Principle:Microsoft Onnxruntime Optimized Inference
Metadata
| Field | Value |
|---|---|
| Principle Name | Optimized_Inference |
| Repository | Microsoft_Onnxruntime |
| Source Repository | https://github.com/microsoft/onnxruntime |
| Domain | ML_Inference, Model_Optimization |
| Last Updated | 2026-02-10 |
| Workflow | Train_Convert_Predict |
| Pair | 5 of 5 |
Overview
Leveraging ONNX Runtime's optimized execution engine for faster model inference compared to the original framework.
Description
After conversion and validation, the ONNX model can be used for production inference. ONNX Runtime applies graph optimizations, operator fusion, and hardware-specific kernels to achieve faster inference than the source framework.
The optimized inference workflow is the final step in the train-convert-predict pipeline. It demonstrates the practical benefit of the ONNX conversion process: the same model can run significantly faster under ONNX Runtime than under the original training framework, especially for:
- Single-sample inference -- Common in web service scenarios where one prediction is made per request.
- Batch inference -- Processing multiple samples at once for throughput optimization.
- Ensemble models -- Models like RandomForest with many internal components benefit from ONNX Runtime's optimized tree traversal implementations.
The optimized inference pattern is demonstrated at docs/python/examples/plot_train_convert_predict.py:L79-101, where both labels and probabilities are retrieved from the ONNX model.
Theoretical Basis
ONNX Runtime achieves performance improvements over source frameworks through several mechanisms:
- Graph optimization -- Constant folding, dead code elimination, and common subexpression elimination reduce the computational graph before execution.
- Operator fusion -- Multiple operators are merged into single optimized kernels (e.g., MatMul + Add + ReLU fused into a single operation).
- Memory planning -- Pre-computed memory allocation patterns minimize dynamic allocation overhead during inference.
- Hardware-specific kernels -- Optimized operator implementations leverage CPU vector instructions (SSE, AVX), GPU compute shaders (CUDA), or specialized accelerators (TensorRT).
- Thread management -- Configurable intra-op and inter-op parallelism enables efficient utilization of multi-core processors.
The performance advantage is most pronounced for:
- Models with many sequential operations that can be fused.
- Latency-sensitive workloads where per-inference overhead matters.
- Deployment scenarios where the source framework's overhead (Python interpretation, GIL) is a bottleneck.
Usage
Optimized inference uses the same InferenceSession API after loading the validated ONNX model:
import onnxruntime as rt
import numpy
sess = rt.InferenceSession("logreg_iris.onnx", providers=rt.get_available_providers())
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name
prob_name = sess.get_outputs()[1].name
pred = sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]
probs = sess.run([prob_name], {input_name: X_test.astype(numpy.float32)})[0]