On Sunday, July 16, 2017 at 1:43:41 AM UTC+2, Pascal Lamblin wrote:
>
> Your original example seems to work for me, though, so it may have to do
> with your setup:
>
I got it work when I removed device and contexts flags from my theanorc
config file and used the command
THEANO_FLAGS="init_gpu_device=cuda" python t.py
If I add the device flag set to cuda or cuda0, it gives me a seg fault.
I found this information when running the test below: "If you want
GPU-related tests to run on a specific GPU device, and not the default one,
you should use the init_gpu_device theano flag."
What does that mean for my configuration ? What shall I change ?
Yes, it gets tested in our daily buildbot and on several pull requests per
> week, by our continuous integration systems. I also just launched it
> manually:
> $ theano-nose theano/gpuarray/tests/test_basic_ops.py:test_gpueye
> Can not use cuDNN on context None: Disabled by dnn.enabled flag
> Mapped name None to device cuda: GeForce GTX 580 (:02:00.0)
> .
> --
> Ran 45 tests in 21.645s
>
> OK
>
I get :
ImportError: No module named test_basic_ops
When I run
THEANO_FLAGS="init_gpu_device=cuda" theano-nose
/usr/local/lib/python2.7/dist-packages/theano/gpuarray/tests/test_basic_ops.py:test_gpueye
I get
if hasattr(theano.tests, "TheanoNoseTester"):
AttributeError: 'module' object has no attribute 'tests'
> You do not specify C and GPU implementations for the same Op, what we have
> in general is two different Ops, one that has CPU inputs and outputs, and
> computes on CPU, and another one with GPU inputs and outputs, that computes
> on GPU.
> This is necessary because the Variables in Theano are strongly typed, and
> the device is part of the type.
> There are optimizations that replace CPU Ops by GPU ones, inserting
> transfer Ops (GpuFromHost, HostFromGpu) if necessary.
> GPU Ops, like CPU ones, can have C (using CUDA) or Python implementations
> (or both).
>
Are the rules name-based ? If there is the string Gpu in the name? Or is
there any registration as other framework ?
Thanks a lot for clarification on the optimization rules.
> What surprises me is to get seg faults in the theano function, while I
>> would have expected them to occur during evaluation on values...
>>
>
> It is strange indeed. It may be possible that some GPU operations are
> executed on GPU during the compilation phase, for constant folding
> (constant propagation) for instance.
> Does it happen as well with the latest master from GitHub?
>
Installing the latest dev version from github did not improve results.
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