onnxruntime-rs
tract
| onnxruntime-rs | tract | |
|---|---|---|
| 1 | 20 | |
| 2 | 2,060 | |
| - | 1.6% | |
| 1.1 | 9.8 | |
| about 1 year ago | 3 days ago | |
| Rust | Rust | |
| Apache License 2.0 | Apache 2.0/MIT |
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.
onnxruntime-rs
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onnxruntime
Having worked a lot with the original onnxruntime-rs crate I made a fork with a lot of changes (mainly focused on using NVIDIA and io-binding). It much more closely aligns with the onnxruntime c api: https://github.com/seddonm1/onnxruntime-rs
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?
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
tch-rs - Rust bindings for the C++ api of PyTorch.