I tried disabling it. I didn't observe any speedup.
On Wednesday, December 7, 2016 at 12:49:15 AM UTC+2, Pascal Lamblin wrote:
>
> 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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> >
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>
> --
> Pascal
>
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