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Learn Onnx - 10 Code Examples & CST Typing Practice Test

ONNX (Open Neural Network Exchange) is an open-source format and ecosystem for representing machine learning models, enabling interoperability between frameworks like PyTorch, TensorFlow, and scikit-learn, and allowing deployment across diverse platforms.

View all 10 Onnx code examples →
ONNX Model Inference ExampleONNX Image Classification InferenceONNX Batch Inference ExampleONNX Regression Model InferenceONNX GPU Inference ExampleONNX Multiple Outputs ExampleONNX Dynamic Input Shape ExampleONNX Softmax Output ExampleONNX Text Model Inference ExampleONNX Model Warmup Example

Learn ONNX with Real Code Examples

Updated Nov 24, 2025

Explain

ONNX provides a standard format for models, allowing them to be trained in one framework and deployed in another.

It supports operators for deep learning, classical ML, and other computational graphs.

ONNX enables cross-platform deployment, including edge devices, mobile, and cloud inference environments.

Core Features

Interoperability between frameworks (PyTorch, TensorFlow, scikit-learn, etc.)

Graph-based computational representation

Model optimization and runtime acceleration

Cross-platform support for cloud, mobile, and edge

Extensible via custom operators for advanced use cases

Basic Concepts Overview

ModelProto: ONNX serialized model format

Graph: computational graph representing model operations

Node: operator within the graph (e.g., Conv, Add, Relu)

Tensor: multi-dimensional array data flowing between nodes

OperatorSet: collection of supported operators

Project Structure

scripts/ - model training and conversion scripts

models/ - exported ONNX model files

datasets/ - data used for testing inference

notebooks/ - experiments and validation

logs/ - inference performance metrics

Building Workflow

Train a model in your preferred framework

Export the trained model to ONNX format

Optional: optimize the model using ONNX Runtime tools

Run inference using ONNX Runtime across supported hardware

Deploy model on cloud, edge, or mobile platforms

Difficulty Use Cases

Beginner: export simple PyTorch/TensorFlow models

Intermediate: optimize models for runtime performance

Advanced: handle custom operators and conversion issues

Expert: deploy models on heterogeneous edge devices

Enterprise: integrate ONNX into ML production pipelines

Comparisons

ONNX vs PyTorch: PyTorch for training; ONNX for interoperable deployment

ONNX vs TensorFlow SavedModel: ONNX is cross-framework; TF SavedModel is TF-specific

ONNX vs CoreML: CoreML targets Apple devices; ONNX is cross-platform

ONNX vs TensorRT: TensorRT optimizes for NVIDIA hardware; ONNX is model format

ONNX vs TFLite: TFLite is for mobile; ONNX supports broader deployment targets

Versioning Timeline

2017 - Initial release by Microsoft and Facebook

2018 - ONNX Runtime introduced for high-performance inference

2019 - Added support for more operators and frameworks

2020 - Expanded optimization tools and quantization support

2025 - Latest version with broad framework and hardware interoperability

Glossary

ONNX: Open Neural Network Exchange

Operator: computation unit/node

Graph: connected nodes and tensors

Runtime: execution engine for ONNX models

Quantization: reducing precision for optimization

Installation Setup

Install Python 3.7+

Install ONNX library: `pip install onnx`

Install ONNX Runtime for inference: `pip install onnxruntime`

Optional: Install framework converters like `onnx-tf` or `torch.onnx`

Verify installation by exporting a sample model and running inference

Environment Setup

Install Python 3.7+

Install ONNX and ONNX Runtime

Verify framework version compatibility

Test sample model export and inference

Install optional optimization and conversion tools

Config Files

models/ - ONNX model files

scripts/ - export and inference scripts

datasets/ - input data for testing

logs/ - performance and debug logs

optimizations/ - optional optimization configurations

Cli Commands

pip install onnx onnxruntime

python export_model.py

onnxruntime_test.py to run inference

Use environment variables to select GPU/CPU device

Monitor logs and performance metrics

Internationalization

Unicode dataset support

Global adoption for cross-platform deployment

Documentation in English, community translations available

Compatible with international ML standards

Supported by multiple cloud and edge providers

Accessibility

Cross-platform Python support

ONNX Runtime supports multiple hardware backends

Open-source and free

Framework-agnostic for diverse teams

Accessible for enterprise and research

Ui Styling

Jupyter/Colab for experimentation

Visualize model graphs using Netron

Monitor inference latency/throughput

Export plots for reporting

Integrate dashboards for real-time metrics

State Management

Version ONNX models for reproducibility

Track runtime performance metrics

Backup exported models

Log inference results

Ensure reproducibility across deployments

Data Management

Preprocess input datasets compatible with exported model

Maintain consistent data types and shapes

Use batching for efficient inference

Cache intermediate results if needed

Track feature transformations for reproducibility

Architecture

Graph-based model representation (nodes = operators, edges = tensors)

Supports standard data types and tensor shapes

Includes metadata for inputs, outputs, and training parameters

Extensible operator set for custom computations

ONNX Runtime executes graphs with hardware-specific optimizations

Rendering Model

Graph-based representation of operators and tensors

Supports standard and custom operator sets

Serialization in ModelProto format

Executable via ONNX Runtime or compatible engines

Optimizable via quantization and graph transformations

Architectural Patterns

Graph of nodes representing operations

Tensors as data flowing between nodes

Separation of model definition and runtime execution

Operator sets versioned for backward compatibility

Extensible architecture for custom layers

Real World Architectures

Cross-framework ML model deployment

Mobile and edge AI applications

Cloud inference pipelines

IoT devices with constrained resources

Hybrid models combining multiple frameworks

Design Principles

Framework-agnostic model representation

Interoperable across hardware and software

Optimized for inference performance

Extensible operator set for custom use cases

Simplify cross-platform deployment

Scalability Guide

Optimize graphs for faster inference

Quantize models for reduced memory footprint

Use batching for high-throughput inference

Deploy across multiple CPUs/GPUs or cloud instances

Leverage ONNX Runtime distributed execution for large workloads

Migration Guide

Upgrade ONNX via pip

Ensure runtime compatibility with model opset

Test exported models for inference correctness

Update deployment pipelines for new ONNX version

Verify performance on target hardware

Performance Notes

ONNX Runtime can outperform native frameworks for inference

Quantization reduces model size and improves latency

Graph optimizations improve throughput

GPU and accelerator support is available for high-performance deployment

Batching inputs increases inference efficiency

Security Notes

Validate model inputs to prevent inference attacks

Use secure storage for exported ONNX models

Ensure runtime environment is trusted

Follow enterprise data governance for deployment

Monitor inference pipelines for anomalies

Monitoring Analytics

Log inference latency and throughput

Monitor GPU/CPU utilization

Track batch performance

Visualize model predictions vs expected outputs

Audit model deployment pipelines

Code Quality

Document model export steps

Validate inference against training framework outputs

Maintain versioned ONNX models

Use automated tests for inference consistency

Monitor runtime performance and logs

Practical Examples

Convert a PyTorch CNN to ONNX and run inference on CPU

Optimize a BERT model using ONNX Runtime for GPU

Deploy a scikit-learn RandomForest model using ONNX

Run quantized ONNX model on mobile device for reduced memory

Integrate ONNX model into cloud-based inference service

Troubleshooting

Verify all operators are supported in ONNX version

Check input/output tensor shapes match original model

Debug custom layers using ONNX Runtime custom ops

Ensure correct version of ONNX and runtime

Validate inference results against original framework outputs

Testing Guide

Compare outputs of ONNX model to original framework

Test with different batch sizes

Validate on multiple hardware backends

Check performance before and after optimization

Ensure numerical precision matches original model

Deployment Options

Run ONNX model via ONNX Runtime on server CPU/GPU

Deploy quantized models on mobile or embedded devices

Integrate with cloud ML inference pipelines

Use containerized ONNX Runtime environments

Combine multiple ONNX models in a pipeline for ensemble inference

Tools Ecosystem

ONNX converters for PyTorch, TensorFlow, scikit-learn, Keras

ONNX Runtime for CPU/GPU inference

ONNX Model Zoo with pre-trained models

Quantization and optimization tools (`onnxruntime-tools`)

Integration with cloud ML services and mobile SDKs

Integrations

PyTorch, TensorFlow, Keras for model export

ONNX Runtime for cross-platform inference

TensorRT and OpenVINO for hardware acceleration

Edge devices like NVIDIA Jetson, Intel Movidius

Cloud deployment on AWS, Azure, GCP

Productivity Tips

Use ONNX Runtime for optimized inference

Apply quantization to reduce model size

Validate models after each conversion

Use graph optimization tools for speed

Batch inputs to maximize throughput

Challenges

Handling unsupported or custom operators

Debugging conversion discrepancies

Optimizing for hardware-specific inference

Ensuring numerical precision matches original model

Maintaining model versioning across multiple deployments

Learning Path

Understand ML model training in PyTorch or TensorFlow

Learn ONNX model export and import

Practice inference using ONNX Runtime

Experiment with model optimization and quantization

Deploy ONNX models on edge and cloud platforms

Skill Improvement Plan

Week 1: Export simple models to ONNX

Week 2: Validate ONNX model inference matches original framework

Week 3: Apply optimizations and quantization

Week 4: Deploy models on GPU/CPU backends

Week 5: Integrate ONNX models into production pipelines

Interview Questions

What is ONNX and why is it used?

How do you export a PyTorch model to ONNX?

Explain ONNX Runtime and its advantages

How do you optimize an ONNX model for inference?

What are limitations of ONNX for deployment?

Cheat Sheet

ModelProto = serialized ONNX model

Graph = computation graph of nodes

Node = operator in the graph

Tensor = multi-dimensional data array

ONNX Runtime = inference engine

Books

Practical ONNX

Deploying AI Models with ONNX Runtime

Cross-Framework Machine Learning with ONNX

ONNX for Edge and Cloud AI

Optimizing Inference with ONNX

Tutorials

ONNX official tutorials

ONNX Runtime performance examples

PyTorch to ONNX export notebooks

TensorFlow to ONNX conversion guides

Hands-on optimization and quantization exercises

Official Docs

https://onnx.ai/

https://github.com/onnx/onnx

https://onnxruntime.ai/

Community Links

ONNX GitHub repository

ONNX Runtime GitHub

StackOverflow ONNX tag

ONNX Slack/Discord

Microsoft AI developer forums

Community Support

ONNX GitHub repository

ONNX Runtime GitHub repository

StackOverflow ONNX tag

ONNX Slack and forums

Microsoft and Facebook ML developer communities

Monetization

Cross-platform AI model deployment services

Enterprise AI solutions with ONNX Runtime

Optimization consulting for inference performance

Edge AI deployment for mobile/IoT

Commercial support and training for ONNX ecosystem

Future Roadmap

Expanded operator support across frameworks

Enhanced optimization and quantization tools

Better edge device compatibility

Improved runtime performance for multi-GPU/TPU

Integration with emerging ML frameworks

When Not To Use

Training new models (ONNX is primarily for inference)

Projects not requiring cross-framework deployment

When custom operators cannot be converted easily

For extremely small-scale local models where overhead is unnecessary

When using framework-native runtime is sufficient

Final Summary

ONNX standardizes ML model representation for cross-framework deployment.

Enables optimized, hardware-accelerated inference across CPU, GPU, and edge devices.

Supports deep learning and classical ML operators with extensibility.

Facilitates production-ready deployment without framework lock-in.

Widely adopted in enterprise, edge AI, and cloud ML pipelines.

Faq

Is ONNX free?

Yes - open-source under MIT license.

Which frameworks support ONNX?

PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, LightGBM, and more.

Can ONNX models run on mobile devices?

Yes - supported via ONNX Runtime Mobile or other accelerators.

Does ONNX support GPU acceleration?

Yes - ONNX Runtime supports GPU, CUDA, TensorRT, and other backends.

Is ONNX used for training?

Primarily for model interoperability and inference, not training.

Code Sample Descriptions

1

ONNX Model Inference Example

import onnxruntime as ort
import numpy as np

# Load ONNX model
session = ort.InferenceSession('model.onnx')

# Prepare input
input_name = session.get_inputs()[0].name
input_data = np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32)

# Run inference
outputs = session.run(None, {input_name: input_data})
print('Model output:', outputs)

A minimal example loading an ONNX model and performing inference using ONNX Runtime.

Let’s Try →
2

ONNX Image Classification Inference

import onnxruntime as ort
import numpy as np
from PIL import Image

# Load image
image = Image.open('image.jpg').resize((224,224))
input_data = np.array(image).astype(np.float32)
input_data = np.expand_dims(input_data, axis=0)

# Load model
session = ort.InferenceSession('resnet50.onnx')
input_name = session.get_inputs()[0].name

# Run inference
outputs = session.run(None, {input_name: input_data})
print('Predicted class:', np.argmax(outputs[0]))

Performing image classification using a pretrained ONNX model.

Let’s Try →
3

ONNX Batch Inference Example

import onnxruntime as ort
import numpy as np

# Batch input
batch_input = np.array([[1,2,3,4],[5,6,7,8]], dtype=np.float32)

# Load model
session = ort.InferenceSession('model.onnx')
input_name = session.get_inputs()[0].name

# Run batch inference
outputs = session.run(None, {input_name: batch_input})
print('Batch outputs:', outputs)

Performing inference on a batch of inputs using ONNX Runtime.

Let’s Try →
4

ONNX Regression Model Inference

import onnxruntime as ort
import numpy as np

# Sample input
input_data = np.array([[10.0, 20.0, 30.0]], dtype=np.float32)

# Load model
session = ort.InferenceSession('regression_model.onnx')
input_name = session.get_inputs()[0].name

# Run inference
pred = session.run(None, {input_name: input_data})
print('Regression prediction:', pred)

Inference example for a regression ONNX model.

Let’s Try →
5

ONNX GPU Inference Example

import onnxruntime as ort
import numpy as np

# Load model on GPU
session = ort.InferenceSession('model.onnx', providers=['CUDAExecutionProvider'])
input_name = session.get_inputs()[0].name
input_data = np.random.rand(1,4).astype(np.float32)

# Run inference
outputs = session.run(None, {input_name: input_data})
print('GPU inference output:', outputs)

Running ONNX model inference on GPU using ONNX Runtime with CUDA provider.

Let’s Try →
6

ONNX Multiple Outputs Example

import onnxruntime as ort
import numpy as np

# Load model
session = ort.InferenceSession('multi_output_model.onnx')
input_name = session.get_inputs()[0].name
input_data = np.random.rand(1,10).astype(np.float32)

# Run inference
outputs = session.run(None, {input_name: input_data})
print('Output 1:', outputs[0])
print('Output 2:', outputs[1])

Running inference for an ONNX model with multiple outputs.

Let’s Try →
7

ONNX Dynamic Input Shape Example

import onnxruntime as ort
import numpy as np

# Input with variable batch size
input_data = np.random.rand(5,4).astype(np.float32)

# Load model
session = ort.InferenceSession('dynamic_model.onnx')
input_name = session.get_inputs()[0].name

# Run inference
outputs = session.run(None, {input_name: input_data})
print('Dynamic input output:', outputs)

Inference with dynamic input shapes in ONNX Runtime.

Let’s Try →
8

ONNX Softmax Output Example

import onnxruntime as ort
import numpy as np

# Load model
session = ort.InferenceSession('classification_model.onnx')
input_name = session.get_inputs()[0].name
input_data = np.random.rand(1,10).astype(np.float32)

# Run inference
logits = session.run(None, {input_name: input_data})[0]
softmax = np.exp(logits) / np.sum(np.exp(logits), axis=1, keepdims=True)
print('Softmax probabilities:', softmax)

Performing inference and applying softmax to ONNX model outputs.

Let’s Try →
9

ONNX Text Model Inference Example

import onnxruntime as ort
import numpy as np

# Example input
input_data = np.random.rand(1,128).astype(np.float32)  # e.g., token embeddings

# Load model
session = ort.InferenceSession('text_model.onnx')
input_name = session.get_inputs()[0].name

# Run inference
outputs = session.run(None, {input_name: input_data})
print('Text classification output:', outputs)

Performing inference using an ONNX text classification model.

Let’s Try →
10

ONNX Model Warmup Example

import onnxruntime as ort
import numpy as np

# Load model
session = ort.InferenceSession('model.onnx')
input_name = session.get_inputs()[0].name

# Warmup pass
for _ in range(5):
    input_data = np.random.rand(1,4).astype(np.float32)
    _ = session.run(None, {input_name: input_data})

print('ONNX model warmed up and ready for real inference.')

Performing a warmup pass for an ONNX model to optimize runtime performance.

Let’s Try →

Frequently Asked Questions about Onnx

What is Onnx?

ONNX (Open Neural Network Exchange) is an open-source format and ecosystem for representing machine learning models, enabling interoperability between frameworks like PyTorch, TensorFlow, and scikit-learn, and allowing deployment across diverse platforms.

What are the primary use cases for Onnx?

Exporting models from PyTorch, TensorFlow, or other frameworks. Cross-framework deployment without retraining. Hardware-accelerated inference on CPUs, GPUs, and specialized accelerators. Optimizing models with ONNX Runtime for production. Edge AI and mobile deployment of ML models

What are the strengths of Onnx?

Simplifies model transfer between different ML frameworks. Optimized inference using ONNX Runtime. Supports deployment on multiple hardware backends. Reduces need to rewrite models for different environments. Strong ecosystem with converter tools and runtime support

What are the limitations of Onnx?

Not all framework-specific features/operators are supported. Complex custom layers may require manual conversion. Primarily focused on inference; less used for training. Debugging model conversion issues can be tricky. Smaller community compared to primary frameworks like PyTorch/TensorFlow

How can I practice Onnx typing speed?

CodeSpeedTest offers 10+ real Onnx code examples for typing practice. You can measure your WPM, track accuracy, and improve your coding speed with guided exercises.

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