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Section Navigation
Introduction to ONNX
ONNX Concepts
ONNX with Python
Proto
Goals
Detailed Differences between
onnx
and
onnx_light
Protobuf format applied to ONNX
ORT flatbuffer format: parallelization and alignment
ModelProto creation and no-copy ownership
Complex loading and saving scenarios
Fuzzing
Expressions
Compute
Value-as-shape propagation
Symbolic-dimension constraint mechanism
Sequences, maps and subgraphs
Shape-inference events (
ShapeEvent
)
Shape-inference coverage
Patterns
GraphBuilder
Pattern optimization
Gradient
Runtime
Buffer-reuse arenas
Backend test-case coverage
Runtime test coverage (onnxruntime and shape_inference)
Tuning
Processor-aware kernel tuning
Backend calibration profile storage
Technical
Thread-pool dispatch in onnx-light, OpenMP, and ONNX Runtime
Overlapping ONNX loading and prepacking
Design
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