def time_computation(inputs, outputs, numeric_inputs, niter):
f = theano.function(inputs, outputs, mode=mode)
starttime = time.time()
debugprint("Computing")
for n in xrange(niter):
outputs = f(*numeric_inputs)
endtime = time.time()
duration = endtime - starttime
return duration / niter
def time_computation(inputs, outputs, numeric_inputs, niter):
f = theano.function(inputs, outputs, mode=mode)
starttime = time.time()
debugprint("Computing")
for n in xrange(niter):
outputs = f(*numeric_inputs)
endtime = time.time()
duration = endtime - starttime
return duration/niter
def time_computation(inputs, outputs, numeric_inputs, niter):
gpu_inputs, gpu_outputs = cpu_to_gpu_graph(inputs, outputs)
# TODO: replace this with a can_run_on(job, machine) function
# use this function in tompkins
if not all(isinstance(n.op, theano.sandbox.cuda.GpuOp)
for n in theano.gof.graph.list_of_nodes(gpu_inputs, gpu_outputs)):
return 99999.9
gpu_numeric_inputs = map(togpu_data, numeric_inputs)
f = theano.function(gpu_inputs, gpu_outputs)
starttime = time.time()
debugprint("Computing")
for n in xrange(niter):
outputs = f(*numeric_inputs)
endtime = time.time()
duration = endtime - starttime
return duration/niter
def time_computation(inputs, outputs, numeric_inputs, niter):
gpu_inputs, gpu_outputs = cpu_to_gpu_graph(inputs, outputs)
# TODO: replace this with a can_run_on(job, machine) function
# use this function in tompkins
if not all(
isinstance(n.op, theano.sandbox.cuda.GpuOp)
for n in theano.gof.graph.list_of_nodes(gpu_inputs, gpu_outputs)):
return 99999.9
gpu_numeric_inputs = map(togpu_data, numeric_inputs)
f = theano.function(gpu_inputs, gpu_outputs)
starttime = time.time()
debugprint("Computing")
for n in xrange(niter):
outputs = f(*numeric_inputs)
endtime = time.time()
duration = endtime - starttime
return duration / niter
