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@mlcompute attribute

🔌dp.kinect3 dp.oak
signature
mlcompute ENGINE_STRING [ DEVICE_SUBSTRING | DEVICE_INDEX ]
values
directml 0 default
examples
@mlcompute directml        <- DirectML, first GPU
@mlcompute directml 1      <- DirectML, second GPU
@mlcompute directml "2070" <- DirectML, "2070" GPU
@mlcompute directml intel  <- DirectML, "intel" GPU
@mlcompute directml rtx    <- DirectML, "rtx" GPU

Machine learning compute engine and device. Enable multiple compute devices for different tasks using attributes like @mlcompute, @transcoder, and @opencl. For example…

  • Track animals using a model on the discrete Nvidia GPU @mlcompute nvidia
  • Decode color frames on the Intel CPU harware decoder @transcoder intelmedia
  • Flip and undistort frames on integrated Intel GPU @opencl intel
  • and the remaining features run on your CPU

Test to discover which settings meet your needs for hardware, latency, and throughput. You can have significant performance improvements! 🙂

DirectML is built into Windows and requires no downloads. Other compute engines like CUDA and TensorRT are not tested or supported – use at your own risk.

📝 The second parameter of @mlcompute is the name of the device or the numeric index (starting with 0) of the device. Name of the device is recommended. The index may change because the Windows Graphics Performance Preference default is “Let Windows decide”. Windows may decide to change the order of GPUs and therefore the indices change. The index is consistent when you choose a Graphics Performance Preference. This setting is in Windows settings, System, Display, Graphics settings.

Performance

DirectML works very well across most GPUs. You may have a delay the first-time your model is started while the engine is optimizing it. Later starts should be quicker.