theano.gpuarray.use('cudaN') should be equivalent.
I forgot if it is in Theano 0.9 or just the dev version. So if it isn't in
0.9, just update to Theano dev version.
Fred
On Tue, Apr 11, 2017 at 3:26 PM <[email protected]> wrote:
> 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.+
>
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