tract
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| tract | MTuner | |
|---|---|---|
| 20 | 5 | |
| 2,046 | 2,547 | |
| 2.7% | 1.2% | |
| 9.8 | 8.6 | |
| 7 days ago | 3 months ago | |
| Rust | C++ | |
| Apache 2.0/MIT | BSD 2-clause "Simplified" License |
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.
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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.
MTuner
- MTuner: C/C memory profiler and memory leak finder
- MTuner: C/C++ memory profiler and memory leak finder
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What do you want out of a Rust profiler?
I would really love to have some memory profiler that is capable of dealing of huge amount of data and allocations, robust and stable with stack traces on allocations. Similar to https://github.com/milostosic/MTuner
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Hey Rustaceans! Got an easy question? Ask here (39/2021)!
For memory profiling, MTuner is a good tool (worked much better for me than Visual Studio's memory profiler), and it's even open source.
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We Trace a KV Database with Less Than 5% Performance Impact
Remotery - https://github.com/Celtoys/Remotery
Visual Studio's built-in profiler is an ok sampling profiler. It doesn't give you a nice multi-thread view which is a huge advantage to a span based profiler.
MTuner is quite nice for debugging memory usage. Which is another gaping hole in the Rust ecosystem. https://github.com/milostosic/mtuner
Lots of tools generate data in a format viewable by the Chrome trace viewer. I think Chrome's tracer viewer is not great. Maybe someday someone will create a viewer for the format that's good. I get cranky when large traces don't render at 60fps. Web-based viewers are almost all very very slow and it makes me sad.
What are some alternatives?
onnxruntime-rs - Rust wrapper for Microsoft's ONNX Runtime (version 1.8)
palanteer - Visual Python and C++ nanosecond profiler, logger, tests enabler
wonnx - A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
PresentMon - Capture and analyze the high-level performance characteristics of graphics applications on Windows.
ncurses-rs - A low-level ncurses wrapper for Rust
tracy - Frame profiler
linfa - A Rust machine learning framework.
Remotery - Single C file, Realtime CPU/GPU Profiler with Remote Web Viewer
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
meta - 🦀 GitHub Actions for Rust - recipes, discussions, questions and ideas
tangram - Tangram makes it easy for programmers to train, deploy, and monitor machine learning models.
coz - Coz: Causal Profiling