https://github.com/sdpython/mlprodict

Productionize machine learning predictions, with ONNX or without
https://github.com/sdpython/mlprodict

machine-learning onnx python3 scikit-learn

Last synced: 3 months ago
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Productionize machine learning predictions, with ONNX or without

• Host: GitHub
• URL: https://github.com/sdpython/mlprodict
• Owner: sdpython
• License: mit
• Archived: true
• Created: 2017-11-29T11:57:10.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2024-01-11T19:19:46.000Z (12 months ago)
• Last Synced: 2024-09-23T06:33:15.177Z (3 months ago)
• Topics: machine-learning, onnx, python3, scikit-learn
• Language: Python
• Homepage: http://www.xavierdupre.fr/app/mlprodict/helpsphinx/index.html
• Size: 22 MB
• Stars: 66
• Watchers: 9
• Forks: 13
• Open Issues: 19
• Metadata Files:
  • Readme: README.rst
  • Changelog: HISTORY.rst
  • License: LICENSE.txt

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README

        

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mlprodict

=========

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*mlprodict* was initially started to help implementing converters

to *ONNX*. The main features is a python runtime for

*ONNX* (class `OnnxInference

`_),

visualization tools

(see `Visualization

`_),

and a `numpy API for ONNX

`_).

The package also provides tools to compare

predictions, to benchmark models converted with

`sklearn-onnx `_.

::

    import numpy

    from sklearn.linear_model import LinearRegression

    from sklearn.datasets import load_iris

    from mlprodict.onnxrt import OnnxInference

    from mlprodict.onnxrt.validate.validate_difference import measure_relative_difference

    from mlprodict import __max_supported_opset__, get_ir_version

    iris = load_iris()

    X = iris.data[:, :2]

    y = iris.target

    lr = LinearRegression()

    lr.fit(X, y)

    # Predictions with scikit-learn.

    expected = lr.predict(X[:5])

    print(expected)

    # Conversion into ONNX.

    from mlprodict.onnx_conv import to_onnx

    model_onnx = to_onnx(lr, X.astype(numpy.float32),

                         black_op={'LinearRegressor'},

                         target_opset=__max_supported_opset__)

    print("ONNX:", str(model_onnx)[:200] + "\n...")

    # Predictions with onnxruntime

    model_onnx.ir_version = get_ir_version(__max_supported_opset__)

    oinf = OnnxInference(model_onnx, runtime='onnxruntime1')

    ypred = oinf.run({'X': X[:5].astype(numpy.float32)})

    print("ONNX output:", ypred)

    # Measuring the maximum difference.

    print("max abs diff:", measure_relative_difference(expected, ypred['variable']))

    # And the python runtime

    oinf = OnnxInference(model_onnx, runtime='python')

    ypred = oinf.run({'X': X[:5].astype(numpy.float32)},

                     verbose=1, fLOG=print)

    print("ONNX output:", ypred)

**Installation**

Installation from *pip* should work unless you need the latest

development features.

::

    pip install mlprodict

The package includes a runtime for *ONNX*. That's why there

is a limited number of dependencies. However, some features

relies on *sklearn-onnx*, *onnxruntime*, *scikit-learn*.

They can be installed with the following instructions:

::

    pip install mlprodict[all]

The code is available at

`GitHub/mlprodict `_

and has `online documentation `_.