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< title > Train, convert and predict with ONNX Runtime — ONNX Runtime 1.11.0 documentation< / title >
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< div class = "sphx-glr-download-link-note admonition note" >
< p class = "admonition-title" > Note< / p >
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< p > Click < a class = "reference internal" href = "#sphx-glr-download-auto-examples-plot-train-convert-predict-py" > < span class = "std std-ref" > here< / span > < / a >
to download the full example code< / p >
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< / div >
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< section class = "sphx-glr-example-title" id = "train-convert-and-predict-with-onnx-runtime" >
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< span id = "l-logreg-example" > < / span > < span id = "sphx-glr-auto-examples-plot-train-convert-predict-py" > < / span > < h1 > Train, convert and predict with ONNX Runtime< a class = "headerlink" href = "#train-convert-and-predict-with-onnx-runtime" title = "Permalink to this headline" > ¶< / a > < / h1 >
< p > This example demonstrates an end to end scenario
starting with the training of a machine learned model
to its use in its converted from.< / p >
< div class = "contents local topic" id = "contents" >
< ul class = "simple" >
< li > < p > < a class = "reference internal" href = "#train-a-logistic-regression" id = "id1" > Train a logistic regression< / a > < / p > < / li >
< li > < p > < a class = "reference internal" href = "#conversion-to-onnx-format" id = "id2" > Conversion to ONNX format< / a > < / p > < / li >
< li > < p > < a class = "reference internal" href = "#probabilities" id = "id3" > Probabilities< / a > < / p > < / li >
< li > < p > < a class = "reference internal" href = "#benchmark-with-randomforest" id = "id4" > Benchmark with RandomForest< / a > < / p > < / li >
< / ul >
< / div >
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< section id = "train-a-logistic-regression" >
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< h2 > < a class = "toc-backref" href = "#id1" > Train a logistic regression< / a > < a class = "headerlink" href = "#train-a-logistic-regression" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > The first step consists in retrieving the iris datset.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > sklearn.datasets< / span > < span class = "k" > import< / span > < span class = "n" > load_iris< / span >
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< span class = "n" > iris< / span > < span class = "o" > =< / span > < span class = "n" > load_iris< / span > < span class = "p" > ()< / span >
< span class = "n" > X< / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "o" > =< / span > < span class = "n" > iris< / span > < span class = "o" > .< / span > < span class = "n" > data< / span > < span class = "p" > ,< / span > < span class = "n" > iris< / span > < span class = "o" > .< / span > < span class = "n" > target< / span >
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< span class = "kn" > from< / span > < span class = "nn" > sklearn.model_selection< / span > < span class = "k" > import< / span > < span class = "n" > train_test_split< / span >
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< span class = "n" > X_train< / span > < span class = "p" > ,< / span > < span class = "n" > X_test< / span > < span class = "p" > ,< / span > < span class = "n" > y_train< / span > < span class = "p" > ,< / span > < span class = "n" > y_test< / span > < span class = "o" > =< / span > < span class = "n" > train_test_split< / span > < span class = "p" > (< / span > < span class = "n" > X< / span > < span class = "p" > ,< / span > < span class = "n" > y< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p > Then we fit a model.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > sklearn.linear_model< / span > < span class = "k" > import< / span > < span class = "n" > LogisticRegression< / span >
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< span class = "n" > clr< / span > < span class = "o" > =< / span > < span class = "n" > LogisticRegression< / span > < span class = "p" > ()< / span >
< span class = "n" > clr< / span > < span class = "o" > .< / span > < span class = "n" > fit< / span > < span class = "p" > (< / span > < span class = "n" > X_train< / span > < span class = "p" > ,< / span > < span class = "n" > y_train< / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
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< p class = "sphx-glr-script-out" > Out:< / p >
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< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > LogisticRegression()
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< / pre > < / div >
< / div >
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< p > We compute the prediction on the test set
and we show the confusion matrix.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > sklearn.metrics< / span > < span class = "k" > import< / span > < span class = "n" > confusion_matrix< / span >
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< span class = "n" > pred< / span > < span class = "o" > =< / span > < span class = "n" > clr< / span > < span class = "o" > .< / span > < span class = "n" > predict< / span > < span class = "p" > (< / span > < span class = "n" > X_test< / span > < span class = "p" > )< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > confusion_matrix< / span > < span class = "p" > (< / span > < span class = "n" > y_test< / span > < span class = "p" > ,< / span > < span class = "n" > pred< / span > < span class = "p" > ))< / span >
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< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
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< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [[13 0 0]
[ 0 10 1]
[ 0 0 14]]
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< / pre > < / div >
< / div >
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< / section >
< section id = "conversion-to-onnx-format" >
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< h2 > < a class = "toc-backref" href = "#id2" > Conversion to ONNX format< / a > < a class = "headerlink" href = "#conversion-to-onnx-format" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > We use module
< a class = "reference external" href = "https://github.com/onnx/sklearn-onnx" > sklearn-onnx< / a >
to convert the model into ONNX format.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > skl2onnx< / span > < span class = "k" > import< / span > < span class = "n" > convert_sklearn< / span >
< span class = "kn" > from< / span > < span class = "nn" > skl2onnx.common.data_types< / span > < span class = "k" > import< / span > < span class = "n" > FloatTensorType< / span >
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< span class = "n" > initial_type< / span > < span class = "o" > =< / span > < span class = "p" > [(< / span > < span class = "s1" > ' float_input' < / span > < span class = "p" > ,< / span > < span class = "n" > FloatTensorType< / span > < span class = "p" > ([< / span > < span class = "kc" > None< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / span > < span class = "p" > ]))]< / span >
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< span class = "n" > onx< / span > < span class = "o" > =< / span > < span class = "n" > convert_sklearn< / span > < span class = "p" > (< / span > < span class = "n" > clr< / span > < span class = "p" > ,< / span > < span class = "n" > initial_types< / span > < span class = "o" > =< / span > < span class = "n" > initial_type< / span > < span class = "p" > )< / span >
< span class = "k" > with< / span > < span class = "nb" > open< / span > < span class = "p" > (< / span > < span class = "s2" > " logreg_iris.onnx" < / span > < span class = "p" > ,< / span > < span class = "s2" > " wb" < / span > < span class = "p" > )< / span > < span class = "k" > as< / span > < span class = "n" > f< / span > < span class = "p" > :< / span >
< span class = "n" > f< / span > < span class = "o" > .< / span > < span class = "n" > write< / span > < span class = "p" > (< / span > < span class = "n" > onx< / span > < span class = "o" > .< / span > < span class = "n" > SerializeToString< / span > < span class = "p" > ())< / span >
< / pre > < / div >
< / div >
< p > We load the model with ONNX Runtime and look at
its input and output.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > onnxruntime< / span > < span class = "k" > as< / span > < span class = "nn" > rt< / span >
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< span class = "n" > sess< / span > < span class = "o" > =< / span > < span class = "n" > rt< / span > < span class = "o" > .< / span > < span class = "n" > InferenceSession< / span > < span class = "p" > (< / span > < span class = "s2" > " logreg_iris.onnx" < / span > < span class = "p" > ,< / span > < span class = "n" > providers< / span > < span class = "o" > =< / span > < span class = "n" > rt< / span > < span class = "o" > .< / span > < span class = "n" > get_available_providers< / span > < span class = "p" > ())< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " input name=' < / span > < span class = "si" > {}< / span > < span class = "s2" > ' and shape=< / span > < span class = "si" > {}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span >
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< span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_inputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > name< / span > < span class = "p" > ,< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_inputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > ))< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " output name=' < / span > < span class = "si" > {}< / span > < span class = "s2" > ' and shape=< / span > < span class = "si" > {}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span >
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< span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_outputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > name< / span > < span class = "p" > ,< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_outputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > ))< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > input name=' float_input' and shape=[None, 4]
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output name=' output_label' and shape=[None]
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< / pre > < / div >
< / div >
< p > We compute the predictions.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > input_name< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_inputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > name< / span >
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< span class = "n" > label_name< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_outputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > name< / span >
< span class = "kn" > import< / span > < span class = "nn" > numpy< / span >
< span class = "n" > pred_onx< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > label_name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > input_name< / span > < span class = "p" > :< / span > < span class = "n" > X_test< / span > < span class = "o" > .< / span > < span class = "n" > astype< / span > < span class = "p" > (< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )})[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > confusion_matrix< / span > < span class = "p" > (< / span > < span class = "n" > pred< / span > < span class = "p" > ,< / span > < span class = "n" > pred_onx< / span > < span class = "p" > ))< / span >
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< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
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< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [[13 0 0]
[ 0 10 0]
[ 0 0 15]]
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< / pre > < / div >
< / div >
< p > The prediction are perfectly identical.< / p >
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< / section >
< section id = "probabilities" >
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< h2 > < a class = "toc-backref" href = "#id3" > Probabilities< / a > < a class = "headerlink" href = "#probabilities" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > Probabilities are needed to compute other
relevant metrics such as the ROC Curve.
Let’
scikit-learn.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > prob_sklearn< / span > < span class = "o" > =< / span > < span class = "n" > clr< / span > < span class = "o" > .< / span > < span class = "n" > predict_proba< / span > < span class = "p" > (< / span > < span class = "n" > X_test< / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > prob_sklearn< / span > < span class = "p" > [:< / span > < span class = "mi" > 3< / span > < span class = "p" > ])< / span >
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< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
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< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [[2.95951945e-05 5.69800366e-02 9.42990368e-01]
[1.84923705e-05 3.93636566e-02 9.60617851e-01]
[1.30004554e-02 8.03678159e-01 1.83321386e-01]]
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< / pre > < / div >
< / div >
< p > And then with ONNX Runtime.
The probabilies appear to be< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > prob_name< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > get_outputs< / span > < span class = "p" > ()[< / span > < span class = "mi" > 1< / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > name< / span >
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< span class = "n" > prob_rt< / span > < span class = "o" > =< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > prob_name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > input_name< / span > < span class = "p" > :< / span > < span class = "n" > X_test< / span > < span class = "o" > .< / span > < span class = "n" > astype< / span > < span class = "p" > (< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )})[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
< span class = "kn" > import< / span > < span class = "nn" > pprint< / span >
< span class = "n" > pprint< / span > < span class = "o" > .< / span > < span class = "n" > pprint< / span > < span class = "p" > (< / span > < span class = "n" > prob_rt< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "p" > :< / span > < span class = "mi" > 3< / span > < span class = "p" > ])< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
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< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > [{0: 2.9595235901069827e-05, 1: 0.05698008090257645, 2: 0.9429903030395508},
{0: 1.8492364688427188e-05, 1: 0.03936365991830826, 2: 0.9606178402900696},
{0: 0.013000461272895336, 1: 0.8036782741546631, 2: 0.1833212822675705}]
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< / pre > < / div >
< / div >
< p > Let’ < / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > timeit< / span > < span class = "k" > import< / span > < span class = "n" > Timer< / span >
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< span class = "k" > def< / span > < span class = "nf" > speed< / span > < span class = "p" > (< / span > < span class = "n" > inst< / span > < span class = "p" > ,< / span > < span class = "n" > number< / span > < span class = "o" > =< / span > < span class = "mi" > 10< / span > < span class = "p" > ,< / span > < span class = "n" > repeat< / span > < span class = "o" > =< / span > < span class = "mi" > 20< / span > < span class = "p" > ):< / span >
< span class = "n" > timer< / span > < span class = "o" > =< / span > < span class = "n" > Timer< / span > < span class = "p" > (< / span > < span class = "n" > inst< / span > < span class = "p" > ,< / span > < span class = "nb" > globals< / span > < span class = "o" > =< / span > < span class = "nb" > globals< / span > < span class = "p" > ())< / span >
< span class = "n" > raw< / span > < span class = "o" > =< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > (< / span > < span class = "n" > timer< / span > < span class = "o" > .< / span > < span class = "n" > repeat< / span > < span class = "p" > (< / span > < span class = "n" > repeat< / span > < span class = "p" > ,< / span > < span class = "n" > number< / span > < span class = "o" > =< / span > < span class = "n" > number< / span > < span class = "p" > ))< / span >
< span class = "n" > ave< / span > < span class = "o" > =< / span > < span class = "n" > raw< / span > < span class = "o" > .< / span > < span class = "n" > sum< / span > < span class = "p" > ()< / span > < span class = "o" > /< / span > < span class = "nb" > len< / span > < span class = "p" > (< / span > < span class = "n" > raw< / span > < span class = "p" > )< / span > < span class = "o" > /< / span > < span class = "n" > number< / span >
< span class = "n" > mi< / span > < span class = "p" > ,< / span > < span class = "n" > ma< / span > < span class = "o" > =< / span > < span class = "n" > raw< / span > < span class = "o" > .< / span > < span class = "n" > min< / span > < span class = "p" > ()< / span > < span class = "o" > /< / span > < span class = "n" > number< / span > < span class = "p" > ,< / span > < span class = "n" > raw< / span > < span class = "o" > .< / span > < span class = "n" > max< / span > < span class = "p" > ()< / span > < span class = "o" > /< / span > < span class = "n" > number< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Average < / span > < span class = "si" > %1.3g< / span > < span class = "s2" > min=< / span > < span class = "si" > %1.3g< / span > < span class = "s2" > max=< / span > < span class = "si" > %1.3g< / span > < span class = "s2" > " < / span > < span class = "o" > %< / span > < span class = "p" > (< / span > < span class = "n" > ave< / span > < span class = "p" > ,< / span > < span class = "n" > mi< / span > < span class = "p" > ,< / span > < span class = "n" > ma< / span > < span class = "p" > ))< / span >
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< span class = "k" > return< / span > < span class = "n" > ave< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for clr.predict" < / span > < span class = "p" > )< / span >
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< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " clr.predict(X_test)" < / span > < span class = "p" > )< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for ONNX Runtime" < / span > < span class = "p" > )< / span >
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< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]" < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > Execution time for clr.predict
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Average 4.38e-05 min=4.25e-05 max=6.07e-05
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Execution time for ONNX Runtime
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Average 1.97e-05 min=1.92e-05 max=2.46e-05
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1.9671264999914226e-05
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< / pre > < / div >
< / div >
< p > Let’
experiences: the model has to do one prediction at a time
as opposed to a batch of prediction.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "k" > def< / span > < span class = "nf" > loop< / span > < span class = "p" > (< / span > < span class = "n" > X_test< / span > < span class = "p" > ,< / span > < span class = "n" > fct< / span > < span class = "p" > ,< / span > < span class = "n" > n< / span > < span class = "o" > =< / span > < span class = "kc" > None< / span > < span class = "p" > ):< / span >
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< span class = "n" > nrow< / span > < span class = "o" > =< / span > < span class = "n" > X_test< / span > < span class = "o" > .< / span > < span class = "n" > shape< / span > < span class = "p" > [< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
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< span class = "k" > if< / span > < span class = "n" > n< / span > < span class = "ow" > is< / span > < span class = "kc" > None< / span > < span class = "p" > :< / span >
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< span class = "n" > n< / span > < span class = "o" > =< / span > < span class = "n" > nrow< / span >
< span class = "k" > for< / span > < span class = "n" > i< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "mi" > 0< / span > < span class = "p" > ,< / span > < span class = "n" > n< / span > < span class = "p" > ):< / span >
< span class = "n" > im< / span > < span class = "o" > =< / span > < span class = "n" > i< / span > < span class = "o" > %< / span > < span class = "n" > nrow< / span >
< span class = "n" > fct< / span > < span class = "p" > (< / span > < span class = "n" > X_test< / span > < span class = "p" > [< / span > < span class = "n" > im< / span > < span class = "p" > :< / span > < span class = "n" > im< / span > < span class = "o" > +< / span > < span class = "mi" > 1< / span > < span class = "p" > ])< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for clr.predict" < / span > < span class = "p" > )< / span >
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< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, clr.predict, 100)" < / span > < span class = "p" > )< / span >
< span class = "k" > def< / span > < span class = "nf" > sess_predict< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "k" > return< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > label_name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > input_name< / span > < span class = "p" > :< / span > < span class = "n" > x< / span > < span class = "o" > .< / span > < span class = "n" > astype< / span > < span class = "p" > (< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )})[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for sess_predict" < / span > < span class = "p" > )< / span >
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< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, sess_predict, 100)" < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > Execution time for clr.predict
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Average 0.00404 min=0.00402 max=0.00409
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Execution time for sess_predict
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Average 0.000881 min=0.000874 max=0.000912
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0.0008813192099997735
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< / pre > < / div >
< / div >
< p > Let’ < / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for predict_proba" < / span > < span class = "p" > )< / span >
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< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, clr.predict_proba, 100)" < / span > < span class = "p" > )< / span >
< span class = "k" > def< / span > < span class = "nf" > sess_predict_proba< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "k" > return< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > prob_name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > input_name< / span > < span class = "p" > :< / span > < span class = "n" > x< / span > < span class = "o" > .< / span > < span class = "n" > astype< / span > < span class = "p" > (< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )})[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for sess_predict_proba" < / span > < span class = "p" > )< / span >
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< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, sess_predict_proba, 100)" < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > Execution time for predict_proba
2022-01-04 17:32:40 +00:00
Average 0.00599 min=0.00597 max=0.00606
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Execution time for sess_predict_proba
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Average 0.000883 min=0.000876 max=0.000916
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0.0008830492349999729
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< / pre > < / div >
< / div >
< p > This second comparison is better as
ONNX Runtime, in this experience,
computes the label and the probabilities
in every case.< / p >
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< / section >
< section id = "benchmark-with-randomforest" >
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< h2 > < a class = "toc-backref" href = "#id4" > Benchmark with RandomForest< / a > < a class = "headerlink" href = "#benchmark-with-randomforest" title = "Permalink to this headline" > ¶< / a > < / h2 >
< p > We first train and save a model in ONNX format.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > sklearn.ensemble< / span > < span class = "k" > import< / span > < span class = "n" > RandomForestClassifier< / span >
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< span class = "n" > rf< / span > < span class = "o" > =< / span > < span class = "n" > RandomForestClassifier< / span > < span class = "p" > ()< / span >
< span class = "n" > rf< / span > < span class = "o" > .< / span > < span class = "n" > fit< / span > < span class = "p" > (< / span > < span class = "n" > X_train< / span > < span class = "p" > ,< / span > < span class = "n" > y_train< / span > < span class = "p" > )< / span >
< span class = "n" > initial_type< / span > < span class = "o" > =< / span > < span class = "p" > [(< / span > < span class = "s1" > ' float_input' < / span > < span class = "p" > ,< / span > < span class = "n" > FloatTensorType< / span > < span class = "p" > ([< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / span > < span class = "p" > ]))]< / span >
< span class = "n" > onx< / span > < span class = "o" > =< / span > < span class = "n" > convert_sklearn< / span > < span class = "p" > (< / span > < span class = "n" > rf< / span > < span class = "p" > ,< / span > < span class = "n" > initial_types< / span > < span class = "o" > =< / span > < span class = "n" > initial_type< / span > < span class = "p" > )< / span >
< span class = "k" > with< / span > < span class = "nb" > open< / span > < span class = "p" > (< / span > < span class = "s2" > " rf_iris.onnx" < / span > < span class = "p" > ,< / span > < span class = "s2" > " wb" < / span > < span class = "p" > )< / span > < span class = "k" > as< / span > < span class = "n" > f< / span > < span class = "p" > :< / span >
< span class = "n" > f< / span > < span class = "o" > .< / span > < span class = "n" > write< / span > < span class = "p" > (< / span > < span class = "n" > onx< / span > < span class = "o" > .< / span > < span class = "n" > SerializeToString< / span > < span class = "p" > ())< / span >
< / pre > < / div >
< / div >
< p > We compare.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > sess< / span > < span class = "o" > =< / span > < span class = "n" > rt< / span > < span class = "o" > .< / span > < span class = "n" > InferenceSession< / span > < span class = "p" > (< / span > < span class = "s2" > " rf_iris.onnx" < / span > < span class = "p" > ,< / span > < span class = "n" > providers< / span > < span class = "o" > =< / span > < span class = "n" > rt< / span > < span class = "o" > .< / span > < span class = "n" > get_available_providers< / span > < span class = "p" > ())< / span >
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< span class = "k" > def< / span > < span class = "nf" > sess_predict_proba_rf< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > ):< / span >
< span class = "k" > return< / span > < span class = "n" > sess< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > ([< / span > < span class = "n" > prob_name< / span > < span class = "p" > ],< / span > < span class = "p" > {< / span > < span class = "n" > input_name< / span > < span class = "p" > :< / span > < span class = "n" > x< / span > < span class = "o" > .< / span > < span class = "n" > astype< / span > < span class = "p" > (< / span > < span class = "n" > numpy< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )})[< / span > < span class = "mi" > 0< / span > < span class = "p" > ]< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for predict_proba" < / span > < span class = "p" > )< / span >
2019-12-21 01:11:46 +00:00
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, rf.predict_proba, 100)" < / span > < span class = "p" > )< / span >
2020-08-13 02:12:50 +00:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " Execution time for sess_predict_proba" < / span > < span class = "p" > )< / span >
2019-12-21 01:11:46 +00:00
< span class = "n" > speed< / span > < span class = "p" > (< / span > < span class = "s2" > " loop(X_test, sess_predict_proba_rf, 100)" < / span > < span class = "p" > )< / span >
< / pre > < / div >
< / div >
< p class = "sphx-glr-script-out" > Out:< / p >
< div class = "sphx-glr-script-out highlight-none notranslate" > < div class = "highlight" > < pre > < span > < / span > Execution time for predict_proba
2022-01-04 17:32:40 +00:00
Average 0.717 min=0.715 max=0.72
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Execution time for sess_predict_proba
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Average 0.00108 min=0.00107 max=0.00111
2020-08-13 02:12:50 +00:00
2022-01-04 17:32:40 +00:00
0.0010817126199989956
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< / pre > < / div >
< / div >
< p > Let’ < / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > measures< / span > < span class = "o" > =< / span > < span class = "p" > []< / span >
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< span class = "k" > for< / span > < span class = "n" > n_trees< / span > < span class = "ow" > in< / span > < span class = "nb" > range< / span > < span class = "p" > (< / span > < span class = "mi" > 5< / span > < span class = "p" > ,< / span > < span class = "mi" > 51< / span > < span class = "p" > ,< / span > < span class = "mi" > 5< / span > < span class = "p" > ):< / span >
2020-08-13 02:12:50 +00:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > n_trees< / span > < span class = "p" > )< / span >
2019-12-21 01:11:46 +00:00
< span class = "n" > rf< / span > < span class = "o" > =< / span > < span class = "n" > RandomForestClassifier< / span > < span class = "p" > (< / span > < span class = "n" > n_estimators< / span > < span class = "o" > =< / span > < span class = "n" > n_trees< / span > < span class = "p" > )< / span >
< span class = "n" > rf< / span > < span class = "o" > .< / span > < span class = "n" > fit< / span > < span class = "p" > (< / span > < span class = "n" > X_train< / span > < span class = "p" > ,< / span > < span class = "n" > y_train< / span > < span class = "p" > )< / span >
< span class = "n" > initial_type< / span > < span class = "o" > =< / span > < span class = "p" > [(< / span > < span class = "s1" > ' float_input' < / span > < span class = "p" > ,< / span > < span class = "n" > FloatTensorType< / span > < span class = "p" > ([< / span > < span class = "mi" > 1< / span > < span class = "p" > ,< / span > < span class = "mi" > 4< / span > < span class = "p" > ]))]< / span >
< span class = "n" > onx< / span > < span class = "o" > =< / span > < span class = "n" > convert_sklearn< / span > < span class = "p" > (< / span > < span class = "n" > rf< / span > < span class = "p" > ,< / span > < span class = "n" > initial_types< / span > < span class = "o" > =< / span > < span class = "n" > initial_type< / span > < span class = "p" > )< / span >
< span class = "k" > with< / span > < span class = "nb" > open< / span > < span class = "p" > (< / span > < span class = "s2" > " rf_iris_< / span > < span class = "si" > %d< / span > < span class = "s2" > .onnx" < / span > < span class = "o" > %< / span > < span class = "n" > n_trees< / span > < span class = "p" > ,< / span > < span class = "s2" > " wb" < / span > < span class = "p" > )< / span > < span class = "k" > as< / span > < span class = "n" > f< / span > < span class = "p" > :< / span >
< span class = "n" > f< / span > < span class = "o" > .< / span > < span class = "n" > write< / span > < span class = "p" > (< / span > < span class = "n" > onx< / span > < span class = "o" > .< / span > < span class = "n" > SerializeToString< / span > < span class = "p" > ())< / span >
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