There was a fix to the random stream in the new back-end. This could fix
that.

But you should use not use_cuda=True with the new back-end, this is for the
old back-end only.

On Sat, Nov 12, 2016 at 9:50 PM, Ragav Venkatesan <
ragav.venkate...@gmail.com> wrote:

> I'm sorry I want to correct myself. use_cuda =None is not creating a new
> process on another GPU and is running as usual. Only using use_cuda = True
> in libgpuarray mode has this issue.
>
>
> On Saturday, November 12, 2016 at 7:46:17 PM UTC-7, Ragav Venkatesan wrote:
>>
>> same result from what I can see. But I was under the assumption that
>> use_cuda = None is going make the thing run on CPU no matter what.
>>
>> On Saturday, November 12, 2016 at 9:31:12 AM UTC-7, Pascal Lamblin wrote:
>>>
>>> What about the default of "use_cuda=None"?
>>>
>>> On Fri, Nov 11, 2016, Ragav Venkatesan wrote:
>>> > I use theano.sandbox.rng_mrg.MRG_RandomStreams to generate a 2D (and
>>> 1D )
>>> > dropout masks for applying dropouts. I create it so,
>>> >
>>> > srng = MRG_RandomStreams(rng.randint(1,89324723894), use_cuda = True)
>>> > mask = srng.binomial (n=1, p=1-dropout_rate,size=params.shape,dtype =
>>> > floatX)
>>> >
>>> > otuput = params *mask.
>>> >
>>> > doing this operation is doing two things.
>>> >
>>> > 1. If I use the libgpuarray backend, and I have two GPUS, the mask is
>>> being
>>> > created in another GPU from the one I have used in config.device.
>>> > 2. If I use it with use_cuda = False, it is creating the data in the
>>> same
>>> > GPU, but then the code does not run on GPU..
>>> >
>>> > Appreciate any help.
>>> >
>>> > --
>>> >
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>>>
>>> --
>>> Pascal
>>>
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