I was able to solve the issue by setting CPLUS_INCLUDE_PATH. Now the output
looks like this
Using cuDNN version 5110 on context None
Mapped name None to device cuda0: GeForce GTX 960 (0000:01:00.0)
[GpuElemwise{exp,no_inplace}(<GpuArrayType(float32, (False,))>),
HostFromGpu(gpuarray)(GpuElemwise{exp,no_inplace}.0)]
Looping 1000 times took 0.196515 seconds
Result is [ 1.23178029 1.61879349 1.52278066 ..., 2.20771813 2.29967761
1.62323296]
Used the cpu
The file is unable to detect that the program ran on gpu but it's a
seperate issue. I am attaching the file gputest.py.
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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 range(iters):
r = f()
t1 = time.time()
print("Looping %d times took %f seconds" % (iters, t1 - t0))
print("Result is %s" % (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')
