tract
linfa


tract | linfa | |
---|---|---|
20 | 15 | |
2,322 | 3,944 | |
2.0% | 3.0% | |
9.9 | 6.4 | |
3 days ago | 7 days ago | |
Rust | Rust | |
Apache 2.0/MIT | 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.
tract
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Are there any ML crates that would compile to WASM?
Tract is the most well known ML crate in Rust, which I believe can compile to WASM - https://github.com/sonos/tract/. Burn may also be useful - https://github.com/burn-rs/burn.
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[Discussion] What crates would you like to see?
tract!!
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tract VS burn - a user suggested alternative
2 projects | 25 Mar 2023
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Machine Learning Inference Server in Rust?
we use tract for inference, integrated into our runtime and services.
- onnxruntime
- Rust Native ML Frameworks?
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Neural networks - what crates to use?
Not for training, but for inference this looks nice: https://github.com/sonos/tract
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Brain.js: GPU Accelerated Neural Networks in JavaScript
There's also tract, from sonos[0]. 100% rust.
I'm currently trying to use it to do speech recognition with a variant of the Conformer architecture (exported to ONNX).
The final goal is to do it in WASM client-side.
[0] https://github.com/sonos/tract
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Serving ML at the Speed of Rust
As the article notes, there isn't any official Rust-native support for any common frameworks.
tract (https://github.com/sonos/tract) seems like the most mature for ONNX (for which TF/PT export is good nowadays), and recently it successfully implemented BERT.
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Run deep neural network models from scratch
There are some DL libraries written in Rust: https://github.com/sonos/tract , https://docs.rs/neuronika/latest/neuronika/index.html . The second one could be used for training, I think.
linfa
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Rust Linfa: The Rising Star of Machine Learning in Systems Programming
Linfa is a modular approach to machine learning in Rust, offering a collection of statistical learning algorithms and tools. Unlike monolithic frameworks, Linfa follows Rust's philosophy of small, focused crates that can be composed together.
- 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.
What are some alternatives?
onnxruntime-rs - Rust wrapper for Microsoft's ONNX Runtime (version 1.8)
smartcore - A comprehensive library for machine learning and numerical computing. Apply Machine Learning with Rust leveraging first principles.
wonnx - A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
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. 🦀
tractjs - Run ONNX and TensorFlow inference in the browser.
rust-ndarray - ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations
bevy_webgl2 - WebGL2 renderer plugin for Bevy game engine
Enzyme - High-performance automatic differentiation of LLVM and MLIR.
MTuner - MTuner is a C/C++ memory profiler and memory leak finder for Windows, PlayStation 3/4/5, Nintendo Switch, Android and other platforms
tch-rs - Rust bindings for the C++ api of PyTorch.
gamma - Computational graphs with reverse automatic differentation in the GPU
tangram - Tangram makes it easy for programmers to train, deploy, and monitor machine learning models.

