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ONNX Runtime is a high-performance inference and training engine for machine learning models. Understanding its core concepts is essential for effectively deploying and optimizing your models.

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What is ONNX Runtime?

ONNX Runtime is a cross-platform, high-performance scoring engine for Open Neural Network Exchange (ONNX) models. It enables:
  • Fast inference across different hardware platforms
  • Flexible deployment from cloud to edge devices
  • Hardware acceleration through execution providers
  • Model optimization via graph transformations

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Core Architecture

ONNX Runtime’s architecture consists of several key components working together:

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Key Components

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1. InferenceSession

The InferenceSession is the main entry point for running models. It:
  • Loads and parses ONNX models
  • Manages execution providers
  • Handles model initialization and optimization
  • Executes inference requests
import onnxruntime as ort

# Create inference session
session = ort.InferenceSession("model.onnx")

# Run inference
outputs = session.run(["output"], {"input": input_data})

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2. Execution Providers

Execution Providers (EPs) are the hardware acceleration interfaces that enable ONNX Runtime to run on different hardware platforms:

CPU

Default provider for general-purpose execution

CUDA

NVIDIA GPU acceleration

TensorRT

Optimized inference on NVIDIA GPUs

DirectML

Hardware acceleration on Windows

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3. Graph Optimizations

ONNX Runtime applies multiple levels of graph optimizations:
  • Level 1 (Basic): Constant folding, redundant node elimination
  • Level 2 (Extended): Node fusions, operator transformations
  • Level 3 (Layout): Data layout optimizations for specific hardware

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4. ONNX Format

The ONNX format is an open standard for representing machine learning models:
  • Protobuf-based: Efficient serialization format
  • Framework-agnostic: Works with PyTorch, TensorFlow, etc.
  • Operator standard: Well-defined operator specifications
  • Extensible: Support for custom operators

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Execution Flow

Here’s how ONNX Runtime processes an inference request:
1

Model Loading

The ONNX model is loaded and parsed into an internal graph representation
2

Graph Optimization

Multiple optimization passes transform the graph for better performance
3

Graph Partitioning

The graph is partitioned across available execution providers based on their capabilities
4

Session Initialization

Kernels are instantiated and memory is allocated
5

Inference Execution

Input data flows through the graph, with each node executed by its assigned execution provider

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Performance Considerations

ONNX Runtime uses two types of thread pools:
  • Intra-op threads: Parallelize computation within a single operator
  • Inter-op threads: Execute independent operators in parallel
Configure via SessionOptions:
sess_options = ort.SessionOptions()
sess_options.intra_op_num_threads = 4
sess_options.inter_op_num_threads = 2
  • Automatic memory planning and reuse
  • Arena-based allocators for efficient allocation
  • Support for pre-allocated memory binding
Supported tensor data types include:
  • Floating point: float32, float16, bfloat16
  • Integer: int8, int16, int32, int64
  • Quantized: uint8, int8 for quantization
  • Special: float8 variants for modern hardware

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Runtime Configuration

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Session Options

The SessionOptions class provides extensive configuration: 
sess_options = ort.SessionOptions()

# Optimization level
sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL

# Execution mode
sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL

# Enable profiling
sess_options.enable_profiling = True
The default optimization level ORT_ENABLE_ALL enables all graph optimizations. For debugging, use ORT_DISABLE_ALL.

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Run Options

Control individual inference runs: 
run_options = ort.RunOptions()
run_options.log_severity_level = 0  # Verbose logging
run_options.terminate = False  # Don't terminate on timeout

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Model Validation

ONNX Runtime validates models during loading:
  • Graph structure: Ensures valid connections between nodes
  • Type checking: Verifies operator input/output types
  • Shape inference: Propagates tensor shapes through the graph
  • Operator availability: Checks that required operators are supported
If your model uses custom operators, you must register them before creating the session.

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Next Steps

ONNX Format

Learn about the ONNX model format specification

Execution Providers

Explore available execution providers and their capabilities

Sessions

Deep dive into InferenceSession and session management

Graph Optimizations

Understand optimization techniques and transformations

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Additional Resources