uom VS onnx

uom (type-safe zero-cost dimensional analysis) v0.35.0 got released today!

It happened! v0.34.0 (crates.io) has been released.

My hope is to release uom (type-safe zero-cost dimensional analysis) v0.34.0 this week. There have been a huge number of new quantities and units added since v0.33.0.

I have been reviewing lots of PRs recently submitted to add many new units and quantities to uom (type-safe zero-cost dimensional analysis).

I reviewed some PRs to add new units to uom (type-safe zero-cost dimensional analysis) yesterday and am really hoping to make progress on logarithmic units this week. no_std support is slowing down the later.

Working on a PR to uom (type-safe zero-cost dimensional analysis) to support logarithmic units.

A great type system enables things like unit preserving calculations and Formal Methods.

Is it the error messages, or other parts of uom that make it unwieldy to use? Feedback welcome here or as a new issue.

I'm working through reviewing the open PRs for uom (type-safe zero-cost dimensional analysis).

Once your model has been trained and validated using Anomalib, the next step is to prepare it for real-time implementation. This is where ONNX (Open Neural Network Exchange) or OpenVINO (Open Visual Inference and Neural network Optimization) comes into play.

ONNX is an open format from the Linux Foundation to represent machine learning models. It is becoming extensively adopted by the Machine Learning community and is compatible with most of the machine learning frameworks like PyTorch, TensorFlow, etc. Converting a model between any of those formats and ONNX is really simple and can be done in most cases with a single command.

ONNX[0], model-as-protosbufs, continuing to gain adoption will hopefully solve this issue.

[0] https://github.com/onnx/onnx

Running inference for large models on CPU is not a new problem and fortunately there has been great development in many different optimization frameworks for speeding up matrix and tensor computations on CPU. We explored multiple optimization frameworks and methods to improve latency, namely TorchScript, BetterTransformer and ONNX.

ONNX is a format for representing machine learning models in a portable way. Additionally, ONNX models can be easily optimized and thus become smaller and faster.

I would say onnx.ai [0] provides more information about ONNX for those who aren’t working with ML/DL.

[0] https://onnx.ai

It's not clear why you thing this sub is appropriate for some third party system with a Python interface. Why don't you try their discussion group: https://github.com/onnx/onnx/discussions

This kind of indirection through standardisation is pretty common to make compatibility between different kinds of software components easier. Some other good examples are the LSP project from Microsoft and ONNX to represent machine learning models. The first provides a standard so that IDEs don't have to re-invent the weel for every programming language. The latter decouples training frameworks from inference frameworks. Going back to WSGI, you can find a pretty extensive rationale for the WSGI standard here if interested.