Setting up Theano on Ubuntu 14.04

After 2 days of non-stop struggle, I was able to make theano work with my GPU NVIDIA GeForce GTX 860M.

TRY AT YOUR OWN RISK!!

Settings which worked are as follows.
DONOT install bumblebee

Run Following command to see if your NVIDIA GPU device is being detected. If not, this BLOG wont help. Sorry.

lspci | grep -i NVIDIA

Install the following:
Driver: NVIDIA 340.76
Cuda : 6.5 toolkit

• Switch to NVIDIA card (If you dont have this command try to get it, without changing the graphics drivers, conflicting drivers can be blacklisted, see next point).

prime-switch nvidia

• Blacklist other driver which can create conflicts:

create /etc/modprobe.d/blacklist-file-drivers.conf File with blacklisted drivers. Use command ubuntu-drivers devices to get a list of nvidia drivers:

blacklist nvidia-349
blacklist nvidia-346
blacklist xserver-xorg-video-nouveau 

To list all installed Graphics Drivers (Useful while blacklisting drivers)  

ubuntu-drivers devices

Note: DONOT blacklist nvidia-340

• Make sure the following command works without error.

nvidia-modprobe
nvidia-settings
nvidia-smi

•  Find /usr/local/cuda-6.5/samples/1_Utilities/deviceQuery folder for your system.

use make command to create executable. 

cd /usr/local/cuda-6.5/samples/1_Utilities/deviceQuery/
sudo make 

run /usr/local/cuda-6.5/samples/1_Utilities/deviceQuery/deviceQuery

You should get the following results: 

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 6.5, CUDA Runtime Version = 6.0, NumDevs = 1, Device0 = GeForce GTX 860M

Result = PASS

• Install theano from GIT (First install dependencies from theano website):

sudo apt-get install python-numpy python-scipy python-dev python-pip python-nose g++ libopenblas-dev git

sudo pip uninstall theano 
sudo pip install git+git://github.com/Theano/Theano.git

In /usr/local/cuda-6.5/targets/x86_64-linux/include/host_config.h, cuda-6.5 supports gcc-4.8 g++-4.8 so one needs to install these and make links to gcc and g++ respectively.
example:  sudo ln -s /usr/bin/gcc-4.8 /usr/local/cuda/bin/gcc

• Create ~/.theanorc File with following content:

[global]
floatX = float32
device = gpu

[nvcc]
fastmath = True

[cuda]
root=/usr/local/cuda-6.5/

•  Make a python file to test gpu say test.py: 

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 xrange(iters):
    r = f()
t1 = time.time()
print 'Looping %d times took' % iters, t1 - t0, 'seconds'
print 'Result is', r
if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):
    print 'Used the cpu'
else:
    print 'Used the gpu'

Once you run the above python file:

sudo python test.py

You might get an error:

Failed to compile cuda_ndarray.cu: libcublas.so.6.5: cannot open shared object file: No such file or directory

So you need to locate and configure that path:

locate libcublas.so.6.5

Add the following library path to ~/.bashrc (Second path has libcublas.so.6.5)

sudo ldconfig /usr/local/cuda-6.5/lib64/
sudo ldconfig /usr/local/cuda-6.5/targets/x86_64-linux/lib/

export LD_LIBRARY_PATH=/usr/local/cuda-6.5/targets/x86_64-linux/lib:$\$$LD_LIBRARY_PATH

export PATH=/usr/local/cuda-6.5/bin:$\$$PATH
export PATH=/usr/local/cuda-6.5/targets/x86_64-linux/lib:$\$$PATH

Now run the gpu code.

sudo python test.py

I get roughly 10x speedup with GPU.

I screwed up many times with different versions of drivers, if you do the same. You might not get a login screen (black screen). The use ctrl + Alt + F1 to goto command mode.
Remove  xorg.conf
sudo rm /etc/X11/xorg.conf

sudo service lightdm stop
sudo service lightdm start

You possibly have got you login screen. You might also be trapped in loop, where login screen asks for password and goes in and again login screen asks for password. I hope you dont get this situation.
(I added:
nvidia-modprobe  
prime-switch intel
to end of ~/.bashrc because there were login loop issues with got resolved by this. I am not sure but it works.)

Although I have written this for my record but I hope it might help someone. :)

Hope this helps.