• noarch v1.6.0
• linux-32 v1.5.1
• linux-64 v1.5.1
• linux-ppc64le v1.5.1
• osx-64 v1.5.1
• win-32 v1.5.1
• win-64 v1.5.1

conda install

To install this package run one of the following:
conda install main::perf

---

---

perf

---

.. image:: https://img.shields.io/pypi/v/perf.svg :alt: Latest release on the Python Cheeseshop (PyPI) :target: https://pypi.python.org/pypi/perf

.. image:: https://travis-ci.org/vstinner/perf.svg?branch=master :alt: Build status of perf on Travis CI :target: https://travis-ci.org/vstinner/perf

The Python perf module is a toolkit to write, run and analyze benchmarks.

Features

• Simple API to run reliable benchmarks
• Automatically 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.

Usage

To run a benchmark_ use the perf timeit command (result written into bench.json)::

$ python3 -m perf timeit '[1,2]*1000' -o bench.json
.....................
Mean +- std dev: 4.22 us +- 0.08 us

Or write a benchmark script bench.py:

.. code:: python

#!/usr/bin/env python3
import perf

runner = perf.Runner()
runner.timeit("sorted(list(range(1000)), key=lambda x: x)",
              stmt="sorted(s, key=f)",
              setup="f = lambda x: x; s = list(range(1000))")

Run the script using (result written into bench.json)::

$ python3 bench.py -o bench.json

To analyze benchmark results_ use the perf stats command::

$ python3 -m perf stats bench.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): 0

There's also:

• perf compare_to command tests if a difference is significant. It supports comparison between multiple benchmark suites (made of multiple benchmarks) ::

  $ python3 -m perf compare_to py2.json py3.json --table +-----------+---------+------------------------------+ | Benchmark | py2 | py3 | +===========+=========+==============================+ | timeit | 4.70 us | 4.22 us: 1.11x faster (-10%) | +-----------+---------+------------------------------+

• perf system tune command to tune your system to run stable benchmarks.

• Automatically collect metadata on the computer and the benchmark: use the perf metadata command to display them, or the perf collect_metadata command to manually collect them.
• --track-memory and --tracemalloc options to track the memory usage of a benchmark.

Quick Links

• perf documentation <https://perf.readthedocs.io/>_
• perf project homepage at GitHub <https://github.com/vstinner/perf>_ (code, bugs)
• Download latest perf release at the Python Cheeseshop (PyPI) <https://pypi.python.org/pypi/perf>_

Command to install perf on Python 3::

python3 -m pip install perf

perf supports Python 2.7 and Python 3. It is distributed under the MIT license.

.. run a benchmark: https://perf.readthedocs.io/en/latest/runbenchmark.html .. _analyze benchmark results: https://perf.readthedocs.io/en/latest/analyze.html