浅谈Python实时检测CPU和GPU的功耗
作者:小锋学长生活大爆炸
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 version version = pynvml.nvmlSystemGetDriverVersion() values.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 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 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 json 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'))
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 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._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.socket(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 else 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}')
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