smarty_pants
tract
| smarty_pants | tract | |
|---|---|---|
| 1 | 20 | |
| 3 | 2,060 | |
| - | 1.6% | |
| 0.0 | 9.8 | |
| about 2 years ago | 6 days ago | |
| Rust | Rust | |
| MIT License | Apache 2.0/MIT |
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smarty_pants
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I wrote a Neural Network library.
The idea is that you can simply use this crate with your project to easily train a neural network using your project. The library supports creating, training, parsing, and running. It may gain more functionality in the future. As it stands it's quite small and pretty fast with 5 NeuralNetworks taking nano-seconds to train 1000 generations in the example program. I've tried to make sure that it is "complete" and as such, I've documented nearly every function, method, and struct. I've also written an example project and tried to make it relatively easy to use.
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.
What are some alternatives?
crates.io - The Rust package registry
onnxruntime-rs - Rust wrapper for Microsoft's ONNX Runtime (version 1.8)
MTuner - MTuner is a C/C++ memory profiler and memory leak finder for Windows, PlayStation 4 and 3, Android and other platforms
wonnx - A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
ncurses-rs - A low-level ncurses wrapper for Rust
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.
bevy_webgl2 - WebGL2 renderer plugin for Bevy game engine
tractjs - Run ONNX and TensorFlow inference in the browser.
pytorch-rpi - Share PyTorch binaries built for Raspberry Pi
gamma - Computational graphs with reverse automatic differentation in the GPU