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Ubuntu – Python is slower in docker container than host

I have this Dockerfile:

FROM python:3.12.4-slim-bookworm

COPY ./python_bench.py ./pystone.py performance.py /home/

CMD [ "/bin/bash" ]

this is pystone.py:

#! /usr/bin/env python3

"""
"PYSTONE" Benchmark Program

Version:        Python/1.1 (corresponds to C/1.1 plus 2 Pystone fixes)

Author:         Reinhold P. Weicker,  CACM Vol 27, No 10, 10/84 pg. 1013.

                Translated from ADA to C by Rick Richardson.
                Every method to preserve ADA-likeness has been used,
                at the expense of C-ness.

                Translated from C to Python by Guido van Rossum.

Version History:

                Version 1.1 corrects two bugs in version 1.0:

                First, it leaked memory: in Proc1(), NextRecord ends
                up having a pointer to itself.  I have corrected this
                by zapping NextRecord.PtrComp at the end of Proc1().

                Second, Proc3() used the operator != to compare a
                record to None.  This is rather inefficient and not
                true to the intention of the original benchmark (where
                a pointer comparison to None is intended; the !=
                operator attempts to find a method __cmp__ to do value
                comparison of the record).  Version 1.1 runs 5-10
                percent faster than version 1.0, so benchmark figures
                of different versions can't be compared directly.

"""

LOOPS = 50000

from time import perf_counter

__version__ = "1.1"

[Ident1, Ident2, Ident3, Ident4, Ident5] = range(1, 6)

class Record:

    def __init__(self, PtrComp = None, Discr = 0, EnumComp = 0,
                       IntComp = 0, StringComp = 0):
        self.PtrComp = PtrComp
        self.Discr = Discr
        self.EnumComp = EnumComp
        self.IntComp = IntComp
        self.StringComp = StringComp

    def copy(self):
        return Record(self.PtrComp, self.Discr, self.EnumComp,
                      self.IntComp, self.StringComp)

TRUE = 1
FALSE = 0

def main(loops=LOOPS):
    benchtime, stones = pystones(loops)
    print("Pystone(%s) time for %d passes = %g" % 
          (__version__, loops, benchtime))
    print("This machine benchmarks at %g pystones/second" % stones)


def pystones(loops=LOOPS):
    return Proc0(loops)

IntGlob = 0
BoolGlob = FALSE
Char1Glob = ''
Char2Glob = ''
Array1Glob = [0]*51
Array2Glob = [x[:] for x in [Array1Glob]*51]
PtrGlb = None
PtrGlbNext = None

def Proc0(loops=LOOPS):
    global IntGlob
    global BoolGlob
    global Char1Glob
    global Char2Glob
    global Array1Glob
    global Array2Glob
    global PtrGlb
    global PtrGlbNext

    starttime = perf_counter()
    for i in range(loops):
        pass
    nulltime = perf_counter() - starttime

    PtrGlbNext = Record()
    PtrGlb = Record()
    PtrGlb.PtrComp = PtrGlbNext
    PtrGlb.Discr = Ident1
    PtrGlb.EnumComp = Ident3
    PtrGlb.IntComp = 40
    PtrGlb.StringComp = "DHRYSTONE PROGRAM, SOME STRING"
    String1Loc = "DHRYSTONE PROGRAM, 1'ST STRING"
    Array2Glob[8][7] = 10

    starttime = perf_counter()

    for i in range(loops):
        Proc5()
        Proc4()
        IntLoc1 = 2
        IntLoc2 = 3
        String2Loc = "DHRYSTONE PROGRAM, 2'ND STRING"
        EnumLoc = Ident2
        BoolGlob = not Func2(String1Loc, String2Loc)
        while IntLoc1 < IntLoc2:
            IntLoc3 = 5 * IntLoc1 - IntLoc2
            IntLoc3 = Proc7(IntLoc1, IntLoc2)
            IntLoc1 = IntLoc1 + 1
        Proc8(Array1Glob, Array2Glob, IntLoc1, IntLoc3)
        PtrGlb = Proc1(PtrGlb)
        CharIndex = 'A'
        while CharIndex <= Char2Glob:
            if EnumLoc == Func1(CharIndex, 'C'):
                EnumLoc = Proc6(Ident1)
            CharIndex = chr(ord(CharIndex)+1)
        IntLoc3 = IntLoc2 * IntLoc1
        IntLoc2 = IntLoc3 / IntLoc1
        IntLoc2 = 7 * (IntLoc3 - IntLoc2) - IntLoc1
        IntLoc1 = Proc2(IntLoc1)

    benchtime = perf_counter() - starttime - nulltime
    if benchtime == 0.0:
        loopsPerBenchtime = 0.0
    else:
        loopsPerBenchtime = (loops / benchtime)
    return benchtime, loopsPerBenchtime

def Proc1(PtrParIn):
    PtrParIn.PtrComp = NextRecord = PtrGlb.copy()
    PtrParIn.IntComp = 5
    NextRecord.IntComp = PtrParIn.IntComp
    NextRecord.PtrComp = PtrParIn.PtrComp
    NextRecord.PtrComp = Proc3(NextRecord.PtrComp)
    if NextRecord.Discr == Ident1:
        NextRecord.IntComp = 6
        NextRecord.EnumComp = Proc6(PtrParIn.EnumComp)
        NextRecord.PtrComp = PtrGlb.PtrComp
        NextRecord.IntComp = Proc7(NextRecord.IntComp, 10)
    else:
        PtrParIn = NextRecord.copy()
    NextRecord.PtrComp = None
    return PtrParIn

def Proc2(IntParIO):
    IntLoc = IntParIO + 10
    while 1:
        if Char1Glob == 'A':
            IntLoc = IntLoc - 1
            IntParIO = IntLoc - IntGlob
            EnumLoc = Ident1
        if EnumLoc == Ident1:
            break
    return IntParIO

def Proc3(PtrParOut):
    global IntGlob

    if PtrGlb is not None:
        PtrParOut = PtrGlb.PtrComp
    else:
        IntGlob = 100
    PtrGlb.IntComp = Proc7(10, IntGlob)
    return PtrParOut

def Proc4():
    global Char2Glob

    BoolLoc = Char1Glob == 'A'
    BoolLoc = BoolLoc or BoolGlob
    Char2Glob = 'B'

def Proc5():
    global Char1Glob
    global BoolGlob

    Char1Glob = 'A'
    BoolGlob = FALSE

def Proc6(EnumParIn):
    EnumParOut = EnumParIn
    if not Func3(EnumParIn):
        EnumParOut = Ident4
    if EnumParIn == Ident1:
        EnumParOut = Ident1
    elif EnumParIn == Ident2:
        if IntGlob > 100:
            EnumParOut = Ident1
        else:
            EnumParOut = Ident4
    elif EnumParIn == Ident3:
        EnumParOut = Ident2
    elif EnumParIn == Ident4:
        pass
    elif EnumParIn == Ident5:
        EnumParOut = Ident3
    return EnumParOut

def Proc7(IntParI1, IntParI2):
    IntLoc = IntParI1 + 2
    IntParOut = IntParI2 + IntLoc
    return IntParOut

def Proc8(Array1Par, Array2Par, IntParI1, IntParI2):
    global IntGlob

    IntLoc = IntParI1 + 5
    Array1Par[IntLoc] = IntParI2
    Array1Par[IntLoc+1] = Array1Par[IntLoc]
    Array1Par[IntLoc+30] = IntLoc
    for IntIndex in range(IntLoc, IntLoc+2):
        Array2Par[IntLoc][IntIndex] = IntLoc
    Array2Par[IntLoc][IntLoc-1] = Array2Par[IntLoc][IntLoc-1] + 1
    Array2Par[IntLoc+20][IntLoc] = Array1Par[IntLoc]
    IntGlob = 5

def Func1(CharPar1, CharPar2):
    CharLoc1 = CharPar1
    CharLoc2 = CharLoc1
    if CharLoc2 != CharPar2:
        return Ident1
    else:
        return Ident2

def Func2(StrParI1, StrParI2):
    IntLoc = 1
    while IntLoc <= 1:
        if Func1(StrParI1[IntLoc], StrParI2[IntLoc+1]) == Ident1:
            CharLoc = 'A'
            IntLoc = IntLoc + 1
    if CharLoc >= 'W' and CharLoc <= 'Z':
        IntLoc = 7
    if CharLoc == 'X':
        return TRUE
    else:
        if StrParI1 > StrParI2:
            IntLoc = IntLoc + 7
            return TRUE
        else:
            return FALSE

def Func3(EnumParIn):
    EnumLoc = EnumParIn
    if EnumLoc == Ident3: return TRUE
    return FALSE

if __name__ == '__main__':
    import sys
    def error(msg):
        print(msg, end=' ', file=sys.stderr)
        print("usage: %s [number_of_loops]" % sys.argv[0], file=sys.stderr)
        sys.exit(100)
    nargs = len(sys.argv) - 1
    if nargs > 1:
        error("%d arguments are too many;" % nargs)
    elif nargs == 1:
        try: loops = int(sys.argv[1])
        except ValueError:
            error("Invalid argument %r;" % sys.argv[1])
    else:
        loops = LOOPS
    main(loops)

this is python_bench.py:

#!/usr/bin/env python3

import math
from time import time

N = range(10)
N_i = range(1_000)
N_j = range(1_000)
x = 1

start = time()
for _ in N:
    for i in N_i:
        for j in N_j:
            x += -1**j * math.sqrt(i)/max(j,2)
stop = time()
print(stop-start)

this is performance.py: 
from multiprocessing import Pool, cpu_count
from datetime import datetime

def stress_test(args):
    cpu, value = args
    start_time = datetime.now()
    for i in range(value):
        value = value * i
    print(f"cpu: {cpu} time: {datetime.now() - start_time}")

if __name__ == '__main__':
    start_time = datetime.now()
    cpu_count = cpu_count()
    with Pool(cpu_count) as mp_pool:
        mp_pool.map(stress_test, [(cpu, 100000000) for cpu in range(cpu_count)])
    print(f"total: {datetime.now() - start_time}")

the Host is linux laptop with Ubuntu 22.04.4 LTS.

python on host:

$ file /usr/bin/python3.12

 ELF 64-bit LSB executable, x86-64, version 1 (SYSV), dynamically linked, interpreter /lib64/ld-linux-x86-64.so.2, BuildID[sha1]=6c2ae97f8a91e9bdc027a8d877e9abef191aa422, for GNU/Linux 3.2.0, stripped

python on container:

$ file  /usr/local/bin/python3.12 


> ELF 64-bit LSB pie executable, x86-64, version 1 (SYSV), dynamically linked, interpreter /lib64/ld-linux-x86-64.so.2, BuildID[sha1]=e1466a54058de9be791ef96f61c8e185388684eb, for GNU/Linux 3.2.0, stripped

Results

performance.py:

host:

cpu: 9 time: 0:00:02.379246
cpu: 3 time: 0:00:02.387882
cpu: 5 time: 0:00:02.388341
cpu: 6 time: 0:00:02.393504
cpu: 10 time: 0:00:02.400015
cpu: 8 time: 0:00:02.411345
cpu: 7 time: 0:00:02.412351
cpu: 4 time: 0:00:02.449361
cpu: 0 time: 0:00:02.666412
cpu: 11 time: 0:00:02.671237
cpu: 2 time: 0:00:02.763631
cpu: 1 time: 0:00:02.775081
total: 0:00:02.790534

Container:

cpu: 3 time: 0:00:03.275465
cpu: 4 time: 0:00:03.280247
cpu: 2 time: 0:00:03.280876
cpu: 11 time: 0:00:03.362792
cpu: 7 time: 0:00:03.365540
cpu: 8 time: 0:00:03.374378
cpu: 6 time: 0:00:03.377563
cpu: 9 time: 0:00:03.380101
cpu: 1 time: 0:00:03.574691
cpu: 0 time: 0:00:03.673995
cpu: 10 time: 0:00:03.773611
cpu: 5 time: 0:00:03.783492
total: 0:00:03.802251

pystone.py:

host:

Pystone(1.1) time for 50000 passes = 0.0531104
This machine benchmarks at 941435 pystones/second

container:

Pystone(1.1) time for 50000 passes = 0.062226
This machine benchmarks at 803522 pystones/second

python_bench.py:

host:

1.9780004024505615

container:

3.0173771381378174

Docker info

Client: Docker Engine - Community
 Version:    27.1.1
 Context:    default
 Debug Mode: false
 Plugins:
  buildx: Docker Buildx (Docker Inc.)
    Version:  v0.16.1
    Path:     /usr/libexec/docker/cli-plugins/docker-buildx
  compose: Docker Compose (Docker Inc.)
    Version:  v2.29.1
    Path:     /usr/libexec/docker/cli-plugins/docker-compose

Server:
 Containers: 6
  Running: 4
  Paused: 0
  Stopped: 2
 Images: 24
 Server Version: 27.1.1
 Storage Driver: overlay2
  Backing Filesystem: extfs
  Supports d_type: true
  Using metacopy: false
  Native Overlay Diff: true
  userxattr: false
 Logging Driver: json-file
 Cgroup Driver: systemd
 Cgroup Version: 2
 Plugins:
  Volume: local
  Network: bridge host ipvlan macvlan null overlay
  Log: awslogs fluentd gcplogs gelf journald json-file local splunk syslog
 Swarm: inactive
 Runtimes: runc io.containerd.runc.v2
 Default Runtime: runc
 Init Binary: docker-init
 containerd version: 2bf793ef6dc9a18e00cb12efb64355c2c9d5eb41
 runc version: v1.1.13-0-g58aa920
 init version: de40ad0
 Security Options:
  apparmor
  seccomp
   Profile: builtin
  cgroupns
 Kernel Version: 6.5.0-45-generic
 Operating System: Ubuntu 22.04.4 LTS
 OSType: linux
 Architecture: x86_64
 CPUs: 12
 Total Memory: 15.23GiB
 Name: farhad-LIFEBOOK-E5512
 ID: 28068456-89b2-497e-8c89-da5bf898b96a
 Docker Root Dir: /var/lib/docker
 Debug Mode: false
 Experimental: false
 Insecure Registries:
  127.0.0.0/8
 Live Restore Enabled: false

 $ lsb_release  -a

No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 22.04.4 LTS
Release: 22.04
Codename: jammy

$ uname -r

6.5.0-45-generic

first I ran all 3 on consecutively on host and then on container. why the results are much lower than on the host? specially by pystone.py and the last script?

Tags:

2

Answers


  1. 0

    Could it be container overhead? Meaning python+local machine runs faster than docker+python+local machine. If not, then maybe check for CPU limits? By default, there should be none.

    docker inspect <container_id>

    Look for CpuShares, CpuQuota, and/or CpuPeriod.
    You can also run docker stats and check if memory is limited.

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  2. 0

    Ensure that your Docker container is allowed to use enough CPU and memory. You can check this by running:

    docker run --cpus=4 --memory=8g <your_image>

    And then see if there is any performance differences and tweak around this.

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