tract
friedrich
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| tract | friedrich | |
|---|---|---|
| 20 | 2 | |
| 2,050 | 50 | |
| 2.9% | - | |
| 9.8 | 0.0 | |
| 5 days ago | 4 months 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.
friedrich
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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).
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Interactive Visualization of Gaussian Processes
For me the good reason to use gaussian regression is the fact that you get an uncertainty on the output.
The big downside is that it takes expert knowledge (to design a proper kernel) and a solid implementation (to avoid the various numerical problems they can produce) to apply them to practical problem. Most implementation either break down very quickly or are not flexible enough for my taste.
I have a Rust implementation [0] which tries to help with the flexibility aspect but it is still very far from perfect.
[0]: https://github.com/nestordemeure/friedrich
What are some alternatives?
onnxruntime-rs - Rust wrapper for Microsoft's ONNX Runtime (version 1.8)
awesome-rust - A curated list of Rust code and resources.
MTuner - MTuner is a C/C++ memory profiler and memory leak finder for Windows, PlayStation 4 and 3, Android and other platforms
Peroxide - Rust numeric library with R, MATLAB & Python syntax
wonnx - A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
rust - Rust for the xtensa architecture. Built in targets for the ESP32 and ESP8266
ncurses-rs - A low-level ncurses wrapper for Rust
tch-rs - Rust bindings for the C++ api of PyTorch.
linfa - A Rust machine learning framework.
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
tangram - Tangram makes it easy for programmers to train, deploy, and monitor machine learning models.