基于cpu使用代码@Hrabal，这是我使用的:

from subprocess import Popen, PIPE

def get_cpu_usage():
    ''' Get CPU usage on Linux by reading /proc/stat '''

    sub = Popen(('grep', 'cpu', '/proc/stat'), stdout=PIPE, stderr=PIPE)
    top_vals = [int(val) for val in sub.communicate()[0].split('\n')[0].split[1:5]]

    return (top_vals[0] + top_vals[2]) * 100. /(top_vals[0] + top_vals[2] + top_vals[3])

关于CPU的详细信息，请使用psutil库 https://psutil.readthedocs.io/en/latest/#cpu 对于RAM频率(以MHz为单位)，使用内置的Linux库dmidecode并操作输出位;)。此命令需要root权限，因此也需要提供您的密码。只需复制以下推荐替换mypass与您的密码

进口操作系统 操作系统。system("echo mpass | sudo -S dmidecode -t memory | grep 'Clock Speed' | cut -d ':' -f2") ------------------- 输出 --------------------------- 1600吨/秒 未知的 1600吨/秒 未知的0

更具体的 [i在os中的i。]popen("echo mpass | sudo -S dmidecode -t memory | grep 'Clock Speed' | cut -d ':' -f2").read()。if i.isdigit()]

-------------------------- 输出 ------------------------- [' 1600 ', ' 1600 ']

下面的代码，没有外部库为我工作。我在Python 2.7.9测试

CPU使用率

import os
    
CPU_Pct=str(round(float(os.popen('''grep 'cpu ' /proc/stat | awk '{usage=($2+$4)*100/($2+$4+$5)} END {print usage }' ''').readline()),2))
print("CPU Usage = " + CPU_Pct)  # print results

和Ram使用，总，使用和免费

import os
mem=str(os.popen('free -t -m').readlines())
"""
Get a whole line of memory output, it will be something like below
['             total       used       free     shared    buffers     cached\n', 
'Mem:           925        591        334         14         30        355\n', 
'-/+ buffers/cache:        205        719\n', 
'Swap:           99          0         99\n', 
'Total:        1025        591        434\n']
 So, we need total memory, usage and free memory.
 We should find the index of capital T which is unique at this string
"""
T_ind=mem.index('T')
"""
Than, we can recreate the string with this information. After T we have,
"Total:        " which has 14 characters, so we can start from index of T +14
and last 4 characters are also not necessary.
We can create a new sub-string using this information
"""
mem_G=mem[T_ind+14:-4]
"""
The result will be like
1025        603        422
we need to find first index of the first space, and we can start our substring
from from 0 to this index number, this will give us the string of total memory
"""
S1_ind=mem_G.index(' ')
mem_T=mem_G[0:S1_ind]
"""
Similarly we will create a new sub-string, which will start at the second value. 
The resulting string will be like
603        422
Again, we should find the index of first space and than the 
take the Used Memory and Free memory.
"""
mem_G1=mem_G[S1_ind+8:]
S2_ind=mem_G1.index(' ')
mem_U=mem_G1[0:S2_ind]

mem_F=mem_G1[S2_ind+8:]
print 'Summary = ' + mem_G
print 'Total Memory = ' + mem_T +' MB'
print 'Used Memory = ' + mem_U +' MB'
print 'Free Memory = ' + mem_F +' MB'

要逐行分析程序的内存和时间，我建议使用memory_profiler和line_profiler。

安装:

# Time profiler
$ pip install line_profiler
# Memory profiler
$ pip install memory_profiler
# Install the dependency for a faster analysis
$ pip install psutil

常见的部分是，通过使用各自的装饰器指定要分析的函数。

例子:我有几个函数在我的Python文件main.py，我想分析。其中之一是linearRegressionfit()。我需要使用装饰器@profile，它可以帮助我分析关于时间和内存的代码。

对函数定义进行以下更改

@profile
def linearRegressionfit(Xt,Yt,Xts,Yts):
    lr=LinearRegression()
    model=lr.fit(Xt,Yt)
    predict=lr.predict(Xts)
    # More Code

对于时间分析，

Run:

$ kernprof -l -v main.py

输出

Total time: 0.181071 s
File: main.py
Function: linearRegressionfit at line 35

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
    35                                           @profile
    36                                           def linearRegressionfit(Xt,Yt,Xts,Yts):
    37         1         52.0     52.0      0.1      lr=LinearRegression()
    38         1      28942.0  28942.0     75.2      model=lr.fit(Xt,Yt)
    39         1       1347.0   1347.0      3.5      predict=lr.predict(Xts)
    40                                           
    41         1       4924.0   4924.0     12.8      print("train Accuracy",lr.score(Xt,Yt))
    42         1       3242.0   3242.0      8.4      print("test Accuracy",lr.score(Xts,Yts))

对于内存剖析，

Run:

$ python -m memory_profiler main.py

输出

Filename: main.py

Line #    Mem usage    Increment   Line Contents
================================================
    35  125.992 MiB  125.992 MiB   @profile
    36                             def linearRegressionfit(Xt,Yt,Xts,Yts):
    37  125.992 MiB    0.000 MiB       lr=LinearRegression()
    38  130.547 MiB    4.555 MiB       model=lr.fit(Xt,Yt)
    39  130.547 MiB    0.000 MiB       predict=lr.predict(Xts)
    40                             
    41  130.547 MiB    0.000 MiB       print("train Accuracy",lr.score(Xt,Yt))
    42  130.547 MiB    0.000 MiB       print("test Accuracy",lr.score(Xts,Yts))

此外，还可以使用matplotlib using绘制内存分析器结果

$ mprof run main.py
$ mprof plot

注:测试于

Line_profiler version == 3.0.2

Memory_profiler version == 0.57.0

Psutil版本== 5.7.0

编辑:可以使用TAMPPA包解析分析器的结果。使用它，我们可以逐行得到所需的图

你可以在subprocess中使用psutil或psmem 示例代码

import subprocess
cmd =   subprocess.Popen(['sudo','./ps_mem'],stdout=subprocess.PIPE,stderr=subprocess.PIPE) 
out,error = cmd.communicate() 
memory = out.splitlines()

参考

https://github.com/Leo-g/python-flask-cmd

psutil库提供了各种平台上关于CPU、RAM等的信息:

psutil是一个模块，提供了一个接口，通过使用Python以可移植的方式检索正在运行的进程和系统利用率(CPU，内存)的信息，实现了ps、top和Windows任务管理器等工具提供的许多功能。 它目前支持Linux, Windows, OSX, Sun Solaris, FreeBSD, OpenBSD和NetBSD, 32位和64位架构，Python版本从2.6到3.5 (Python 2.4和2.5的用户可能使用2.1.3版本)。

一些例子:

#!/usr/bin/env python
import psutil
# gives a single float value
psutil.cpu_percent()
# gives an object with many fields
psutil.virtual_memory()
# you can convert that object to a dictionary 
dict(psutil.virtual_memory()._asdict())
# you can have the percentage of used RAM
psutil.virtual_memory().percent
79.2
# you can calculate percentage of available memory
psutil.virtual_memory().available * 100 / psutil.virtual_memory().total
20.8

以下是其他文档，提供了更多的概念和感兴趣的概念:

https://psutil.readthedocs.io/en/latest/