onnxjs VS onnxruntime

Tensorflow (and by extension Keras) offload most of the actual work to C++ or C, so having those compile to WebAssembly would (I imagine) be a herculean effort.

Instead, The TF team maintains TFJS, which can run on WebAssembly[0].

There are also tractjs[1], and onnyxjs[2], both of which allow you to run (most) ONNX models (which is an open standard for specifying ML models) using WebAssembly and WebGL(only onnyxjs supports WebGL). A bunch of frameworks (caffe, pytorch, TF) support exporting to/importing from ONNX.

[0] https://blog.tensorflow.org/2020/03/introducing-webassembly-...

[1] https://github.com/bminixhofer/tractjs

[2] https://github.com/microsoft/onnxjs

ONNX Runtime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

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.

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.

- 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/

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.

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).