Unable to export Random Forest model to ONNX format in Azure ML Studio

Question

Hello, I am using Azure Machine Learning Studio and have built a Random Forest model. I would like to export this model to the ONNX format for use in an external development environment. However, I cannot find the option to export the model to ONNX within the studio. Could you please guide me on how to achieve this?

Answer 1

Hi @Liron Hagbi

Welcome to the Microsoft Q&A and thank you for posting your questions here.

I understand that you're having trouble exporting your Random Forest model to the ONNX format in Azure Machine Learning Studio. While Azure ML Studio doesn't provide a direct option to export Random Forest models to ONNX, you can achieve this using the sklearn-onnx package in Python. Here’s a step-by-step guide to help you:

Step 1: Install Required Packages: First, you need to install the sklearn-onnx package. You can do this using pip:

pip install skl2onnx onnxruntime

Step 2: Convert the Model to ONNX: Next, you can convert your trained Random Forest model to the ONNX format using the sklearn-onnx package. Here’s an example:

import skl2onnx
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
import joblib

# Load your trained Random Forest model
model = joblib.load('path_to_your_model.pkl')

# Define the initial type
initial_type = [('float_input', FloatTensorType([None, model.n_features_in_]))]

# Convert the model
onnx_model = convert_sklearn(model, initial_types=initial_type)

# Save the ONNX model
with open("random_forest_model.onnx", "wb") as f:
    f.write(onnx_model.SerializeToString())

Step 3: Verify the ONNX Model: You can verify the ONNX model using the onnxruntime package to ensure it works correctly:

import onnxruntime as rt
import numpy as np

# Load the ONNX model
sess = rt.InferenceSession("random_forest_model.onnx")

# Prepare input data
input_name = sess.get_inputs().name
label_name = sess.get_outputs().name
data = np.array([[...]], dtype=np.float32)  # Replace with your input data

# Run the model
pred = sess.run([label_name], {input_name: data})
print(pred)

For more detailed guidance, you can refer to the official Microsoft documentation:

• Convert and export models to ONNX format
• ONNX Runtime for Python

I hope this is helpful! Do not hesitate to let me know if you have any other questions.

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Thanks