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onnx-light 0.1.31 documentation - Home onnx-light 0.1.31 documentation - Home
  • Quick tour
  • Start
  • Design
  • API
  • ByOp
  • How-To
  • Next Steps
  • Miscellaneous
  • GitHub
  • Quick tour
  • Start
  • Design
  • API
  • ByOp
  • How-To
  • Next Steps
  • Miscellaneous
  • GitHub

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
  • Proto

Proto#

The proto design covers the ONNX concepts and compatibility goals, binary formats, model ownership, loading and validation.

  • 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

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ONNX with Python

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Goals
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