pycape VS onnxruntime

Cape has recently deployed a confidential optical character recognition (OCR) service. Anyone can try it through the Cape UI after signing up through Cape’s website. You can also integrate the confidential OCR service with your application using the cape-js and pycape SDKs. In this blog post, we will use this OCR service example to demonstrate how you can benefit from a machine learning service while maintaining users’ data confidentiality.

Cape provides a command line interface (CLI) and also a Python and JavaScript software development kits (SDKs) called pycape and cape-js that allow developers to deploy their apps and allow users to interact with them in a secure manner.

In addition to the platform itself, Cape also provides a CLI that enables its users to easily encrypt their input data, and deploy and run serverless functions with easy commands: cape encrypt, cape deploy, and cape run. Additionally, Cape also provides two SDKs: pycape and cape-js, which allow for using cape within Python and JavaScript programs respectively.

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).