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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