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  • Installation
  • Tutorials
  • API
  • Examples Gallery
  • Notebooks Gallery
  • Other pages
  • Blog Gallery

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  • Fast runtime with onnxruntime
  • Benchmark inference for scikit-learn models
  • What is the opset number?
  • Train and deploy a scikit-learn pipeline
  • Convert a pipeline with a LightGBM classifier
  • Store arrays in one onnx graph
  • Intermediate results and investigation
  • Black list operators when converting
  • Forward backward on a neural network on GPU
  • Train a scikit-learn neural network with onnxruntime-training on GPU
  • Converter for WOE
  • Modify the ONNX graph
  • Train a linear regression with forward backward
  • Benchmark ONNX conversion
  • Dataframe as an input
  • Funny discrepancies
  • Train a linear regression with onnxruntime-training
  • SerializeToString and ParseFromString
  • Forward backward on a neural network on GPU (Nesterov) and penalty
  • Implement a new converter using other converters
  • Batch predictions vs one-off predictions
  • Change the number of outputs by adding a parser
  • Profile onnxruntime execution
  • One model, many possible conversions with options
  • Two ways to implement a converter
  • Choose appropriate output of a classifier
  • A new converter with options
  • Transfer Learning with ONNX
  • Convert a pipeline with a XGBoost model
  • Profiling of ONNX graph with onnxruntime
  • Batch predictions without parallelization
  • Convert a pipeline with a LightGBM regressor
  • Implement a new converter
  • Issues when switching to float
  • Benchmark onnxruntime optimization
  • Benchmark, comparison scikit-learn - forward-backward
  • Benchmark, comparison scikit-learn - onnxruntime-training
  • Benchmark operator LeakyRelu
  • Dealing with discrepancies (tf-idf)
  • Benchmark operator Slice
  • Converter for WOEEncoder from categorical_encoder
  • A model
  • Benchmark onnxruntime API: run or run_with_ort_values
  • Benchmark, comparison torch - forward-backward
  • Benchmark, comparison sklearn - forward-backward - classification
  • TfIdf and sparse matrices
  • Benchmark inference for a linear regression
  • Fast design with a python runtime
  • Compares numpy to onnxruntime on simple functions
  • Benchmark and profile of operator Slice
  • Add a parser to handle dataframes
  • Multithreading with onnxruntime
  • Train a linear regression with onnxruntime-training on GPU in details
  • Benchmark onnxruntime API: eager mode
  • Train a linear regression with onnxruntime-training in details
  • Multithreading with onnxruntime and big models

Examples Gallery#

Fast runtime with onnxruntime

Fast runtime with onnxruntime

Fast runtime with onnxruntime
Benchmark inference for scikit-learn models

Benchmark inference for scikit-learn models

Benchmark inference for scikit-learn models
What is the opset number?

What is the opset number?

What is the opset number?
Train and deploy a scikit-learn pipeline

Train and deploy a scikit-learn pipeline

Train and deploy a scikit-learn pipeline
Convert a pipeline with a LightGBM classifier

Convert a pipeline with a LightGBM classifier

Convert a pipeline with a LightGBM classifier
Store arrays in one onnx graph

Store arrays in one onnx graph

Store arrays in one onnx graph
Intermediate results and investigation

Intermediate results and investigation

Intermediate results and investigation
Black list operators when converting

Black list operators when converting

Black list operators when converting
Forward backward on a neural network on GPU

Forward backward on a neural network on GPU

Forward backward on a neural network on GPU
Train a scikit-learn neural network with onnxruntime-training on GPU

Train a scikit-learn neural network with onnxruntime-training on GPU

Train a scikit-learn neural network with onnxruntime-training on GPU
Converter for WOE

Converter for WOE

Converter for WOE
Modify the ONNX graph

Modify the ONNX graph

Modify the ONNX graph
Train a linear regression with forward backward

Train a linear regression with forward backward

Train a linear regression with forward backward
Benchmark ONNX conversion

Benchmark ONNX conversion

Benchmark ONNX conversion
Dataframe as an input

Dataframe as an input

Dataframe as an input
Funny discrepancies

Funny discrepancies

Funny discrepancies
Train a linear regression with onnxruntime-training

Train a linear regression with onnxruntime-training

Train a linear regression with onnxruntime-training
SerializeToString and ParseFromString

SerializeToString and ParseFromString

SerializeToString and ParseFromString
Forward backward on a neural network on GPU (Nesterov) and penalty

Forward backward on a neural network on GPU (Nesterov) and penalty

Forward backward on a neural network on GPU (Nesterov) and penalty
Implement a new converter using other converters

Implement a new converter using other converters

Implement a new converter using other converters
Batch predictions vs one-off predictions

Batch predictions vs one-off predictions

Batch predictions vs one-off predictions
Change the number of outputs by adding a parser

Change the number of outputs by adding a parser

Change the number of outputs by adding a parser
Profile onnxruntime execution

Profile onnxruntime execution

Profile onnxruntime execution
One model, many possible conversions with options

One model, many possible conversions with options

One model, many possible conversions with options
Two ways to implement a converter

Two ways to implement a converter

Two ways to implement a converter
Choose appropriate output of a classifier

Choose appropriate output of a classifier

Choose appropriate output of a classifier
A new converter with options

A new converter with options

A new converter with options
Transfer Learning with ONNX

Transfer Learning with ONNX

Transfer Learning with ONNX
Convert a pipeline with a XGBoost model

Convert a pipeline with a XGBoost model

Convert a pipeline with a XGBoost model
Profiling of ONNX graph with onnxruntime

Profiling of ONNX graph with onnxruntime

Profiling of ONNX graph with onnxruntime
Batch predictions without parallelization

Batch predictions without parallelization

Batch predictions without parallelization
Convert a pipeline with a LightGBM regressor

Convert a pipeline with a LightGBM regressor

Convert a pipeline with a LightGBM regressor
Implement a new converter

Implement a new converter

Implement a new converter
Issues when switching to float

Issues when switching to float

Issues when switching to float
Benchmark onnxruntime optimization

Benchmark onnxruntime optimization

Benchmark onnxruntime optimization
Benchmark, comparison scikit-learn - forward-backward

Benchmark, comparison scikit-learn - forward-backward

Benchmark, comparison scikit-learn - forward-backward
Benchmark, comparison scikit-learn - onnxruntime-training

Benchmark, comparison scikit-learn - onnxruntime-training

Benchmark, comparison scikit-learn - onnxruntime-training
Benchmark operator LeakyRelu

Benchmark operator LeakyRelu

Benchmark operator LeakyRelu
Dealing with discrepancies (tf-idf)

Dealing with discrepancies (tf-idf)

Dealing with discrepancies (tf-idf)
Benchmark operator Slice

Benchmark operator Slice

Benchmark operator Slice
Converter for WOEEncoder from categorical_encoder

Converter for WOEEncoder from categorical_encoder

Converter for WOEEncoder from categorical_encoder
Quantization with onnxruntime

sphx_glr_gyexamples_plot_quantization.py

Quantization with onnxruntime
Benchmark onnxruntime API: run or run_with_ort_values

Benchmark onnxruntime API: run or run_with_ort_values

Benchmark onnxruntime API: run or run_with_ort_values
Benchmark, comparison torch - forward-backward

Benchmark, comparison torch - forward-backward

Benchmark, comparison torch - forward-backward
Benchmark, comparison sklearn - forward-backward - classification

Benchmark, comparison sklearn - forward-backward - classification

Benchmark, comparison sklearn - forward-backward - classification
TfIdf and sparse matrices

TfIdf and sparse matrices

TfIdf and sparse matrices
Benchmark inference for a linear regression

Benchmark inference for a linear regression

Benchmark inference for a linear regression
Fast design with a python runtime

Fast design with a python runtime

Fast design with a python runtime
Compares numpy to onnxruntime on simple functions

Compares numpy to onnxruntime on simple functions

Compares numpy to onnxruntime on simple functions
Benchmark and profile of operator Slice

Benchmark and profile of operator Slice

Benchmark and profile of operator Slice
Add a parser to handle dataframes

Add a parser to handle dataframes

Add a parser to handle dataframes
Multithreading with onnxruntime

Multithreading with onnxruntime

Multithreading with onnxruntime
Train a linear regression with onnxruntime-training on GPU in details

Train a linear regression with onnxruntime-training on GPU in details

Train a linear regression with onnxruntime-training on GPU in details
Benchmark onnxruntime API: eager mode

Benchmark onnxruntime API: eager mode

Benchmark onnxruntime API: eager mode
Train a linear regression with onnxruntime-training in details

Train a linear regression with onnxruntime-training in details

Train a linear regression with onnxruntime-training in details
Multithreading with onnxruntime and big models

Multithreading with onnxruntime and big models

Multithreading with onnxruntime and big models

Download all examples in Python source code: gyexamples_python.zip

Download all examples in Jupyter notebooks: gyexamples_jupyter.zip

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