I recently replaced a Titan X, which was on loan, with a GTX 980. After messing with drivers for nearly a day I was able to get my dual monitor setup running again. Unfortunately whatever i did freaked out Theano yielding the error:
dustin@Cortex ~ $ ipython
Python 2.7.6 (default, Mar 22 2014, 22:59:56)
Type "copyright", "credits" or "license" for more information.
IPython 1.2.1 -- 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
WARNING (theano.sandbox.cuda): CUDA is installed, but device gpu0 \
is not available (error: Unable to get the number of gpus available: \
unknown error)
I tried upgrading the Nvidia driver to 346.59 and CUDA from 6.5 to 7.0 with no luck. So I decided to start fresh since I had been wanting to upgrade from Linux Mint 17 to 17.1 anyway. Following are the steps I used to get my system running Theano again. I have not replicated these results so hopefully I am not overlooking any major steps.
Nvidia Driver
I used the xorg-edgers PPA to install Nvidia drivers 346.59 as described on
Noobs Lab. In short, add the new repository:
sudo add-apt-repository ppa:xorg-edgers/ppa
Update to get the list of available packages:
sudo apt-get update
Install 346:
sudo apt-get install nvidia-346 nvidia-settings
I have a dual monitor setup that require I execute
nvidia-settings to
Enable Xinerama via the
X Server Display Configuration page.
CUDA
I found some
helpful instructions for installing CUDA. In short, start by installing the GNU Compiler Collection tools with:
sudo apt-get install build-essential
Download the
Nvidia CUDA 7.0 DEB. Though I'm running Linux Mint 17.1 I used the
Ubuntu 14.04 Network DEB. Install it:
sudo dpkg -i cuda-repo-ubuntu1404_6.5-14_amd64.deb
Update to get the list of packages:
sudo apt-get update
Install CUDA:
sudo apt-get install cuda
Finally alter your .bashrc to add CUDA to your
PATH and
LD_LIBRARY_PATH environment variables. Theano will also want to know where CUDA is located so now is a good time to setup the
CUDA_ROOT environment variables as well.
export PATH=/usr/local/cuda-7.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-7.0/lib64:$LD_LIBRARY_PATH
export CUDA_ROOT=/usr/local/cuda-7.0
cuDNN
Installing cuDNN for use with Theano can be found on the
cuDNN page of deeplearning.net. I used the first method currently suggested on that page which is to copy
*.h to
$CUDA_ROOT/includes and
*.so* to $
CUDA_ROOT/lib64.
