# We can save the model into ONNX
# format and compute the same predictions in many
# platform using :epkg:`onnxruntime`.
####################################
# Python runtime
# ++++++++++++++
#
# A python runtime can be used as well to compute
# the prediction. It is not meant to be used into
# production (it still relies on python), but it is
# useful to investigate why the conversion went wrong.
# It uses module :epkg:`mlprodict`.
oinf = OnnxInference(onx, runtime="python_compiled")
print(oinf)
##########################################
# It works almost the same way.
pred_pyrt = oinf.run({'X': X_test.astype(numpy.float32)})['variable']
print(diff(pred_skl, pred_pyrt))
#############################
# Final graph
# +++++++++++
ax = plot_graphviz(oinf.to_dot())
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# We can save the model into ONNX
# format and compute the same predictions in many
# platform using :epkg:`onnxruntime`.
####################################
# Python runtime
# ++++++++++++++
#
# A python runtime can be used as well to compute
# the prediction. It is not meant to be used into
# production (it still relies on python), but it is
# useful to investigate why the conversion went wrong.
# It uses module :epkg:`mlprodict`.
oinf = OnnxInference(onx, runtime="python_compiled")
print(oinf)
##########################################
# It works almost the same way.
pred_pyrt = oinf.run({'X': X_test.astype(numpy.float32)})['variable']
print(diff(pred_skl, pred_pyrt))
#############################
# Final graph
# +++++++++++
ax = plot_graphviz(oinf.to_dot(), dpi=100)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)