Ctrl+K
Logo image Logo image

Site Navigation

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

Site Navigation

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

Section Navigation

  • Converts a logistic regression into C
  • Discrepencies with ONNX
  • Einsum decomposition
  • Fast TopK elements
  • Infer operator computation cost
  • Introduction to a numpy API for ONNX: CustomClassifier
  • Introduction to a numpy API for ONNX: FunctionTransformer
  • Lightgbm, double, discrepencies
  • Loss function in ONNX
  • Memory usage
  • ONNX FFTs
  • ONNX and FFT
  • ONNX graph, single or double floats
  • ONNX side by side
  • ONNX visualization
  • Pairwise distances with ONNX (pdist)
  • Precision loss due to float32 conversion with ONNX
  • Profiling with onnxruntime
  • Time processing for every ONNX nodes in a graph
  • Transfer Learning with ONNX
  • Tricky detail when converting a random forest from scikit-learn into ONNX
  • Use function when converting into ONNX
  • Notebooks Coverage

Notebook Gallery#

Notebooks Coverage

  • Converts a logistic regression into C
  • Discrepencies with ONNX
  • Einsum decomposition
  • Fast TopK elements
  • Infer operator computation cost
  • Introduction to a numpy API for ONNX: CustomClassifier
  • Introduction to a numpy API for ONNX: FunctionTransformer
  • Lightgbm, double, discrepencies
  • Loss function in ONNX
  • Memory usage
  • ONNX FFTs
  • ONNX and FFT
  • ONNX graph, single or double floats
  • ONNX side by side
  • ONNX visualization
  • Pairwise distances with ONNX (pdist)
  • Precision loss due to float32 conversion with ONNX
  • Profiling with onnxruntime
  • Time processing for every ONNX nodes in a graph
  • Transfer Learning with ONNX
  • Tricky detail when converting a random forest from scikit-learn into ONNX
  • Use function when converting into ONNX
_images/sklearn_grammar_lr.thumb.png

Converts a logistic regression into C#

_images/onnx_discrepencies.thumb.png

Discrepencies with ONNX#

_images/einsum_decomposition.thumb.png

Einsum decomposition#

_images/topk_cpp.thumb.png

Fast TopK elements#

_images/onnx_operator_cost.thumb.png

Infer operator computation cost#

_images/numpy_api_onnx_ccl.thumb.png

Introduction to a numpy API for ONNX: CustomClassifier#

_images/numpy_api_onnx_ftr.thumb.png

Introduction to a numpy API for ONNX: FunctionTransformer#

_images/lightgbm_double.thumb.png

Lightgbm, double, discrepencies#

_images/loss_functions.thumb.png

Loss function in ONNX#

_images/onnx_profile.thumb.png

Memory usage#

_images/onnx_ffts.thumb.png

ONNX FFTs#

_images/onnx_fft.thumb.png

ONNX and FFT#

_images/onnx_float32_and_64.thumb.png

ONNX graph, single or double floats#

_images/onnx_sbs.thumb.png

ONNX side by side#

_images/onnx_visualization.thumb.png

ONNX visualization#

_images/onnx_pdist.thumb.png

Pairwise distances with ONNX (pdist)#

_images/onnx_shaker.thumb.png

Precision loss due to float32 conversion with ONNX#

_images/onnx_profile_ort.thumb.png

Profiling with onnxruntime#

_images/onnx_node_time.thumb.png

Time processing for every ONNX nodes in a graph#

_images/transfer_learning.thumb.png

Transfer Learning with ONNX#

_images/onnx_float_double_skl_decision_trees.thumb.png

Tricky detail when converting a random forest from scikit-learn into ONNX#

_images/onnx_sklearn_functions.thumb.png

Use function when converting into ONNX#

previous

TreeEnsembleRegressor and parallelisation

next

Converts a logistic regression into C
Show Source