python-benchmark-harness
line_profiler
python-benchmark-harness | line_profiler | |
---|---|---|
29 | 17 | |
153 | 2,481 | |
- | 1.3% | |
0.0 | 8.2 | |
over 2 years ago | 7 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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.
python-benchmark-harness
- Sprucing up my read me for my Python project
- Sunday Daily Thread: What's everyone working on this week?
- My Python micro benchmarking framework.
- renaming my Python micro-benchmarking project
- My Python micro-benchmarking project
- How do I achieve true concurrency in Python?
- How do you know if your open-source library is doing well?
- Any tips for my project and how can I exactly measure the succes of a small open-source project?
- Sponsoring open source projects, share about your project
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Testing CLI created with Python Click
Oh my! https://github.com/JoeyHendricks/QuickPotato/blob/295d8150eca588e8816c3c5168177cb07a286a57/examples/example_code.py#L18
line_profiler
- Ask HN: C/C++ developer wanting to learn efficient Python
- New version of line_profiler: 4.1.0
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Making Python 100x faster with less than 100 lines of Rust
LineProfiler is the best tool to learn how to write performant Python and code optimization.
https://github.com/pyutils/line_profiler
You can literally see the hot spot of your code, then you can grind different algorithms or change the whole architecture to make it faster.
For example replace short for loops to list comprehensions, vectorize all numpy operations (only vectorize partially do not help the issue), using 'not any()' instead or 'all()' for boolean, etc.
Doing this for like 2 weeks, basically you can automatically recognized most bad code patterns in a glance.
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Why is my Pubmed plant search app so slow?
You may want to try using a package like line_profiler to narrow down where the time is spent.
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How to make nested for loops run faster
When tuning for performance, always measure. Never assume you know where the slow parts are. Run a line profiler and see where all the time is actually going.
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I'm working on a world map generator, but I have one function in particular that is very slow and keeping me from being able to scale my maps to as large as I'd like... is there a way that I can optimize this depth first search function, or another way of grouping contiguous cells based on criteria?
Either way I would highly recommend running a profiler on your code to see where the program is spending most of its time. line_profiler is a very nice one, as it shows you execution time for each line.
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Is it possible to make a function to check how many lines of code have been executed in the program so far (including said function’s lines)?
There are dedicated tools like line_profiler for python - if this doesn't do exactly what you need it can be easily modified.
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Why does sklearn.Pipeline with regex outperform spacy for text preprocessing?
It's surprising to me that an sklearn pipeline and a spacy pipeline both doing simple regexing are vastly different in performance. I would go one layer deeper with measurement with something like line_profiler, which I've used to great effect to get line-by-line perf stats. This should illuminate why.
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Hot profiling for Python
This looks really nice! Does it use line_profiler or is it a different implementation for the profiling? Either way the interface is fantastic!
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Profiling and Analyzing Performance of Python Programs
# https://github.com/pyutils/line_profiler pip install line_profiler kernprof -l -v some-code.py # This might take a while... Wrote profile results to some-code.py.lprof Timer unit: 1e-06 s Total time: 13.0418 s File: some-code.py Function: exp at line 3 Line # Hits Time Per Hit % Time Line Contents ============================================================== 3 @profile 4 def exp(x): 5 1 4.0 4.0 0.0 getcontext().prec += 2 6 1 0.0 0.0 0.0 i, lasts, s, fact, num = 0, 0, 1, 1, 1 7 5818 4017.0 0.7 0.0 while s != lasts: 8 5817 1569.0 0.3 0.0 lasts = s 9 5817 1837.0 0.3 0.0 i += 1 10 5817 6902.0 1.2 0.1 fact *= i 11 5817 2604.0 0.4 0.0 num *= x 12 5817 13024902.0 2239.1 99.9 s += num / fact 13 1 5.0 5.0 0.0 getcontext().prec -= 2 14 1 2.0 2.0 0.0 return +s
What are some alternatives?
palanteer - Visual Python and C++ nanosecond profiler, logger, tests enabler
SnakeViz - An in-browser Python profile viewer
pytest-austin - Python Performance Testing with Austin
memory_profiler - Monitor Memory usage of Python code
image-actions - A Github Action that automatically compresses JPEGs, PNGs and WebPs in Pull Requests.
reloadium - Hot Reloading and Profiling for Python
JEval - ⚡ JEval helps you to evaluate your JMeter test plan and provides recommendation before you start your performance testing. All contributions welcome 🙏.
pprofile - Line-granularity, thread-aware deterministic and statistic pure-python profiler
react-native-vision-camera - 📸 The Camera library that sees the vision. [Moved to: https://github.com/mrousavy/react-native-vision-camera]
psutil - Cross-platform lib for process and system monitoring in Python
react-native-reanimated - React Native's Animated library reimplemented
prometeo - An experimental Python-to-C transpiler and domain specific language for embedded high-performance computing