Thanks for the hint:) Your are right.
I just searched the code for Theano 0.9
(link: http://deeplearning.net/software/theano/tutorial/using_gpu.html) and
used it for another test. Unfortunately the effect is the same.
Maybe it really works for this example code, but for my application it does
not seem to work. It is as slow with the GPU flag as with the CPU flag.
With older versions of theano (and lasagne) it worked, but I also changed
the GPU (GTX 780 to Titan X pascal).
Am Samstag, 17. Juni 2017 00:37:29 UTC+2 schrieb Daniel Seita:
>
> Not sure if this affects the result but note that the link you provided is
> for theano 0.8.X, not theano 0.9.0 as your title implies.
>
> On Thursday, June 15, 2017 at 2:45:26 PM UTC-7, Meier Benjamin wrote:
>>
>> Hello,
>>
>> I use the follwing test program:
>> https://theano.readthedocs.io/en/0.8.x/tutorial/using_gpu.html
>>
>> from theano import function, config, shared, sandbox
>> import theano.tensor as T
>> import numpy
>> import time
>>
>> vlen = 10 * 30 * 768 # 10 x #cores x # threads per core
>> iters = 1000
>>
>> rng = numpy.random.RandomState(22)
>> x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
>> f = function([], T.exp(x))
>> print(f.maker.fgraph.toposort())
>> t0 = time.time()
>> for i in range(iters):
>> r = f()
>> t1 = time.time()
>> print("Looping %d times took %f seconds" % (iters, t1 - t0))
>> print("Result is %s" % (r,))
>> if numpy.any([isinstance(x.op, T.Elemwise) for x in
>> f.maker.fgraph.toposort()]):
>> print('Used the cpu')
>> else:
>> print('Used the gpu')
>>
>> And I get this output:
>>
>> root@21cfc9b009d4:/code/tmp/test# THEANO_FLAGS='floatX=float32,device=cuda0'
>> python gpu_test.py
>> Using cuDNN version 5105 on context None
>> Mapped name None to device cuda0: TITAN X (Pascal) (:87:00.0)
>> [GpuElemwise{exp,no_inplace}(),
>> HostFromGpu(gpuarray)(GpuElemwise{exp,no_inplace}.0)]
>> Looping 1000 times took 0.221684 seconds
>> Result is [ 1.23178029 1.61879349 1.52278066 ..., 2.20771813 2.29967761
>> 1.62323296]
>> Used the cpu
>>
>>
>> For some reason theano still uses the CPU? But it already prints the GPU
>> infos? Do I do something wrong?
>>
>> Thank you very much
>>
>>
>>
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