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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