Workflow:Microsoft Onnxruntime Train Convert Predict
| Knowledge Sources | |
|---|---|
| Domains | ML_Inference, Model_Conversion, Model_Deployment |
| Last Updated | 2026-02-10 04:30 GMT |
Overview
End-to-end pipeline for training a scikit-learn model, converting it to ONNX format, and running optimized inference predictions with ONNX Runtime.
Description
This workflow demonstrates the complete machine learning lifecycle from model training through deployment with ONNX Runtime. A model is trained using scikit-learn (or a similar framework), converted to the ONNX interchange format using skl2onnx or a similar converter, and then loaded into ONNX Runtime for optimized inference. The workflow includes validation by comparing predictions between the original framework and ONNX Runtime to ensure conversion fidelity. This pattern applies to any framework with ONNX export support, including PyTorch and TensorFlow.
Usage
Execute this workflow when you need to deploy a trained model for production inference with better performance than the original training framework provides. This is particularly valuable when moving from scikit-learn or PyTorch models to a lightweight, optimized inference engine, or when you need cross-platform model portability.
Execution Steps
Step 1: Train Model in Source Framework
Train a machine learning model using your preferred framework (scikit-learn, PyTorch, TensorFlow, etc.). This step produces a trained model object with learned weights and parameters. The model should be fully trained and validated before conversion.
Key considerations:
- Ensure the model uses operators that have ONNX equivalents
- Record the model's input schema (feature names, types, shapes)
- Validate model accuracy before proceeding to conversion
Step 2: Define Input Schema
Specify the model's input types and shapes for the ONNX converter. For scikit-learn, this means defining the initial types (e.g., FloatTensorType with shape). For PyTorch, this means creating example input tensors. The input schema must exactly match the data the model expects during inference.
Key considerations:
- Shape dimensions can be None for dynamic axes
- Data types must match training data types
- For pipelines, define inputs for the first stage
Step 3: Convert Model to ONNX
Use the appropriate conversion tool to transform the trained model into ONNX format. For scikit-learn, use skl2onnx's convert_sklearn function. For PyTorch, use torch.onnx.export. The converter maps framework-specific operations to ONNX operators and serializes the model graph with weights.
Key considerations:
- Specify the target ONNX opset version for compatibility
- Complex pipelines (preprocessing + model) can be converted as a single unit
- Custom operators may require additional converter registration
Step 4: Validate Conversion
Compare predictions between the original framework model and the ONNX Runtime inference session to verify conversion accuracy. Run the same test inputs through both and check that outputs match within acceptable numerical tolerance. This catches conversion errors before deployment.
Key considerations:
- Use representative test data covering edge cases
- Allow small floating-point tolerance (e.g., 1e-6)
- Check both predicted labels and probability distributions
Step 5: Run Optimized Inference
Load the ONNX model into an InferenceSession and run predictions. Configure execution providers and session options for optimal performance. The ONNX Runtime applies graph optimizations (operator fusion, constant folding, memory planning) that typically yield significant speedups over the original framework.
Key considerations:
- Select appropriate execution providers for target hardware
- Graph optimization happens automatically at session creation
- Batch predictions for maximum throughput