ravel
py-spy
Our great sponsors
| ravel | py-spy | |
|---|---|---|
| 1 | 25 | |
| 27 | 11,850 | |
| - | - | |
| 0.0 | 6.4 | |
| over 2 years ago | 15 days ago | |
| C++ | Rust | |
| GNU General Public License v3.0 or later | MIT 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.
ravel
py-spy
- Minha jornada de otimização de uma aplicação django
- Graphical Python Profiler
-
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
-
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
-
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.
-
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 ;)
-
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
-
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.
-
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.
-
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?
faasm - High-performance stateful serverless runtime based on WebAssembly
pyflame
timemory - Modular C++ Toolkit for Performance Analysis and Logging. Profiling API and Tools for C, C++, CUDA, Fortran, and Python. The C++ template API is essentially a framework to creating tools: it is designed to provide a unifying interface for recording various performance measurements alongside data logging and interfaces to other tools.
pyinstrument - 🚴 Call stack profiler for Python. Shows you why your code is slow!
dmtcp - DMTCP: Distributed MultiThreaded CheckPointing
python-uncompyle6 - A cross-version Python bytecode decompiler
amgcl - C++ library for solving large sparse linear systems with algebraic multigrid method
memory_profiler - Monitor Memory usage of Python code
libgrape-lite - 🍇 A C++ library for parallel graph processing (GRAPE) 🍇
icecream - 🍦 Never use print() to debug again.
line_profiler
profiling