Comments (4)
Thanks for posting.
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The preprocessing model used in the sample was created using LearningModelBuilder which is part of the WinML Experimental API. An example of using LearningModelBuilder to create a model can be found here. Alternatively, you could also use the ONNX python API.
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Writing a compute shader is possible, but will not be optimized with other shaders in the dml graph. There may be performance gains but it's hard to say.
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WinML uses DML as a backend. The sample showcases how to use ORT with the DML EP, but ORT can be use with other EPs (CUDA, TensorRT, etc.).
from windows-machine-learning.
Thanks for posting.
- The preprocessing model used in the sample was created using LearningModelBuilder which is part of the WinML Experimental API. An example of using LearningModelBuilder to create a model can be found here. Alternatively, you could also use the ONNX python API.
- Writing a compute shader is possible, but will not be optimized with other shaders in the dml graph. There may be performance gains but it's hard to say.
- WinML uses DML as a backend. The sample showcases how to use ORT with the DML EP, but ORT can be use with other EPs (CUDA, TensorRT, etc.).
Thanks for your reply @nums11!
Would you please confirm for the third bullet point above that if we want to use any providers other than DML, we can't use WinML, only a level down using ORT directly is the way to go? I am asking because for your sample code, the ORT API specifically mentions only DML is supported at this moment. Thanks!
from windows-machine-learning.
Yes, if you want to use a provider other than DML you cannot use WinML and you can use ORT. Here is an example from the ORT docs of using ORT with CUDA as a backend.
from windows-machine-learning.
Yes, if you want to use a provider other than DML you cannot use WinML and you can use ORT. Here is an example from the ORT docs of using ORT with CUDA as a backend.
Thanks for your reply @nums11!
from windows-machine-learning.
Related Issues (20)
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