dtparse
rayon
dtparse | rayon | |
---|---|---|
1 | 67 | |
73 | 10,277 | |
- | 1.9% | |
0.0 | 9.0 | |
8 months ago | 10 days ago | |
Python | Rust | |
MIT License | 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.
dtparse
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PyO3: Rust Bindings for the Python Interpreter
ciso8601 is blazingly fast, and also its wall time is very stable. By all means, use ciso8601 if the format allows :)
On my machine, ciso8601 always runs in 240ns, and the Rust lib median time is 1250ns.
You can run a benchcmark too! Just call pytest, and it will generate an .svg report: https://github.com/gukoff/dtparse/blob/master/tests/test_per...
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?
tokenizers - 💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
crossbeam - Tools for concurrent programming in Rust
PyO3 - Rust bindings for the Python interpreter
tokio - A runtime for writing reliable asynchronous applications with Rust. Provides I/O, networking, scheduling, timers, ...
pythran - Ahead of Time compiler for numeric kernels
RxRust - The Reactive Extensions for the Rust Programming Language
setuptools-rust - Setuptools plugin for Rust support
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
CheeseShop - Examples of using PyO3 Rust bindings for Python with little to no silliness.
tokio-rayon - Mix async code with CPU-heavy thread pools using Tokio + Rayon
coroutine-rs - Coroutine Library in Rust