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73 lines
2.2 KiB
ReStructuredText
73 lines
2.2 KiB
ReStructuredText
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ONNX Runtime
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============
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ONNX Runtime
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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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Documentation is available at
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`Python Bindings for ONNX Runtime <https://aka.ms/onnxruntime-python>`_.
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Example
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-------
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The following example demonstrates an end-to-end example
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in a very common scenario. A model is trained with *scikit-learn*
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but it has to run very fast in a optimized environment.
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The model is then converted into ONNX format and ONNX Runtime
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replaces *scikit-learn* to compute the predictions.
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::
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# Train a model.
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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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from sklearn.ensemble import RandomForest
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iris = load_iris()
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X, y = iris.data, iris.target
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X_train, X_test, y_train, y_test = train_test_split(X, y)
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clr = RandomForest()
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clr.fit(X_train, y_train)
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# Convert into ONNX format with onnxmltools
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from onnxmltools import convert_sklearn
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from onnxmltools.utils import save_model
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from onnxmltools.convert.common.data_types import FloatTensorType
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initial_type = [('float_input', FloatTensorType([1, 4]))]
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onx = convert_sklearn(clr, initial_types=initial_type)
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save_model(onx, "rf_iris.onnx")
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# Compute the prediction with ONNX Runtime
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import onnxruntime as rt
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import numpy
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sess = rt.InferenceSession("rf_iris.onnx")
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input_name = sess.get_inputs()[0].name
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label_name = sess.get_outputs()[0].name
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pred_onx = sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]
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Changes
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-------
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0.1.4
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^^^^^
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GA release as part of open sourcing onnxruntime.
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0.1.3
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^^^^^
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Fixes a crash on machines which do not support AVX instructions.
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0.1.2
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^^^^^
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First release on Ubuntu 16.04 for CPU and GPU with Cuda 9.1 and Cudnn 7.0,
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supports runtime for deep learning models architecture such as AlexNet, ResNet,
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XCeption, VGG, Inception, DenseNet, standard linear learner,
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standard ensemble learners,
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and transform scaler, imputer.
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