def _run_example_with(self, start_method):
spawn_ctx = mp.get_context(start_method)
mp_queue = spawn_ctx.Queue()
server = timer.LocalTimerServer(mp_queue, max_interval=0.01)
server.start()
world_size = 8
processes = []
for i in range(0, world_size):
if i % 2 == 0:
p = spawn_ctx.Process(target=_stuck_function, args=(i, mp_queue))
else:
p = spawn_ctx.Process(target=_happy_function, args=(i, mp_queue))
p.start()
processes.append(p)
for i in range(0, world_size):
p = processes[i]
p.join()
if i % 2 == 0:
self.assertEqual(-signal.SIGKILL, p.exitcode)
else:
self.assertEqual(0, p.exitcode)
server.stop()
def test_torch_mp_example(self):
# in practice set the max_interval to a larger value (e.g. 60 seconds)
mp_queue = mp.get_context("spawn").Queue()
server = timer.LocalTimerServer(mp_queue, max_interval=0.01)
server.start()
world_size = 8
# all processes should complete successfully
# since start_process does NOT take context as parameter argument yet
# this method WILL FAIL (hence the test is disabled)
torch_mp.spawn(fn=_happy_function,
args=(mp_queue, ),
nprocs=world_size,
join=True)
with self.assertRaises(Exception):
# torch.multiprocessing.spawn kills all sub-procs
# if one of them gets killed
torch_mp.spawn(fn=_stuck_function,
args=(mp_queue, ),
nprocs=world_size,
join=True)
server.stop()
def setUp(self):
self.mp_queue = mp.Queue()
self.max_interval = 0.01
self.server = timer.LocalTimerServer(self.mp_queue, self.max_interval)
self.server.start()