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CUDA Version: 12.0 #101
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This is a common problem with #105 which will be answered here |
MocapNET needs a version of the libtensorflow2.x C library. The version of tensorflow defines what version of CUDA is compatible or not, MocapNET does not directly need or use any CUDA version. What needs to be done for CUDA/GPU support thus it to replace the folder dependencies/libtensorflow with a good working tensorflow version/build that includes the correct CUDA version support. If GPU support is malfunctioning/not present MocapNET will fall back to CPU execution, which has the side effect that the 2D joint estimator will run much slower. This is the reason for the very low framerate on the 2D joint estimation you observed. To the best of my knowledge CUDA 12 is not supported by tensorflow AT ALL at the moment as seen from their official list here : As you might imagine I am not a Google or NVIDIA employee so unfortunately I cannot do a lot about their version compatibility etc. The best that can be done seems to be CUDA 11.8 with Tensorflow 2.13 . Now the latest C library for Tensorflow 2 that can be downloaded pre-compiled from here : Now to make things work on your CUDA 12 machine
Once again I am sorry for the inconvenience but if Google and NVIDIA with their huge budgets can't fix their compatibility issues I sure can't :) further more unfortunately my development PC has a really old GPU (GTX1050) at the moment, so I am not able to reproduce an environment with CUDA 12 to try and help fixing this myself! Looking forward to your comments and further questions! |
My PC has GPU of which CUDA Version is 12.0.
But it seems not to be used.
Please tell me how to use CUDA Version: 12.0.
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