Train a model with PyTorch and export to ONNX

With the PyTorch framework and Azure Machine Learning, you can train a model in the cloud and download it as an ONNX file to run locally with Windows Machine Learning.

Train the model

With Azure ML, you can train a PyTorch model in the cloud, getting the benefits of rapid scale-out, deployment, and more. See Train and register PyTorch models at scale with Azure Machine Learning for more information.

Export to ONNX

Once you've trained the model, you can export it as an ONNX file so you can run it locally with Windows ML. See Export PyTorch models for Windows ML for instructions on how to natively export from PyTorch.

Integrate with Windows ML

After you've exported the model to ONNX, you're ready to integrate it into a Windows ML application. Windows ML is available in several different programming languages, so check out a tutorial in the language you're most comfortable with.

• C#: Create a Windows Machine Learning UWP application (C#)

• Python: Create a Windows Machine Learning application with Python

• C++: Create a Windows Machine Learning Desktop application (C++)

Note

Use the following resources for help with Windows ML:

• To ask or answer technical questions about Windows ML, please use the windows-machine-learning tag on Stack Overflow.
• To report a bug, please file an issue on our GitHub.