gpu.js
onnxruntime
gpu.js | onnxruntime | |
---|---|---|
9 | 58 | |
14,989 | 13,102 | |
0.2% | 2.3% | |
0.0 | 10.0 | |
3 months ago | about 1 hour ago | |
JavaScript | C++ | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
gpu.js
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Deep Learning in JavaScript
You might already be familiar, but a GPU.js backend can provide some speedups via good old WebGL -- no need for WebGPU just yet!
[0]: https://github.com/gpujs/gpu.js/
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Show HN: Shadeup – A language that makes WebGPU easier
Very cool project.
I learned WebGL three years ago but before I dove into the underlying concepts I used GPU.js [1] to quickly prototype my project. Eventually, the abstraction prevented necessary performance optimizations so I switched to vanilla GLSL and these vanilla GLSL "shaders" were initially ejected from GPU.js.
Writing JS code then looking at the generated WebGPU output is a great way to get familiar with WebGPU. Thanks for this.
[1] https://github.com/gpujs/gpu.js/
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Gpu.js: GPU Accelerated JavaScript
I used this library on my project but I think it's no longer maintained. I PRed a fix for buggy atan2 over a year ago and no movement [1]. I do highly recommend it if you're a web developer interested in harnessing parallel processing.
[1] https://github.com/gpujs/gpu.js/pull/683
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Brain.js: GPU Accelerated Neural Networks in JavaScript
Thanks for pointing this out. I've submitted a PR to resolve this: https://github.com/gpujs/gpu.js/issues/757
That being said, if you're not building from source (you're running an LTS version of node on a supported platform), you don't need to worry about python or many of the build deps.
- GPU.js
- For what projects, Nodejs is an absolute No No?
onnxruntime
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Machine Learning with PHP
ONNX Runtime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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AI Inference now available in Supabase Edge Functions
Embedding generation uses the ONNX runtime under the hood. This is a cross-platform inferencing library that supports multiple execution providers from CPU to specialized GPUs.
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Deep Learning in JavaScript
tfjs is dead, looking at the commit history. The standard now is to convert PyTorch to onnx, then use onnxruntime (https://github.com/microsoft/onnxruntime/tree/main/js/web) to run the model on the browsdr.
- FLaNK Stack 05 Feb 2024
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Vcc – The Vulkan Clang Compiler
- slang[2] has the potential, but the meta programming part is not as strong as C++, existing libraries cannot be used.
The above conclusion is drawn from my work https://github.com/microsoft/onnxruntime/tree/dev/opencl, purely nightmare to work with thoes drivers and jit compilers. Hopefully Vcc can take compute shader more seriously.
[1]: https://www.circle-lang.org/
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Oracle-samples/sd4j: Stable Diffusion pipeline in Java using ONNX Runtime
I did. It depends what you want, for an overview of how ONNX Runtime works then Microsoft have a bunch of things on https://onnxruntime.ai, but the Java content is a bit lacking on there as I've not had time to write much. Eventually I'll probably write something similar to the C# SD tutorial they have on there but for the Java API.
For writing ONNX models from Java we added an ONNX export system to Tribuo in 2022 which can be used by anything on the JVM to export ONNX models in an easier way than writing a protobuf directly. Tribuo doesn't have full coverage of the ONNX spec, but we're happy to accept PRs to expand it, otherwise it'll fill out as we need it.
- Mamba-Chat: A Chat LLM based on State Space Models
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VectorDB: Vector Database Built by Kagi Search
What about models besides GPT? Most of the popular vector encoding models aren't using this architecture.
If you really didn't want PyTorch/Transformers, you could consider exporting your models to ONNX (https://github.com/microsoft/onnxruntime).
- ONNX runtime: Cross-platform accelerated machine learning
- Onnx Runtime: “Cross-Platform Accelerated Machine Learning”
What are some alternatives?
numjs - Like NumPy, in JavaScript
onnx - Open standard for machine learning interoperability
headless-gl - 🎃 Windowless WebGL for node.js
onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX
math-clamp - Clamp a number
onnx-simplifier - Simplify your onnx model
aladino - 🧞♂️ Your magic WebGL carpet
ONNX-YOLOv7-Object-Detection - Python scripts performing object detection using the YOLOv7 model in ONNX.
math-sum - Sum numbers
onnx-tensorflow - Tensorflow Backend for ONNX
Brain.js - 🤖 GPU accelerated Neural networks in JavaScript for Browsers and Node.js
MLflow - Open source platform for the machine learning lifecycle