Memory and variables

>>> theano.config.floatX = 'float32'

>>> a = T.matrix()

>>> b = a.transfer(None)

>>> b.eval({a:numpy.ones((2,2)).astype(theano.config.floatX)})
gpuarray.array([[ 1.  1.]
 [ 1.  1.]], dtype=float32)

 >>> theano.printing.debugprint(b)
GpuFromHost<None> [id A] ''   
 |<TensorType(float32, matrix)> [id B]

>>> a = T.matrix('a')

>>> b = a ** 2

>>> sq = theano.function([a],b)

>>> theano.printing.debugprint(sq)
HostFromGpu(gpuarray) [id A] ''   2
 |GpuElemwise{Sqr}[(0, 0)]<gpuarray> [id B] ''   1
   |GpuFromHost<None> [id C] ''   0
     |a [id D]

>>> b = b.transfer(None)

>>> sq = theano.function([a],b)

>>> theano.printing.debugprint(sq)
GpuElemwise{Sqr}[(0, 0)]<gpuarray> [id A] ''   1
 |GpuFromHost<None> [id B] ''   0
   |a [id C]

>>> b = a.transfer('cpu')

>>> theano.printing.debugprint(b)
<TensorType(float32, matrix)> [id A]

>>> state = shared(0)

>>> state

<TensorType(int64, scalar)>

>>> state.get_value()
array(0)

>>> state.set_value(1)

>>> state.get_value()
array(1)

PATH=/usr/local/cuda-8.0-cudnn-5.1/bin:$PATH THEANO_FLAGS="contexts=dev0->cuda0;dev1->cuda1,floatX=float32,gpuarray.preallocate=0.8" python

>>> from theano import theano
Using cuDNN version 5110 on context dev0
Preallocating 9151/11439 Mb (0.800000) on cuda0
Mapped name dev0 to device cuda0: Tesla K80 (0000:83:00.0)
Using cuDNN version 5110 on context dev1
Preallocating 9151/11439 Mb (0.800000) on cuda1
Mapped name dev1 to device cuda1: Tesla K80 (0000:84:00.0)

>>> import theano.tensor as T

>>> import numpy

>>> theano.shared(numpy.random.random((1024, 1024)).astype('float32'),target='dev1')
<GpuArrayType<dev1>(float32, (False, False))>