Python进程使用的总内存？

Is there a way for a Python program to determine how much memory it’s currently using? I’ve seen discussions about memory usage for a single object, but what I need is total memory usage for the process, so that I can determine when it’s necessary to start discarding cached data.

Here is a useful solution that works for various operating systems, including Linux, Windows, etc.:

import os
import psutil
process = psutil.Process(os.getpid())
print(process.memory_info().rss)  # in bytes 

With Python 2.7 and psutil 5.6.3, the last line should be

print(process.memory_info()[0])

instead (there was a change in the API later).

Note: do pip install psutil if it is not installed yet.

For Unix based systems (Linux, Mac OS X, Solaris), you can use the getrusage() function from the standard library module resource. The resulting object has the attribute ru_maxrss, which gives the peak memory usage for the calling process:

>>> resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
2656  # peak memory usage (kilobytes on Linux, bytes on OS X)

The Python docs don’t make note of the units. Refer to your specific system’s man getrusage.2 page to check the unit for the value. On Ubuntu 18.04, the unit is noted as kilobytes. On Mac OS X, it’s bytes.

The getrusage() function can also be given resource.RUSAGE_CHILDREN to get the usage for child processes, and (on some systems) resource.RUSAGE_BOTH for total (self and child) process usage.

If you care only about Linux, you can alternatively read the /proc/self/status or /proc/self/statm file as described in other answers for this question and this one too.

On Windows, you can use WMI (home page, cheeseshop):

def memory():
    import os
    from wmi import WMI
    w = WMI('.')
    result = w.query("SELECT WorkingSet FROM Win32_PerfRawData_PerfProc_Process WHERE IDProcess=%d" % os.getpid())
    return int(result[0].WorkingSet)

On Linux (from python cookbook http://code.activestate.com/recipes/286222/:

import os
_proc_status = '/proc/%d/status' % os.getpid()

_scale = {'kB': 1024.0, 'mB': 1024.0*1024.0,
          'KB': 1024.0, 'MB': 1024.0*1024.0}

def _VmB(VmKey):
    '''Private.
    '''
    global _proc_status, _scale
     # get pseudo file  /proc/<pid>/status
    try:
        t = open(_proc_status)
        v = t.read()
        t.close()
    except:
        return 0.0  # non-Linux?
     # get VmKey line e.g. 'VmRSS:  9999  kB\n ...'
    i = v.index(VmKey)
    v = v[i:].split(None, 3)  # whitespace
    if len(v) < 3:
        return 0.0  # invalid format?
     # convert Vm value to bytes
    return float(v[1]) * _scale[v[2]]


def memory(since=0.0):
    '''Return memory usage in bytes.
    '''
    return _VmB('VmSize:') - since


def resident(since=0.0):
    '''Return resident memory usage in bytes.
    '''
    return _VmB('VmRSS:') - since


def stacksize(since=0.0):
    '''Return stack size in bytes.
    '''
    return _VmB('VmStk:') - since

On unix, you can use the ps tool to monitor it:

$ ps u -p 1347 | awk '{sum=sum+$6}; END {print sum/1024}'

where 1347 is some process id. Also, the result is in MB.

Current memory usage of the current process on Linux, for Python 2, Python 3, and pypy, without any imports:

def getCurrentMemoryUsage():
    ''' Memory usage in kB '''

    with open('/proc/self/status') as f:
        memusage = f.read().split('VmRSS:')[1].split('\n')[0][:-3]

    return int(memusage.strip())

It reads the status file of the current process, takes everything after VmRSS:, then takes everything before the first newline (isolating the value of VmRSS), and finally cuts off the last 3 bytes which are a space and the unit (kB).
To return, it strips any whitespace and returns it as a number.

Tested on Linux 4.4 and 4.9, but even an early Linux version should work: looking in man proc and searching for the info on the /proc/$PID/status file, it mentions minimum versions for some fields (like Linux 2.6.10 for “VmPTE”), but the “VmRSS” field (which I use here) has no such mention. Therefore I assume it has been in there since an early version.

I like it, thank you for @bayer. I get a specific process count tool, now.

# Megabyte.
$ ps aux | grep python | awk '{sum=sum+$6}; END {print sum/1024 " MB"}'
87.9492 MB

# Byte.
$ ps aux | grep python | awk '{sum=sum+$6}; END {print sum " KB"}'
90064 KB

Attach my process list.

$ ps aux  | grep python
root       943  0.0  0.1  53252  9524 ?        Ss   Aug19  52:01 /usr/bin/python /usr/local/bin/beaver -c /etc/beaver/beaver.conf -l /var/log/beaver.log -P /var/run/beaver.pid
root       950  0.6  0.4 299680 34220 ?        Sl   Aug19 568:52 /usr/bin/python /usr/local/bin/beaver -c /etc/beaver/beaver.conf -l /var/log/beaver.log -P /var/run/beaver.pid
root      3803  0.2  0.4 315692 36576 ?        S    12:43   0:54 /usr/bin/python /usr/local/bin/beaver -c /etc/beaver/beaver.conf -l /var/log/beaver.log -P /var/run/beaver.pid
jonny    23325  0.0  0.1  47460  9076 pts/0    S+   17:40   0:00 python
jonny    24651  0.0  0.0  13076   924 pts/4    S+   18:06   0:00 grep python

Reference

• memory – Linux: find out what process is using all the RAM? – Super User
• Total memory used by Python process? – Stack Overflow
• linux – ps aux output meaning – Super User

For Python 3.6 and psutil 5.4.5 it is easier to use memory_percent() function listed here.

import os
import psutil
process = psutil.Process(os.getpid())
print(process.memory_percent())

Even easier to use than /proc/self/status: /proc/self/statm. It’s just a space delimited list of several statistics. I haven’t been able to tell if both files are always present.

/proc/[pid]/statm

Provides information about memory usage, measured in pages. The columns are:

• size (1) total program size (same as VmSize in /proc/[pid]/status)
• resident (2) resident set size (same as VmRSS in /proc/[pid]/status)
• shared (3) number of resident shared pages (i.e., backed by a file) (same as RssFile+RssShmem in /proc/[pid]/status)
• text (4) text (code)
• lib (5) library (unused since Linux 2.6; always 0)
• data (6) data + stack
• dt (7) dirty pages (unused since Linux 2.6; always 0)

Here’s a simple example:

from pathlib import Path
from resource import getpagesize

PAGESIZE = getpagesize()
PATH = Path('/proc/self/statm')


def get_resident_set_size() -> int:
    """Return the current resident set size in bytes."""
    # statm columns are: size resident shared text lib data dt
    statm = PATH.read_text()
    fields = statm.split()
    return int(fields[1]) * PAGESIZE


data = []
start_memory = get_resident_set_size()
for _ in range(10):
    data.append('X' * 100000)
    print(get_resident_set_size() - start_memory)

That produces a list that looks something like this:

0
0
368640
368640
368640
638976
638976
909312
909312
909312

You can see that it jumps by about 300,000 bytes after roughly 3 allocations of 100,000 bytes.

Below is my function decorator which allows to track how much memory this process consumed before the function call, how much memory it uses after the function call, and how long the function is executed.

import time
import os
import psutil


def elapsed_since(start):
    return time.strftime("%H:%M:%S", time.gmtime(time.time() - start))


def get_process_memory():
    process = psutil.Process(os.getpid())
    return process.memory_info().rss


def track(func):
    def wrapper(*args, **kwargs):
        mem_before = get_process_memory()
        start = time.time()
        result = func(*args, **kwargs)
        elapsed_time = elapsed_since(start)
        mem_after = get_process_memory()
        print("{}: memory before: {:,}, after: {:,}, consumed: {:,}; exec time: {}".format(
            func.__name__,
            mem_before, mem_after, mem_after - mem_before,
            elapsed_time))
        return result
    return wrapper

So, when you have some function decorated with it

from utils import track

@track
def list_create(n):
    print("inside list create")
    return [1] * n

You will be able to see this output:

inside list create
list_create: memory before: 45,928,448, after: 46,211,072, consumed: 282,624; exec time: 00:00:00

import os, win32api, win32con, win32process
han = win32api.OpenProcess(win32con.PROCESS_QUERY_INFORMATION|win32con.PROCESS_VM_READ, 0, os.getpid())
process_memory = int(win32process.GetProcessMemoryInfo(han)['WorkingSetSize'])

For Unix systems command time (/usr/bin/time) gives you that info if you pass -v. See Maximum resident set size below, which is the maximum (peak) real (not virtual) memory that was used during program execution:

$ /usr/bin/time -v ls /

    Command being timed: "ls /"
    User time (seconds): 0.00
    System time (seconds): 0.01
    Percent of CPU this job got: 250%
    Elapsed (wall clock) time (h:mm:ss or m:ss): 0:00.00
    Average shared text size (kbytes): 0
    Average unshared data size (kbytes): 0
    Average stack size (kbytes): 0
    Average total size (kbytes): 0
    Maximum resident set size (kbytes): 0
    Average resident set size (kbytes): 0
    Major (requiring I/O) page faults: 0
    Minor (reclaiming a frame) page faults: 315
    Voluntary context switches: 2
    Involuntary context switches: 0
    Swaps: 0
    File system inputs: 0
    File system outputs: 0
    Socket messages sent: 0
    Socket messages received: 0
    Signals delivered: 0
    Page size (bytes): 4096
    Exit status: 0

Using sh and os to get into python bayer’s answer.

float(sh.awk(sh.ps('u','-p',os.getpid()),'{sum=sum+$6}; END {print sum/1024}'))

Answer is in megabytes.