skl2onnx

import numpy as np from sklearn.datasets import load_iris from sklearn.ensemble import RandomForestClassifier from skl2onnx import to_onnx import onnxruntime as rt # 1. Train a scikit-learn model iris = load_iris() X, y = iris.data, iris.target X = X.astype(np.float32) # ONNX typically uses float32 model = RandomForestClassifier(n_estimators=10, random_state=42) model.fit(X, y) # 2. Convert the scikit-learn model to ONNX format # `X[:1]` is used to infer the input types and shapes onx_model = to_onnx(model, X[:1]) # 3. Save the ONNX model to a file with open("rf_iris.onnx", "wb") as f: f.write(onx_model.SerializeToString()) # 4. Load and make predictions with ONNX Runtime sess = rt.InferenceSession("rf_iris.onnx", providers=["CPUExecutionProvider"]) input_name = sess.get_inputs()[0].name output_names = [output.name for output in sess.get_outputs()] # Make a prediction predictions = sess.run(output_names, {input_name: X[0:1].astype(np.float32)}) print(f"Original model prediction: {model.predict(X[0:1])}") print(f"ONNX Runtime prediction (label): {predictions[0]}") print(f"ONNX Runtime prediction (probabilities): {predictions[1]}")