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perf
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/run_benchmark.html .. _analyze benchmark results: https://perf.readthedocs.io/en/latest/analyze.html