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A benchmark utility used in speed / performance tests.
When measuring execution time, the result depends on the computer hardware. To be able to produce a universal measure, the simplest way is to benchmark the speed of a fixed sequence of code and calculate a ratio out of it. From there, the time taken by a function can be translated to a universal value that can be compared on any computer. Python provides a benchmark utility in its test package that measures the duration of a sequence of well-chosen operations. pybenchmark designed to provide a simple and pythonic way to get performance data.
- Simple usage workflow
- Decorator for profiling
- A la carte usage of decorator in place
- Provide a simple way to get CPU and memory details information
Install from PyPI:
$ pip install pybenchmark
Or using alternative command:
$ pip install https://github.com/duboviy/pybenchmark/archive/master.zip
Or from source use:
$ python setup.py install
- 2.7
- 3.3
- 3.4
- 3.5
- 3.6
- PyPy
You can use profile decorator to wrap your code:
from pybenchmark import profile
@profile()
def some_code():
time.sleep(0.5)
some_code()
print(stats)
{'stats': {'kstones': 0.50012803077697754, 'time': 24.278059746455238, 'memory': 0}Also you can use decorator à la carte, if you don't want to edit/disturb your source code (for example, when writing tests):
import pybenchmark
eat_cpu_time = lambda: 2**100000000
# using decorator à la carte below
eat_it = pybenchmark.profile('you bad boy!')(eat_cpu_time)
please = eat_it()
pybenchmark.stats
{'you bad boy!': {'kstones': 14.306935999128555, 'time': 0.30902981758117676, 'memory': 8096}}You can use module for visualizing Python code profiles using the Chrome developer tools:
from pybenchmark import GProfiler
profiler = GProfiler()
profiler.start()
my_expensive_code()
profiler.stop()
with open('my.cpuprofile', 'w') as f:
f.write(profiler.output())Or you can use context manager:
with GProfiler() as profiler:
my_expensive_code()
# File with name './pybenchmark_%s_.cpuprofile' % os.getpid() would be createdThen load json file into chrome developer tools timeline.
To get the timeline chart load the file into Profiles tool from Chrome Dev Tools.
There is a "Load" button just under the list of profiles.
Then timeline-chart can be obtained by changing Heavy(Bottom Up) option to Chart.
You can use it with or without gevent framework.
You can get CPU machine details information (on LINUX-based systems):
>>> from pybenchmark import CpuInfo >>> cpu = CpuInfo() >>> cpu processor : 0 vendor_id : GenuineIntel cpu family : 6 model : 42 model name : Intel(R) Core(TM) i3-2130 CPU @ 3.40GHz stepping : 7 microcode : 0x28 cpu MHz : 1600.257 cache size : 3072 KB physical id : 0 siblings : 4 core id : 0 cpu cores : 2 apicid : 0 initial apicid : 0 fpu : yes fpu_exception : yes cpuid level : 13 wp : yes flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm pcid sse4_1 sse4_2 popcnt tsc_deadline_timer xsave avx lahf_lm arat epb xsaveopt pln pts dtherm tpr_shadow vnmi flexpriority ept vpid bogomips : 6784.56 clflush size : 64 cache_alignment : 64 address sizes : 36 bits physical, 48 bits virtual power management: processor : 1 vendor_id : GenuineIntel cpu family : 6 model : 42 model name : Intel(R) Core(TM) i3-2130 CPU @ 3.40GHz stepping : 7 microcode : 0x28 cpu MHz : 1600.523 cache size : 3072 KB physical id : 0 siblings : 4 core id : 1 cpu cores : 2 apicid : 2 initial apicid : 2 fpu : yes fpu_exception : yes cpuid level : 13 wp : yes flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm pcid sse4_1 sse4_2 popcnt tsc_deadline_timer xsave avx lahf_lm arat epb xsaveopt pln pts dtherm tpr_shadow vnmi flexpriority ept vpid bogomips : 6784.56 clflush size : 64 cache_alignment : 64 address sizes : 36 bits physical, 48 bits virtual power management: processor : 2 vendor_id : GenuineIntel cpu family : 6 model : 42 model name : Intel(R) Core(TM) i3-2130 CPU @ 3.40GHz stepping : 7 microcode : 0x28 cpu MHz : 1595.476 cache size : 3072 KB physical id : 0 siblings : 4 core id : 0 cpu cores : 2 apicid : 1 initial apicid : 1 fpu : yes fpu_exception : yes cpuid level : 13 wp : yes flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm pcid sse4_1 sse4_2 popcnt tsc_deadline_timer xsave avx lahf_lm arat epb xsaveopt pln pts dtherm tpr_shadow vnmi flexpriority ept vpid bogomips : 6784.56 clflush size : 64 cache_alignment : 64 address sizes : 36 bits physical, 48 bits virtual power management: processor : 3 vendor_id : GenuineIntel cpu family : 6 model : 42 model name : Intel(R) Core(TM) i3-2130 CPU @ 3.40GHz stepping : 7 microcode : 0x28 cpu MHz : 1599.062 cache size : 3072 KB physical id : 0 siblings : 4 core id : 1 cpu cores : 2 apicid : 3 initial apicid : 3 fpu : yes fpu_exception : yes cpuid level : 13 wp : yes flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm pcid sse4_1 sse4_2 popcnt tsc_deadline_timer xsave avx lahf_lm arat epb xsaveopt pln pts dtherm tpr_shadow vnmi flexpriority ept vpid bogomips : 6784.56 clflush size : 64 cache_alignment : 64 address sizes : 36 bits physical, 48 bits virtual
Return output as dict:
>>> cpu.dict()
{
"1": {
"cpu cores": "2",
"bogomips": "6784.56",
"core id": "1",
"apicid": "2",
"fpu_exception": "yes",
"stepping": "7",
"cache_alignment": "64",
"clflush size": "64",
"microcode": "0x28",
"cache size": "3072 KB",
"cpuid level": "13",
"fpu": "yes",
"model name": "Intel(R) Core(TM) i3-2130 CPU @ 3.40GHz",
"siblings": "4",
"physical id": "0",
"address sizes": "36 bits physical, 48 bits virtual",
"cpu family": "6",
"vendor_id": "GenuineIntel",
"wp": "yes",
"power management": "",
"flags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm pcid sse4_1 sse4_2 popcnt tsc_deadline_timer xsave avx lahf_lm arat epb xsaveopt pln pts dtherm tpr_shadow vnmi flexpriority ept vpid",
"cpu MHz": "1683.398",
"model": "42",
"processor": "1",
"initial apicid": "2"
},
"0": {
"cpu cores": "2",
"bogomips": "6784.56",
"core id": "0",
"apicid": "0",
"fpu_exception": "yes",
"stepping": "7",
"cache_alignment": "64",
"clflush size": "64",
"microcode": "0x28",
"cache size": "3072 KB",
"cpuid level": "13",
"fpu": "yes",
"model name": "Intel(R) Core(TM) i3-2130 CPU @ 3.40GHz",
"siblings": "4",
"physical id": "0",
"address sizes": "36 bits physical, 48 bits virtual",
"cpu family": "6",
"vendor_id": "GenuineIntel",
"wp": "yes",
"power management": "",
"flags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm pcid sse4_1 sse4_2 popcnt tsc_deadline_timer xsave avx lahf_lm arat epb xsaveopt pln pts dtherm tpr_shadow vnmi flexpriority ept vpid",
"cpu MHz": "1601.187",
"model": "42",
"processor": "0",
"initial apicid": "0"
},
"3": {
"cpu cores": "2",
"bogomips": "6784.56",
"core id": "1",
"apicid": "3",
"fpu_exception": "yes",
"stepping": "7",
"cache_alignment": "64",
"clflush size": "64",
"microcode": "0x28",
"cache size": "3072 KB",
"cpuid level": "13",
"fpu": "yes",
"model name": "Intel(R) Core(TM) i3-2130 CPU @ 3.40GHz",
"siblings": "4",
"physical id": "0",
"address sizes": "36 bits physical, 48 bits virtual",
"cpu family": "6",
"vendor_id": "GenuineIntel",
"wp": "yes",
"power management": "",
"flags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm pcid sse4_1 sse4_2 popcnt tsc_deadline_timer xsave avx lahf_lm arat epb xsaveopt pln pts dtherm tpr_shadow vnmi flexpriority ept vpid",
"cpu MHz": "1612.476",
"model": "42",
"processor": "3",
"initial apicid": "3"
},
"2": {
"cpu cores": "2",
"bogomips": "6784.56",
"core id": "0",
"apicid": "1",
"fpu_exception": "yes",
"stepping": "7",
"cache_alignment": "64",
"clflush size": "64",
"microcode": "0x28",
"cache size": "3072 KB",
"cpuid level": "13",
"fpu": "yes",
"model name": "Intel(R) Core(TM) i3-2130 CPU @ 3.40GHz",
"siblings": "4",
"physical id": "0",
"address sizes": "36 bits physical, 48 bits virtual",
"cpu family": "6",
"vendor_id": "GenuineIntel",
"wp": "yes",
"power management": "",
"flags": "fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 cx16 xtpr pdcm pcid sse4_1 sse4_2 popcnt tsc_deadline_timer xsave avx lahf_lm arat epb xsaveopt pln pts dtherm tpr_shadow vnmi flexpriority ept vpid",
"cpu MHz": "1600.125",
"model": "42",
"processor": "2",
"initial apicid": "1"
}
}
Search (is case insensitive):
>>> cpu.search('CPU Mhz')
['cpu MHz\t\t: 1599.062\n', 'cpu MHz\t\t: 1600.125\n', 'cpu MHz\t\t: 1598.398\n', 'cpu MHz\t\t: 1601.320\n']
You can also get memory machine details information (on LINUX-based systems):
>>> from pybenchmark import MemInfo >>> mem = MemInfo() >>> mem MemTotal: 8092460 kB MemFree: 499880 kB MemAvailable: 5454920 kB Buffers: 219088 kB Cached: 4980040 kB SwapCached: 7576 kB Active: 5647392 kB Inactive: 1628708 kB Active(anon): 1794356 kB Inactive(anon): 492656 kB Active(file): 3853036 kB Inactive(file): 1136052 kB Unevictable: 200 kB Mlocked: 200 kB SwapTotal: 16776188 kB SwapFree: 16639112 kB Dirty: 172 kB Writeback: 0 kB AnonPages: 2070440 kB Mapped: 204800 kB Shmem: 210036 kB Slab: 247884 kB SReclaimable: 219356 kB SUnreclaim: 28528 kB KernelStack: 4144 kB PageTables: 11904 kB NFS_Unstable: 0 kB Bounce: 0 kB WritebackTmp: 0 kB CommitLimit: 20822416 kB Committed_AS: 3317504 kB VmallocTotal: 34359738367 kB VmallocUsed: 362844 kB VmallocChunk: 34359347296 kB HardwareCorrupted: 0 kB AnonHugePages: 0 kB HugePages_Total: 0 HugePages_Free: 0 HugePages_Rsvd: 0 HugePages_Surp: 0 Hugepagesize: 2048 kB DirectMap4k: 83644 kB DirectMap2M: 8202240 kB
Return output as dict:
>>> mem.dict()
{
"WritebackTmp": "0 kB",
"SwapTotal": "16776188 kB",
"Active(anon)": "1794356 kB",
"SwapFree": "16639112 kB",
"DirectMap4k": "83644 kB",
"KernelStack": "4144 kB",
"MemFree": "499880 kB",
"HugePages_Rsvd": "0",
"Committed_AS": "3317504 kB",
"SUnreclaim": "28528 kB",
"NFS_Unstable": "0 kB",
"VmallocChunk": "34359347296 kB",
"Writeback": "0 kB",
"Inactive(file)": "1136052 kB",
"MemTotal": "8092460 kB",
"VmallocUsed": "362844 kB",
"HugePages_Free": "0",
"AnonHugePages": "0 kB",
"Shmem": "210036 kB",
"AnonPages": "2070440 kB",
"Active": "5647392 kB",
"Inactive(anon)": "492656 kB",
"HugePages_Total": "0",
"Hugepagesize": "2048 kB",
"Cached": "4980040 kB",
"SwapCached": "7576 kB",
"VmallocTotal": "34359738367 kB",
"Dirty": "172 kB",
"Mapped": "204800 kB",
"Unevictable": "200 kB",
"SReclaimable": "219356 kB",
"MemAvailable": "5454920 kB",
"Slab": "247884 kB",
"DirectMap2M": "8202240 kB",
"HugePages_Surp": "0",
"Bounce": "0 kB",
"Inactive": "1628708 kB",
"PageTables": "11904 kB",
"HardwareCorrupted": "0 kB",
"CommitLimit": "20822416 kB",
"Mlocked": "200 kB",
"Buffers": "219088 kB",
"Active(file)": "3853036 kB"
}
Search (is case insensitive):
>>> mem.search('Swap')
['SwapCached: 7576 kB\n', 'SwapTotal: 16776188 kB\n', 'SwapFree: 16639112 kB\n']
Get memory usage as int (is case sensitive):
>>> mem.get('Inactive(anon)')
492656
MIT licensed library. See LICENSE.txt for details.
If you have suggestions for improving the pybenchmark, please open an issue or pull request on GitHub.