ONNX & TensorRT Research¶

What you'll learn¶

• The training-to-inference gap
• What ONNX is (a portable model format)
• What TensorRT does (GPU optimization)
• The export-and-optimize pipeline
• When to use them

You train a model in PyTorch or TensorFlow — but for inference (serving predictions), you often want something faster and more portable. ONNX is a standard format to export models, and TensorRT is NVIDIA's engine to run them fast on GPUs.

The training-to-inference gap¶

The training-to-inference gap — a key concept in ONNX & TensorRT.

Training and inference have different needs:

• Training — flexibility, autograd, experimentation. Framework-specific (PyTorch, TF).
• Inference — speed, low latency, small footprint, running anywhere (server, mobile, browser, edge). You don't need autograd or training machinery.

Shipping your full training framework to production is heavy and slow. The fix: export the trained model to an optimized, portable form.

ONNX: a portable model format¶

A standard graph format so models move between frameworks and runtimes.

ONNX (Open Neural Network Exchange) is a standard file format for models. Train in one framework, export to ONNX, run anywhere that supports ONNX — decoupling training from serving.

# Export a PyTorch model to ONNX (documented API)
import torch
torch.onnx.export(model, example_input, "model.onnx")

# Run it with ONNX Runtime — no PyTorch needed
import onnxruntime as ort
session = ort.InferenceSession("model.onnx")
outputs = session.run(None, {"input": data})

   PyTorch/TF model ──export──▶ model.onnx ──run on──▶ ONNX Runtime
                                              (CPU, GPU, mobile, browser, edge)

Why it's valuable: one exported .onnx file runs via ONNX Runtime on almost any hardware, often faster than the original framework because ONNX Runtime applies graph optimizations (operator fusion, constant folding). It also frees you from shipping the training framework to production.

TensorRT: maximum GPU speed¶

NVIDIA's optimizer/runtime that fuses and quantizes for fastest GPU inference.

TensorRT (NVIDIA) takes optimization further specifically for NVIDIA GPUs. It compiles a model into a highly-optimized inference "engine" using:

• Layer fusion — combine operations into single GPU kernels (fewer memory round-trips — echoing GPU Kernels).
• Precision calibration — run in FP16 or INT8 (Quantization) for big speedups.
• Kernel auto-tuning — pick the fastest kernel for your specific GPU.
• Memory optimization — reuse buffers.

The result can be several times faster than the original model on the same GPU — critical for high-throughput or low-latency serving.

The export-and-optimize pipeline¶

Train → export to ONNX → optimize to a target runtime.

A typical production path:

   1. Train (PyTorch/TF)
   2. Export to ONNX
   3. (optional) Quantize
   4. Optimize with ONNX Runtime  — or —  compile with TensorRT (NVIDIA GPUs)
   5. Serve the optimized model  (see ML Deployment / ML Inference Graphs)

Each step trades flexibility for speed. You lock in the model (no more training) in exchange for a lean, fast artifact.

When to use them¶

A core question explored in ONNX & TensorRT: When to use them.

• ONNX — almost always worth it for production inference: portability + free graph optimizations, and it decouples serving from your training framework. Especially valuable for cross-platform (mobile, browser via ONNX Runtime Web, edge).
• TensorRT — when you serve on NVIDIA GPUs and need maximum throughput/lowest latency, and can invest in the (NVIDIA-specific) optimization step.

They compose: export to ONNX, then optionally compile that to a TensorRT engine. Connects to ML Deployment and ML Inference Graphs.

Practice exercises¶

1. Explain why you'd export a model to ONNX instead of shipping the PyTorch training code.
2. List three optimizations ONNX Runtime or TensorRT applies and why each speeds inference.
3. Describe how quantization fits into the export-and-optimize pipeline.
4. Explain the tradeoff you accept when compiling to a fixed TensorRT engine.
5. Decide: for a model served in a browser vs on an NVIDIA server, which tooling fits each?