cs492-concur
py-spy
| cs492-concur | py-spy | |
|---|---|---|
| 1 | 25 | |
| 1,346 | 11,864 | |
| 0.7% | - | |
| 8.6 | 6.4 | |
| 24 days ago | 22 days ago | |
| Rust | Rust | |
| - | MIT License |
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cs492-concur
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Rusticles #10 - Wed Sep 09 2020
kaist-cp/cs492-concur (Rust): undefined
py-spy
- Minha jornada de otimização de uma aplicação django
- Graphical Python Profiler
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Grasshopper – An Open Source Python Library for Load Testing
For CPU cycles, py-spy[0] is getting more and more used. For RAM, I would like to known too...
[0] -- https://github.com/benfred/py-spy
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Debugging a Mixed Python and C Language Stack
Theres also Py Spy, a profiling tool that can generate flame charts containing a mix of python and C (or C++) calls.
https://github.com/benfred/py-spy
It's worked really well for my needs
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python to rust migration
You should profile your consumer to check the bottlenecks. You can use the excellent py-spy(written in Rust). IMO a few usage of Numba there and there should solve your performance issues.
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Has anyone switched from numpy to Rust?
So as a first step you'll want to profile your program to figure out where it's slow, and hopefully that'll also tell you why it's slow. I'm the (biased) author of the Sciagraph profiler which is designed for this sort of application (https://sciagraph.com) but you can also try py-spy, which isn't as well designed for data processing/analysis applications (e.g. it won't visualize parallelism at all) but can still be informative (https://github.com/benfred/py-spy). Both are written in Rust ;)
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Trace your Python process line by line with minimal overhead!
Any advantages/disadvantages compared to py-spy [1]?
[1]: https://github.com/benfred/py-spy
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Python 3.11 delivers.
Python profiling is enabled primarily through cprofile, and can be visualized with help of tools like snakeviz (output flame graph can look like this). There are also memory profilers like memray which does in-depth traces, or sampling profilers like py-spy.
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Tales of serving ML models with low-latency
A good profiler would be https://github.com/benfred/py-spy . If you run your app/benchmark with it, it should be able to draw a flamegraph telling you where the majority of time is spent. The info here is quite fine grained so it would already tell you where the bottleneck is. Without a full-fledged profiler you can also measure the timings in various parts of the code to understand where the bottleneck is.
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Profiling a Python library written in Rust (Maturin)
Might be worth raising an issue on py-spy (a python profiler written in rust which "supports profiling native python extensions written in languages like C/C++ or Cython" to see if that can close the loop.
What are some alternatives?
polkadot - Polkadot Node Implementation
pyflame
awesome-rust - A curated list of Rust code and resources.
pyinstrument - 🚴 Call stack profiler for Python. Shows you why your code is slow!
findomain - The fastest and complete solution for domain recognition. Supports screenshoting, port scan, HTTP check, data import from other tools, subdomain monitoring, alerts via Discord, Slack and Telegram, multiple API Keys for sources and much more.
python-uncompyle6 - A cross-version Python bytecode decompiler
Parity - (deprecated) The fast, light, and robust client for the Ethereum mainnet.
memory_profiler - Monitor Memory usage of Python code
rust - Rust language bindings for TensorFlow
icecream - 🍦 Never use print() to debug again.
MeiliSearch - A lightning-fast search API that fits effortlessly into your apps, websites, and workflow
line_profiler