ML Model Optimization • Model Compilation (TensorRT, ONNX, TVM)Easy⏱️ ~2 min
ONNX: The Universal Intermediate Format
Open Neural Network Exchange (ONNX) solves a critical portability problem in machine learning: training frameworks and deployment runtimes are tightly coupled. If you train in PyTorch, you historically needed PyTorch at serving time. If you wanted to switch to a faster runtime or target a different hardware accelerator, you faced expensive retraining or complex conversion work. ONNX breaks this coupling by providing a common intermediate format that any framework can export to and any runtime can consume.
ONNX defines a standardized graph representation with typed operators and versioned operator sets (opsets). When you export a PyTorch model to ONNX, the framework translates its internal graph nodes into ONNX operators like Conv, MatMul, or Softmax with explicit shapes, types, and attributes. This ONNX file becomes a portable artifact that TensorRT, ONNX Runtime, TVM, or any other compliant runtime can import and optimize for their target hardware. The key benefit is flexibility: train once, deploy anywhere without being locked into a single stack.
In production pipelines, ONNX sits between training and serving. A typical flow is: train a model in PyTorch or TensorFlow, export to ONNX with explicit input shapes and named inputs, then pass that ONNX file to your compiler of choice. NVIDIA uses ONNX as input to TensorRT for GPU acceleration. Microsoft built ONNX Runtime with execution providers for CPU, CUDA, TensorRT, and DirectML, enabling the same ONNX file to run optimized across diverse hardware. Amazon Inferentia accepts ONNX models through the Neuron compiler, and Google supports ONNX import in some XLA pipelines for TPU deployment.
The abstraction is not perfect. Dynamic control flow, custom operators, and certain post processing operations may not have standard ONNX representations. Teams often export models before non standard stages like Non Maximum Suppression (NMS) in object detection and handle those in separate runtime code. Despite these limitations, ONNX has become the de facto interchange format, with broad adoption across cloud providers and hardware vendors.
💡 Key Takeaways
•ONNX decouples training frameworks from deployment runtimes, enabling train once and deploy anywhere without retraining or vendor lock in
•Defines standardized graph representation with versioned operator sets (opsets) for interoperability across tools and hardware
•Production adoption: Microsoft ONNX Runtime supports CPU, CUDA, TensorRT, and DirectML execution providers from a single ONNX file
•Common export flow: PyTorch model to ONNX with explicit shapes, then ONNX to TensorRT engine for NVIDIA GPUs or Neuron compiler for AWS Inferentia
•Limitations include dynamic control flow and custom operators; teams often export before post processing stages like NMS and handle those separately
•ONNX has become the de facto interchange format with support from NVIDIA, Microsoft, Amazon, and Google across cloud and edge deployments
📌 Examples
Export a PyTorch ResNet50: torch.onnx.export(model, dummy_input, 'resnet50.onnx', input_names=['image'], output_names=['logits'], dynamic_axes={'image': {0: 'batch'}})Microsoft Azure Machine Learning uses ONNX Runtime to serve models trained in any framework on CPU and GPU instances with automatic hardware acceleration
NVIDIA Jetson devices accept ONNX models that TensorRT converts to optimized engines, enabling the same model artifact to deploy across cloud and edge