The code team on the GPU. This code is very simple, I'm not surprised that it don't get always speed up.
You use the GPU well. The problem is in the detection that select the print. It need to be updated for the new backend. Le dim. 18 juin 2017 17:31, Meier Benjamin <[email protected]> a écrit : > 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) (0000:87:00.0) >>> [GpuElemwise{exp,no_inplace}(<GpuArrayType<None>(float32, (False,))>), >>> 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 >>> >>> >>> -- > > --- > You received this message because you are subscribed to the Google Groups > "theano-users" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > For more options, visit https://groups.google.com/d/optout. > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
