Morph MORPH
SIGN IN SIGN UP
microsoft / onnxruntime UNCLAIMED

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

0 0 1 C++
Normal View History Raw
Initial bootstrap commit.
2018-11-19 16:48:22 -08:00
========
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.
Propagate documentation modification from rel-1.0.0 (#2713)
2019-12-21 00:25:45 +01:00
.. _l-logreg-example:
Initial bootstrap commit.
2018-11-19 16:48:22 -08:00
Step 1: Train a model using your favorite framework
+++++++++++++++++++++++++++++++++++++++++++++++++++
We'll use the famous iris datasets.
Update the documentation, run all examples during the generation of the documentation (replace #89) (#103) * Minor update in the documentation * Run examples during the generation of the documentation.
2018-12-05 19:12:25 +01:00
.. runpython::
:showcode:
:store:
:warningout: ImportWarning FutureWarning
Initial bootstrap commit.
2018-11-19 16:48:22 -08:00
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)
Update the documentation, run all examples during the generation of the documentation (replace #89) (#103) * Minor update in the documentation * Run examples during the generation of the documentation.
2018-12-05 19:12:25 +01:00
print(clr)
Initial bootstrap commit.
2018-11-19 16:48:22 -08:00
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>`_.
Update the documentation, run all examples during the generation of the documentation (replace #89) (#103) * Minor update in the documentation * Run examples during the generation of the documentation.
2018-12-05 19:12:25 +01:00
.. runpython::
:showcode:
:restore:
:store:
:warningout: ImportWarning FutureWarning
Initial bootstrap commit.
2018-11-19 16:48:22 -08:00
Update documentation to reflect the latest changes (#311) - removes markdown output - rename intro into index - uses skl2onnx anywhere possible instead of onnxmltools
2019-01-11 12:41:42 +01:00
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
Initial bootstrap commit.
2018-11-19 16:48:22 -08:00
Propagate documentation modification from rel-1.0.0 (#2713)
2019-12-21 00:25:45 +01:00
initial_type = [('float_input', FloatTensorType([None, 4]))]
Initial bootstrap commit.
2018-11-19 16:48:22 -08:00
onx = convert_sklearn(clr, initial_types=initial_type)
Update documentation to reflect the latest changes (#311) - removes markdown output - rename intro into index - uses skl2onnx anywhere possible instead of onnxmltools
2019-01-11 12:41:42 +01:00
with open("logreg_iris.onnx", "wb") as f:
f.write(onx.SerializeToString())
Initial bootstrap commit.
2018-11-19 16:48:22 -08:00
Step 3: Load and run the model using ONNX Runtime
+++++++++++++++++++++++++++++++++++++++++++++++++
We will use *ONNX Runtime* to compute the predictions
for this machine learning model.
Update the documentation, run all examples during the generation of the documentation (replace #89) (#103) * Minor update in the documentation * Run examples during the generation of the documentation.
2018-12-05 19:12:25 +01:00
.. runpython::
:showcode:
:restore:
:store:
Initial bootstrap commit.
2018-11-19 16:48:22 -08:00
Update the documentation, run all examples during the generation of the documentation (replace #89) (#103) * Minor update in the documentation * Run examples during the generation of the documentation.
2018-12-05 19:12:25 +01:00
import numpy
Initial bootstrap commit.
2018-11-19 16:48:22 -08:00
import onnxruntime as rt
Update the documentation, run all examples during the generation of the documentation (replace #89) (#103) * Minor update in the documentation * Run examples during the generation of the documentation.
2018-12-05 19:12:25 +01:00
Fix Training Packaging pipeline (#9885) * Fix Training Packaging pipeline
2021-11-30 15:26:10 -08:00
sess = rt.InferenceSession("logreg_iris.onnx", providers=rt.get_available_providers())
Update the documentation, run all examples during the generation of the documentation (replace #89) (#103) * Minor update in the documentation * Run examples during the generation of the documentation.
2018-12-05 19:12:25 +01:00
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
Fix Training Packaging pipeline (#9885) * Fix Training Packaging pipeline
2021-11-30 15:26:10 -08:00
sess = rt.InferenceSession("logreg_iris.onnx", providers=rt.get_available_providers())
Initial bootstrap commit.
2018-11-19 16:48:22 -08:00
input_name = sess.get_inputs()[0].name
Update the documentation, run all examples during the generation of the documentation (replace #89) (#103) * Minor update in the documentation * Run examples during the generation of the documentation.
2018-12-05 19:12:25 +01:00
label_name = sess.get_outputs()[0].name
Initial bootstrap commit.
2018-11-19 16:48:22 -08:00
pred_onx = sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]
Update the documentation, run all examples during the generation of the documentation (replace #89) (#103) * Minor update in the documentation * Run examples during the generation of the documentation.
2018-12-05 19:12:25 +01:00
print(pred_onx)
Reference in New Issue Copy Permalink