ONNX Goes Open Source On Windows Machine Learning
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ONNX Goes Open Source On Windows Machine Learning: What It Means For Developers
ONNX, or Open Neural Network Exchange, is a standard format for representing machine learning models that can be used across different frameworks and platforms. ONNX was created by Microsoft, Facebook, and Amazon in 2017 to enable interoperability and portability of machine learning models.
Recently, Microsoft announced that ONNX is now open source on Windows Machine Learning (WinML), a platform that allows developers to run machine learning models on Windows devices. This means that developers can now use ONNX models on WinML without any conversion or modification, and benefit from the performance and compatibility of WinML.
Why is this important for developers Here are some of the advantages of using ONNX on WinML:
ONNX models can run on any Windows device, from PCs to laptops to tablets to phones. This gives developers more flexibility and reach for their applications.
ONNX models can leverage the hardware acceleration of WinML, which uses DirectX 12 and DirectML to optimize the execution of machine learning models on GPUs and CPUs. This results in faster and more efficient inference of machine learning models.
ONNX models can be easily integrated with other Windows technologies, such as Windows UI, Windows ML APIs, and Visual Studio. This simplifies the development and deployment of machine learning applications on Windows.
ONNX goes open source on WinML is a significant milestone for the machine learning community, as it promotes collaboration and innovation among different frameworks and platforms. Developers can now use ONNX to create and share machine learning models that can run on Windows devices with high performance and compatibility.
How can developers use ONNX on WinML The process is simple and straightforward. Developers can use any framework that supports ONNX, such as PyTorch, TensorFlow, Keras, or Scikit-learn, to create and train their machine learning models. Then, they can export their models to ONNX format using the ONNX Runtime or the ONNX Converter. Finally, they can load and run their ONNX models on WinML using the Windows ML APIs or the WinML Dashboard.
What are some examples of applications that use ONNX on WinML There are many applications that showcase the power and versatility of ONNX on WinML. For instance, Microsoft's Seeing AI app uses ONNX models to provide visual assistance to people who are blind or have low vision. The app can recognize faces, objects, text, colors, and more using the camera of a Windows device. Another example is Adobe Photoshop Elements, which uses ONNX models to enhance and edit photos using features such as smart selection, auto colorization, and auto skin smoothing.
ONNX goes open source on WinML is a game-changer for developers who want to create and deploy machine learning applications on Windows devices. With ONNX, developers can use their preferred framework and tools to build machine learning models that can run on any Windows device with high performance and compatibility. ONNX also enables developers to share and reuse machine learning models across different platforms and frameworks, fostering collaboration and innovation in the machine learning community. aa16f39245