Pascal:
your plan worked! I previously had:
$ echo $LD_LIBRARY_PATH
/usr/local/lib:/usr/local/cuda-8.0:/usr/local/cuda-8.0/lib64:/usr/local/cuda
-8.0/include:/home/daniel/mjpro131/bin:/home/daniel/mjpro131
~$ echo $LIBRARY_PATH
/usr/local/cuda/lib64
AH! My library path actually had `cuda` and not `cuda-8.0`. Gaaaah. I reset
my bashrc file with the correct cuda folder and:
$ cat ~/.theanorc
[global]
device = cuda
floatX = float64
[cuda]
root = /usr/local/cuda-8.0
~$ ipython
Python 2.7.13 |Anaconda custom (64-bit)| (default, Dec 20 2016, 23:09:15)
Type "copyright", "credits" or "license" for more information.
IPython 5.3.0 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: import theano
Using cuDNN version 5105 on context None
Mapped name None to device cuda: TITAN X (Pascal) (0000:01:00.0)
In [2]:
Awesome! No wonder this was a problem, it was a bit hard for me to see.
Anyway I think things look OK. Thank you very much.
Pascal and nouiz, just one last thing, this GPU backend is the first time
that Theano can run float64 with the GPU, right? I'm hoping to take
existing code which uses float64 only and running it on the GPU. (That's
why I've been using the CPU recently, to use float64.)
On Friday, June 30, 2017 at 6:48:08 PM UTC-7, Daniel Seita wrote:
>
> Hi nouiz and Pascal, thanks for the responses. I've been busy using the
> CPU version of Theano in the meantime, so sorry for the delay in responding.
>
> nouiz:
>
> I actually had my `cudnn.h` file in both a `lib64` directory and an
> `include` directory:
>
> ~$ ls -lh /usr/local/cuda-8.0/include/cudnn.h
> -r--r--r-- 1 root root 98K Oct 17 2016 /usr/local/cuda-8.0/include/cudnn.
> h
> ~$ ls -lh /usr/local/cuda-8.0/lib64/cudnn.h
> -r--r--r-- 1 root root 98K Oct 17 2016 /usr/local/cuda-8.0/lib64/cudnn.h
>
> I must have copied them to both when I was installing it. Also, here is my
> error message in full, assuming that my `~/.theanorc` file is
>
> ~$ cat ~/.theanorc
> [global]
> device = cuda
> floatX = float64
>
> [cuda]
> root = /usr/local/cuda-8.0
> ~$ ipython
> Python 2.7.13 |Anaconda custom (64-bit)| (default, Dec 20 2016, 23:09:15)
> Type "copyright", "credits" or "license" for more information.
>
> IPython 5.3.0 -- An enhanced Interactive Python.
> ? -> Introduction and overview of IPython's features.
> %quickref -> Quick reference.
> help -> Python's own help system.
> object? -> Details about 'object', use 'object??' for extra details.
>
> In [1]: import theano
> ERROR (theano.gpuarray): Could not initialize pygpu, support disabled
> Traceback (most recent call last):
> File
> "/home/daniel/anaconda2/lib/python2.7/site-packages/theano/gpuarray/__init__.py"
> , line 164, in <module>
> use(config.device)
> File
> "/home/daniel/anaconda2/lib/python2.7/site-packages/theano/gpuarray/__init__.py"
> , line 151, in use
> init_dev(device)
> File
> "/home/daniel/anaconda2/lib/python2.7/site-packages/theano/gpuarray/__init__.py"
> , line 68, in init_dev
> context.cudnn_handle = dnn._make_handle(context)
> File
> "/home/daniel/anaconda2/lib/python2.7/site-packages/theano/gpuarray/dnn.py"
> , line 80, in _make_handle
> cudnn = _dnn_lib()
> File
> "/home/daniel/anaconda2/lib/python2.7/site-packages/theano/gpuarray/dnn.py"
> , line 67, in _dnn_lib
> raise RuntimeError('Could not find cudnn library (looked for v5[.1])')
> RuntimeError: Could not find cudnn library (looked for v5[.1])
>
> This happens if I also set the device to be `cuda0` instead of `cuda`, and
> trying with `float32` instead of `float64`.
>
>
>
>
> On Friday, June 30, 2017 at 5:43:35 PM UTC-7, Pascal Lamblin wrote:
>>
>> Something weird seems to be happening:
>> - theano detects that cuDNN is available, in fact it called
>> _dnn_check_compile() and _dnn_check_version() successfully
>> - however, calling _dnn_lib() failed, which means ctypes did not manage
>> to find the cudnn library.
>>
>> Is /usr/local/cuda-8.0/lib64 in your LIBRARY_PATH (in addition to
>> LD_LIBRARY_PATH)?
>>
>> On Monday, June 19, 2017 at 6:16:37 PM UTC-4, nouiz wrote:
>>>
>>> Your cudnn.h file should not be in the lib64 directory, but in an
>>> include directory. Tensorflow does none standard stuff related to import
>>> and cause problem in other setup, but it seem to tolerate your non standard
>>> setup. Theano does the standard setup.
>>>
>>> You can use the Theano flag dnn.include_path and dnn.library_path to
>>> tell Theano where your cudnn.h and cudnn.so* files are.
>>>
>>> I did not see your last error in full.
>>>
>>> Le ven. 16 juin 2017 19:35, Daniel Seita <[email protected]> a écrit :
>>>
>>>> Ack, sorry, half of my post got deleted! Hopefully you can still see it
>>>> (i can find it by looking at the original post but it's in a really ugly
>>>> format, sorry).
>>>>
>>>>
>>>>
>>>> On Friday, June 16, 2017 at 4:33:20 PM UTC-7, Daniel Seita wrote:
>>>>
>>>>> I was running into some more difficulties, so I gave up on getting
>>>>> this to work and tried to uninstall and then reinstall Theano. Just to be
>>>>> extra clear, here is my setup:
>>>>>
>>>>> - Ubuntu 16.04
>>>>> - Cuda 8.0, stored in `usr/local/cuda-8.0`
>>>>> - Titan X GPU with Pascal
>>>>>
>>>>> cuDNN is here:
>>>>>
>>>>> $ ls /usr/local/cuda-8.0/lib64/cudnn.h
>>>>> /usr/local/cuda-8.0/lib64/cudnn.h
>>>>>
>>>>> To verify that I can use my GPU I started this quick TensorFlow
>>>>> computation:
>>>>>
>>>>> In [1]: import tensorflow as tf
>>>>>
>>>>> In [2]: tf.__version__
>>>>> Out[2]: '1.1.0'
>>>>>
>>>>> In [3]: tf.GPUOptions
>>>>> Out[3]: tensorflow.core.protobuf.config_pb2.GPUOptions
>>>>>
>>>>> In [4]: with tf.device('/gpu:0'):
>>>>> ...: a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2,
>>>>> 3], name='a')
>>>>> ...: b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3,
>>>>> 2], name='b')
>>>>> ...: c = tf.matmul(a,b)
>>>>> ...:
>>>>>
>>>>> In [5]: with tf.Session() as sess:
>>>>> ...: print(sess.run(c))
>>>>> ...:
>>>>> 2017-06-16 16:10:54.402311: W tensorflow/core/platform/
>>>>> cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to
>>>>> use SSE4.1 instructions, but these are available on your machine and
>>>>> could
>>>>> speed up CPU computations.
>>>>> 2017-06-16 16:10:54.402328: W
>>>>> tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library
>>>>> wasn't compiled to use SSE4.2 instructions, but these are available
>>>>> on your machine and could speed up CPU computations.
>>>>> 2017-06-16 16:10:54.402346: W tensorflow/core/platform/
>>>>> cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to
>>>>> use AVX instructions, but these are available on your machine and could
>>>>> speed up CPU computations.
>>>>> 2017-06-16 16:10:54.402350: W
>>>>> tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library
>>>>> wasn't compiled to use AVX2 instructions, but these are available on
>>>>> your machine and could speed up CPU computations.
>>>>> 2017-06-16 16:10:54.402356: W tensorflow/core/platform/
>>>>> cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to
>>>>> use FMA instructions, but these are available on your machine and could
>>>>> speed up CPU computations.
>>>>> 2017-06-16 16:10:54.527167: I
>>>>> tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful NUMA
>>>>> node read from SysFS had negative value (-1), but there must be at least
>>>>> one NUMA node, so returning NUMA node zero
>>>>> 2017-06-16 16:10:54.527553: I
>>>>> tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with
>>>>> properties:
>>>>> name: TITAN X (Pascal)
>>>>> major: 6 minor: 1 memoryClockRate (GHz) 1.531
>>>>> pciBusID 0000:01:00.0
>>>>> Total memory: 11.90GiB
>>>>> Free memory: 11.38GiB
>>>>> 2017-06-16 16:10:54.527565: I
>>>>> tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
>>>>> 2017-06-16 16:10:54.527568: I
>>>>> tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
>>>>> 2017-06-16 16:10:54.527590: I
>>>>> tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow
>>>>> device (/gpu:0) -> (device: 0, name: TITAN X (Pascal), pci bus id:
>>>>> 0000:01:00.0)
>>>>> [[ 22. 28.]
>>>>> [ 49. 64.]]
>>>>>
>>>>>
>>>>> This looks like it indicates a successful GPU and/or cuDNN
>>>>> installation.
>>>>>
>>>>> Great, now let's install the *development version* of Theano. The
>>>>> instructions I'm following step-by-step:
>>>>> http://deeplearning.net/software/theano_versions/dev/install_ubuntu.html
>>>>>
>>>>> The first step seems t
>>>>>
>>>>
--
---
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 [email protected].
For more options, visit https://groups.google.com/d/optout.
