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

我觉得这些答案是为Python 2编写的，而且在任何情况下，都没有人提到Python 3可用的标准资源包。它提供了获取给定进程(默认为调用Python进程)的资源限制的命令。这与获取整个系统当前的资源使用情况是不同的，但它可以解决一些相同的问题，例如:“我想确保这个脚本只使用X个内存。”

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'

通过结合tqdm和psutil，可以获得实时的CPU和RAM监控。当运行繁重的计算/处理时，它可能很方便。

它也可以在Jupyter中工作，无需任何代码更改:

from tqdm import tqdm
from time import sleep
import psutil

with tqdm(total=100, desc='cpu%', position=1) as cpubar, tqdm(total=100, desc='ram%', position=0) as rambar:
    while True:
        rambar.n=psutil.virtual_memory().percent
        cpubar.n=psutil.cpu_percent()
        rambar.refresh()
        cpubar.refresh()
        sleep(0.5)

使用多处理库将这些进度条放在单独的进程中是很方便的。

此代码片段也可作为要点。

从第一反应中获得反馈，并做一些小的改变

#!/usr/bin/env python
#Execute commond on windows machine to install psutil>>>>python -m pip install psutil
import psutil

print ('                                                                   ')
print ('----------------------CPU Information summary----------------------')
print ('                                                                   ')

# gives a single float value
vcc=psutil.cpu_count()
print ('Total number of CPUs :',vcc)

vcpu=psutil.cpu_percent()
print ('Total CPUs utilized percentage :',vcpu,'%')

print ('                                                                   ')
print ('----------------------RAM Information summary----------------------')
print ('                                                                   ')
# you can convert that object to a dictionary 
#print(dict(psutil.virtual_memory()._asdict()))
# gives an object with many fields
vvm=psutil.virtual_memory()

x=dict(psutil.virtual_memory()._asdict())

def forloop():
    for i in x:
        print (i,"--",x[i]/1024/1024/1024)#Output will be printed in GBs

forloop()
print ('                                                                   ')
print ('----------------------RAM Utilization summary----------------------')
print ('                                                                   ')
# you can have the percentage of used RAM
print('Percentage of used RAM :',psutil.virtual_memory().percent,'%')
#79.2
# you can calculate percentage of available memory
print('Percentage of available RAM :',psutil.virtual_memory().available * 100 / psutil.virtual_memory().total,'%')
#20.8

@CodeGench的解决方案不需要外壳，所以假设Linux和Python的标准库:

def cpu_load(): 
    with open("/proc/stat", "r") as stat:
        (key, user, nice, system, idle, _) = (stat.readline().split(None, 5))
    assert key == "cpu", "'cpu ...' should be the first line in /proc/stat"
    busy = int(user) + int(nice) + int(system)
    return 100 * busy / (busy + int(idle))