scalene
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scalene | CPython | |
---|---|---|
32 | 1,309 | |
11,125 | 59,431 | |
1.6% | 1.5% | |
9.3 | 10.0 | |
2 days ago | 2 days ago | |
Python | Python | |
Apache License 2.0 | 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.
scalene
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Memray – A Memory Profiler for Python
I collected a list of profilers (also memory profilers, also specifically for Python) here: https://github.com/albertz/wiki/blob/master/profiling.md
Currently I actually need a Python memory profiler, because I want to figure out whether there is some memory leak in my application (PyTorch based training script), and where exactly (in this case, it's not a problem of GPU memory, but CPU memory).
I tried Scalene (https://github.com/plasma-umass/scalene), which seems to be powerful, but somehow the output it gives me is not useful at all? It doesn't really give me a flamegraph, or a list of the top lines with memory allocations, but instead it gives me a listing of all source code lines, and prints some (very sparse) information on each line. So I need to search through that listing now by hand to find the spots? Maybe I just don't know how to use it properly.
I tried Memray, but first ran into an issue (https://github.com/bloomberg/memray/issues/212), but after using some workaround, it worked now. I get a flamegraph out, but it doesn't really seem accurate? After a while, there don't seem to be any new memory allocations at all anymore, and I don't quite trust that this is correct.
There is also Austin (https://github.com/P403n1x87/austin), which I also wanted to try (have not yet).
Somehow this experience so far was very disappointing.
(Side node, I debugged some very strange memory allocation behavior of Python before, where all local variables were kept around after an exception, even though I made sure there is no reference anymore to the exception object, to the traceback, etc, and I even called frame.clear() for all frames to really clear it. It turns out, frame.f_locals will create another copy of all the local variables, and the exception object and all the locals in the other frame still stay alive until you access frame.f_locals again. At that point, it will sync the f_locals again with the real (fast) locals, and then it can finally free everything. It was quite annoying to find the source of this problem and to find workarounds for it. https://github.com/python/cpython/issues/113939)
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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.
CPython
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Featured Mod of the Month: Phil Ashby
After that, with the basics of software engineering understood, I would move on to a wider use language, with a bigger ecosystem to employ, most likely Python. This would expose me to large system design / distributed systems and architectural challenges...
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Convert Images Into Pencil Sketch
Have you ever felt like your photos needed a little extra touch to stand out? Well, get ready because we're about to learn a cool Python trick! We're going to take ordinary photos and turn them into awesome pencil sketches using Python and OpenCV. This will make your pictures look like they were drawn by hand!
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Backdoor in upstream xz/liblzma leading to SSH server compromise
Python for Windows bundles liblzma from this project, but it appears to be version 5.2.5 [0] vendored into the Python project's repo on 2022-04-18 [1], so that should be fine, right?
[0] https://github.com/python/cpython/blob/main/PCbuild/get_exte...
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How to Develop a User Data Storage Registration Form Using Python.
When working in Visual Studio Code (VS Code), start by creating a new Python file for your registration form project. It's helpful to have separate files for different parts of your project.
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Open source at Fastly is getting opener
Through the Fast Forward program, we give free services and support to open source projects and the nonprofits that support them. We support many of the world’s top programming languages (like Python, Rust, Ruby, and the wonderful Scratch), foundational technologies (cURL, the Linux kernel, Kubernetes, OpenStreetMap), and projects that make the internet better and more fun for everyone (Inkscape, Mastodon, Electronic Frontier Foundation, Terms of Service; Didn’t Read).
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C++ Safety, in Context
In my understanding, no. I believe it was bpo-4489 [1], and I couldn't find a matching advisory from the PSF's database [2] which should contain all historical advisories as well.
- The GIL can now be disabled in Python's main branch
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Eloquent JavaScript 4th edition (2024)
How do you mean? CPython uses karatsuba's for large numbers which should be asymptotically fast
https://github.com/python/cpython/blob/d864b0094f9875c5613cb...
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Top Paying Programming Technologies 2024
24. Python - $78,331
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What is really an API? Examples, Code + History
a. Setting Up: Make sure you have Python and pip (package installer) installed. If you do not have Python, you can install the latest version from the Python ecosystem here
What are some alternatives?
RustPython - A Python Interpreter written in Rust
flask-profiler - a flask profiler which watches endpoint calls and tries to make some analysis.
ipython - Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.
Vulpix - Fast, unopinionated, minimalist web framework for .NET core inspired by express.js
palanteer - Visual Python and C++ nanosecond profiler, logger, tests enabler
Visual Studio Code - Visual Studio Code
pytest-austin - Python Performance Testing with Austin
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Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Camunda BPM - Flexible framework for workflow and decision automation with BPMN and DMN. Integration with Quarkus, Spring, Spring Boot, CDI.
Django - The Web framework for perfectionists with deadlines.
go - The Go programming language