scalene
hyperfine
scalene | hyperfine | |
---|---|---|
32 | 74 | |
11,174 | 20,020 | |
1.4% | - | |
9.2 | 8.1 | |
7 days ago | 3 days ago | |
Python | Rust | |
Apache License 2.0 | Apache License 2.0 |
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)
- Scalene: A high-performance CPU GPU and memory profiler for Python
- Scalene: A high-performance, CPU, GPU, and memory profiler for Python
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How can I find out why my python is so slow?
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.
https://github.com/plasma-umass/scalene
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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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Cum as putea sa imbunatatesc timpul de rulare al pitonului?
Ai vazut "Python Performance Matters" by Emery Berger (Strange Loop 2022)? E in principiu o prezentare si demo cu Scalene.
- Scalene - A Python CPU/GPU/memory profiler with optimization proposals
- Scalene: A Python CPU/GPU/memory profiler with optimization proposals
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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
hyperfine
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Measuring startup and shutdown overhead of several code interpreters
Check out the official hyperfine Github repo
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Bun - The One Tool for All Your JavaScript/Typescript Project's Needs?
And then I used hyperfine to run the benchmarks on my MacBook Pro 14 M2 Max, and here are the results:
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Faster tetranucleotide (k-mer) frequencies!
Search "benchmarking tools for linux" and decide that hyperfine is good for what I'm doing. Run Jennifer's new python script against my refactored perl and find that the python is 1.26 times faster for k=3 and 1.47 times faster for k=4. For the Covid-19 sequence, these are both on the order of hundreds of milliseconds.
- Hyperfine: A command-line benchmarking tool
- FLaNK Weekly 08 Jan 2024
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Show HN: Inshellisense – IDE style shell autocomplete
> It is very possible to write sub 100ms procedures in TS, […]
I will not disagree with this statement because I don’t have a way to test inshellisense right now. Could you (or anyone with a working Node + NPM installation) please install inshellisense and post the actual numbers? Perhaps using a tool like hyperfine (https://github.com/sharkdp/hyperfine).
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Firefox has surpassed Chrome on Speedometer
Yeah, while it's not as thorough as these tools, the method is at least reproducible and sane, and with ~10 or so samples, you get an interval with a nice confidence.
Another through method will be hyperfine[0], yet I wanted to provide a method which requires no installation and can be done in a whim, without jumps and hoops, with the tools already at hand.
[0]: https://github.com/sharkdp/hyperfine
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How to optimize your config? What are mistakes to avoid when optimizing your config?
That is native and inbuild but I would suggest below options instead 1. Using lazy's Profile tab instead https://github.com/folke/lazy.nvim 2. Using a dedicated plugin to do this https://github.com/dstein64/vim-startuptime. 3. Using an external program hyperfine is one that I use https://github.com/sharkdp/hyperfine
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How to remove all <br> from all of my .html files
Fair enough, although might I recommend using hyperfine for your testing? ;p
What are some alternatives?
flask-profiler - a flask profiler which watches endpoint calls and tries to make some analysis.
criterion.rs - Statistics-driven benchmarking library for Rust
palanteer - Visual Python and C++ nanosecond profiler, logger, tests enabler
fd - A simple, fast and user-friendly alternative to 'find'
pytest-austin - Python Performance Testing with Austin
ripgrep - ripgrep recursively searches directories for a regex pattern while respecting your gitignore
memray - Memray is a memory profiler for Python
awesome-mac - Now we have become very big, Different from the original idea. Collect premium software in various categories.
pyshader - Write modern GPU shaders in Python!
kubeconform - A FAST Kubernetes manifests validator, with support for Custom Resources!
viztracer - VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
quinn - Async-friendly QUIC implementation in Rust