def _compute_time_on_machine(runfile, i, o, input_shapes, machine, niter):
""" Computes computation time of funciton graph on a remote machine
Returns average duration of the computation (time)
inputs:
runfile - The program to run on the remote machine
i - A Theano graph
o - A Theano graph
input_shapes - A dict mapping input variable to array shape
machine - A machine on which to run the graph
niter - The number of times to run the computation in sampling
outputs:
A dict mapping apply node to average runtime
>>> _compute_time_on_machine(runfile, i, o, {x: (10, 10)}, 'receiver.univ.edu', 10)
{dot(x, Add(x, y)): 0.133, Add(x, y): .0012}
See Also
--------
comptime_dict_cpu
comptime_dict_gpu
"""
file = open('_machinefile.txt', 'w')
file.write(machine)
file.close()
# stringify the keys
variables = theano.gof.graph.variables(i, o)
if len(set(map(str, variables))) != len(variables):
raise ValueError("Not all variables have unique names"
"Look into theano.gof.utils.give_variables_names")
known_shapes = shape_of_variables(i, o, input_shapes)
known_shapes_str = str({str(k): v for k, v in known_shapes.items()})
stdin, stdout, stderr = os.popen3(
'''mpiexec -np 1 -machinefile _machinefile.txt python %s%s "%s" %d''' %
(ape_dir, runfile, known_shapes_str, niter))
# Send the fgraphs as strings (they will be unpacked on the other end)
nodes = theano.gof.graph.list_of_nodes(i, o)
fgraphs = graph_iter(nodes)
pack_many(fgraphs, stdin) # This writes to stdin
stdin.close() # send termination signal
# Receive the output from the compute node
# return stdout.read() + stderr.read()
message = stdout.read()
times = ast.literal_eval(message)
return dict(zip(map(str, nodes), times))
def _compute_time_on_machine(runfile, i, o, input_shapes, machine, niter):
""" Computes computation time of funciton graph on a remote machine
Returns average duration of the computation (time)
inputs:
runfile - The program to run on the remote machine
i - A Theano graph
o - A Theano graph
input_shapes - A dict mapping input variable to array shape
machine - A machine on which to run the graph
niter - The number of times to run the computation in sampling
outputs:
A dict mapping apply node to average runtime
>>> _compute_time_on_machine(runfile, i, o, {x: (10, 10)}, 'receiver.univ.edu', 10)
{dot(x, Add(x, y)): 0.133, Add(x, y): .0012}
See Also
--------
comptime_dict_cpu
comptime_dict_gpu
"""
file = open('_machinefile.txt', 'w')
file.write(machine)
file.close()
# stringify the keys
variables = theano.gof.graph.variables(i, o)
if len(set(map(str, variables))) != len(variables):
raise ValueError("Not all variables have unique names"
"Look into theano.gof.utils.give_variables_names")
known_shapes = shape_of_variables(i, o, input_shapes)
known_shapes_str = str({str(k):v for k,v in known_shapes.items()})
stdin, stdout, stderr = os.popen3('''mpiexec -np 1 -machinefile _machinefile.txt python %s%s "%s" %d'''%(ape_dir, runfile, known_shapes_str, niter))
# Send the fgraphs as strings (they will be unpacked on the other end)
nodes = theano.gof.graph.list_of_nodes(i, o)
fgraphs = graph_iter(nodes)
pack_many(fgraphs, stdin) # This writes to stdin
stdin.close() # send termination signal
# Receive the output from the compute node
# return stdout.read() + stderr.read()
message = stdout.read()
times = ast.literal_eval(message)
return dict(zip(map(str, nodes), times))
def test_comptime_run():
x = theano.tensor.matrix('x')
y = theano.tensor.matrix('y')
z = theano.tensor.dot(x, y)
inputs, outputs = (x, y), (z,)
variables = theano.gof.graph.variables(inputs, outputs)
nodes = theano.gof.graph.list_of_nodes(inputs, outputs)
theano.gof.utils.give_variables_names(variables)
map(clean_variable, variables)
input_shapes = {x: (10, 10), y: (10, 10)}
known_shapes = shape_of_variables(inputs, outputs, input_shapes)
known_shapes = {str(k): v for k,v in known_shapes.items()}
time_comp_fn = lambda ins, outs, num_ins, niter: 1
fgraphs = list(graph_iter(nodes))
niter = 3
results = comptime_run(known_shapes, niter, fgraphs, time_comp_fn)
assert results == [1]*len(fgraphs)
