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< title > ONNX Runtime Backend for ONNX< / title >
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< li class = "toctree-l1" > < a class = "reference internal" href = "../tutorial.html" > Tutorial< / a > < ul >
< li class = "toctree-l2" > < a class = "reference internal" href = "../tutorial.html#step-1-train-a-model-using-your-favorite-framework" > Step 1: Train a model using your favorite framework< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../tutorial.html#step-2-convert-or-export-the-model-into-onnx-format" > Step 2: Convert or export the model into ONNX format< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../tutorial.html#step-3-load-and-run-the-model-using-onnx-runtime" > Step 3: Load and run the model using ONNX Runtime< / a > < / li >
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< li class = "toctree-l1" > < a class = "reference internal" href = "../api_summary.html" > API Summary< / a > < ul >
< li class = "toctree-l2" > < a class = "reference internal" href = "../api_summary.html#device" > Device< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../api_summary.html#examples-and-datasets" > Examples and datasets< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../api_summary.html#load-and-run-a-model" > Load and run a model< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "../api_summary.html#backend" > Backend< / a > < / li >
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< li class = "toctree-l2 current" > < a class = "current reference internal" href = "#" > ONNX Runtime Backend for ONNX< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_pipeline.html" > Draw a pipeline< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_load_and_predict.html" > Load and predict with ONNX Runtime and a very simple model< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_profiling.html" > Profile the execution of a simple model< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_metadata.html" > Metadata< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_dl_keras.html" > ONNX Runtime for Keras< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_convert_pipeline_vectorizer.html" > Train, convert and predict with ONNX Runtime< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_common_errors.html" > Common errors with onnxruntime< / a > < / li >
< li class = "toctree-l2" > < a class = "reference internal" href = "plot_train_convert_predict.html" > Train, convert and predict with ONNX Runtime< / a > < / li >
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< div class = "sphx-glr-download-link-note admonition note" >
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< p class = "admonition-title" > Note< / p >
< p > Click < a class = "reference internal" href = "#sphx-glr-download-auto-examples-plot-backend-py" > < span class = "std std-ref" > here< / span > < / a > to download the full example code< / p >
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< / div >
< div class = "sphx-glr-example-title section" id = "onnx-runtime-backend-for-onnx" >
< span id = "l-example-backend-api" > < / span > < span id = "sphx-glr-auto-examples-plot-backend-py" > < / span > < h1 > ONNX Runtime Backend for ONNX< a class = "headerlink" href = "#onnx-runtime-backend-for-onnx" title = "Permalink to this headline" > ¶< / a > < / h1 >
< p > < em > ONNX Runtime< / em > extends the
< a class = "reference external" href = "https://github.com/onnx/onnx/blob/master/docs/ImplementingAnOnnxBackend.md" > onnx backend API< / a >
to run predictions using this runtime.
Let’
of a simple logistic regression model.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > numpy< / span > < span class = "k" > as< / span > < span class = "nn" > np< / span >
< span class = "kn" > from< / span > < span class = "nn" > onnxruntime< / span > < span class = "k" > import< / span > < span class = "n" > datasets< / span >
< span class = "kn" > import< / span > < span class = "nn" > onnxruntime.backend< / span > < span class = "k" > as< / span > < span class = "nn" > backend< / span >
< span class = "kn" > from< / span > < span class = "nn" > onnx< / span > < span class = "k" > import< / span > < span class = "n" > load< / span >
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< span class = "n" > name< / span > < span class = "o" > =< / span > < span class = "n" > datasets< / span > < span class = "o" > .< / span > < span class = "n" > get_example< / span > < span class = "p" > (< / span > < span class = "s2" > " logreg_iris.onnx" < / span > < span class = "p" > )< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > load< / span > < span class = "p" > (< / span > < span class = "n" > name< / span > < span class = "p" > )< / span >
< span class = "n" > rep< / span > < span class = "o" > =< / span > < span class = "n" > backend< / span > < span class = "o" > .< / span > < span class = "n" > prepare< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "s1" > ' CPU' < / span > < span class = "p" > )< / span >
< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "n" > np< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([[< / span > < span class = "o" > -< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "o" > -< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > np< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )< / span >
< span class = "n" > label< / span > < span class = "p" > ,< / span > < span class = "n" > proba< / span > < span class = "o" > =< / span > < span class = "n" > rep< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " label=< / span > < span class = "si" > {}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > label< / span > < span class = "p" > ))< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " probabilities=< / span > < span class = "si" > {}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > proba< / span > < span class = "p" > ))< / span >
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< / 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 > label=[1]
probabilities=[{0: 0.02731134556233883, 1: 0.5175684094429016, 2: 0.4551202654838562}]
< / pre > < / div >
< / div >
< p > The device depends on how the package was compiled,
GPU or CPU.< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > from< / span > < span class = "nn" > onnxruntime< / span > < span class = "k" > import< / span > < span class = "n" > get_device< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "n" > get_device< / 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 > CPU
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< / pre > < / div >
< / div >
< p > The backend can also directly load the model
without using < em > onnx< / em > .< / p >
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< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > rep< / span > < span class = "o" > =< / span > < span class = "n" > backend< / span > < span class = "o" > .< / span > < span class = "n" > prepare< / span > < span class = "p" > (< / span > < span class = "n" > name< / span > < span class = "p" > ,< / span > < span class = "s1" > ' CPU' < / span > < span class = "p" > )< / span >
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< span class = "n" > x< / span > < span class = "o" > =< / span > < span class = "n" > np< / span > < span class = "o" > .< / span > < span class = "n" > array< / span > < span class = "p" > ([[< / span > < span class = "o" > -< / span > < span class = "mf" > 1.0< / span > < span class = "p" > ,< / span > < span class = "o" > -< / span > < span class = "mf" > 2.0< / span > < span class = "p" > ]],< / span > < span class = "n" > dtype< / span > < span class = "o" > =< / span > < span class = "n" > np< / span > < span class = "o" > .< / span > < span class = "n" > float32< / span > < span class = "p" > )< / span >
< span class = "n" > label< / span > < span class = "p" > ,< / span > < span class = "n" > proba< / span > < span class = "o" > =< / span > < span class = "n" > rep< / span > < span class = "o" > .< / span > < span class = "n" > run< / span > < span class = "p" > (< / span > < span class = "n" > x< / span > < span class = "p" > )< / span >
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< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " label=< / span > < span class = "si" > {}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > label< / span > < span class = "p" > ))< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "s2" > " probabilities=< / span > < span class = "si" > {}< / span > < span class = "s2" > " < / span > < span class = "o" > .< / span > < span class = "n" > format< / span > < span class = "p" > (< / span > < span class = "n" > proba< / span > < span class = "p" > ))< / span >
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< / 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 > label=[1]
probabilities=[{0: 0.02731134556233883, 1: 0.5175684094429016, 2: 0.4551202654838562}]
< / pre > < / div >
< / div >
< p > The backend API is implemented by other frameworks
and makes it easier to switch between multiple runtimes
with the same API.< / p >
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< p class = "sphx-glr-timing" > < strong > Total running time of the script:< / strong > ( 0 minutes 0.078 seconds)< / p >
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< p > < a class = "reference download internal" download = "" href = "../_downloads/07414d774e7573917fe9f036ed647ad9/plot_backend.py" > < code class = "xref download docutils literal notranslate" > < span class = "pre" > Download< / span > < span class = "pre" > Python< / span > < span class = "pre" > source< / span > < span class = "pre" > code:< / span > < span class = "pre" > plot_backend.py< / span > < / code > < / a > < / p >
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< p > < a class = "reference download internal" download = "" href = "../_downloads/d2eead6e164573e054253b729a99fe25/plot_backend.ipynb" > < code class = "xref download docutils literal notranslate" > < span class = "pre" > Download< / span > < span class = "pre" > Jupyter< / span > < span class = "pre" > notebook:< / span > < span class = "pre" > plot_backend.ipynb< / span > < / code > < / a > < / p >
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< p class = "sphx-glr-signature" > < a class = "reference external" href = "https://sphinx-gallery.github.io" > Gallery generated by Sphinx-Gallery< / a > < / p >
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