Simplifying ONNX Model Deployment with the Windows Machine Learning Framework

Jan 14, 2026

Challenges with Traditional ONNX Runtime Deployment

Deploying ONNX models efficiently across diverse hardware platforms has always been a challenge. While ONNX Runtime provides a powerful and flexible inference framework, its traditional deployment workflow introduces complexity that can hinder scalability, portability, and reliability, especially in heterogeneous device environments.

ONNX Runtime supports multiple hardware backend through Execution Providers (EPs), enabling models to run on CPUs, GPUs, NPUs, and other accelerators from different vendors. A typical deployment workflow involves the following steps:

  1. Detect available hardware on the target device
  2. Determine which Execution Providers are supported
  3. Manually install and configure:
    • Vendor-specific dynamic libraries
    • Configuration files
    • Environment variables
  4. Explicitly specify the Execution Provider via SessionOptions

While flexible, this approach introduces several practical issues:

1. Limited Cross-Device Compatibility

Different devices support different Execution Providers. A deployment that works on one machine may fail on another due to missing or incompatible EPs, making cross-device deployment fragile and error-prone.

2. Manual Dependency Management

Manually managing dynamic libraries and configuration files is tedious and risky. Common problems include:

  • Library version mismatches
  • Binary compatibility issues
  • Incorrect environment configuration
  • Hidden runtime failures that are hard to debug

These challenges become increasingly severe as projects scale across teams and hardware platforms.

In practice, developers must carefully manage Execution Providers (EPs), hardware dependencies, and environment configurations to ensure models run correctly across different systems. As deployment scenarios scale across teams and devices, this manual process becomes increasingly fragile and difficult to maintain.

In this blog, we introduce a model deployment and performance testing solution built on Windows Machine Learning (WinML). By leveraging WinML’s hardware abstraction and automation capabilities, this approach significantly simplifies ONNX model deployment while improving robustness, portability, and user experience.


A WinML-Based Solution

To address these problems, we introduce a model deployment and performance testing solution built on Windows Machine Learning (WinML). By leveraging WinML’s hardware abstraction and automation capabilities, this approach significantly simplifies ONNX model deployment while improving robustness, portability, and user experience.

WinML is a Windows-native model deployment framework that abstracts many of the complexities involved in hardware acceleration. It provides several key advantages:

  • Automatically queries device hardware capabilities
  • Determines supported Execution Providers at runtime
  • Automatically downloads required EP support packages when needed
  • Manages dynamic library paths, versioning, and upgrades internally

By leveraging WinML, users no longer need to manually configure hardware-specific dependencies for each device. This significantly reduces the deployment friction and improves reliability across heterogeneous environments

A User-Friendly Model Performance Testing Tool

Based on WinML, we further developed a model performance testing tool designed for simplicity, flexibility, and scalability.

Deployment Workflow

This section outlines the end-to-end workflow for deploying and running on ONNX model using the WinML-based approach.

1. Prepare the ONNX Model

  • Train the model using a supported framework (e.g., PyTorch).
  • Export the trained model to ONNX format.
  • Ensure the model is compatible with Windows ML / DirectML operators.

2. Initialize the ONNX Runtime Environment

  • Create an Ort::Env object as the global runtime environment.
  • This environment instance is responsible for:
  • Logging and threading
  • Execution Provider registration
  • Device enumeration
		Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "winml_ep");

3. Query Available Execution Provider Devices

  • Call env->GetEpDevices() to enumerate all supported EP devices.
  • Each returned device describes:
  • Execution Provider type (e.g., WinML)
  • Hardware backend (CPU, GPU, NPU)
  • Device ID and capability flags
		auto devices = env.GetEpDevices();

Purpose:

  • Enable dynamic hardware selection
  • Support heterogeneous deployment across different Windows devices
  • Avoid hardcoding GPU/CPU assumptions

4. Select the Target Device

  • Iterate through the device list and select a suitable device based on:
  • Provider name (e.g., "VitisAIExecutionProvider")
  • Device type (NPU preferred over CPU)
  • Performance or power constraints
		for (const auto& device : devices) {
// Match VitisAI EP + desired hardware
}

5. Download the related EP package 

If a required EP package is not installed on the system, the tool automatically downloads and installs it at runtime using WinML APIs. This eliminates manual dependency management and reduces deployment errors. 

		for (const auto& p : providers) {
  std::wstring name(p.Name().c_str());
  std::wcout << "Found support for External Provider: " << name << std::endl;
  if (p.ReadyState() == winrt::Microsoft::Windows::AI::MachineLearning::ExecutionProviderReadyState::NotPresent) {
    std::wcout << "Provider " << name << " is not present!" << std::endl;
    try {
      std::wcout << "Downloading provider " << name <<   s  td::endl;   p.EnsureReadyAsync().get();
    } catch (const std::exception& e) {
      std::cerr << "ERROR when downloading new EP: " <<   e.what() << std::endl;
      throw std::runtime_error("Error when downloading new EP: " + utils::ws2s(name));
    }
  }
}

6. Configure Session Options with the Selected EP Device

  • Create Ort::SessionOptions
  • Append the selected EP device to the session configuration
  • Bind the session explicitly to the chosen device
		Ort::SessionOptions session_options; // Append WinML EP with selected device info

This ensures inference runs exactly on the selected Windows ML backend.

7. Create the Inference Session

  • Load the ONNX model using the configured session options
  • The session is now bound to the selected WinML device
		Ort::Session session(env, model_path, session_options);

8. Prepare Inputs and Outputs

  • Convert application data into ONNX Runtime tensors
  • Match input shapes and data types defined in the model
  • Bind input and output tensors to the session

9. Run Inference

  • Execute inference synchronously or asynchronously
  • ONNX Runtime dispatches execution to Windows ML via the selected EP device
		session.Run(...);

Key Features

  • Minimal Configuration 
    • Users can deploy models with only basic configuration, without worrying about environment setup.
  • Flexible Execution Provider Selection
    • Users may explicitly specify an Execution Provider.
    • If the specified EP is not supported on the device, the system can automatically fall back to a default supported EP.

Adoption and Impact

Thanks to its simple setup, strong compatibility, and user-friendly design, this tool has already been widely adopted across multiple teams. It enables engineers to focus on model performance and optimization, rather than spending time on environment configuration and debugging deployment issues.

Conclusion

By combining the flexibility of ONNX models with the robustness and automation of WinML, we have significantly improved the model deployment experience on Windows platforms. This approach reduces friction, improves portability, and scales naturally across diverse hardware environments.

For developers struggling with Execution Provider compatibility or complex deployment pipelines, a WinML-based solution may be the key to unlocking a smoother and more reliable workflow.

 

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WenJie He

AI Group Engineering

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