ONNX Runtime
enables high-performance evaluation of trained machine learning (ML)
models while keeping resource usage low.
Building on Microsoft’s dedication to the
Open Neural Network Exchange (ONNX)
community, it supports traditional ML models as well
as Deep Learning algorithms in the
ONNX-ML format.
Documentation is available at
Python Bindings for ONNX Runtime.
Example
The following example demonstrates an end-to-end example
in a very common scenario. A model is trained with scikit-learn
but it has to run very fast in a optimized environment.
The model is then converted into ONNX format and ONNX Runtime
replaces scikit-learn to compute the predictions.
# Train a model.
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForest
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y)
clr = RandomForest()
clr.fit(X_train, y_train)
# Convert into ONNX format with onnxmltools
from onnxmltools import convert_sklearn
from onnxmltools.utils import save_model
from onnxmltools.convert.common.data_types import FloatTensorType
initial_type = [('float_input', FloatTensorType([1, 4]))]
onx = convert_sklearn(clr, initial_types=initial_type)
save_model(onx, "rf_iris.onnx")
# Compute the prediction with ONNX Runtime
import onnxruntime as rt
import numpy
sess = rt.InferenceSession("rf_iris.onnx")
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]
Changes
0.1.4
GA release as part of open sourcing onnxruntime.
0.1.3
Fixes a crash on machines which do not support AVX instructions.
0.1.2
First release on Ubuntu 16.04 for CPU and GPU with Cuda 9.1 and Cudnn 7.0,
supports runtime for deep learning models architecture such as AlexNet, ResNet,
XCeption, VGG, Inception, DenseNet, standard linear learner,
standard ensemble learners,
and transform scaler, imputer.