linfa
not-yet-awesome-rust
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linfa | not-yet-awesome-rust | |
---|---|---|
14 | 18 | |
3,381 | 1,297 | |
3.5% | 1.8% | |
6.3 | 0.0 | |
25 days ago | about 1 year ago | |
Rust | ||
Apache License 2.0 | The Unlicense |
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.
linfa
- Why is Rust not more popular in ML and secure edge computing?
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Polars vs ndarray performance
I've been playing with data analytics and ml in rust for the last couple of weeks. A typical ML job requires transforming some data to feed the ml model to the then train the model. For ML I've been using linfa (https://github.com/rust-ml/linfa) which is surprisingly nice. I've been experimenting with ndarray and polars for data transformation (linfa uses ndarray) - from a UX standpoint. I'm pretty surprised by polars' performance (https://h2oai.github.io/db-benchmark/), which sits on top of arrow2, and it's definitely a great candidate for OLAP tasks. But I couldn't find any comparison between ndarray and polars, has anyone had any meaningful experience with the two or/and can point me to a benchmark comparison?
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Ask HN: What is the job market like, for niche languages (Nim, crystal)?
The most comprehensive current view of the Rust machine learning ecosystem at the moment is probably at https://www.arewelearningyet.com/ (I sometimes help maintain this site)
Rust has a weird mix at the moment, and not one that's likely to significantly change within the next 12 months, at least. Certain tools are genuinely best-in-class, especially around simple operations on insane amounts of data. Rust kills it in that space due to its native speed and focus on concurrency.
There's also growing projects like Linfa [1]. that while not at the level of scikit-learn, have significantly increased their coverage on common data science/classical ML problems in the past couple years, along with improved tooling. The space does have a few pure-Rust projects coming down the pipeline around autodifferentiation, GPU compute, etc. that are likely to yield some really valuable results in deep learning, but that aren't quite available and will take some time to pick up some traction even once they're released. At the same time, areas like data visualization are unlikely to reach parity with something like matplotlib/pyplot in the near future.
Python is the de-facto standard, and will be for some time, but Rust's ability to build accessible high-level APIs on top of performant, language-native libraries is attracting some attention and I wouldn't be surprised to start seeing ingress in the certain areas over the next few years, where instead of the Python/C++ combination, it's just Rust all the way down.
[1] https://github.com/rust-ml/linfa
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Is RUST aiming to build an ecosystem on scientific computing?
take a look at https://github.com/rust-ml/linfa for machine learning related crates
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What is a FOSS which is needed but doesn't exist yet/needs contributers?
Check out smartcore and linfa. At work I was badly in need of an NMF function similar to MATLAB's one these days but not enough time to write one myself. If you're good at math and machine learning, this sounds like a task you could try tackling.
- Any role that Rust could have in the Data world (Big Data, Data Science, Machine learning, etc.)?
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How far along is the ML ecosystem with Rust?
For other algorithms, there is not yet a single library to rule them all (linfa might become that at some point) but searching for the algorithm you need on crate.io is likely to give you some results (obligatory plug to Friedrich, my gaussian process implementation).
- Linfa: A Rust machine learning framework
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AII4DEVS #10: Diverse knowledge is the key to grow the next generation of ML practitioners into AI engineers.
To all folks in love with Rust programming language, **linfa** is a promising library to check out: a complete porting of the well known scikit-learn library, which enables common preprocessing tasks and classical ML algorithms such as clustering, linear learners, logistic regression, and decision trees as well as support vector machines and Bayesian algorithms such as Naive Bayes. We all know that Python has the 98% of the machine learning languages market share, but if I looked to something else, a super-fast Rust implementation would be my first stop.
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Linfa has a website now!
for a start I will implement the TryFrom for Dataset under a feature flag. But to be really useful some of the algorithms have to start using something like DatasetBase here Records are currently bounded by an associated type for the element type, we would have to relax that too. Just read your blogpost on polars 👍
not-yet-awesome-rust
- Not-yet-Awesome Rust
- What are some libraries/crates that Rust still doesn't have readily available?
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Which crate should most not exist?
https://github.com/not-yet-awesome-rust/not-yet-awesome-rust might give ideas
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Looking to contribute to the arm ecosystem.
My suggestion would be to find something on Not Yet Awesome Rust that is not ARM-specific, but would be useful on ARM, and which interests you, and then do that.
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Library/app/feature wishlist for Rust newcomers?
For a wishlist, you can check Not Yet Awesome Rust or Not Yet Awesome Embedded Rust.
- Beginner projects
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What is a FOSS which is needed but doesn't exist yet/needs contributers?
Maybe take a look at Not Yet Awesome Rust? (A list of libraries people want in Rust but which don't exist yet.)
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What sort of mature, open-source libraries do you feel Rust should have but currently lacks?
Aside from that, there's always Not-Yet-Awesome Rust. (Check the issues too. The README is lagging behind.)
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I would like to revive a Rust project(rewrite something in Rust if not) if possible. Any ideas?
hostapd
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What libraries do you miss from other languages?
Not Yet Awesome Rust
What are some alternatives?
smartcore - A comprehensive library for machine learning and numerical computing. The library provides a set of tools for linear algebra, numerical computing, optimization, and enables a generic, powerful yet still efficient approach to machine learning.
LiteDB - LiteDB - A .NET NoSQL Document Store in a single data file
Awesome-Rust-MachineLearning - This repository is a list of machine learning libraries written in Rust. It's a compilation of GitHub repositories, blogs, books, movies, discussions, papers, etc. 🦀
ceres-solver - A large scale non-linear optimization library
rust-ndarray - ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations
tiny-skia - A tiny Skia subset ported to Rust
rusty-machine - Machine Learning library for Rust
Ink - 🌈 React for interactive command-line apps
Enzyme - High-performance automatic differentiation of LLVM and MLIR.
okapi - OpenAPI (AKA Swagger) document generation for Rust projects
tract - Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference
pjproject - PJSIP project