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Deploy with ONNX

Technical Resources

Quick Reference

  • One-sentence definition: ONNX (Open Neural Network Exchange) provides an open-source framework for model interoperability, enabling seamless deployment of AI models across platforms and devices.
  • Key use cases: Simplifies moving AI models from training frameworks like PyTorch or TensorFlow to inference environments like TensorRT or OpenVINO.
  • Prerequisites: Understanding of Python, basic AI concepts, and familiarity with ONNX-compatible frameworks.

Table of Contents

  1. Introduction
  2. Core Concepts
  3. What is ONNX?
  4. Why Use ONNX for Edge AI?
  5. ONNX Workflow Overview
  6. Visual Architecture
  7. ONNX Model Deployment Pipeline
  8. Implementation Details
  9. Model Conversion Example
  10. Running Inference with ONNX Runtime
  11. Tools & Resources
  12. References

Introduction

What

ONNX is an open standard that allows AI models to be portable between different frameworks and optimized inference runtimes.

Why

With ONNX, developers can optimize and deploy models efficiently across diverse edge devices, enabling robust real-time AI applications.

Where

  • Healthcare: Image diagnostics on portable devices.
  • Retail: Customer behavior analysis via AI cameras.
  • Smart Homes: Voice recognition on low-power IoT devices.

Core Concepts

What is ONNX?

ONNX is a unified format for AI models, supported by a growing ecosystem of tools and runtimes.

Why Use ONNX for Edge AI?

  • Interoperability: Models can be exported and run in diverse environments.
  • Optimization: Compatible with performance-focused runtimes like TensorRT or OpenVINO.
  • Scalability: Simplifies deployment across multiple devices.

ONNX Workflow Overview

  1. Export: Convert a trained model from frameworks like PyTorch or TensorFlow to ONNX.
  2. Optimize: Use ONNX tools to optimize the model.
  3. Deploy: Run the model on edge devices using ONNX Runtime.

Visual Architecture

ONNX Model Deployment Pipeline

graph TD
A[Trained Model] -->|Export to ONNX| B[ONNX Model]
B -->|Optimize| C[ONNX Optimized Model]
C -->|Run Inference| D[Edge Device]

Implementation Details

Model Conversion Example

Step 1: Convert a PyTorch Model to ONNX

import torch
import onnx

# Load a pre-trained PyTorch model
model = torch.load("model.pth")
model.eval()

# Dummy input for ONNX export
dummy_input = torch.randn(1, 3, 224, 224)

# Export the model
onnx.export(model, dummy_input, "model.onnx", opset_version=11)

Step 2: Validate the ONNX Model

onnxruntime_tools check model.onnx

Running Inference with ONNX Runtime

Step 1: Install ONNX Runtime

pip install onnxruntime

Step 2: Run Inference

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.random.randn(1, 3, 224, 224).astype(np.float32)

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

Tools & Resources

Tools

  • ONNX Runtime: High-performance inference engine.
  • Netron: Visualize ONNX models.

Learning Resources

References