f-stack VS onnxruntime

So, we try to use Photon coroutine lib to simplify the development of DPDK applications with the new concurrency model, and provide more functionalities, such as lock, timer and file I/O. First of all, we need to choose a userspace network protocol stack. After investigation, we have chosen Tencent's open source F-Stack project, which has ported the entire FreeBSD 11.0 network protocol stack on top of DPDK. It also has made some code cuts, providing a set of POSIX APIs, such as socket, epoll, kqueue, etc. Of course, its epoll is also simulated by kqueue, since it is essentially FreeBSD.

I agree most HTTP server benchmarks are highly misleading in that way, and mention in my post how disappointed I am at the lack of good benchmarks. I also agree that typical HTTP servers would fall over at much lower new connection loads.

I'm talking about a hypothetical HTTPS server that used optimized kernel-bypass networking. Here's a kernel-bypass HTTP server benchmarked doing 50k new connections per core second while re-using nginx code: https://github.com/F-Stack/f-stack. But I don't know of anyone who's done something similar with HTTPS support.

Software for Internet Service Providers. The current project is based on DPDK, on top of it we use modified version of F-stack and then our application logic. There is some application logic "under" the F-stack too.

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).