What is ONNX?

ONNX (Open Neural Network Exchange) is a free, open-source file format that lets you move machine-learning models between different tools and platforms. Think of it as a universal “language” that lets a model built in one program run in another without having to rebuild it.

Let's break it down

  • Open - anyone can use it, see the code, and add to it; it isn’t owned by a single company.
  • Neural Network - a type of AI model that mimics how brain cells work, used for tasks like image or speech recognition.
  • Exchange - the act of swapping or sharing something; here it means sharing a model.
  • Format - a specific way of organizing data so computers can read it, like a PDF for documents.
  • File - the actual saved model that you can copy, upload, or download.

Why does it matter?

Because it saves time and money: you can build a model with the tool you like best, then run it wherever it’s needed-on a phone, a server, or a different framework-without rewriting code. This flexibility speeds up development and makes AI more accessible.

Where is it used?

  • A data-science team trains a model in PyTorch, exports it to ONNX, and a mobile app team imports it into TensorFlow Lite to run on Android phones.
  • Cloud providers (e.g., Azure, AWS) accept ONNX models so customers can deploy them instantly on scalable GPU instances.
  • Companies convert legacy models into ONNX to run on specialized hardware accelerators that only support the ONNX runtime.
  • Researchers share their latest models on public repositories in ONNX format so anyone can reproduce results without matching the original software stack.

Good things about it

  • Interoperability - works across many frameworks (PyTorch, TensorFlow, Scikit-learn, etc.).
  • Hardware flexibility - many accelerators (GPU, FPGA, edge chips) provide ONNX runtimes, enabling fast inference.
  • Open community - continuous improvements from a global group of contributors.
  • Standardized ops - a common set of operations reduces surprises when moving models.
  • Versioning - newer ONNX versions add features while keeping backward compatibility.

Not-so-good things

  • Some newer or exotic operations aren’t yet supported, requiring custom work-arounds.
  • Compatibility issues can arise when moving between different ONNX versions or framework exporters.
  • Performance may be slightly lower than a model fine-tuned directly for a specific framework or hardware.
  • Learning the conversion process adds an extra step for beginners unfamiliar with model export tools.