In the era of Industrial IoT (IIoT), moving AI from the cloud to the edge is crucial for real-time decision-making. Deploying pre-trained AI models on Industrial Edge Boards reduces latency, improves security, and saves bandwidth.
This guide explores the workflow of deploying high-performance AI models using ONNX Runtime, which is the industry standard for cross-platform compatibility.
Step 1: Preparing the Pre-trained Model
Most industrial applications start with models trained in PyTorch or TensorFlow. To ensure compatibility with edge hardware like NVIDIA Jetson or Intel OpenVINO, we convert them to the ONNX format.
# Example: Exporting a PyTorch model to ONNX
import torch
import torchvision.models as models
# Load a pre-trained ResNet model
model = models.resnet50(pretrained=True)
model.eval()
# Create dummy input for shape reference
dummy_input = torch.randn(1, 3, 224, 224)
# Export to ONNX
torch.onnx.export(model, dummy_input, "industrial_model.onnx", verbose=True)Step 2: Optimizing for Edge Hardware
Industrial boards have limited resources. Using Quantization (converting FP32 to INT8) can significantly speed up inference without losing much accuracy. This is essential for Edge AI boards handling high-speed production lines.
Step 3: Running Inference on the Edge
Once you have your .onnx file, you can run it on your target board using the following Python snippet:
import onnxruntime as ort
import numpy as np
# Load the model on the Edge Board
session = ort.InferenceSession("industrial_model.onnx")
# Prepare your industrial sensor/camera data
input_name = session.get_inputs()[0].name
data = np.random.randn(1, 3, 224, 224).astype(np.float32)
# Run Inference
result = session.run(None, {input_name: data})
print("AI Prediction Successful:", result)Conclusion
Deploying AI on the Edge allows industrial systems to react instantly to anomalies. By leveraging pre-trained models and optimizing them for specific hardware, manufacturers can achieve smarter automation with minimal infrastructure costs.
Edge AI, Industrial IoT, AI Deployment, Pre-trained Models, NVIDIA Jetson, Raspberry Pi, ONNX, OpenVINO, Machine Learning
