# ONNX Runtime — Cross-Platform ML Model Inference Engine

> ONNX Runtime is a high-performance inference engine for machine learning models in the ONNX format. Developed by Microsoft, it accelerates model serving across CPU, GPU, and specialized hardware with a unified API for Python, C++, C#, Java, and JavaScript.

## Install

Save as a script file and run:

# ONNX Runtime — Cross-Platform ML Model Inference Engine

## Quick Use
```bash
pip install onnxruntime
python -c "
import onnxruntime as ort
import numpy as np
# Example: run a model exported from PyTorch/TF
# session = ort.InferenceSession('model.onnx')
# result = session.run(None, {'input': np.random.randn(1, 3, 224, 224).astype(np.float32)})
print(f'ONNX Runtime {ort.__version__} ready, providers: {ort.get_available_providers()}')
"
```

## Introduction
ONNX Runtime is Microsoft's cross-platform inference accelerator for machine learning models. It supports the Open Neural Network Exchange (ONNX) format, enabling models trained in PyTorch, TensorFlow, scikit-learn, and other frameworks to run on any target hardware with optimized performance and minimal code changes.

## What ONNX Runtime Does
- Executes ONNX-format models with automatic graph optimizations and kernel fusion
- Accelerates inference on CPUs (x86, ARM), NVIDIA GPUs (CUDA, TensorRT), AMD GPUs, and NPUs
- Provides execution providers that plug in hardware-specific acceleration without code changes
- Supports quantization (INT8, FP16) to reduce model size and increase throughput
- Enables on-device inference for mobile (iOS, Android) and web (WebAssembly, WebGPU) targets

## Architecture Overview
ONNX Runtime loads an ONNX graph and applies optimization passes (constant folding, operator fusion, layout transformation) before partitioning subgraphs to available execution providers. Each execution provider handles hardware-specific code generation. A session-based API manages model loading, input binding, and output retrieval. The runtime uses a thread pool for parallel operator execution on CPU and streams for GPU overlap.

## Self-Hosting & Configuration
- CPU inference: `pip install onnxruntime`; GPU: `pip install onnxruntime-gpu`
- Convert PyTorch models: `torch.onnx.export(model, dummy_input, 'model.onnx')` or use the Optimum library
- Select execution provider: `ort.InferenceSession('model.onnx', providers=['CUDAExecutionProvider'])`
- Quantize models with `onnxruntime.quantization.quantize_dynamic()` for INT8 inference
- Deploy to edge via ONNX Runtime Mobile with a reduced operator set for smaller binary size

## Key Features
- Broad hardware support through 20+ execution providers including TensorRT, DirectML, and OpenVINO
- Graph optimizations that automatically fuse operators for up to 3x speedup over naive execution
- Supports ONNX opset versions 7 through 21 for wide model compatibility
- Training support via ORTModule for accelerating PyTorch training with ONNX Runtime kernels
- ONNX Runtime Web enables ML inference in the browser via WebAssembly and WebGPU

## Comparison with Similar Tools
- **PyTorch (eager)** — flexible for research but ONNX Runtime typically delivers 2-3x faster inference
- **TensorRT** — best raw NVIDIA GPU performance but NVIDIA-only and requires more setup
- **OpenVINO** — Intel-optimized inference but narrower hardware coverage than ONNX Runtime
- **TFLite** — lightweight for mobile but limited to TensorFlow models and fewer optimizations
- **Triton Inference Server** — model serving platform that can use ONNX Runtime as a backend

## FAQ
**Q: Which model frameworks can export to ONNX?**
A: PyTorch (via torch.onnx or torch.export), TensorFlow (via tf2onnx), scikit-learn (via skl2onnx), and many others. Hugging Face Optimum automates export for transformer models.

**Q: Does ONNX Runtime support dynamic input shapes?**
A: Yes, ONNX models can have dynamic axes (e.g., variable batch size or sequence length), and ONNX Runtime handles them efficiently.

**Q: How much speedup can I expect over PyTorch?**
A: Typically 1.5-3x for inference, depending on the model and hardware. Graph optimization and operator fusion drive most gains.

**Q: Can I use ONNX Runtime for LLM inference?**
A: Yes, ONNX Runtime GenAI provides optimized LLM inference with support for models like Phi, LLaMA, and Mistral in ONNX format.

## Sources
- https://github.com/microsoft/onnxruntime
- https://onnxruntime.ai

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Author: Script Depot