问题：如何在Python中获得当前的CPU和RAM使用率？
在Python中获取当前系统状态（当前CPU，RAM，可用磁盘空间等）的首选方式是什么？* nix和Windows平台的奖励积分。
似乎有几种方法可以从我的搜索中提取出来：
使用PSI之类的库（目前似乎尚未积极开发并且在多个平台上不受支持）或pystatgrab之类的库（自2007年以来一直没有活动，它似乎也不支持Windows）。
使用平台特定的代码，例如
os.popen("ps")在* nix系统和MEMORYSTATUSin中使用a 或类似代码ctypes.windll.kernel32（请参阅ActiveState上的此食谱对于Windows平台使用）。可以将Python类与所有这些代码段放在一起。
并不是说这些方法不好，而是已经有一种受支持的，跨平台的方法来做同样的事情？
回答 0
psutil库将为您提供各种平台上的一些系统信息（CPU /内存使用情况）：
psutil是一个模块，提供了一个接口，该接口通过使用Python以可移植的方式检索有关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。
它当前支持32位和64位体系结构的Linux，Windows，OSX，Sun Solaris，FreeBSD，OpenBSD和NetBSD，Python版本从2.6到3.5（Python 2.4和2.5的用户可以使用2.1.3版本）。
更新：这是一些示例用法psutil：
#!/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())回答 1
使用psutil库。在Ubuntu 18.04上，截至2019年1月30日，pip安装了5.5.0（最新版本）。较旧的版本可能会有所不同。您可以通过在Python中执行以下操作来检查psutil的版本：
from __future__ import print_function # for Python2
import psutil
print(psutil.__version__)要获取一些内存和CPU统计信息：
from __future__ import print_function
import psutil
print(psutil.cpu_percent())
print(psutil.virtual_memory()) # physical memory usage
print('memory % used:', psutil.virtual_memory()[2])所述virtual_memory（元组）将具有％的内存使用的全系统。在Ubuntu 18.04上，这似乎被我高估了几个百分点。
您还可以获取当前Python实例使用的内存：
import os
import psutil
pid = os.getpid()
py = psutil.Process(pid)
memoryUse = py.memory_info()[0]/2.**30 # memory use in GB...I think
print('memory use:', memoryUse)这给出了您的Python脚本的当前内存使用情况。
pypi的pypi页面上有一些更深入的示例。
回答 2
仅适用于Linux：仅使用stdlib依赖项就可以保证RAM使用情况：
import os
tot_m, used_m, free_m = map(int, os.popen('free -t -m').readlines()[-1].split()[1:])编辑：指定解决方案操作系统依赖性
回答 3
下面的代码，没有外部库为我工作。我在Python 2.7.9上进行了测试
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 results
print("CPU Usage = " + CPU_Pct)和Ram使用情况，总计，二手和免费
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'回答 4
这是我前几天整理的东西，仅是Windows，但可以帮助您获得部分所需的工作。
派生自：“用于sys的可用mem” http://msdn2.microsoft.com/zh-cn/library/aa455130.aspx
“单个过程信息和python脚本示例” http://www.microsoft.com/technet/scriptcenter/scripts/default.mspx?mfr=true
注意：WMI界面/过程也可用于执行类似的任务，因为当前的方法可以满足我的需求，所以我在这里不使用它，但是如果有朝一日需要扩展或改进它，则可能需要研究可用的WMI工具。 。
适用于python的WMI：
http://tgolden.sc.sabren.com/python/wmi.html
编码：
'''
Monitor window processes
derived from:
>for sys available mem
http://msdn2.microsoft.com/en-us/library/aa455130.aspx
> individual process information and python script examples
http://www.microsoft.com/technet/scriptcenter/scripts/default.mspx?mfr=true
NOTE: the WMI interface/process is also available for performing similar tasks
I'm not using it here because the current method covers my needs, but if someday it's needed
to extend or improve this module, then may want to investigate the WMI tools available.
WMI for python:
http://tgolden.sc.sabren.com/python/wmi.html
'''
__revision__ = 3
import win32com.client
from ctypes import *
from ctypes.wintypes import *
import pythoncom
import pywintypes
import datetime
class MEMORYSTATUS(Structure):
_fields_ = [
('dwLength', DWORD),
('dwMemoryLoad', DWORD),
('dwTotalPhys', DWORD),
('dwAvailPhys', DWORD),
('dwTotalPageFile', DWORD),
('dwAvailPageFile', DWORD),
('dwTotalVirtual', DWORD),
('dwAvailVirtual', DWORD),
]
def winmem():
x = MEMORYSTATUS() # create the structure
windll.kernel32.GlobalMemoryStatus(byref(x)) # from cytypes.wintypes
return x
class process_stats:
'''process_stats is able to provide counters of (all?) the items available in perfmon.
Refer to the self.supported_types keys for the currently supported 'Performance Objects'
To add logging support for other data you can derive the necessary data from perfmon:
---------
perfmon can be run from windows 'run' menu by entering 'perfmon' and enter.
Clicking on the '+' will open the 'add counters' menu,
From the 'Add Counters' dialog, the 'Performance object' is the self.support_types key.
--> Where spaces are removed and symbols are entered as text (Ex. # == Number, % == Percent)
For the items you wish to log add the proper attribute name in the list in the self.supported_types dictionary,
keyed by the 'Performance Object' name as mentioned above.
---------
NOTE: The 'NETFramework_NETCLRMemory' key does not seem to log dotnet 2.0 properly.
Initially the python implementation was derived from:
http://www.microsoft.com/technet/scriptcenter/scripts/default.mspx?mfr=true
'''
def __init__(self,process_name_list=[],perf_object_list=[],filter_list=[]):
'''process_names_list == the list of all processes to log (if empty log all)
perf_object_list == list of process counters to log
filter_list == list of text to filter
print_results == boolean, output to stdout
'''
pythoncom.CoInitialize() # Needed when run by the same process in a thread
self.process_name_list = process_name_list
self.perf_object_list = perf_object_list
self.filter_list = filter_list
self.win32_perf_base = 'Win32_PerfFormattedData_'
# Define new datatypes here!
self.supported_types = {
'NETFramework_NETCLRMemory': [
'Name',
'NumberTotalCommittedBytes',
'NumberTotalReservedBytes',
'NumberInducedGC',
'NumberGen0Collections',
'NumberGen1Collections',
'NumberGen2Collections',
'PromotedMemoryFromGen0',
'PromotedMemoryFromGen1',
'PercentTimeInGC',
'LargeObjectHeapSize'
],
'PerfProc_Process': [
'Name',
'PrivateBytes',
'ElapsedTime',
'IDProcess',# pid
'Caption',
'CreatingProcessID',
'Description',
'IODataBytesPersec',
'IODataOperationsPersec',
'IOOtherBytesPersec',
'IOOtherOperationsPersec',
'IOReadBytesPersec',
'IOReadOperationsPersec',
'IOWriteBytesPersec',
'IOWriteOperationsPersec'
]
}
def get_pid_stats(self, pid):
this_proc_dict = {}
pythoncom.CoInitialize() # Needed when run by the same process in a thread
if not self.perf_object_list:
perf_object_list = self.supported_types.keys()
for counter_type in perf_object_list:
strComputer = "."
objWMIService = win32com.client.Dispatch("WbemScripting.SWbemLocator")
objSWbemServices = objWMIService.ConnectServer(strComputer,"root\cimv2")
query_str = '''Select * from %s%s''' % (self.win32_perf_base,counter_type)
colItems = objSWbemServices.ExecQuery(query_str) # "Select * from Win32_PerfFormattedData_PerfProc_Process")# changed from Win32_Thread
if len(colItems) > 0:
for objItem in colItems:
if hasattr(objItem, 'IDProcess') and pid == objItem.IDProcess:
for attribute in self.supported_types[counter_type]:
eval_str = 'objItem.%s' % (attribute)
this_proc_dict[attribute] = eval(eval_str)
this_proc_dict['TimeStamp'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.') + str(datetime.datetime.now().microsecond)[:3]
break
return this_proc_dict
def get_stats(self):
'''
Show process stats for all processes in given list, if none given return all processes
If filter list is defined return only the items that match or contained in the list
Returns a list of result dictionaries
'''
pythoncom.CoInitialize() # Needed when run by the same process in a thread
proc_results_list = []
if not self.perf_object_list:
perf_object_list = self.supported_types.keys()
for counter_type in perf_object_list:
strComputer = "."
objWMIService = win32com.client.Dispatch("WbemScripting.SWbemLocator")
objSWbemServices = objWMIService.ConnectServer(strComputer,"root\cimv2")
query_str = '''Select * from %s%s''' % (self.win32_perf_base,counter_type)
colItems = objSWbemServices.ExecQuery(query_str) # "Select * from Win32_PerfFormattedData_PerfProc_Process")# changed from Win32_Thread
try:
if len(colItems) > 0:
for objItem in colItems:
found_flag = False
this_proc_dict = {}
if not self.process_name_list:
found_flag = True
else:
# Check if process name is in the process name list, allow print if it is
for proc_name in self.process_name_list:
obj_name = objItem.Name
if proc_name.lower() in obj_name.lower(): # will log if contains name
found_flag = True
break
if found_flag:
for attribute in self.supported_types[counter_type]:
eval_str = 'objItem.%s' % (attribute)
this_proc_dict[attribute] = eval(eval_str)
this_proc_dict['TimeStamp'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.') + str(datetime.datetime.now().microsecond)[:3]
proc_results_list.append(this_proc_dict)
except pywintypes.com_error, err_msg:
# Ignore and continue (proc_mem_logger calls this function once per second)
continue
return proc_results_list
def get_sys_stats():
''' Returns a dictionary of the system stats'''
pythoncom.CoInitialize() # Needed when run by the same process in a thread
x = winmem()
sys_dict = {
'dwAvailPhys': x.dwAvailPhys,
'dwAvailVirtual':x.dwAvailVirtual
}
return sys_dict
if __name__ == '__main__':
# This area used for testing only
sys_dict = get_sys_stats()
stats_processor = process_stats(process_name_list=['process2watch'],perf_object_list=[],filter_list=[])
proc_results = stats_processor.get_stats()
for result_dict in proc_results:
print result_dict
import os
this_pid = os.getpid()
this_proc_results = stats_processor.get_pid_stats(this_pid)
print 'this proc results:'
print this_proc_resultshttp://monkut.webfactional.com/blog/archive/2009/1/21/windows-process-memory-logging-python
回答 5
我们之所以选择使用常规信息源，是因为我们可以发现空闲内存中的瞬时波动，并且认为查询meminfo数据源很有帮助。这也帮助我们获得了一些预先准备的相关参数。
码
import os
linux_filepath = "/proc/meminfo"
meminfo = dict(
(i.split()[0].rstrip(":"), int(i.split()[1]))
for i in open(linux_filepath).readlines()
)
meminfo["memory_total_gb"] = meminfo["MemTotal"] / (2 ** 20)
meminfo["memory_free_gb"] = meminfo["MemFree"] / (2 ** 20)
meminfo["memory_available_gb"] = meminfo["MemAvailable"] / (2 ** 20)输出供参考（我们删除了所有换行符以进行进一步分析）
内存总数：1014500 kB内存空闲：562680 kB可用内存：646364 kB缓冲区：15144 kB缓存：210720 kB交换缓存：0 kB活动：261476 kB非活动：128888 kB活动（匿名）：167092 kB非活动（匿名）：20888 kB活动（文件） ：94384 kB无效（文件）：108000 kB无法启动：3652 kB锁定：3652 kB交换总量：0 kB交换免费：0 kB脏污：0 kB写回：0 kB AnonPages：168160 kB Mapped：81352 kB Shmem：21060 kB Slab：34492 kB SReclaimable：18044 kB SUnreclaim：16448 kB KernelStack：2672 kB PageTables：8180 kB NFS_Unstable：0 kB Bounce：0 kB WritebackTmp：0 kB CommitLimit：507248 kB Committed_AS：1038756 kB VmallocTotal：34359738367 kB kB Vmallocd： 0 kB AnonHugePages：88064 kB Cma总计：0 kB CmaFree：0 kB HugePages_Total：0 HugePages_Free：0 HugePages_Rsvd：0 HugePages_Surp：0 HugePagesize：2048 kB DirectMap4k：43008 kB DirectMap2M：1005568 kB
回答 6
我觉得这些答案是针对Python 2编写的，无论如何都没有人提及resource可用于Python 3 的标准软件包。它提供了用于获取给定进程（默认情况下为调用Python进程）的资源限制的命令。这与整个系统当前对资源的使用情况不同，但是可以解决一些相同的问题，例如“我想确保此脚本只使用X个RAM。”
回答 7
“ …当前系统状态（当前CPU，RAM，可用磁盘空间等）”和“ * nix和Windows平台”可能很难实现。
操作系统在管理这些资源的方式上根本不同。实际上，它们在核心概念方面有所不同，例如定义什么才算是系统和什么才算是应用程序时间。
“可用磁盘空间”？什么算作“磁盘空间”？所有设备的所有分区？多重引导环境中的外部分区呢？
我认为Windows和* nix之间没有足够清晰的共识使之成为可能。实际上，在称为Windows的各种操作系统之间甚至可能没有达成共识。是否有一个适用于XP和Vista的Windows API？
回答 8
此脚本用于CPU使用率：
import os
def get_cpu_load():
""" Returns a list CPU Loads"""
result = []
cmd = "WMIC CPU GET LoadPercentage "
response = os.popen(cmd + ' 2>&1','r').read().strip().split("\r\n")
for load in response[1:]:
result.append(int(load))
return result
if __name__ == '__main__':
print get_cpu_load()回答 9
有关CPU的详细信息，请使用psutil库
对于RAM频率（以MHz为单位），请使用内置的Linux库dmidecode并稍微控制输出;）。此命令需要root权限，因此也要提供密码。只需复制以下命令，将mypass替换为您的密码
import os
os.system("echo mypass | sudo -S dmidecode -t memory | grep 'Clock Speed' | cut -d ':' -f2")——————-输出—————————
1600 MT / s
未知
1600 MT / s
未知0
- 更具体地说
[i for i in os.popen("echo mypass | sudo -S dmidecode -t memory | grep 'Clock Speed' | cut -d ':' -f2").read().split(' ') if i.isdigit()]
————————–输出———————– –
[‘1600’，’1600’]
回答 10
为了获得程序的逐行存储和时间分析，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共同的部分是，您可以使用相应的修饰符指定要分析的功能。
示例：我的Python文件main.py中有几个要分析的功能。其中之一是linearRegressionfit()。我需要使用装饰器@profile，该装饰器可帮助我针对以下两个方面分析代码：时间和内存。
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，
跑：
$ kernprof -l -v main.py输出量
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))对于内存分析，
跑：
$ python -m memory_profiler main.py输出量
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))同样，也可以matplotlib使用
$ mprof run main.py
$ mprof plot
line_profiler 版本== 3.0.2
memory_profiler 版本== 0.57.0
psutil 版本== 5.7.0
To get a line-by-line memory and time analysis of your program, I suggest using memory_profiler and line_profiler.
Installation:
# Time profiler
$ pip install line_profiler
# Memory profiler
$ pip install memory_profiler
# Install the dependency for a faster analysis
$ pip install psutil
The common part is, you specify which function you want to analyse by using the respective decorators.
Example: I have several functions in my Python file main.py that I want to analyse. One of them is linearRegressionfit(). I need to use the decorator @profile that helps me profile the code with respect to both: Time & Memory.
Make the following changes to the function definition
@profile
def linearRegressionfit(Xt,Yt,Xts,Yts):
lr=LinearRegression()
model=lr.fit(Xt,Yt)
predict=lr.predict(Xts)
# More Code
For Time Profiling,
Run:
$ kernprof -l -v main.py
Output
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))
For Memory Profiling,
Run:
$ python -m memory_profiler main.py
Output
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))
Also, the memory profiler results can also be plotted using matplotlib using
$ mprof run main.py
$ mprof plot
line_profiler version == 3.0.2
memory_profiler version == 0.57.0
psutil version == 5.7.0
回答 11
您可以将psutil或psmem与子流程示例代码一起使用
import subprocess
cmd = subprocess.Popen(['sudo','./ps_mem'],stdout=subprocess.PIPE,stderr=subprocess.PIPE)
out,error = cmd.communicate()
memory = out.splitlines()参考 http://techarena51.com/index.php/how-to-install-python-3-and-flask-on-linux/
回答 12
基于@Hrabal的cpu使用代码，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])回答 13
我认为没有可用的受支持的多平台库。请记住，Python本身是用C编写的，因此，任何库都将像上面建议的那样，明智地决定要运行哪个特定于操作系统的代码段。
