asv
pytest-benchmark
| asv | pytest-benchmark | |
|---|---|---|
| 3 | 2 | |
| 840 | 1,199 | |
| 1.1% | - | |
| 9.1 | 6.0 | |
| 9 days ago | about 1 month ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | BSD 2-clause "Simplified" 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.
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.
pytest-benchmark
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Pinpoint performance regressions with CI-Integrated differential profiling
pytest-benchmark
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Investigating Pydantic v2's Bold Performance Claims
To test this, we will setup some benchmarks using pytest-benchmark, some sample data with a simple schema, and compare results between Python's dataclass, Pydantic v1, and v2.
What are some alternatives?
pyperformance - Python Performance Benchmark Suite
pytest-codspeed - Pytest plugin to create CodSpeed benchmarks
pybench - Python benchmark tool inspired by Geekbench.
pydantic-core - Core validation logic for pydantic written in rust
scikit-image - Image processing in Python
action - Github Actions for running CodSpeed in your CI
fashion-mnist - A MNIST-like fashion product database. Benchmark :point_down:
cloud_benchmarker - Cloud Benchmarker automates performance testing of cloud instances, offering insightful charts and tracking over time.
pyeventbus - Python Eventbus
pyperf - Toolkit to run Python benchmarks
git-appraise-eclipse - Distributed code review for Eclipse
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production