onnxruntime VS onnx-tensorflow

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).

How to solve this? I found below github issue which they solved i think, but im not to able to find the solution https://github.com/onnx/onnx-tensorflow/pull/1022

Okay, maybe it worked some years ago. The issue currently is that the trainable weights get lost...which is by design https://github.com/onnx/onnx-tensorflow/issues/1002

TLDR: I'm using onnx-tf to convert an onnx model to tensorflow. During the conversion I lose important information such as inputs, outputs and the names of operators. Please read on if you have experience with this library or you've experienced similar issues. :)

We had a few small issues with ONNX. Export worked but when running with e.g. tflite stumbled for example across this https://github.com/onnx/onnx-tensorflow/issues/853 Also the support for sampling from distributions is generally still pretty weak, but we were able to work around that.

https://pytorch.org/tutorials/advanced/super\_resolution\_with\_onnxruntime.html https://github.com/onnx/onnx-tensorflow

https://github.com/onnx/onnx-tensorflow Haven’t tried it, let me know if it works.