[theano-users] Re: Implementing a GPU op
Moreover if you install Theano from scratch : conda install theano pygpu Fetching package metadata . Solving package specifications: . Package plan for installation in environment /root/miniconda2: The following NEW packages will be INSTALLED: libgpuarray: 0.6.8-0 pygpu: 0.6.8-py27_0 theano: 0.9.0-py27_0 you need to run the following correction after : conda install libgpuarray=0.6.4-0 pygpu=0.6.4 And now that works on the server I was initially working on. -- --- 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 theano-users+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
[theano-users] Re: Implementing a GPU op
I found another server on which the code works as on your computer :
Using cuDNN version 5110 on context None
Preallocating 9151/11439 Mb (0.80) on cuda0
Mapped name None to device cuda0: Tesla K80 (:83:00.0)
GpuEye{dtype='float32', context_name=None} [id A] ''
|x [id B]
|y [id C]
Compiling
GpuEye{dtype='float32', context_name=None} [id A] '' 0
|x [id B]
|y [id C]
Results
[[ 1. 0. 0.]
[ 0. 1. 0.]
[ 0. 0. 1.]
To reproduce the segmentation fault :
*conda install pygpu*
Fetching package metadata .
Solving package specifications: .
Package plan for installation in environment /root/miniconda2:
The following packages will be UPDATED:
libgpuarray: 0.6.4-0 --> 0.6.8-0
pygpu: 0.6.4-py27_1 --> 0.6.8-py27_0
Proceed ([y]/n)? y
And then you can run again :
import theano
from theano.gpuarray.basic_ops import GpuEye
x = theano.tensor.iscalar('x')
y = theano.tensor.iscalar('y')
z = GpuEye(dtype='float32', context_name=None)(x,y,
theano.tensor.constant(0))
theano.printing.debugprint(z)
print("Compiling")
f = theano.function( [x,y], z)
theano.printing.debugprint(f)
print("Results")
print(f(3, 3))
Using cuDNN version 5110 on context None
Preallocating 9151/11439 Mb (0.80) on cuda0
Mapped name None to device cuda0: Tesla K80 (:83:00.0)
GpuEye{dtype='float32', context_name=None} [id A] ''
|x [id B]
|y [id C]
Compiling
Segmentation fault (core dumped)
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[theano-users] Re: Implementing a GPU op
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. -- --- 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 theano-users+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
[theano-users] Re: Implementing a GPU op
What surprises me is to get seg faults in the theano function, while I
would have expected them to occur during evaluation on values...
On Wednesday, July 12, 2017 at 10:05:30 AM UTC+2, Christopher Bourez wrote:
>
> A second thing that is not clear to me in the documentation of Theano is
> how you specify a C implementation and GPU implementation of the same own
> op. Thank you
>
> On Wednesday, July 12, 2017 at 9:58:34 AM UTC+2, Christopher Bourez wrote:
>>
>> I've also tried to create an example with theano.gpuarray.nnet.GpuSoftmax
>> but after compilation it got replaced another implementation*GpuDnnSoftmax
>> : *
>>
>>
>> *Elemwise{mul,no_inplace} [id A] ''|HostFromGpu(gpuarray) [id B] ''
>>| |GpuSoftmax [id C] ''| |GpuFromHost [id D] ''| |x
>> [id E] |InplaceDimShuffle{x,x} [id F] '' |TensorConstant{2} [id
>> G]CompilingHostFromGpu(gpuarray) [id A] '' 5 |GpuElemwise{Mul}[(0,
>> 1)] [id B] '' 4 |GpuArrayConstant{[[ 2.]]} [id C]
>> |InplaceGpuDimShuffle{0,1} [id D] '' 3
>> |GpuDnnSoftmax{mode='channel', algo='accurate'} [id E] '' 2
>> |GpuContiguous [id F] '' 1 |InplaceGpuDimShuffle{0,1,x,x} [id G]
>> '' 0 |<GpuArrayType(float32, (False, False))> [id H]*I'm
>> looking of a good example with a GPU Kernel.
>>
>> On Wednesday, July 12, 2017 at 9:56:08 AM UTC+2, Christopher Bourez wrote:
>>>
>>> I don't know what you mean by "not modifying" the source for GpuEye:
>>> - In this example, I'm importing a not modifyed GpuEye op from Theano
>>> basic ops
>>> - If I'm using theano.tensor.eye, then it does not use the GpuEye
>>>
>>> Also, are you sure this test
>>>
>>> https://github.com/Theano/Theano/blob/2625464534147fd70da60a3a3ddcb63ed8e5a416/theano/gpuarray/tests/test_basic_ops.py#L401
>>> works well ?
>>>
>>> On Wednesday, July 12, 2017 at 2:48:44 AM UTC+2, Pascal Lamblin wrote:
>>>>
>>>> Does it work if you do not modify the source for GpuEye at all?
>>>> If it does, then maybe sharing your new source would get you more help.
>>>>
>>>> On Tuesday, July 11, 2017 at 12:12:03 PM UTC-4, Christopher Bourez
>>>> wrote:
>>>>>
>>>>> Hi,
>>>>>
>>>>> I'm trying to implement a simple GPU op but it always gives me a
>>>>> Segmentation fault during compilation, without other message.
>>>>>
>>>>> For example :
>>>>> import theano
>>>>> from theano.gpuarray.basic_ops import GpuEye
>>>>>
>>>>> x = theano.tensor.iscalar('x')
>>>>> y = theano.tensor.iscalar('y')
>>>>> z = GpuEye(dtype='float32', context_name=None)(x,y,
>>>>> theano.tensor.constant(0))
>>>>>
>>>>> theano.printing.debugprint(z)
>>>>> print("Compiling")
>>>>> f = theano.function( [x,y], z)
>>>>> theano.printing.debugprint(f)
>>>>> print("Results")
>>>>> print(f(3, 3))
>>>>>
>>>>> I've also tried with the softmax gpu function. Is there something I'm
>>>>> missing ?
>>>>>
>>>>> I copied the file, created a complete new op, and the segmentation
>>>>> fault appears when I'm defining a Kernel in gpu_kernels() method of the
>>>>> op.
>>>>>
>>>>> Thank you a lot for your help
>>>>>
>>>>
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[theano-users] Re: Implementing a GPU op
A second thing that is not clear to me in the documentation of Theano is
how you specify a C implementation and GPU implementation of the same own
op. Thank you
On Wednesday, July 12, 2017 at 9:58:34 AM UTC+2, Christopher Bourez wrote:
>
> I've also tried to create an example with theano.gpuarray.nnet.GpuSoftmax but
> after compilation it got replaced another implementation*GpuDnnSoftmax : *
>
>
> *Elemwise{mul,no_inplace} [id A] ''|HostFromGpu(gpuarray) [id B] ''
>| |GpuSoftmax [id C] ''| |GpuFromHost [id D] ''| |x
> [id E] |InplaceDimShuffle{x,x} [id F] '' |TensorConstant{2} [id
> G]CompilingHostFromGpu(gpuarray) [id A] '' 5 |GpuElemwise{Mul}[(0,
> 1)] [id B] '' 4 |GpuArrayConstant{[[ 2.]]} [id C]
> |InplaceGpuDimShuffle{0,1} [id D] '' 3
> |GpuDnnSoftmax{mode='channel', algo='accurate'} [id E] '' 2
> |GpuContiguous [id F] '' 1 |InplaceGpuDimShuffle{0,1,x,x} [id G]
> '' 0 |<GpuArrayType(float32, (False, False))> [id H]*I'm
> looking of a good example with a GPU Kernel.
>
> On Wednesday, July 12, 2017 at 9:56:08 AM UTC+2, Christopher Bourez wrote:
>>
>> I don't know what you mean by "not modifying" the source for GpuEye:
>> - In this example, I'm importing a not modifyed GpuEye op from Theano
>> basic ops
>> - If I'm using theano.tensor.eye, then it does not use the GpuEye
>>
>> Also, are you sure this test
>>
>> https://github.com/Theano/Theano/blob/2625464534147fd70da60a3a3ddcb63ed8e5a416/theano/gpuarray/tests/test_basic_ops.py#L401
>> works well ?
>>
>> On Wednesday, July 12, 2017 at 2:48:44 AM UTC+2, Pascal Lamblin wrote:
>>>
>>> Does it work if you do not modify the source for GpuEye at all?
>>> If it does, then maybe sharing your new source would get you more help.
>>>
>>> On Tuesday, July 11, 2017 at 12:12:03 PM UTC-4, Christopher Bourez wrote:
>>>>
>>>> Hi,
>>>>
>>>> I'm trying to implement a simple GPU op but it always gives me a
>>>> Segmentation fault during compilation, without other message.
>>>>
>>>> For example :
>>>> import theano
>>>> from theano.gpuarray.basic_ops import GpuEye
>>>>
>>>> x = theano.tensor.iscalar('x')
>>>> y = theano.tensor.iscalar('y')
>>>> z = GpuEye(dtype='float32', context_name=None)(x,y,
>>>> theano.tensor.constant(0))
>>>>
>>>> theano.printing.debugprint(z)
>>>> print("Compiling")
>>>> f = theano.function( [x,y], z)
>>>> theano.printing.debugprint(f)
>>>> print("Results")
>>>> print(f(3, 3))
>>>>
>>>> I've also tried with the softmax gpu function. Is there something I'm
>>>> missing ?
>>>>
>>>> I copied the file, created a complete new op, and the segmentation
>>>> fault appears when I'm defining a Kernel in gpu_kernels() method of the op.
>>>>
>>>> Thank you a lot for your help
>>>>
>>>
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[theano-users] Re: Implementing a GPU op
I don't know what you mean by "not modifying" the source for GpuEye:
- In this example, I'm importing a not modifyed GpuEye op from Theano
basic ops
- If I'm using theano.tensor.eye, then it does not use the GpuEye
Also, are you sure this test
https://github.com/Theano/Theano/blob/2625464534147fd70da60a3a3ddcb63ed8e5a416/theano/gpuarray/tests/test_basic_ops.py#L401
works well ?
On Wednesday, July 12, 2017 at 2:48:44 AM UTC+2, Pascal Lamblin wrote:
>
> Does it work if you do not modify the source for GpuEye at all?
> If it does, then maybe sharing your new source would get you more help.
>
> On Tuesday, July 11, 2017 at 12:12:03 PM UTC-4, Christopher Bourez wrote:
>>
>> Hi,
>>
>> I'm trying to implement a simple GPU op but it always gives me a
>> Segmentation fault during compilation, without other message.
>>
>> For example :
>> import theano
>> from theano.gpuarray.basic_ops import GpuEye
>>
>> x = theano.tensor.iscalar('x')
>> y = theano.tensor.iscalar('y')
>> z = GpuEye(dtype='float32', context_name=None)(x,y,
>> theano.tensor.constant(0))
>>
>> theano.printing.debugprint(z)
>> print("Compiling")
>> f = theano.function( [x,y], z)
>> theano.printing.debugprint(f)
>> print("Results")
>> print(f(3, 3))
>>
>> I've also tried with the softmax gpu function. Is there something I'm
>> missing ?
>>
>> I copied the file, created a complete new op, and the segmentation fault
>> appears when I'm defining a Kernel in gpu_kernels() method of the op.
>>
>> Thank you a lot for your help
>>
>
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[theano-users] Implementing a GPU op
Hi,
I'm trying to implement a simple GPU op but it always gives me a
Segmentation fault during compilation, without other message.
For example :
import theano
from theano.gpuarray.basic_ops import GpuEye
x = theano.tensor.iscalar('x')
y = theano.tensor.iscalar('y')
z = GpuEye(dtype='float32', context_name=None)(x,y,
theano.tensor.constant(0))
theano.printing.debugprint(z)
print("Compiling")
f = theano.function( [x,y], z)
theano.printing.debugprint(f)
print("Results")
print(f(3, 3))
I've also tried with the softmax gpu function. Is there something I'm
missing ?
I copied the file, created a complete new op, and the segmentation fault
appears when I'm defining a Kernel in gpu_kernels() method of the op.
Thank you a lot for your help
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