py-spy
Poetry
py-spy | Poetry | |
---|---|---|
25 | 377 | |
11,864 | 29,552 | |
- | 1.1% | |
6.4 | 9.7 | |
21 days ago | about 9 hours ago | |
Rust | Python | |
MIT License | MIT License |
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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.
Poetry
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Understanding Dependencies in Programming
You can manage dependencies in Python with the package manager pip, which comes pre-installed with Python. Pip allows you to install and uninstall Python packages, and it uses a requirements.txt file to keep track of which packages your project depends on. However, pip does not have robust dependency resolution features or isolate dependencies for different projects; this is where tools like pipenv and poetry come in. These tools create a virtual environment for each project, separating the project's dependencies from the system-wide Python environment and other projects.
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Implementing semantic image search with Amazon Titan and Supabase Vector
Poetry provides packaging and dependency management for Python. If you haven't already, install poetry via pip:
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From Kotlin Scripting to Python
Poetry
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How to Enhance Content with Semantify
The Semantify repository provides an example Astro.js project. Ensure you have poetry installed, then build the project from the root of the repository:
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Uv: Python Packaging in Rust
Has anyone else been paying attention to how hilariously hard it is to package PyTorch in poetry?
https://github.com/python-poetry/poetry/issues/6409
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Boring Python: dependency management (2022)
Based on this comment 5 days ago[0], it's working? I'm not sure didn't dig in too far but based on that comment it seems fair to say that it's not fully Poetry's fault because torch removed hashes (which poetry needs to be effective) for a while only recently adding it back in.
Not sure where I would stand if I fully investigated it tho.
[0] https://github.com/python-poetry/poetry/issues/6409#issuecom...
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Fun with Avatars: Crafting the core engine | Part. 1
We will be running this project in Python 3.10 on Mac/Linux, and we will use Poetry to manage our dependencies. Later, we will bundle our app into a container using docker for deployment.
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Python Packaging, One Year Later: A Look Back at 2023 in Python Packaging
Here are the two main packaging issues I run into, specifically when using Poetry:
1) Lack of support for building extension modules (as mentioned by the article). There is a workaround using an undocumented feature [0], which I've tried, but ultimately decided it was not the right approach. I still use Poetry, but build the extension as a separate step in CI, rather than kludging it into Poetry.
2) Lack of support for offline installs [1], e.g. being able to download the dependencies, copy them to another machine, and perform the install from the downloaded dependencies (similar to using "pip --no-index --find-links=."). Again, you can work around this (by using "poetry export --with-credentials" and "pip download" for fetching the dependencies, then firing up pypiserver [2] to run a local PyPI server on the offline machine), but ideally this would all be a first class feature of Poetry, similar to how it is in pip.
I don't have the capacity to create Pull Requests for addressing these issues with Poetry, and I'm very grateful for the maintainers and those who do contribute. Instead, on the linked issues I share my notes on the matter, in the hope that it may at least help others and potentially get us closer to a solution.
Regardless, I'm sticking with Poetry for now. Though to be fair, the only other Python packaging tools I've used extensively are Pipenv and pip/setuptools. It's time consuming to thoroughly try out these other packaging tools, and is generally lower priority than developing features/fixing bugs, so it's helpful to read about the author's experience with these other tools, such as PDM and Hatch.
[0] https://github.com/python-poetry/poetry/issues/2740
[1] https://github.com/python-poetry/poetry/issues/2184
[2] https://pypi.org/project/pypiserver/
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Introducing Flama for Robust Machine Learning APIs
We believe that poetry is currently the best tool for this purpose, besides of being the most popular one at the moment. This is why we will use poetry to manage the dependencies of our project throughout this series of posts. Poetry allows you to declare the libraries your project depends on, and it will manage (install/update) them for you. Poetry also allows you to package your project into a distributable format and publish it to a repository, such as PyPI. We strongly recommend you to learn more about this tool by reading the official documentation.
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How do you resolve dependency conflicts?
I started using poetry. The problem is poetry will not install if there is dependency conflict and there is no way to ignore: github
What are some alternatives?
pyflame
Pipenv - Python Development Workflow for Humans.
pyinstrument - 🚴 Call stack profiler for Python. Shows you why your code is slow!
PDM - A modern Python package and dependency manager supporting the latest PEP standards
python-uncompyle6 - A cross-version Python bytecode decompiler
hatch - Modern, extensible Python project management
memory_profiler - Monitor Memory usage of Python code
pyenv - Simple Python version management
icecream - 🍦 Never use print() to debug again.
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
line_profiler
virtualenv - Virtual Python Environment builder