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========
Tutorial
========
*ONNX Runtime* provides an easy way to run
machine learned models with high performance on CPU or GPU
without dependencies on the training framework.
Machine learning frameworks are usually optimized for
batch training rather than for prediction, which is a
more common scenario in applications, sites, and services.
At a high level, you can:
1. Train a model using your favorite framework.
2. Convert or export the model into ONNX format.
See `ONNX Tutorials <https://github.com/onnx/tutorials>`_
for more details.
3. Load and run the model using *ONNX Runtime*.
In this tutorial, we will briefly create a
pipeline with *scikit-learn*, convert it into
ONNX format and run the first predictions.
.. _l-logreg-example:
Step 1: Train a model using your favorite framework
+++++++++++++++++++++++++++++++++++++++++++++++++++
We'll use the famous iris datasets.
.. runpython::
:showcode:
:store:
:warningout: ImportWarning FutureWarning
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y)
from sklearn.linear_model import LogisticRegression
clr = LogisticRegression()
clr.fit(X_train, y_train)
print(clr)
Step 2: Convert or export the model into ONNX format
++++++++++++++++++++++++++++++++++++++++++++++++++++
`ONNX <https://github.com/onnx/onnx>`_ is a format to describe
the machine learned model.
It defines a set of commonly used operators to compose models.
There are `tools <https://github.com/onnx/tutorials>`_
to convert other model formats into ONNX. Here we will use
`ONNXMLTools <https://github.com/onnx/onnxmltools>`_.
.. runpython::
:showcode:
:restore:
:store:
:warningout: ImportWarning FutureWarning
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
initial_type = [('float_input', FloatTensorType([None, 4]))]
onx = convert_sklearn(clr, initial_types=initial_type)
with open("logreg_iris.onnx", "wb") as f:
f.write(onx.SerializeToString())
Step 3: Load and run the model using ONNX Runtime
+++++++++++++++++++++++++++++++++++++++++++++++++
We will use *ONNX Runtime* to compute the predictions
for this machine learning model.
.. runpython::
:showcode:
:restore:
:store:
import numpy
import onnxruntime as rt
sess = rt.InferenceSession("logreg_iris.onnx", providers=rt.get_available_providers())
input_name = sess.get_inputs()[0].name
pred_onx = sess.run(None, {input_name: X_test.astype(numpy.float32)})[0]
print(pred_onx)
The code can be changed to get one specific output
by specifying its name into a list.
.. runpython::
:showcode:
:restore:
import numpy
import onnxruntime as rt
sess = rt.InferenceSession("logreg_iris.onnx", providers=rt.get_available_providers())
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name
pred_onx = sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]
print(pred_onx)
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