Learn ONNX with Real Code 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

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

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

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

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

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

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

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