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