I have a system with two GPUs, and am using Keras with Tensorflow backend. Gpu:0 is being allocated to PyCUDA, which is performing a unique operation which is fed forward to Keras, and changes with each batch. As such, I would like to run a Keras model on gpu:1 while leaving gpu:0 allocated to PyCUDA.
Is there any way to do this? Looking through prior threads I've found several depreciated solutions.
So I don't think that this feature is meaningfully implemented in Keras currently. Found a workaround that I recommend whereby you just create multiple processes using Python's default multiprocessing library.
Note: Currently for this setup you need to spawn the new process, rather than fork it, to avoid a weird interaction with one of the PyCUDA backend libraries.