hp2html
scalene
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| hp2html | scalene | |
|---|---|---|
| 0 | 29 | |
| 6 | 7,745 | |
| - | 1.7% | |
| 0.0 | 8.0 | |
| over 10 years ago | 3 days ago | |
| JavaScript | JavaScript | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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.
hp2html
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Tracking mentions began in Dec 2020.
scalene
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How can I find out why my python is so slow?
Try using scalene to find where your code is running slow (and/or consuming lots of memory). You're talking two different OSs here, there are a ton of things that could explain the difference. But profiling will help you find where the bottleneck is
Use this my fren: https://github.com/plasma-umass/scalene
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Making Python 100x faster with less than 100 lines of Rust
You should take a look at Scalene - it's even better.
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Blog Post: Making Python 100x faster with less than 100 lines of Rust
I like seeing another Python profiler. The one I've been playing with is Scalene (GitHub). It does some fun things related to letting you see how much things are moving across the system Python memory boundary.
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OpenAI might be training its AI technology to replace some software engineers, report says
I tried out some features of machine learning models suggesting optimisations on code profiled by scalene and pretty much all of them would make the code less efficient, both time and memory wise. I am not worried. The devil is in the details and ML will not replace all of us anytime soon
- Modules Import and Optimisation
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[2022 day 16 (part 2)] [python 3.10] Can my solution be optimized?
Can't say about the floyd-warshall, since that's from elsewhere. However, I'd suggest profiling the code. For example scalene is pretty decent python profiler.
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What are some features you wish Python had?
How about scalene?
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Dwarf-Based Stack Walking Using eBPF
This is super awesome work and a great technical explanation of a very deep topic.
What happens in the case of JIT or FFI? I think I've only ever seen the Python profiler, scalene[0], handle these cases.
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Slipcover: Near Zero-Overhead Python Code Coverage
The PLASMA lab @ UMass Amherst (home of the Scalene profiler) has released a new version of Slipcover, a super fast code coverage tool for Python. It is by far the fastest code coverage tool: in our tests, its average slowdown is just 5% (compare to the widely used coverage.py, average slowdown 218%!). The latest release performs both line and branch coverage with virtually no overhead. Use it to dramatically speed up your tests and continuous integration!
What are some alternatives?
flask-profiler - a flask profiler which watches endpoint calls and tries to make some analysis.
palanteer - Visual Python and C++ nanosecond profiler, logger, tests enabler
pytest-austin - Python Performance Testing with Austin
pyshader - Write modern GPU shaders in Python!
memray - Memray is a memory profiler for Python
hw-diagnostics
magic-trace - magic-trace collects and displays high-resolution traces of what a process is doing
Keras - Deep Learning for humans
Dask - Parallel computing with task scheduling
viztracer - VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
dask-memusage - A low-impact profiler to figure out how much memory each task in Dask is using
dtale - Visualizer for pandas data structures