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本文整理汇总了Python中pynvml.nvmlDeviceGetName方法的典型用法代码示例。如果您正苦于以下问题：Python pynvml.nvmlDeviceGetName方法的具体用法？Python pynvml.nvmlDeviceGetName怎么用？Python pynvml.nvmlDeviceGetName使用的例子？那么恭喜您, 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模块pynvml的用法示例。

在下文中一共展示了pynvml.nvmlDeviceGetName方法的6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，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

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# 需要导入模块: import pynvml [as 别名]# 或者: from pynvml import nvmlDeviceGetName [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 

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# 需要导入模块: import pynvml [as 别名]# 或者: from pynvml import nvmlDeviceGetName [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) 

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# 需要导入模块: import pynvml [as 别名]# 或者: from pynvml import nvmlDeviceGetName [as 别名]def _get_devices(self): Obtain the name of the installed devices. The quality of this information depends on the backend and OS being used, but it should be sufficient for identifying cards. Returns ------- list List of device names for connected GPUs as corresponding to the values in :attr:`_handles` self._initialize() if self._device_count == 0: names = list() if self._is_plaidml: names = self._plaid.names elif IS_MACOS: names = [pynvx.cudaGetName(handle, ignore=True) for handle in self._handles] else: names = [pynvml.nvmlDeviceGetName(handle).decode( utf-8 ) for handle in self._handles] self._log( debug , GPU Devices: {} .format(names)) return names 

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# 需要导入模块: import pynvml [as 别名]# 或者: from pynvml import nvmlDeviceGetName [as 别名]def device_name_for(device_handle): Get GPU device name  try: return nativestr(pynvml.nvmlDeviceGetName(device_handle)) except pynvml.NVMlError: return NVIDIA 

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# 需要导入模块: import pynvml [as 别名]# 或者: from pynvml import nvmlDeviceGetName [as 别名]def measure_cpu_gpu_instant_load(): # Get current cpu gpu load, as # load = [rank, cpu_load, nvidia_device_id, gpu_load] # result_arr: [load, load, ...] if cg_load_backend_ok: global gpu_a_load global gpu_m_count global p_handler cpu_load = p_handler.cpu_percent() gpu_m_count += 1 try: comm = current_communicator() if comm: index = comm.local_rank elif \'cuda\' in str(nn.get_current_context().backend): index = 0 else: raise Exception handler = pynvml.nvmlDeviceGetHandleByIndex(index) gpu_load = [ [index, pynvml.nvmlDeviceGetUtilizationRates(handler).gpu]] if index in gpu_a_load.keys(): gpu_a_load[index][\'name\'] = pynvml.nvmlDeviceGetName( handler).decode( utf-8 ) o_load = gpu_a_load[index][\'load\'] n_load = gpu_load[0][1] gpu_a_load[index][\'load\'] = ( (gpu_m_count - 1) * o_load + n_load) / gpu_m_count else: gpu_a_load[index] = { \'name\': pynvml.nvmlDeviceGetName(handler).decode( utf-8 ), \'load\': gpu_load[0][1] except Exception: gpu_load = [[-1, -1]] callback.update_status( (\'cpu_gpu_load\', collect_and_shape_result(cpu_load, gpu_load))) 

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# 需要导入模块: import pynvml [as 别名]# 或者: from pynvml import nvmlDeviceGetName [as 别名]def _log_statistics(self, elapsed_steps, elapsed_time, global_step): Collect and store all summary values. Arguments: elapsed_steps (int): The number of steps between the current trigger event and the last one. elapsed_time (float): The number of seconds between the current trigger event and the last one. global_step (tf.Tensor): Global step tensor.