line_profiler
rayon
line_profiler | rayon | |
---|---|---|
17 | 67 | |
2,481 | 10,277 | |
1.3% | 1.9% | |
8.2 | 9.0 | |
8 days ago | 12 days ago | |
Python | Rust | |
GNU General Public License v3.0 or later | 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.
line_profiler
- Ask HN: C/C++ developer wanting to learn efficient Python
- New version of line_profiler: 4.1.0
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Making Python 100x faster with less than 100 lines of Rust
LineProfiler is the best tool to learn how to write performant Python and code optimization.
https://github.com/pyutils/line_profiler
You can literally see the hot spot of your code, then you can grind different algorithms or change the whole architecture to make it faster.
For example replace short for loops to list comprehensions, vectorize all numpy operations (only vectorize partially do not help the issue), using 'not any()' instead or 'all()' for boolean, etc.
Doing this for like 2 weeks, basically you can automatically recognized most bad code patterns in a glance.
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Why is my Pubmed plant search app so slow?
You may want to try using a package like line_profiler to narrow down where the time is spent.
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How to make nested for loops run faster
When tuning for performance, always measure. Never assume you know where the slow parts are. Run a line profiler and see where all the time is actually going.
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I'm working on a world map generator, but I have one function in particular that is very slow and keeping me from being able to scale my maps to as large as I'd like... is there a way that I can optimize this depth first search function, or another way of grouping contiguous cells based on criteria?
Either way I would highly recommend running a profiler on your code to see where the program is spending most of its time. line_profiler is a very nice one, as it shows you execution time for each line.
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Is it possible to make a function to check how many lines of code have been executed in the program so far (including said function’s lines)?
There are dedicated tools like line_profiler for python - if this doesn't do exactly what you need it can be easily modified.
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Why does sklearn.Pipeline with regex outperform spacy for text preprocessing?
It's surprising to me that an sklearn pipeline and a spacy pipeline both doing simple regexing are vastly different in performance. I would go one layer deeper with measurement with something like line_profiler, which I've used to great effect to get line-by-line perf stats. This should illuminate why.
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Hot profiling for Python
This looks really nice! Does it use line_profiler or is it a different implementation for the profiling? Either way the interface is fantastic!
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Profiling and Analyzing Performance of Python Programs
# https://github.com/pyutils/line_profiler pip install line_profiler kernprof -l -v some-code.py # This might take a while... Wrote profile results to some-code.py.lprof Timer unit: 1e-06 s Total time: 13.0418 s File: some-code.py Function: exp at line 3 Line # Hits Time Per Hit % Time Line Contents ============================================================== 3 @profile 4 def exp(x): 5 1 4.0 4.0 0.0 getcontext().prec += 2 6 1 0.0 0.0 0.0 i, lasts, s, fact, num = 0, 0, 1, 1, 1 7 5818 4017.0 0.7 0.0 while s != lasts: 8 5817 1569.0 0.3 0.0 lasts = s 9 5817 1837.0 0.3 0.0 i += 1 10 5817 6902.0 1.2 0.1 fact *= i 11 5817 2604.0 0.4 0.0 num *= x 12 5817 13024902.0 2239.1 99.9 s += num / fact 13 1 5.0 5.0 0.0 getcontext().prec -= 2 14 1 2.0 2.0 0.0 return +s
rayon
- Rayon: Data-race free parallelization of sequential computations in Rust
- Too Dangerous for C++
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Which application/problem would you choose for presenting Rust to newcomers in 1h30min?
Do some operations with .iter() then later use rayon to parallelize. So you can show how easy is to add a dependency and how easy is to parallelize.
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What Are The Rust Crates You Use In Almost Every Project That They Are Practically An Extension of The Standard Library?
rayon: Async CPU runtime for parallelism.
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Moving from Typescript and Langchain to Rust and Loops
In the quest for more efficient solutions, the ONNX runtime emerged as a beacon of performance. The decision to transition from Typescript to Rust was an unconventional yet pivotal one. Driven by Rust's robust parallel processing capabilities using Rayon and seamless integration with ONNX through the ort crate, Repo-Query unlocked a realm of unparalleled efficiency. The result? A transformation from sluggish processing to, I have to say it, blazing-fast performance.
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AreWeMegafactoryYet? I just breached simulating 1M buildings @ 60 fps (If I'm not recording, Ryzen 7 1700X 8 Core)
With a lot of rayon, blood, sweat and tears I finally managed to simulate a million buildings at 60fps :) Feel free to AMA, game is Combine And Conquer
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The Rust I Wanted Had No Future
(see https://github.com/rayon-rs/rayon/tree/master/src/iter/plumbing)
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Parallel event iterator?
I did some very basic testing with this crate : https://crates.io/crates/rayon and it seems to work :
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General Recommendations: Should I Use Tree-sitter as the AST for the LSP I am developing?
Sequentially, generating tree-sitter AST for each file and querying for the links of each file takes around 2.3 seconds. However, I randomly remembered this crate rayon, and I decided to test it. It ended up improving the performance (just by changing 2 lines of code) to 200-300ms by parallelizing the iterators and tree-sitter queries. MAJOR.
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python to rust migration
Now if you really want to use Rust, you can rewrite only the part that are slowing down your consumer. It's easy by using Py03 and maturin. Maybe also rayon to parallelize.
What are some alternatives?
SnakeViz - An in-browser Python profile viewer
crossbeam - Tools for concurrent programming in Rust
memory_profiler - Monitor Memory usage of Python code
tokio - A runtime for writing reliable asynchronous applications with Rust. Provides I/O, networking, scheduling, timers, ...
reloadium - Hot Reloading and Profiling for Python
RxRust - The Reactive Extensions for the Rust Programming Language
pprofile - Line-granularity, thread-aware deterministic and statistic pure-python profiler
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
psutil - Cross-platform lib for process and system monitoring in Python
tokio-rayon - Mix async code with CPU-heavy thread pools using Tokio + Rayon
prometeo - An experimental Python-to-C transpiler and domain specific language for embedded high-performance computing
coroutine-rs - Coroutine Library in Rust