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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