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  • Installation
  • Tutorial
  • API
  • ONNX, Runtime, Backends
  • scikit-learn Converters and Benchmarks
  • Command lines
  • Examples
  • FAQ, code, …
  • Gallery of examples
  • Notebook Gallery
  • History

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  • Conversion of a logistic regression into C
  • Measure ONNX runtime performances
  • A converter for a TransformedTargetRegressor
  • Walk through all methods to export an ONNX model
  • Profile the execution of a runtime
  • Grid search ONNX models
  • Merges benchmarks
  • Speed up scikit-learn inference with ONNX
  • Benchmark Random Forests, Tree Ensemble
  • Compares numba, numpy, onnxruntime for simple functions
  • Compares implementations of Add
  • Compares implementations of ReduceMax
  • Compares implementations of ReduceSumSquare
  • Compares implementations of ReduceMean
  • Compares implementations of Tranpose
  • When to parallelize?
  • Compares implementations of ReduceSum
  • Compares implementations of Where
  • Compares implementations of Einsum
  • TopK benchmark
  • Benchmark Linear Regression
  • Benchmark Random Forests, Tree Ensemble, (AoS and SoA)
  • Benchmark Random Forests, Tree Ensemble, Multi-Classification
  • TreeEnsembleRegressor and parallelisation

Gallery of examples#

Conversion of a logistic regression into C

Conversion of a logistic regression into C

Conversion of a logistic regression into C
Measure ONNX runtime performances

Measure ONNX runtime performances

Measure ONNX runtime performances
A converter for a TransformedTargetRegressor

A converter for a TransformedTargetRegressor

A converter for a TransformedTargetRegressor
Walk through all methods to export an ONNX model

Walk through all methods to export an ONNX model

Walk through all methods to export an ONNX model
Profile the execution of a runtime

Profile the execution of a runtime

Profile the execution of a runtime
Grid search ONNX models

Grid search ONNX models

Grid search ONNX models
Merges benchmarks

Merges benchmarks

Merges benchmarks
Speed up scikit-learn inference with ONNX

Speed up scikit-learn inference with ONNX

Speed up scikit-learn inference with ONNX
Benchmark Random Forests, Tree Ensemble

Benchmark Random Forests, Tree Ensemble

Benchmark Random Forests, Tree Ensemble
Compares numba, numpy, onnxruntime for simple functions

Compares numba, numpy, onnxruntime for simple functions

Compares numba, numpy, onnxruntime for simple functions
Compares implementations of Add

Compares implementations of Add

Compares implementations of Add
Compares implementations of ReduceMax

Compares implementations of ReduceMax

Compares implementations of ReduceMax
Compares implementations of ReduceSumSquare

Compares implementations of ReduceSumSquare

Compares implementations of ReduceSumSquare
Compares implementations of ReduceMean

Compares implementations of ReduceMean

Compares implementations of ReduceMean
Compares implementations of Tranpose

Compares implementations of Tranpose

Compares implementations of Tranpose
When to parallelize?

When to parallelize?

When to parallelize?
Compares implementations of ReduceSum

Compares implementations of ReduceSum

Compares implementations of ReduceSum
Compares implementations of Where

Compares implementations of Where

Compares implementations of Where
Compares implementations of Einsum

Compares implementations of Einsum

Compares implementations of Einsum
TopK benchmark

TopK benchmark

TopK benchmark
Benchmark Linear Regression

Benchmark Linear Regression

Benchmark Linear Regression
Benchmark Random Forests, Tree Ensemble, (AoS and SoA)

Benchmark Random Forests, Tree Ensemble, (AoS and SoA)

Benchmark Random Forests, Tree Ensemble, (AoS and SoA)
Benchmark Random Forests, Tree Ensemble, Multi-Classification

Benchmark Random Forests, Tree Ensemble, Multi-Classification

Benchmark Random Forests, Tree Ensemble, Multi-Classification
TreeEnsembleRegressor and parallelisation

TreeEnsembleRegressor and parallelisation

TreeEnsembleRegressor and parallelisation

Download all examples in Python source code: gyexamples_python.zip

Download all examples in Jupyter notebooks: gyexamples_jupyter.zip

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