I have a linux system with three gpus. I am using keras with theano to run cnn's, In the past when I was using Theano 8.+ , I was able to assign a particular gpu to jupyter notebook window using the following:
import theano.sandbox.cuda theano.sandbox.cuda.use("gpu2")
This allowed me to run three versions of the same cnn model using different hyper-parameters.
I very recently updated both keras (to 2.0) and theano ( to 0.9). This required me to setup the gpuarray backend.
Running just one jupyter notebook with a model works fine. gpu1 is selected by theano. However when I startup a second notebook with the same model, theano tries to use the gpu assigned to the first notebook, causing a memory usage problem and ultimately causing the cnn model to run on the cpu rather than using one of the available two remaining gpus.
Is there a way to select the gpu that I wish the run on each jupyter notebook in theano 0.9 as I was able in theano 8.+
