram = [pynvx.cudaGetMemFree(handle, ignore=True) / (1024 * 1024)
for handle in self.handles]
else:
vram = [pynvml.nvmlDeviceGetMemoryInfo(handle).free / (1024 * 1024)
for handle in self.handles]
self.shutdown()
if self.logger:
self.logger.debug("GPU VRAM free: %s", vram)
return vram
def get_free(self):
""" Return the vram available """
self.initialize()
if is_macos:
vram = [
pynvx.cudaGetMemFree(handle, ignore=True) / (1024 * 1024)
for handle in self.handles
]
else:
vram = [
pynvml.nvmlDeviceGetMemoryInfo(handle).free / (1024 * 1024)
for handle in self.handles
]
self.shutdown()
return vram
def _get_free_vram(self) -> List[int]:
""" Obtain the amount of VRAM that is available, in Megabytes, for each connected Nvidia
GPU.
Returns
-------
list
List of `float`s containing the amount of VRAM available, in Megabytes, for each
connected GPU as corresponding to the values in :attr:`_handles
"""
vram = [
pynvx.cudaGetMemFree(handle, ignore=True) / (1024 * 1024) # pylint:disable=no-member
for handle in self._handles
]
self._log("debug", f"GPU VRAM free: {vram}")
return vram
def get_free(self):
""" Return the vram available """
self.initialize()
if self.plaid:
# NB There is no useful way to get free VRAM on PlaidML.
# OpenCL loads and unloads VRAM as required, so this returns the total memory
# It's not particularly useful
vram = self.plaid.vram
elif IS_MACOS:
vram = [pynvx.cudaGetMemFree(handle, ignore=True) / (1024 * 1024)
for handle in self.handles]
else:
vram = [pynvml.nvmlDeviceGetMemoryInfo(handle).free / (1024 * 1024)
for handle in self.handles]
self.shutdown()
if self.logger:
self.logger.debug("GPU VRAM free: %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_free():
v = m.cudaGetMemFree(0, ignore=True)
assert type(v) in integer_types