Setting up CUDA toolkit and Nvidia drivers on my HP Pavilion 15 Notebook kept messing up with my display manager. After numerous X-Server breakdowns, here is how I got Theano to run on GPU safely. I setup CUDA-7.5 on Ubuntu 14.04.

• Check system compatibility

  • First of all, verify that your GPU is CUDA-compatible. Run

    ` lspci | grep NVIDIA `

    and note the GPU model. Make sure your GPU is listed here.

  • Check for existing Nvidia Drivers by running

    ` nvidia-smi `

    If you already have Nvidia Drivers, then this should show you the Driver Version. Note it.

• Install dependencies

  ` sudo apt-get install build-essential gcc g++ `

• Install Nvidia drivers and CUDA toolkit

  • Download CUDA .run package from Nvidia website. This package contains 3 components:
    • Nvidia GPU Driver
    • CUDA Toolkit
    • CUDA Samples

    It is a good idea to extract these 3 components as individual installers via

    ` sudo sh cuda_*.run -extract=~/cuda_installers `

  • Check the Nvidia driver version from its filename. For eg. in my case it was version 352 ( Filename: NVIDIA-Linux-x86_64-352.39.run ). You can skip installation of the driver if you already have this (or higher) version installed.

  • Nvidia driver installation

    • Remove any programs or configuration starting with nvidia-*.

      ` sudo apt-get –purge remove nvidia-* `

    • Hit Ctrl+Alt+F1 to login to physical terminal.

    • Stop X-Server:

      ` sudo lightdm stop `

    • Install Nvidia Driver

      ` cd cuda_installers `

      ` sudo sh NVIDIA-Linux-x86_64-352.39.run `

      Accept the EULA and follow prompts. If the installers complaints for missing dependencies, you may need to install some extra packages.

      ` sudo apt-get install dkms fakeroot linux-headers-generic `

  • CUDA toolkit installation

    ` sudo sh cuda-linux64-rel-7.5.18-19867135.run `

    Install the samples for testing:

    ` sudo sh cuda-samples-linux-7.5.18-19867135.run `

• Test CUDA installation by running a sample

  ` cd ~/NVIDIA_CUDA-7.5_Samples/1_Utilities/deviceQuery `

  ` ./deviceQuery `

  If all went fine, you should see result PASS. Now, start X-Server and log out of physical terminal

  ` sudo lightdm start `

• Set up the required environment variables

  Append following lines in ~/.bashrc or ~/.zshrc

  ` PATH=/usr/local/CUDA-7.5/bin:$PATH `

  ` LD_LIBRARY_PATH=/usr/local/CUDA-7.5/lib64:$LD_LIBRARY_PATH `

• Install Python SciPy Stack

  ` sudo apt-get install python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose `

• Install the released version of Theano from PyPI

  ` sudo pip install Theano `

• Configure Theano to run on GPU

  Save a file ~/.theanorc with following text.

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[global]
floatX = float32
device = gpu

[nvcc]
fastmath = True

[cuda]
root=/usr/local/cuda-7.5/

• Test to see if GPU is being used

  Copy following program in a file and run it.

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from theano import function, config, shared, sandbox
import theano.tensor as T
import numpy
import time

vlen = 10 * 30 * 768  # 10 x #cores x # threads per core
iters = 1000

rng = numpy.random.RandomState(22)
x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
f = function([], T.exp(x))
print(f.maker.fgraph.toposort())
t0 = time.time()
for i in range(iters):
	r = f()
t1 = time.time()
print("Looping %d times took %f seconds" % (iters, t1 - t0))
print("Result is %s" % (r,))
if numpy.any([isinstance(x.op, tensor.Elemwise) and
              ('Gpu' not in type(x.op).__name__)
              for x in f.maker.fgraph.toposort()]):
    print('Used the cpu')
else:
    print('Used the gpu')

Hopefully this will show ‘Used the gpu’.

Now you are all set to play with your GPU. Enjoy!