If you have ever tried running a pre-trained neural network model on Windows, you have probably faced the question: will it even work? And if so, how fast? AMD decided to simplify this task and introduced a tool for testing ONNX models using Windows ML and DirectML.
Why ONNX Models Need Testing on Windows
Why Is This Necessary 🤔
ONNX is an open format for storing machine learning models. Its convenience lies in the fact that a model can be trained in one framework (say, PyTorch) and then run in another environment without a complete overhaul. However, there is a problem: not all operators used by a model are supported on all platforms.
Windows ML (WinML) is a system built into Windows for running machine learning models. It can use DirectML, which allows utilizing the GPU for acceleration. But before running a model in production, you want to understand two things:
- Is the model compatible with the WinML and DirectML operator set?
- What will the performance be on specific hardware?
That is exactly why AMD created its tool.
Features of the New AMD Tool for ONNX
What the New Tool Does
The tool from AMD helps test an ONNX model before its deployment. Simply put, it checks whether the model will run on Windows ML and how fast it will work.
Key features:
- Checking model compatibility with Windows ML and DirectML operators
- Initializing the ONNX Runtime environment
- Running the model and measuring performance
All this is packed into a fairly simple interface so that you do not have to manually figure out the runtime environment settings and check every operator.
How AMD's ONNX Tool Works in Practice
How It Works in Practice
The process looks roughly like this. First, the tool checks if all operators in your model are supported by DirectML. If there is an incompatibility somewhere, you will find out about it immediately, rather than after hours of debugging.
Next, it initializes ONNX Runtime – a library that can run ONNX models on different backends, including DirectML. After that, you can run the model and see how much time inference takes.
This is particularly useful if you are developing an application for Windows and want to use a GPU from AMD (or other manufacturers supporting DirectML) to accelerate the model's operation.
Who Benefits from This ONNX Tool
Who Will Find This Useful
First and foremost – developers of applications on Windows who utilize machine learning. For example, if you are making a desktop application with image recognition or text processing, and you want it to work fast on the user's graphics card.
It can also be useful for those porting models from a server environment to client devices. Instead of guessing whether the model will run on Windows, you can simply check.
Limitations of the ONNX Testing Tool
What Remains Behind the Scenes
The tool solves a specific task – checking compatibility and basic performance testing. But it does not replace full-fledged benchmarking in real conditions. That is, if you need to understand how the model behaves under load, with different batch sizes, and on different hardware – this will still have to be tested separately.
It is also worth remembering that DirectML is an abstraction over different GPUs. It works on AMD, NVIDIA, and Intel. But in some cases, performance may differ depending on how well a specific operation is optimized for specific hardware.
Summary of AMD's ONNX Tool for Windows
Summary
AMD has made a step towards simplifying work with ONNX models on Windows. The tool is not revolutionary but practical – it saves time at the compatibility check stage and gives an initial idea of how the model will work. For those developing for Windows and using machine learning, this can be a useful addition to the toolkit.