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onnx-extended 0.2.3 documentation
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onnx-extended 0.2.3 documentation

Contents

  • Tutorial
    • Build
      • Build with cython
      • Build with pybind11
      • Build with CUDA
      • Build with onnxruntime
    • CReferenceEvaluator
    • Cython Binding of onnxruntime
    • Custom Kernels for onnxruntime
    • Many Tools
      • External Data and Big Models
      • Onnx Manipulations
      • Quantization
      • Profiling onnxruntime
      • Debug Intermediate Results
      • Compare multiple versions of onnxruntime
      • Trees
    • Examples
      • Measuring CPU performance
      • Measuring CPU performance with a vector sum
      • Measuring CPU performance with a parallelized vector sum
      • Measuring CPU performance with a parallelized vector sum and AVX
      • Measuring CPU/GPU performance with a vector sum
    • Conv
      • Using C implementation of operator Conv
      • How float format has an impact on speed computation
      • Measuring Gemm performance with different input and output tests
      • Measuring performance about Gemm with onnxruntime
      • Profiles a simple onnx graph including a singleGemm
      • TreeEnsemble optimization
  • API
    • check
    • ext_test_case
    • helper
    • ortcy
    • ortops
      • ortops.tutorial
      • ortops.optim
    • reference
    • validation.cpu
    • validation.cuda
    • validation.bench_trees
    • tools
      • Shortcuts
      • tools.graph
      • tools.graph.onnx_graph_transformer
      • tools.onnx_nodes
      • Other tools
    • command lines
  • Technical Details
    • Install CUDA on WSL (2)
    • Useful commands on Linux
    • Gemm and storage order
    • 2023-09-05 - version GLIBCXX_3.4.30 not found
  • ONNX Benchmarks
  • Examples Gallery
    • Measuring CPU performance with a vector sum
    • Measuring CPU performance with a parallelized vector sum
    • Measuring CPU performance with a parallelized vector sum and AVX
    • Measuring CPU performance
    • Using C implementation of operator Conv
    • Measuring onnxruntime performance against a cython binding
    • Measuring CPU/GPU performance with a vector sum
    • Measuring Gemm performance with different input and output tests
    • How float format has an impact on speed computation
    • TreeEnsemble optimization
    • Profiles a simple onnx graph including a singleGemm
    • Measuring performance about Gemm with onnxruntime

More

  • Change Logs
  • LICENSE
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Examples Gallery#

Measuring CPU performance with a vector sum

Measuring CPU performance with a vector sum

Measuring CPU performance with a parallelized vector sum

Measuring CPU performance with a parallelized vector sum

Measuring CPU performance with a parallelized vector sum and AVX

Measuring CPU performance with a parallelized vector sum and AVX

Measuring CPU performance

Measuring CPU performance

Using C implementation of operator Conv

Using C implementation of operator Conv

Measuring onnxruntime performance against a cython binding

Measuring onnxruntime performance against a cython binding

Measuring CPU/GPU performance with a vector sum

Measuring CPU/GPU performance with a vector sum

Measuring Gemm performance with different input and output tests

Measuring Gemm performance with different input and output tests

How float format has an impact on speed computation

How float format has an impact on speed computation

TreeEnsemble optimization

TreeEnsemble optimization

Profiles a simple onnx graph including a singleGemm

Profiles a simple onnx graph including a singleGemm

Measuring performance about Gemm with onnxruntime

Measuring performance about Gemm with onnxruntime

Download all examples in Python source code: auto_examples_python.zip

Download all examples in Jupyter notebooks: auto_examples_jupyter.zip

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