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Installation

pip install onnxruntime
Install ONNX Runtime CPU version

pip install onnxruntime-gpu
Install ONNX Runtime GPU version

pip install onnx
Install ONNX (Open Neural Network Exchange)

Model Loading

import onnxruntime as ort
Import ONNX Runtime

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

session = ort.InferenceSession('model.onnx', providers=['CUDAExecutionProvider'])
Load model with GPU acceleration

Inference

input_name = session.get_inputs()[0].name
Get input name

output_name = session.get_outputs()[0].name
Get output name

results = session.run([output_name], {input_name: input_data})
Run inference

predictions = results[0]
Access prediction results

Model Inspection

session.get_inputs()
Get model input details

session.get_outputs()
Get model output details

session.get_providers()
List available execution providers

ort.get_device()
Get the device being used

Performance Tuning

options = ort.SessionOptions()
Create session options object

options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
Enable all optimizations

options.intra_op_num_threads = 4
Set number of threads

session = ort.InferenceSession('model.onnx', sess_options=options)
Create session with custom options

ONNX Model Conversion

import onnx
Import ONNX

onnx_model = onnx.load('model.onnx')
Load ONNX model

onnx.checker.check_model(onnx_model)
Validate ONNX model

onnx.save(onnx_model, 'new_model.onnx')
Save ONNX model