Quick start

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 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.

Step 1: Train a model using your favorite framework

We’ll use the famous Iris datasets.

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
iris = load_iris()
X, y =,
X_train, X_test, y_train, y_test = train_test_split(X, y)

from sklearn.linear_model import LogisticRegression
clr = LogisticRegression(), y_train)

Step 2: Convert or export the model into ONNX format

ONNX is a format to describe the machine learned model. It defines a set of commonly used operators to compose models. There are tools to convert other model formats into ONNX. Here we will use ONNXMLTools.

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:

Step 3: Load and run the model using ONNX Runtime

We will use ONNX Runtime to compute the predictions for this machine learning model.

import onnxruntime as rt
sess = rt.InferenceSession("logreg_iris.onnx", providers=["CPUExecutionProvider"])
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name

pred_onx =[label_name], {input_name: X_test.astype(numpy.float32)})[0]

convert_sklearn, to_onnx, initial_types

The module implements two functions: convert_sklearn and to_onnx. The first one was used in the previous examples, it requires two mandatory arguments:

  • a scikit-learn model or a pipeline

  • initial types

scikit-learn does not store information about the training dataset. It is not always possible to retrieve the number of features or their types. That’s why the function needs another argument called initial_types. In many cases, the training datasets is a numerical matrix X_train. Then it becomes initial_type=[('X', FloatTensorType([None, X_train.shape[1]]))]. X is the name of this unique input, the second term indicates the type and shape. The shape is [None, X_train.shape[1]], the first dimension is the number of rows followed by the number of features. The number of rows is undefined as the the number of requested predictions is unknown at the time the model is converted. The number of features is usually known. Let’s assume now the input is a string column followed by a matrix, then initial types would be:

    ('S', StringTensorType([None, 1])),
    ('X', FloatTensorType([None, X_train.shape[1]])),

Function to_onnx was implemented after discussions with the core developers of scikit-learn. It also contains a mechanism to infer the proper type based on one row of the training datasets. Then, the following code convert_sklearn(clr, initial_types=[('X', FloatTensorType([None, 4]))]) is usually rewritten into to_onnx(clr, X_train[:1]) where X_train is the training dataset, it can be a matrix or a dataframe. The input name is 'X' by default unless X_train is a dataframe. In that case, the column names are used as input names.