Theano for GPU Computing

In the near future Flux will be offering GPGPU services based on the Nvidia K20x GPU.

A user had requested support for Theano for GPU computing, so we installed it:

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features:

• tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions.
• transparent use of a GPU – Perform data-intensive calculations up to 140x faster than with CPU.(float32 only)
• efficient symbolic differentiation – Theano does your derivatives for function with one or many inputs.
• speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny.
• dynamic C code generation – Evaluate expressions faster.
• extensive unit-testing and self-verification – Detect and diagnose many types of mistake.

 To use Theano for GPU's run it as so:

	#Example for Theano 0.6 on CAEN-HPC FluxG
	#load modules
	module load python/2.7.5 cuda/5.5 theano/0.6
	#use device=gpu to let our system autoselect the GPU to use
	#use device=cpu to run your code on another system
	THEANO_FLAGS='floatX=float32,device=gpu' python script.py

view raw theano-gpu hosted with ❤ by GitHub

Setting device=gpu rather than gpu# lets our system assign the correct GPU for you.  For testing on CPU set device=cpu. Theano has many configuration options but the above are the most common.

 If you want to use Theano on multiple GPUs in a single job contact us.