前言

相关一些检测工具挺多的，比如powertop、powerstat、s-tui等。但如何通过代码的方式来实时检测，是个麻烦的问题。通过许久的搜索和自己的摸索，发现了可以检测CPU和GPU功耗的方法。如果有什么不对，或有更好的方法，欢迎评论留言！

文末附完整功耗分析的示例代码！

GPU功耗检测方法

如果是常规的工具，可以使用官方的NVML。但这里需要Python控制，所以使用了对应的封装：pynvml。

先安装：

pip install pynvml

关于这个库，网上的使用教程挺多的。这里直接给出简单的示例代码：

import pynvml
pynvml.nvmlInit()handle = pynvml.nvmlDeviceGetHandleByIndex(0)
powerusage = pynvml.nvmlDeviceGetPowerUsage(handle) / 1000

这个方法获取的值，跟使用“nvidia-smi”指令得到的是一样的。

附赠一个来自网上的获取更详细信息的函数：

def get_sensor_values():"""get Sensor values:return:"""values = list()# get gpu driver versionversion = pynvml.nvmlSystemGetDriverVersion()values.append("GPU_device_driver_version:" + version.decode())gpucount = pynvml.nvmlDeviceGetCount()  # 显示有几块GPUfor gpu_id in range(gpucount):handle = pynvml.nvmlDeviceGetHandleByIndex(gpu_id)name = pynvml.nvmlDeviceGetName(handle).decode()meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)# print(meminfo.total)  # 显卡总的显存大小gpu_id = str(gpu_id)values.append("GPU " + gpu_id + " " + name + " 总共显存大小:" + str(common.bytes2human(meminfo.total)))# print(meminfo.used)  # 显存使用大小values.append("GPU " + gpu_id + " " + name + " 显存使用大小:" + str(common.bytes2human(meminfo.used)))# print(meminfo.free)  # 显卡剩余显存大小values.append("GPU " + gpu_id + " " + name + " 剩余显存大小:" + str(common.bytes2human(meminfo.free)))values.append("GPU " + gpu_id + " " + name + " 剩余显存比例:" + str(int((meminfo.free / meminfo.total) * 100)))utilization = pynvml.nvmlDeviceGetUtilizationRates(handle)# print(utilization.gpu)  # gpu利用率values.append("GPU " + gpu_id + " " + name + " GPU利用率:" + str(utilization.gpu))powerusage = pynvml.nvmlDeviceGetPowerUsage(handle)# print(powerusage / 1000) # 当前功耗, 原始单位是mWavalues.append("GPU " + gpu_id + " " + name + " 当前功耗(W):" + str(powerusage / 1000))# 当前gpu power capacity# pynvml.nvmlDeviceGetEnforcedPowerLimit(handle)# 通过以下方法可以获取到gpu的温度，暂时采用ipmi sdr获取gpu的温度，此处暂不处理# temp = pynvml.nvmlDeviceGetTemperature(handle,0)print('\n'.join(values))return values

CPU功耗检测方法

这个没有找到开源可以直接用的库。但经过搜索，发现大家都在用的s-tui工具是开源的！通过查看源码，发现他是有获取CPU功耗部分的代码，所以就参考他的源码写了一下。

先安装：

sudo apt install s-tui
pip install s-tui

先直接运行工具看一下效果(不使用sudo是不会出来Power的)：

sudo s-tui

说明这个工具确实能获取到CPU的功耗。其中package就是2个CPU，dram是内存条功耗(一般不准，可以不用)。

直接给出简单的示例代码：

from s_tui.sources.rapl_power_source import RaplPowerSourcesource.update()
summary = dict(source.get_sensors_summary())cpu_power_total = str(sum(list(map(float, [summary[key] for key in summary.keys() if key.startswith('package')]))))

不过注意！由于需要sudo权限，所以运行这个py文件时候，也需要sudo方式，比如：

sudo python demo.py

sudo的困扰与解决

上面提到，由于必须要sudo方式，但sudo python就换了运行脚本的环境了呀，这个比较棘手。后来想了个方法，曲线救国一下。通过sudo运行一个脚本，并开启socket监听；而我们自己真正的脚本，在需要获取CPU功耗时候，连接一下socket就行。

为什么这里使用socket而不是http呢？因为socket更高效一点！

我们写一个“power_listener.py”来监听：

from s_tui.sources.rapl_power_source import RaplPowerSource
import socket
import jsondef output_to_terminal(source):results = {}if source.get_is_available():source.update()source_name = source.get_source_name()results[source_name] = source.get_sensors_summary()for key, value in results.items():print(str(key) + ": ")for skey, svalue in value.items():print(str(skey) + ": " + str(svalue) + ", ")source = RaplPowerSource()
# output_to_terminal(source)s = socket.socket()
host = socket.gethostname()
port = 8888
s.bind((host, port))
s.listen(5)
print("等待客户端连接...")
while True:c, addr = s.accept()source.update()summary = dict(source.get_sensors_summary())#msg = json.dumps(summary)# package表示CPU，dram表示内存(一般不准)power_total = str(sum(list(map(float, [summary[key] for key in summary.keys() if key.startswith('package')]))))print(f'发送给{addr}：{power_total}')c.send(power_total.encode('utf-8'))c.close()                # 关闭连接

因此，在需要获取CPU功耗时候，只需要：

s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
host = socket.gethostname()
port = 8888
s.connect((host, port))
msg = s.recv(1024)
s.close()
power_usage_cpu = float(msg.decode('utf-8'))

完整功耗分析示例代码

提供一个我自己编写和使用的功耗分析代码，仅供参考。（注意上面的power_listener.py需要运行着）

import cv2
import socket
import sys
import threading
import json
import statistics
from psutil import _common as common
import pynvml
pynvml.nvmlInit()class Timer: def __init__(self, name = '', is_verbose = False):self._name = name self._is_verbose = is_verboseself._is_paused = False self._start_time = None self._accumulated = 0 self._elapsed = 0         self.start()def start(self):self._accumulated = 0         self._start_time = cv2.getTickCount()def pause(self): now_time = cv2.getTickCount()self._accumulated += (now_time - self._start_time)/cv2.getTickFrequency() self._is_paused = True   def resume(self): if self._is_paused: # considered only if paused self._start_time = cv2.getTickCount()self._is_paused = False                      def elapsed(self):if self._is_paused:self._elapsed = self._accumulatedelse:now = cv2.getTickCount()self._elapsed = self._accumulated + (now - self._start_time)/cv2.getTickFrequency()        if self._is_verbose is True:      name =  self._nameif self._is_paused:name += ' [paused]'message = 'Timer::' + name + ' - elapsed: ' + str(self._elapsed) timer_print(message)return self._elapsed   class PowerUsage:'''demo:power_usage = PowerUsage()power_usage.analyze_start()time.sleep(2)time_used, power_usage_gpu, power_usage_cpu = power_usage.analyze_end()print(time_used)print(power_usage_gpu)print(power_usage_cpu)'''def __init__(self):self.start_analyze = Falseself.power_usage_gpu_values = list()self.power_usage_cpu_values = list()self.thread = Noneself.timer = Timer(name='GpuPowerUsage', is_verbose=False)def analyze_start(self, gpu_id=0, delay=0.1):handle = pynvml.nvmlDeviceGetHandleByIndex(gpu_id)def start():self.power_usage_gpu_values.clear()self.power_usage_cpu_values.clear()self.start_analyze = Trueself.timer.start()while self.start_analyze:powerusage = pynvml.nvmlDeviceGetPowerUsage(handle)self.power_usage_gpu_values.append(powerusage/1000)s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)host = socket.gethostname()port = 8888s.connect((host, port))msg = s.recv(1024)s.close()self.power_usage_cpu_values.append(float(msg.decode('utf-8')))time.sleep(delay)self.thread = threading.Thread(target=start, daemon=True)self.thread.start()def analyze_end(self, mean=True):self.start_analyze = Falsewhile self.thread and self.thread.isAlive():time.sleep(0.01)time_used = self.timer.elapsed()self.thread = Nonepower_usage_gpu = statistics.mean(self.power_usage_gpu_values) if mean else self.power_usage_gpu_valuespower_usage_cpu = statistics.mean(self.power_usage_cpu_values) if mean else self.power_usage_cpu_valuesreturn time_used, power_usage_gpu, power_usage_cpupower_usage = PowerUsage()
def power_usage_api(func, note=''):@wraps(func)def wrapper(*args, **kwargs):power_usage.analyze_start()result = func(*args, **kwargs)print(f'{note}{power_usage.analyze_end()}')return resultreturn wrapperdef power_usage_api2(note=''):def decorator(func):@wraps(func)def wrapper(*args, **kwargs):power_usage.analyze_start()result = func(*args, **kwargs)print(f'{note}{power_usage.analyze_end()}')return resultreturn wrapperreturn decorator

用法示例：

power_usage = PowerUsage()
power_usage.analyze_start()
# ----------------------
# xxx 某一段待分析的代码
# 这里以sleep表示运行时长
time.sleep(2)
# ----------------------
time_used, power_usage_gpu, power_usage_cpu = power_usage.analyze_end()
print(f'time_used: {time_used}')
print(f'power_usage_gpu: {power_usage_gpu}')
print(f'power_usage_cpu: {power_usage_cpu}')