Gallery of examples

Metadata

Metadata

ONNX Runtime Backend for ONNX

ONNX Runtime Backend for ONNX

Draw a pipeline

Draw a pipeline

Logging, verbose

Logging, verbose

Probabilities or raw scores

Probabilities or raw scores

Train, convert and predict a model

Train, convert and predict a model

Append onnx nodes to the converted model

Append onnx nodes to the converted model

Investigate a pipeline

Investigate a pipeline

Compare CDist with scipy

Compare CDist with scipy

Convert a pipeline with a LightGbm model

Convert a pipeline with a LightGbm model

Probabilities as a vector or as a ZipMap

Probabilities as a vector or as a ZipMap

Convert a model with a reduced list of operators

Convert a model with a reduced list of operators

Custom Operator for NMF Decomposition

Custom Operator for NMF Decomposition

Discrepencies with StandardScaler

Discrepencies with StandardScaler

Benchmark a pipeline

Benchmark a pipeline

Convert a pipeline with a XGBoost model

Convert a pipeline with a XGBoost model

Discrepencies with GaussianProcessorRegressor: use of double

Discrepencies with GaussianProcessorRegressor: use of double

Errors with onnxruntime

Errors with onnxruntime

Play with ONNX operators

Play with ONNX operators

Different ways to convert a model

Different ways to convert a model

Convert a pipeline with ColumnTransformer

Convert a pipeline with ColumnTransformer

TfIdfVectorizer with ONNX

TfIdfVectorizer with ONNX

Walk through intermediate outputs

Walk through intermediate outputs

When a custom model is neither a classifier nor a regressor (alternative)

When a custom model is neither a classifier nor a regressor (alternative)

When a custom model is neither a classifier nor a regressor

When a custom model is neither a classifier nor a regressor

Write your own converter for your own model

Write your own converter for your own model