pipeless VS onnx

After spending some months working on the Pipeless open-source framework, today I bring something new and really cool: Pipeless Agents

pipeless init my-project --template empty # Using the empty template we avoid the interactive shell cd my-project wget -O - https://github.com/pipeless-ai/pipeless/archive/main.tar.gz | tar -xz --strip=2 "pipeless-main/examples/yolo"

wget -O - https://github.com/pipeless-ai/pipeless/archive/main.tar.gz | tar -xz --strip=2 "pipeless-main/examples/onnx-yolo"

Check the live demo in the website

The code is available in the Pipeless GitHub repository: https://github.com/pipeless-ai/pipeless/tree/main/examples/yolo

Recently, a new alternative called Pipeless has appeared. Pipeless is an open-source framework that focuses on providing a great development experience and out-of-the-box performance. It offers a really easy stream management allowing you to add, edit, and remove streams on the fly as well as processing multiple streams. Furthermore, it offers you the possibility to deploy the applications either to the cloud or directly to embedded or edge devices.

You can find the repo here: https://github.com/pipeless-ai/pipeless

Pipeless is an open-source computer vision framework that ships everything you need to build and deploy computer vision applications really fast.

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.