py-spy
PyO3
py-spy | PyO3 | |
---|---|---|
25 | 147 | |
11,864 | 11,044 | |
- | 1.9% | |
6.4 | 9.8 | |
21 days ago | about 19 hours ago | |
Rust | Rust | |
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.
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.
PyO3
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Encapsulation in Rust and Python
Integrating Rust into Python, Edward Wright, 2021-04-12 Examples for making rustpython run actual python code Calling Rust from Python using PyO3 Writing Python inside your Rust code — Part 1, 2020-04-17 RustPython, RustPython Rust for Python developers: Using Rust to optimize your Python code PyO3 (Rust bindings for Python) Musing About Pythonic Design Patterns In Rust, Teddy Rendahl, 2023-07-14
- Rust Bindings for the Python Interpreter
- Polars – A bird's eye view of Polars
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In Rust for Python: A Match from Heaven
This story unfolds as a captivating journey where the agile Flounder, representing the Python programming language, navigates the vast seas of coding under the wise guidance of Sebastian, symbolizing Rust. Central to their adventure are three powerful tridents: cargo, PyO3, and maturin.
- Segunda linguagem
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Calling Rust from Python
I would not recommend FFI + ctypes. Maintaining the bindings is tedious and error-prone. Also, Rust FFI/unsafe can be tricky even for experienced Rust devs.
Instead PyO3 [1] lets you "write a native Python module in Rust", and it works great. A much better choice IMO.
[1] https://github.com/PyO3/pyo3
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Python 3.12
Same w/ Rust and Python, this is really neat because now each thread could have a GIL without doing exactly what you said. The pyO3 commit to allow subinterpreters was merged 21 days ago, so this might "just work" today: https://github.com/PyO3/pyo3/pull/3446
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Removing Garbage Collection from the Rust Language (2013)
I expected someone to write a rust-based scripting language which tightly integrated with rust itself.
In reality, it seems like the python developers and toolchain are embracing rust enough to reduce the benefits to a new alternative.
https://github.com/PyO3/pyo3
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Bytewax: Stream processing library built using Python and Rust
Hey HN! I am one of the people working on Bytewax. Bytewax came out of our experience working with ML infrastructure at GitHub. We wanted to use Python because we could move fast, the team was very fluent in it, and the rest of our tooling was Python-native already. We didn't want to introduce JVM-based solutions into our stack because of the lack of experience and the friction we had trying to get Python-centric tooling working with existing solutions like Flink.
In our research, we found Timely Dataflow (https://timelydataflow.github.io/timely-dataflow/, https://news.ycombinator.com/item?id=24837031) and the Naiad project (https://www.microsoft.com/en-us/research/project/naiad/) as well as PyO3 (https://github.com/PyO3/pyo3) and we thought we found a match made in heaven :). Bytewax leverages both of these projects and builds on them to provide a clean API (at least we think so) and table stakes features like connectors, state recovery, and cloud-native scaling. It has been really cool to learn about the dataflow computation model, Rust, and how to wrangle the GIL with Rust and Python :P.
Would love to get your feedback :).
`pip install bytewax` to get started. We have a page of guides (https://www.bytewax.io/guides) with ready-to-run examples.
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Tell HN: Rust Is the Superglue
You can practice your Rust skills by writing performant and/or gluey extensions for higher-level language such as NodeJS (checkout napi-rs) and Python or complementing JS in the browser if you target Webassembly.
For instance, checkout Llama-node https://github.com/Atome-FE/llama-node for an involved Rust-based NodeJS extension. Python has PyO3, a Rust-Python extension toolset: https://github.com/PyO3/pyo3.
They can help you leverage your Rust for writing cool new stuff.
What are some alternatives?
pyflame
rust-cpython - Rust <-> Python bindings
pyinstrument - 🚴 Call stack profiler for Python. Shows you why your code is slow!
pybind11 - Seamless operability between C++11 and Python
python-uncompyle6 - A cross-version Python bytecode decompiler
RustPython - A Python Interpreter written in Rust
memory_profiler - Monitor Memory usage of Python code
milksnake - A setuptools/wheel/cffi extension to embed a binary data in wheels
icecream - 🍦 Never use print() to debug again.
bincode - A binary encoder / decoder implementation in Rust.
line_profiler
uniffi-rs - a multi-language bindings generator for rust