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浅谈Python实时检测CPU和GPU的功耗

目录
  • 前言
  • GPU功耗检测方法
  • CPU功耗检测方法
  • sudo的困扰与解决
  • 完整功耗分析示例代码

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。

浅谈Python实时检测CPU和GPU的功耗

浅谈Python实时检测CPU和GPU的功耗

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 version
    version = pynvml.nvmlSystemGetDriverVersion()
    valupythones.append("GPU_device_driver_version:" + version.decode())
    gpucount = pynvml.nvmlDeviceGetCount()  # 显示有几块GPU
    for 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mWa
        values.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 编程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

浅谈Python实时检测CPU和GPU的功耗

 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 RaplPowerSource
 
source.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 jsjson
 
def 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'))

浅谈Python实时检测CPU和GPU的功耗

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_verbose
        self._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 r开发者_Go教程esume(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._accumulated
        else:
            now = cv2.getTickCount()
            self._elapsed = self._accumulated + (now - self._start_time)/cv2.getTickFrequency()        
        if self._is_verbose is True:      
            name =  self._name
            if 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 = False
        self.power_usage_gpu_values = list()
        self.power_usage_cpu_values = list()
        self.thread = None
        self.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 = True
            self.timer.start()
            while self.start_analyze:
                powerusage = pynvml.nvmlDeviceGetPowerUsage(handle)
                self.power_usage_gpu_values.append(powerusage/1000)
 
                s = socket.sockpythonet(socket.AF_INET, socket.SOCK_STREAM)
                host = socket.gethostname()
                port = 8888
                s.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 = False
        while self.thread and self.thread.isAlive():
            time.sleep(0.01)
        time_used = self.timer.elapsed()
        self.thread = None
        power_usage_gpu = statistics.mean(self.power_usage_gpu_values) if mean eYiYLbRulse self.power_usage_gpu_values
        power_usage_cpu = statistics.mean(self.power_usage_cpu_values) if mean else self.power_usage_cpu_values
        return time_used, power_usage_gpu, power_usage_cpu
 
 
power_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 result
    return wrapper
 
def 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 result
        return wrapper
    return 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}')

浅谈Python实时检测CPU和GPU的功耗

 到此这篇关于浅谈Python实时检测CPU和GPU的功耗的文章就介绍到这了,更多相关Python CPU和GPU功耗内容请搜索我们以前的文章或继续浏览下面的相关文章希望大家以后多多支持我们!

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