Package: pyperf
The Pythonpyperfmodule is a toolkit to write, run and analyze benchmarks.FeaturesSimple API to run reliable benchmarksAutomatically calibrate a benchmark for a time budget.Spawn multiple worker processes.Compute the mean and standard deviation.Detect if a benchmark result seems unstable.JSON format to store benchmark results.Support multiple units: seconds, bytes and integer.UsageTorun a benchmarkuse thepyperf timeitcommand (result written intobench.json):$ python3 -m pyperf timeit '[1,2]*1000' -o bench.json
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Mean +- std dev: 4.22 us +- 0.08 usOr write a benchmark scriptbench.py:#!/usr/bin/env python3importpyperfrunner=pyperf.Runner()runner.timeit(name="sort a sorted list",stmt="sorted(s, key=f)",setup="f = lambda x: x; s = list(range(1000))")Seethe API docsfor full details on thetimeitfunction and theRunnerclass. To run the script and dump the results into a file namedbench.json:$ python3 bench.py -o bench.jsonToanalyze benchmark resultsuse thepyperf statscommand:$ python3 -m pyperf stats telco.json
Total duration: 29.2 sec
Start date: 2016-10-21 03:14:19
End date: 2016-10-21 03:14:53
Raw value minimum: 177 ms
Raw value maximum: 183 ms

Number of calibration run: 1
Number of run with values: 40
Total number of run: 41

Number of warmup per run: 1
Number of value per run: 3
Loop iterations per value: 8
Total number of values: 120

Minimum:         22.1 ms
Median +- MAD:   22.5 ms +- 0.1 ms
Mean +- std dev: 22.5 ms +- 0.2 ms
Maximum:         22.9 ms

  0th percentile: 22.1 ms (-2% of the mean) -- minimum
  5th percentile: 22.3 ms (-1% of the mean)
 25th percentile: 22.4 ms (-1% of the mean) -- Q1
 50th percentile: 22.5 ms (-0% of the mean) -- median
 75th percentile: 22.7 ms (+1% of the mean) -- Q3
 95th percentile: 22.9 ms (+2% of the mean)
100th percentile: 22.9 ms (+2% of the mean) -- maximum

Number of outlier (out of 22.0 ms..23.0 ms): 0There’s also:pyperf compare_tocommand tests if a difference is
significant. It supports comparison between multiple benchmark suites (made
of multiple benchmarks)$ python3 -m pyperf compare_to --table mult_list_py36.json mult_list_py37.json mult_list_py38.json
+----------------+----------------+-----------------------+-----------------------+
| Benchmark      | mult_list_py36 | mult_list_py37        | mult_list_py38        |
+================+================+=======================+=======================+
| [1]*1000       | 2.13 us        | 2.09 us: 1.02x faster | not significant       |
+----------------+----------------+-----------------------+-----------------------+
| [1,2]*1000     | 3.70 us        | 5.28 us: 1.42x slower | 3.18 us: 1.16x faster |
+----------------+----------------+-----------------------+-----------------------+
| [1,2,3]*1000   | 4.61 us        | 6.05 us: 1.31x slower | 4.17 us: 1.11x faster |
+----------------+----------------+-----------------------+-----------------------+
| Geometric mean | (ref)          | 1.22x slower          | 1.09x faster          |
+----------------+----------------+-----------------------+-----------------------+pyperf system tunecommand to tune your system to run stable benchmarks.Automatically collect metadata on the computer and the benchmark:
use thepyperf metadatacommand to display them, or thepyperf collect_metadatacommand to manually collect them.--track-memoryand--tracemallocoptions to track
the memory usage of a benchmark.Quick Linkspyperf documentationpyperf project homepage at GitHub(code, bugs)Download latest pyperf release at the Python Cheeseshop (PyPI)Command to install pyperf on Python 3:python3 -m pip install pyperfpyperf requires Python 3.7 or newer.Python 2.7 users can use pyperf 1.7.1 which is the last version compatible with
Python 2.7.pyperf is distributed under the MIT license.The pyperf project is covered by thePSF Code of Conduct.