Theano is a very interesting Python library developed mainly for deep learning, which can run calculations on some NVIDIA GPUs by using the CUDA library. Setting up Theano to use the GPU can be a little tricky and take a bit of work.
Install the pre-reqs
sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev
Next, create a symlink to libglut, which will allow you to install the CUDA samples as described on Utkarsh Jaiswal’s blog
sudo ln -s /usr/lib/x86_64-linux-gnu/libglut.so.3 /usr/lib/libglut.so
Download CUDA from the NVIDIA site and then install it:
sudo apt-get remove --purge nvidia* chmod +x cuda_5.0.35_linux_64_ubuntu11.10-1.run sudo service lightdm stop sudo ./cuda_5.0.35_linux_64_ubuntu11.10-1.run
Get the latest released version of Theano:
sudo apt-get install python-dev libopenblas-dev liblapack-dev gfortran sudo pip install --upgrade Theano
Create a ~/.theanorc file to enable the GPU:
[global] floatX = float32 device = gpu
Test it out
Now run the sample program under “Testing Theano with GPU” in the Theano tutorial. It will hopefully tell you that it used your GPU.
A good benchmark to test out the speed of your setup is to run /usr/local/lib/python2.7/dist-packages/theano/misc/check_blas.py
Thanks to the Theano developers for providing this awesome library and to Andrew Ng, Samy Bengio, and the other Googlers who have been taking their time to teach the rest of us more machine learning concepts.