Examples Gallery

Fast runtime with onnxruntime

Fast runtime with onnxruntime

Benchmark inference for scikit-learn models

Benchmark inference for scikit-learn models

What is the opset number?

What is the opset number?

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

Store arrays in one onnx graph

Store arrays in one onnx graph

Intermediate results and investigation

Intermediate results and investigation

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

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

Modify the ONNX graph

Modify the ONNX graph

Train a linear regression with forward backward

Train a linear regression with forward backward

Benchmark ONNX conversion

Benchmark ONNX conversion

Dataframe as an input

Dataframe as an input

Funny discrepancies

Funny discrepancies

Train a linear regression with onnxruntime-training

Train a linear regression with onnxruntime-training

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

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

Change the number of outputs by adding a parser

Change the number of outputs by adding a parser

Profile onnxruntime execution

Profile onnxruntime execution

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

Choose appropriate output of a classifier

Choose appropriate output of a classifier

A new converter with options

A new converter with options

Transfer Learning with ONNX

Transfer Learning with ONNX

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

Batch predictions without parallelization

Batch predictions without parallelization

Convert a pipeline with a LightGBM regressor

Convert a pipeline with a LightGBM regressor

Implement a new converter

Implement a new converter

Issues when switching to float

Issues when switching to float

Benchmark onnxruntime optimization

Benchmark onnxruntime optimization

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 operator LeakyRelu

Benchmark operator LeakyRelu

Dealing with discrepancies (tf-idf)

Dealing with discrepancies (tf-idf)

Benchmark operator Slice

Benchmark operator Slice

Converter for WOEEncoder from categorical_encoder

Converter for WOEEncoder from categorical_encoder

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, comparison torch - forward-backward

Benchmark, comparison torch - forward-backward

Benchmark, comparison sklearn - forward-backward - classification

Benchmark, comparison sklearn - forward-backward - classification

TfIdf and sparse matrices

TfIdf and sparse matrices

Benchmark inference for a linear regression

Benchmark inference for a linear regression

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

Benchmark and profile of operator Slice

Benchmark and profile of operator Slice

Add a parser to handle dataframes

Add a parser to handle dataframes

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

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

Multithreading with onnxruntime and big models

Multithreading with onnxruntime and big models