wonnx
Dannjs
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| wonnx | Dannjs | |
|---|---|---|
| 18 | 10 | |
| 1,478 | 428 | |
| 6.2% | - | |
| 6.5 | 0.0 | |
| 19 days ago | over 1 year ago | |
| Rust | JavaScript | |
| GNU General Public License v3.0 or later | MIT 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.
wonnx
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Intel CEO: 'The entire industry is motivated to eliminate the CUDA market'
The two I know of are IREE and Kompute[1]. I'm not sure how much momentum the latter has, I don't see it referenced much. There's also a growing body of work that uses Vulkan indirectly through WebGPU. This is currently lagging in performance due to lack of subgroups and cooperative matrix mult, but I see that gap closing. There I think wonnx[2] has the most momentum, but I am aware of other efforts.
[1]: https://kompute.cc/
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VkFFT: Vulkan/CUDA/Hip/OpenCL/Level Zero/Metal Fast Fourier Transform Library
To a first approximation, Kompute[1] is that. It doesn't seem to be catching on, I'm seeing more buzz around WebGPU solutions, including wonnx[2] and more hand-rolled approaches, and IREE[3], the latter of which has a Vulkan back-end.
[1]: https://kompute.cc/
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Onnx Runtime: “Cross-Platform Accelerated Machine Learning”
There's also a third-party WebGPU implementation: https://github.com/webonnx/wonnx
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Are there any ML crates that would compile to WASM?
By experimental I meant e.g. using WGPU to run compute shaders like wonnx, which is working fine but only on a very restricted set of devices and browsers.
- WebGPU ONNX inference runtime written in Rust
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PyTorch Primitives in WebGPU for the Browser
https://news.ycombinator.com/item?id=35696031 ... TIL about wonnx: https://github.com/webonnx/wonnx#in-the-browser-using-webgpu...
microsoft/onnxruntime: https://github.com/microsoft/onnxruntime
Apache/arrow has language-portable Tensors for cpp: https://arrow.apache.org/docs/cpp/api/tensor.html and rust: https://docs.rs/arrow/latest/arrow/tensor/struct.Tensor.html and Python: https://arrow.apache.org/docs/python/api/tables.html#tensors https://arrow.apache.org/docs/python/generated/pyarrow.Tenso...
Fwiw it looks like the llama.cpp Tensor is from ggml, for which there are CUDA and OpenCL implementations (but not yet ROCm, or a WebGPU shim for use with emscripten transpilation to WASM): https://github.com/ggerganov/llama.cpp/blob/master/ggml.h
Are the recommendable ways to cast e.g. arrow Tensors to pytorch/tensorflow?
FWIU, Rust has a better compilation to WASM; and that's probably faster than already-compiled-to-JS/ES TensorFlow + WebGPU.
What's a fair benchmark?
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rustformers/llm: Run inference for Large Language Models on CPU, with Rust 🦀🚀🦙
wonnx has done some fantastic work in this regard, so that's where we plan to start once we get there. In terms of general discussion of alternate backends, see this issue.
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I want to talk about WebGPU
> GPU in other ways, such as training ML models and then using them via an inference engine all powered by your local GPU?
Have a look at wonnix https://github.com/webonnx/wonnx
A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
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Chrome Ships WebGPU
Looking forward to your WebGPU ML runtime! Also, why not contribute back to WONNX? (https://github.com/webonnx/wonnx)
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OpenXLA Is Available Now
You can indeed perform inference using WebGPU (see e.g. [1] for GPU-accelerated inference of ONNX models on WebGPU; I am one of the authors).
The point made above is that WebGPU can only be used for GPU's and not really for other types of 'neural accelerators' (like e.g. the ANE on Apple devices).
Dannjs
- Brain.js: GPU Accelerated Neural Networks in JavaScript
- GitHub - matiasvlevi/Dann: Deep Neural Network Library for JavaScript.
- Dannjs - Neural Network library for Javascript
- Dannjs – Deep Neural Network Library for JavaScript
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Train a Deep Neural Network to recognize handwritten digits with Dannjs.
We are also going to install our Neural Network library, Dannjs.
- Dannjs – Neural Network Library for JavaScript
- Dannjs | Deep Neural Networks for Javascript
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DannJS, a cool, user-friendly neural-network library for JavaScript.
DannJS is a neural-network library for JavaScript. Dannjs (Homepage). Dann (GitHub).
- Deep Neural Network library fo JavaScript
- Welcome Contributors – Contribute to Dannjs
What are some alternatives?
stablehlo - Backward compatible ML compute opset inspired by HLO/MHLO
dalle-playground - A playground to generate images from any text prompt using Stable Diffusion (past: using DALL-E Mini)
onnx - Open standard for machine learning interoperability
Tesseract.js - Pure Javascript OCR for more than 100 Languages 📖🎉🖥
tract - Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference
Brain.js - Simple feed-forward neural network in JavaScript
iree - A retargetable MLIR-based machine learning compiler and runtime toolkit.
Keras.js - Run Keras models in the browser, with GPU support using WebGL
burn - Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
ConvNetJS - Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.
blaze - A Rustified OpenCL Experience
Synapses - A group of neural-network libraries for functional and mainstream languages