* add a [cuda] section to your .theanorc file containing the option ``root = /path/to/cuda/root``.
Once that is done, the only thing left is to change the ``device`` option to name the GPU device in your
computer.
For example: ``THEANO_FLAGS='cuda.root=/path/to/cuda/root,device=gpu'``.
You can also set the device option in the .theanorc file's ``[global]`` section.
* If your computer has multiple gpu and use 'device=gpu', the driver select the one to use (normally gpu0)
* You can use the program nvida-smi to change that policy.
* You can choose one specific gpu by giving device the one of those values: gpu0, gpu1, gpu2, or gpu3.
* If you have more than 4 devices you are very lucky but you will have to modify theano's *configdefaults.py* file and define more gpu devices to choose from.
* Using the 'device=gpu*' theano flag make theano fall back to the cpu if there is a problem with the gpu.
You can use the flag 'force_device=True' to have theano raise an error when we cannot use the gpu.
computer, and set the default floating point computations to float32.
For example: ``THEANO_FLAGS='cuda.root=/path/to/cuda/root,device=gpu,floatX=float32'``.
You can also set these options in the .theanorc file's ``[global]`` section:
.. code-block:: cfg
[global]
device = gpu
floatX = float32
Note that:
* If your computer has multiple GPUs and you use 'device=gpu', the driver
selects the one to use (usually gpu0).
* You can use the program nvida-smi to change this policy.
* You can choose one specific GPU by specifying 'device=gpuX', with X the
the corresponding GPU index (0, 1, 2, ...)
* By default, when ``device`` indicates preference for GPU computations,
Theano will fall back to the CPU if there is a problem with the GPU.
You can use the flag 'force_device=True' to instead raise an error when
Theano cannot use the GPU.
Once your setup is complete, head to :ref:`using_gpu` to find how to verify
