本文整理汇总了Python中pynvml.nvmlDeviceGetMemoryInfo方法的典型用法代码示例。如果您正苦于以下问题:Python pynvml.nvmlDeviceGetMemoryInfo方法的具体用法?Python pynvml.nvmlDeviceGetMemoryInfo怎么用?Python pynvml.nvmlDeviceGetMemoryInfo使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pynvml
的用法示例。
在下文中一共展示了pynvml.nvmlDeviceGetMemoryInfo方法的10个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: getGPUUsage
# 需要导入模块: import pynvml [as 别名]
# 或者: from pynvml import nvmlDeviceGetMemoryInfo [as 别名]
def getGPUUsage():
try:
pynvml.nvmlInit()
count = pynvml.nvmlDeviceGetCount()
if count == 0:
return None
result = {"driver": pynvml.nvmlSystemGetDriverVersion(),
"gpu_count": int(count)
}
i = 0
gpuData = []
while i<count:
handle = pynvml.nvmlDeviceGetHandleByIndex(i)
mem = pynvml.nvmlDeviceGetMemoryInfo(handle)
gpuData.append({"device_num": i, "name": pynvml.nvmlDeviceGetName(handle), "total": round(float(mem.total)/1000000000, 2), "used": round(float(mem.used)/1000000000, 2)})
i = i+1
result["devices"] = jsonpickle.encode(gpuData, unpicklable=False)
except Exception as e:
result = {"driver": "No GPU!", "gpu_count": 0, "devices": []}
return result
示例2: gpu_info
# 需要导入模块: import pynvml [as 别名]
# 或者: from pynvml import nvmlDeviceGetMemoryInfo [as 别名]
def gpu_info(self):
# pip install nvidia-ml-py3
if len(self.gpu_ids) >= 0 and torch.cuda.is_available():
try:
import pynvml
pynvml.nvmlInit()
self.config_dic['gpu_driver_version'] = pynvml.nvmlSystemGetDriverVersion()
for gpu_id in self.gpu_ids:
handle = pynvml.nvmlDeviceGetHandleByIndex(gpu_id)
gpu_id_name = "gpu%s" % gpu_id
mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle)
gpu_utilize = pynvml.nvmlDeviceGetUtilizationRates(handle)
self.config_dic['%s_device_name' % gpu_id_name] = pynvml.nvmlDeviceGetName(handle)
self.config_dic['%s_mem_total' % gpu_id_name] = gpu_mem_total = round(mem_info.total / 1024 ** 3, 2)
self.config_dic['%s_mem_used' % gpu_id_name] = gpu_mem_used = round(mem_info.used / 1024 ** 3, 2)
# self.config_dic['%s_mem_free' % gpu_id_name] = gpu_mem_free = mem_info.free // 1024 ** 2
self.config_dic['%s_mem_percent' % gpu_id_name] = round((gpu_mem_used / gpu_mem_total) * 100, 1)
self._set_dict_smooth('%s_utilize_gpu' % gpu_id_name, gpu_utilize.gpu, 0.8)
# self.config_dic['%s_utilize_gpu' % gpu_id_name] = gpu_utilize.gpu
# self.config_dic['%s_utilize_memory' % gpu_id_name] = gpu_utilize.memory
pynvml.nvmlShutdown()
except Exception as e:
print(e)
示例3: _get_vram
# 需要导入模块: import pynvml [as 别名]
# 或者: from pynvml import nvmlDeviceGetMemoryInfo [as 别名]
def _get_vram(self):
""" Obtain the total VRAM in Megabytes for each connected GPU.
Returns
-------
list
List of floats containing the total amount of VRAM in Megabytes for each connected GPU
as corresponding to the values in :attr:`_handles
"""
self._initialize()
if self._device_count == 0:
vram = list()
elif self._is_plaidml:
vram = self._plaid.vram
elif IS_MACOS:
vram = [pynvx.cudaGetMemTotal(handle, ignore=True) / (1024 * 1024)
for handle in self._handles]
else:
vram = [pynvml.nvmlDeviceGetMemoryInfo(handle).total /
(1024 * 1024)
for handle in self._handles]
self._log("debug", "GPU VRAM: {}".format(vram))
return vram
示例4: __query_mem
# 需要导入模块: import pynvml [as 别名]
# 或者: from pynvml import nvmlDeviceGetMemoryInfo [as 别名]
def __query_mem(handle):
"""
Query information on the memory of a GPU.
Arguments:
handle:
NVML device handle.
Returns:
summaries (:obj:`dict`):
Dictionary containing the memory values for ['mem_used', 'mem_free', 'mem_total'].
All values are given in MiB as integers.
"""
# Query information on the GPUs memory usage.
info = nvml.nvmlDeviceGetMemoryInfo(handle)
summaries = dict()
bytes_mib = 1024.0 ** 2
summaries['mem_used'] = int(info.used / bytes_mib)
summaries['mem_free'] = int(info.free / bytes_mib)
summaries['mem_total'] = int(info.total / bytes_mib)
return summaries
示例5: get_appropriate_cuda
# 需要导入模块: import pynvml [as 别名]
# 或者: from pynvml import nvmlDeviceGetMemoryInfo [as 别名]
def get_appropriate_cuda(task_scale='s'):
if task_scale not in {'s','m','l'}:
logger.info('task scale wrong!')
exit(2)
import pynvml
pynvml.nvmlInit()
total_cuda_num = pynvml.nvmlDeviceGetCount()
for i in range(total_cuda_num):
logger.info(i)
handle = pynvml.nvmlDeviceGetHandleByIndex(i) # 这里的0是GPU id
memInfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
utilizationInfo = pynvml.nvmlDeviceGetUtilizationRates(handle)
logger.info(i, 'mem:', memInfo.used / memInfo.total, 'util:',utilizationInfo.gpu)
if memInfo.used / memInfo.total < 0.15 and utilizationInfo.gpu <0.2:
logger.info(i,memInfo.used / memInfo.total)
return 'cuda:'+str(i)
if task_scale=='s':
max_memory=2000
elif task_scale=='m':
max_memory=6000
else:
max_memory = 9000
max_id = -1
for i in range(total_cuda_num):
handle = pynvml.nvmlDeviceGetHandleByIndex(0) # 这里的0是GPU id
memInfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
utilizationInfo = pynvml.nvmlDeviceGetUtilizationRates(handle)
if max_memory < memInfo.free:
max_memory = memInfo.free
max_id = i
if id == -1:
logger.info('no appropriate gpu, wait!')
exit(2)
return 'cuda:'+str(max_id)
# if memInfo.used / memInfo.total < 0.5:
# return
示例6: mem_used_for
# 需要导入模块: import pynvml [as 别名]
# 或者: from pynvml import nvmlDeviceGetMemoryInfo [as 别名]
def mem_used_for(device_handle):
"""Get GPU device memory consumption in percent"""
try:
return pynvml.nvmlDeviceGetMemoryInfo(device_handle).used
except pynvml.NVMLError:
return None
示例7: mem_used_percent_for
# 需要导入模块: import pynvml [as 别名]
# 或者: from pynvml import nvmlDeviceGetMemoryInfo [as 别名]
def mem_used_percent_for(device_handle):
"""Get GPU device memory consumption in percent"""
try:
memory_info = pynvml.nvmlDeviceGetMemoryInfo(device_handle)
return memory_info.used * 100.0 / memory_info.total
except pynvml.NVMLError:
return None
示例8: _get_free_vram
# 需要导入模块: import pynvml [as 别名]
# 或者: from pynvml import nvmlDeviceGetMemoryInfo [as 别名]
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
示例9: query_gpu
# 需要导入模块: import pynvml [as 别名]
# 或者: from pynvml import nvmlDeviceGetMemoryInfo [as 别名]
def query_gpu(handle: int) -> Dict:
memory = pynvml.nvmlDeviceGetMemoryInfo(handle) # in Bytes
utilization = pynvml.nvmlDeviceGetUtilizationRates(handle)
return {
"gpu_{}_memory_free".format(handle): int(memory.free),
"gpu_{}_memory_used".format(handle): int(memory.used),
"gpu_{}_utilization".format(handle): utilization.gpu,
}
示例10: _crawl_in_system
# 需要导入模块: import pynvml [as 别名]
# 或者: from pynvml import nvmlDeviceGetMemoryInfo [as 别名]
def _crawl_in_system(self):
'''
nvidia-smi returns following: MEMORY, UTILIZATION, ECC, TEMPERATURE,
POWER, CLOCK, COMPUTE, PIDS, PERFORMANCE, SUPPORTED_CLOCKS,
PAGE_RETIREMENT, ACCOUNTING
currently, following are requested based on dlaas requirements:
utilization.gpu, utilization.memory,
memory.total, memory.free, memory.used
nvidia-smi --query-gpu=utilization.gpu,utilization.memory,\
memory.total,memory.free,memory.used --format=csv,noheader,nounits
'''
if self._init_nvml() == -1:
return
self.inspect_arr = exec_dockerps()
num_gpus = pynvml.nvmlDeviceGetCount()
for gpuid in range(0, num_gpus):
gpuhandle = pynvml.nvmlDeviceGetHandleByIndex(gpuid)
temperature = pynvml.nvmlDeviceGetTemperature(
gpuhandle, pynvml.NVML_TEMPERATURE_GPU)
memory = pynvml.nvmlDeviceGetMemoryInfo(gpuhandle)
mem_total = memory.total / 1024 / 1024
mem_used = memory.used / 1024 / 1024
mem_free = memory.free / 1024 / 1024
power_draw = pynvml.nvmlDeviceGetPowerUsage(gpuhandle) / 1000
power_limit = pynvml.nvmlDeviceGetEnforcedPowerLimit(
gpuhandle) / 1000
util = pynvml.nvmlDeviceGetUtilizationRates(gpuhandle)
util_gpu = util.gpu
util_mem = util.memory
entry = {
'utilization': {'gpu': util_gpu, 'memory': util_mem},
'memory': {'total': mem_total, 'free': mem_free,
'used': mem_used},
'temperature': temperature,
'power': {'draw': power_draw, 'limit': power_limit}
}
key = self._get_feature_key(gpuhandle, gpuid)
if gpuid == num_gpus - 1:
self._shutdown_nvml()
yield (key, entry, 'gpu')
return