I am trying to develop a 3D StarDist model using the Jupyter notebook however I realized my process is running in the CPU instead of the GPU making my the training process run forever. I realized I did not have gup_tools() installed but so far I have not been able to install it. I already try to install it using pip and the developmental version, also I try the troubleshooting suggestions downloading they suggested in the GitHub gputools website (I am not sure if I did this part correctly). Can you help me with what else I can do to run my process in my GPU?
In this case, I am running 3D StarDist without the augmentation process in order to save time during the training but I do not know if this is affecting the speed of the process. I am using the next configuration
I am trying to develop a 3D StarDist model using the Jupyter notebook however I realized my process is running in the CPU instead of the GPU making my the training process run forever. I realized I did not have gup_tools() installed but so far I have not been able to install it. I already try to install it using pip and the developmental version, also I try the troubleshooting suggestions downloading they suggested in the GitHub gputools website (I am not sure if I did this part correctly). Can you help me with what else I can do to run my process in my GPU?
In this case, I am running 3D StarDist without the augmentation process in order to save time during the training but I do not know if this is affecting the speed of the process. I am using the next configuration
conf = Config3D ( rays = rays, grid = grid, anisotropy = anisotropy, use_gpu = use_gpu, n_channel_in = n_channel,
adjust for your data below (make patch size as large as possible)
)
My images are 2 fluorescence stacks of 85 images of size 340x310 pixels plus 2 of the demo images.
Thank you in advance.