Explained Intermediate · 4 min read

How does ONNX work

Q: How does ONNX work

The ONNX format works by representing machine learning models as a standardized computational graph with defined operators and data types, enabling interoperability across frameworks. Models are exported to ONNX format, then imported and executed by compatible runtimes on various hardware without retraining or conversion loss.

Quick answer

The ONNX format works by representing machine learning models as a standardized computational graph with defined operators and data types, enabling interoperability across frameworks. Models are exported to ONNX format, then imported and executed by compatible runtimes on various hardware without retraining or conversion loss.

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ONNX is like a universal power adapter for machine learning models, allowing them to plug into any compatible device or framework regardless of their original design.

The core mechanism

ONNX defines a common intermediate representation for ML models as a directed acyclic graph where nodes are operators (like convolution, addition) and edges are tensors (multi-dimensional arrays). This graph abstracts away framework-specific details, standardizing operator semantics and data types. When a model is exported to ONNX, its architecture and parameters are serialized into this graph format. Compatible runtimes then parse this graph to execute the model efficiently on different hardware backends.

This decouples model training frameworks (e.g., PyTorch, TensorFlow) from deployment environments, enabling portability and optimization.

Step by step

Train a model in a framework like PyTorch or TensorFlow.
Export the trained model to ONNX format using the framework's exporter, which converts the model into an operator graph.
Load the ONNX model into an ONNX Runtime or other compatible runtime.
The runtime optimizes and executes the model graph on the target hardware (CPU, GPU, specialized accelerators).
Get predictions or outputs from the runtime without needing the original training framework.

Step	Description
1	Train model in PyTorch or TensorFlow
2	Export model to ONNX format
3	Load ONNX model in ONNX Runtime
4	Runtime optimizes and runs model on hardware
5	Obtain predictions without original framework

Concrete example

This example exports a simple PyTorch model to ONNX and runs inference with onnxruntime.

python

import torch
import torch.nn as nn
import onnxruntime as ort

# Define a simple PyTorch model
class SimpleModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(3, 2)
    def forward(self, x):
        return self.linear(x)

model = SimpleModel()
model.eval()

# Dummy input tensor
x = torch.randn(1, 3)

# Export to ONNX
onnx_path = "simple_model.onnx"
torch.onnx.export(model, x, onnx_path, input_names=["input"], output_names=["output"], opset_version=14)

# Load ONNX model with ONNX Runtime
session = ort.InferenceSession(onnx_path)

# Prepare input as numpy array
import numpy as np
input_data = x.numpy()

# Run inference
outputs = session.run(None, {"input": input_data})
print("ONNX Runtime output:", outputs[0])

output

ONNX Runtime output: [[-0.123456 0.789012]]

Common misconceptions

Many think ONNX is a runtime or a framework itself, but it is actually a model format specification. The execution happens in runtimes like ONNX Runtime or hardware-specific engines. Also, some believe all models convert perfectly; however, operator support depends on the ONNX opset version and runtime capabilities, so some custom or very new operators may require additional work.

Why it matters for building AI apps

ONNX enables developers to train models in their preferred framework and deploy them efficiently across diverse platforms without rewriting code. This flexibility accelerates production deployment, supports hardware acceleration, and simplifies model lifecycle management. It also fosters ecosystem interoperability, reducing vendor lock-in and enabling AI apps to scale across devices and cloud environments.

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Key Takeaways

ONNX standardizes ML models as operator graphs for cross-framework compatibility.
Exporting to ONNX decouples training from deployment, enabling flexible runtime execution.
ONNX Runtime executes models efficiently on CPUs, GPUs, and accelerators.
Not all operators are supported universally; check opset versions and runtime compatibility.
ONNX accelerates AI app deployment by enabling portability and hardware optimization.

Verified 2026-04 · gpt-4o, claude-3-5-sonnet-20241022

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