pyperformance
asv
| pyperformance | asv | |
|---|---|---|
| 6 | 3 | |
| 817 | 840 | |
| 0.9% | 1.1% | |
| 6.6 | 9.1 | |
| 20 days ago | 9 days ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pyperformance
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Phoronix: PyPerformance benchmark is on average 32% faster on Python 3.11 compared to 3.10 (on a Ryzen 9 5950X)
PyPerformance benchmark: https://github.com/python/pyperformance
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Faster CPython 3.12 Plan
25% number is from pyperformance benchmark suite, which you can replicate. Whether pyperformance is representative benchmark suite is another question.
https://github.com/python/pyperformance
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The Performance Benchmarks Comparing various combinations of GCC and Python
For each combination, We launch a GCC container and build Python with the GCC. Then run benchmarks using pyperformance and export to a JSON file.
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This Week In Python
pyperformance – Python Performance Benchmark Suite
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Hello, I created a interpreted dynamic programming language in C#. I use a bytecode compiler and a vm for interpretation. Right now I'm trying to optimise it. Any help would be great!
There are some standard benchmarks like fannkuch, deltablue, and so on (see a bunch for Python here) that you can port to your VM. They have adjustable values that you can raise or lower to increase or decrease the amount of time you take.
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Why is python so much slower on MacOS?
So I decided to run some actual benchmark suite. I found pyperformance which would seem to do the trick.
asv
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git-appraise – Distributed Code Review for Git
> All these workflows are a derivation of the source in the repository and keeping them close together has a great aesthetic.
I agree. Version control is a great enabler, so using it to track "sources" other than just code can be useful. A couple of tools I like to use:
- Artemis, for tracking issues http://www.chriswarbo.net/blog/2017-06-14-artemis.html
- ASV, for tracking benchmark results https://github.com/airspeed-velocity/asv (I use this for non-Python projects via my asv-nix plugin http://www.chriswarbo.net/projects/nixos/asv_benchmarking.ht... )
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Is GitHub Actions suitable for running benchmarks?
scikit-image, the project that commissioned this task, uses Airspeed Velocity, or asv, for their benchmark tests.
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Memory benchmarking tools
Problem - The project currently uses Airspeed Velocity for tracking the memory changes. But I am having a lot of trouble setting this up and using this tool for monitoring memory consumption on a regular basis. Are you guys aware of some other open-source tools that I can use instead of this? I am stuck with this thing for some time now. I would appreciate any help.
What are some alternatives?
pybench - Python benchmark tool inspired by Geekbench.
pyperf - Toolkit to run Python benchmarks
scikit-image - Image processing in Python
Nuitka - Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
fashion-mnist - A MNIST-like fashion product database. Benchmark :point_down:
ga-extractor - Tool for extracting Google Analytics data suitable for migrating to other platforms/databases
pyeventbus - Python Eventbus
pytest-benchmark - py.test fixture for benchmarking code
Cython - The most widely used Python to C compiler
git-appraise-eclipse - Distributed code review for Eclipse