It may be possible that ultra_fast_sigmoid has no implementation on the
GPU, or that the corresponding optimization is not applied for some
reason.
On Tue, Dec 06, 2016, Bogdan Budescu wrote:
> Could it be that when specifying
> optimizer_including=local_ultra_fast_sigmoid in theano's config flags, the
> gpu optimization is disabled?
>
> On Tuesday, December 6, 2016 at 5:40:23 PM UTC+2, Bogdan Budescu wrote:
> >
> > When trying to compile a (rather large) neural net with device=cuda, at
> > some point I get the following error:
> >
> > ImportError: ('libamdlibm.so: cannot open shared object file: No such file
> > or directory', '[Elemwise{pow,no_inplace}(<TensorType(float32, scalar)>,
> > <TensorType(float32, scalar)>)]')
> >
> > Now, I don't mind the error itself, as it's probably caused by not running
> > ldconfig, but this suggests that the optimizer might want to run the op on
> > cpu instead of gpu, and I assume that this might be the reason for which my
> > net's training runs so slow (I assume that this also implies some redundant
> > copies between cpu and gpu memory). I also observe that the training
> > process takes 100% of the processor (or, rather, of a single core, as
> > python is not multi-threaded).
> >
> > How can I tell whether there are any ops running on cpu after a successful
> > compilation (I already set assert_no_cpu_op='raise'), and how can I force
> > the ops to be executed on gpu instead?
> >
> >
> >
>
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Pascal
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