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51 lines
1.5 KiB
ReStructuredText
51 lines
1.5 KiB
ReStructuredText
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Python Bindings for ONNX Runtime
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================================
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ONNX Runtime enables high-performance evaluation of trained machine learning (ML)
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models while keeping resource usage low.
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Building on Microsoft's dedication to the
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`Open Neural Network Exchange (ONNX) <https://onnx.ai/>`_
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community, it supports traditional ML models as well
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as Deep Learning algorithms in the
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`ONNX-ML format <https://github.com/onnx/onnx/blob/master/docs/IR.md>`_.
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.. only:: html
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.. toctree::
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:maxdepth: 1
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tutorial
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api_summary
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auto_examples/index
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:ref:`genindex`
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.. only:: md
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.. toctree::
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:maxdepth: 1
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:caption: Contents:
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tutorial
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api_summary
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examples_md
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The core library is implemented in C++.
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*ONNX Runtime* is available on
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PyPi for Linux Ubuntu 16.04, Python 3.5+ for both
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`CPU <https://pypi.org/project/onnxruntime/>`_ and
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`GPU <https://pypi.org/project/onnxruntime-gpu/>`_.
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This example demonstrates a simple prediction for an
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`ONNX-ML format <https://github.com/onnx/onnx/blob/master/docs/IR.md>`_
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model. The following file ``model.onnx`` is taken from
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github `onnx...test_sigmoid <https://github.com/onnx/onnx/tree/master/onnx/backend/test/data/node/test_sigmoid>`_.
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::
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import onnxruntime as rt
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sess = rt.InferenceSession("model.onnx")
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input_name = sess.get_inputs()[0].name
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X = numpy.random.random((3,4,5)).astype(numpy.float32)
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res = sess.run([output_name], {input_name: x})
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pred_onnx = sess.run(None, {input_name: X})
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