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Interactive performance benchmarking in Jupyter

Project description

perfume

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Interactive performance benchmarking in Jupyter

Overview

perfume is a performance benchmarking tool that provides quick feedback on the systems under test.

The primary goals are:

  • Prioritize analysis of distributions of latency, not averages.

  • Support both immediate feedback and robust benchmarking with many samples, through a UI that updates as we collect more information.

  • Provide raw data back to the user, for flexible custom analysis.

  • Provide helpful post-processing analysis and charting tools.

Features

  • Live-updating histogram chart and descriptive statistics during a benchmark run.

  • Jupyter notebook integration.

  • Tunable benchmarking overhead.

  • Comparative analysis of multiple functions under test.

  • Powerful post-processing analysis tools.

Demo

You can check out an example notebook using perfume.

docs/perfume.gif docs/cumulative_quantiles.png

Installing

pip install perfume-bench

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.1.3 (2017-09-10)

  • Actually fix when only benchmarking one function (no K-S test) (oops).

0.1.2 (2017-09-10)

  • Fix when only benchmarking one function (no K-S test).

0.1.1 (2017-08-27)

  • Add dependency on matplotlib.

0.1.0 (2017-08-27)

  • First release on PyPI.

  • Interactive histogram while benchmarking with bokeh.

  • Interactive descriptive stats and K-S test.

  • Cumulative distribution plots.

  • Bucketed resampling.

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