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 <takeshida...@gmail.com> 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 to be to install miniconda. I downloaded the bash
>>> script for Python 2.7 and ran it:
>>>
>>> ~/Downloads$ bash Miniconda2-latest-Linux-x86_64.sh
>>>
>>> Welcome to Miniconda2 4.3.21 (by Continuum Analytics, Inc.)
>>>
>>> In order to continue the installation process, please review the license
>>> agreement.
>>> Please, press ENTER to continue
>>>
>>> and it seemed to work without issues.
>>>
>>> The next step is to install requirements through conda. Here I did:
>>>
>>> $ conda install numpy scipy mkl nose sphinx pydot-ng
>>> Fetching package metadata .........
>>> Solving package specifications: .
>>>
>>> Package plan for installation in environment /home/daniel/miniconda2:
>>>
>>> The following NEW packages will be INSTALLED:
>>>
>>> alabaster: 0.7.10-py27_0
>>> babel: 2.4.0-py27_0
>>> docutils: 0.13.1-py27_0
>>> imagesize: 0.7.1-py27_0
>>> jinja2: 2.9.6-py27_0
>>> libgfortran: 3.0.0-1
>>> markupsafe: 0.23-py27_2
>>> mkl: 2017.0.1-0
>>> nose: 1.3.7-py27_1
>>> numpy: 1.13.0-py27_0
>>> pydot-ng: 1.0.0.15-py27_0
>>> pygments: 2.2.0-py27_0
>>> pytz: 2017.2-py27_0
>>> scipy: 0.19.0-np113py27_0
>>> snowballstemmer: 1.2.1-py27_0
>>> sphinx: 1.6.2-py27_0
>>> sphinxcontrib: 1.0-py27_0
>>> sphinxcontrib-websupport: 1.0.1-py27_0
>>> typing: 3.6.1-py27_0
>>>
>>> The following packages will be UPDATED:
>>>
>>> conda: 4.3.21-py27_0 --> 4.3.22-py27_0
>>>
>>> Proceed <span style="color: #660;" class="styled-by-prettify
>>>
>>> --
>>
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