2025 Python Packaging Survey is now live!  Take the survey now

Python Runtime for ONNX models, other helpers to convert machine learned models in C++.

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for sloaned from gravatar.com sloaned

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Xavier Dupré
  • Tags mlprodict , Xavier Dupré , onnx , scikit-learn , production , machine learning
Classifiers
Report project as malware

Project description

https://github.com/sdpython/mlprodict/blob/master/_doc/sphinxdoc/source/phdoc_static/project_ico.png?raw=true

mlprodict

Build status Build Status Windows https://circleci.com/gh/sdpython/mlprodict/tree/master.svg?style=svg https://dev.azure.com/xavierdupre3/mlprodict/_apis/build/status/sdpython.mlprodict https://badge.fury.io/py/mlprodict.svg MIT License https://codecov.io/github/sdpython/mlprodict/coverage.svg?branch=master GitHub Issues Notebook Coverage Downloads Forks Stars https://mybinder.org/badge_logo.svg size

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.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page