---
type: learning
area: ai
status: learning
date: 2026-06-22
created: 2026-06-22
updated: 2026-07-19
tags:
  - learning
  - ai
  - onnx
---

# ONNX

## What is ONNX?

**ONNX** means **Open Neural Network Exchange**. It is an open format for representing machine-learning models and neural networks.

ONNX provides a common way to describe a model's:

- Operations, such as matrix multiplication, convolution, and activation functions
- Inputs and outputs
- Weights and parameters
- Data types and tensor shapes

The goal is to separate the model from the framework used to create it. For example, a model trained with PyTorch or TensorFlow can be exported to ONNX and then run with another compatible runtime.

## Why ONNX is useful

ONNX is mainly useful for **model portability and deployment**:

- Train a model with PyTorch, TensorFlow, or scikit-learn.
- Export the trained model to `.onnx` format.
- Run it with ONNX Runtime or another ONNX-compatible engine.
- Deploy it in a Python service, .NET application, mobile app, browser, or edge device.

This can reduce framework dependencies and make inference easier to optimize for CPUs, GPUs, and specialized hardware.

## Important distinction

ONNX usually describes the model and its computation graph. It does not replace the training framework or automatically make every model faster.

- **Training:** usually done with a framework such as PyTorch or TensorFlow.
- **Export:** converts the trained model into ONNX format.
- **Inference:** runs the ONNX model with a runtime such as ONNX Runtime.
- **Optimization:** may include quantization, graph optimization, or hardware-specific execution providers.

## Core concepts to learn

1. Tensors, shapes, and data types
2. Computation graphs and operators
3. Model export and supported operators
4. ONNX Runtime sessions
5. Input preprocessing and output postprocessing
6. Model validation after export
7. Quantization and inference optimization
8. Execution providers, such as CPU, CUDA, and TensorRT

## Practical learning path

### 1. Review the foundations

Before learning ONNX, understand:

- Python and NumPy
- Basic neural-network concepts
- Tensor shapes
- Model training versus inference
- Preprocessing and postprocessing

Related notes:

- [[03 Learning/Deep Learning/Introduction]]
- [[03 Learning/Deep Learning/ANN]]
- [[03 Learning/Data Science/Standardizing vs Normalizing]]

### 2. Export a small model

Start with a simple PyTorch model. Learn how to:

- Create a model
- Run a test input through it
- Export it with `torch.onnx.export`
- Inspect the generated graph

### 3. Run it with ONNX Runtime

Install the runtime:

```bash
pip install onnx onnxruntime
```

Then load and execute a model:

```python
import onnxruntime as ort

session = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
input_name = session.get_inputs()[0].name
output = session.run(None, {input_name: input_data})
```

The important parts are the input name, input shape, data type, and returned output.

### 4. Validate the export

Compare the original framework output with the ONNX Runtime output using the same input. Small floating-point differences are normal, but large differences usually indicate an export, preprocessing, or unsupported-operator problem.

### 5. Build a small project

A good first project is:

> Train a small image or tabular classifier, export it to ONNX, run inference with ONNX Runtime, and expose it through a FastAPI endpoint.

This connects ONNX to the deployment ideas in [[01 Projects/PFE/PFE Project]].

## Questions to answer while learning

- Which operators does my model use?
- Is my model supported by the target ONNX opset?
- Are the input shape and data type correct?
- Does ONNX Runtime produce the same predictions?
- Which execution provider should I use?
- Can quantization reduce latency or model size?

## Resources

- [ONNX official website](https://onnx.ai/)
- [ONNX Runtime documentation](https://onnxruntime.ai/docs/)
- [ONNX GitHub repository](https://github.com/onnx/onnx)
- [PyTorch ONNX export documentation](https://pytorch.org/docs/stable/onnx.html)

## Summary

ONNX is a portable model format. A practical way to learn it is to export a small model from PyTorch, run it with ONNX Runtime, compare predictions, and then study optimization and deployment.