def run(opts: typing.Optional[typing.List[str]] = None, predict: bool = False):
"""Run starts a job based on the command passed from the command line.
You can optionally run the mmf job programmatically by passing an optlist as opts.
Args:
opts (typing.Optional[typing.List[str]], optional): Optlist which can be used.
to override opts programmatically. For e.g. if you pass
opts = ["training.batch_size=64", "checkpoint.resume=True"], this will
set the batch size to 64 and resume from the checkpoint if present.
Defaults to None.
predict (bool, optional): If predict is passed True, then the program runs in
prediction mode. Defaults to False.
"""
setup_imports()
if opts is None:
parser = flags.get_parser()
args = parser.parse_args()
else:
args = argparse.Namespace(config_override=None)
args.opts = opts
print(args)
configuration = Configuration(args)
# Do set runtime args which can be changed by MMF
configuration.args = args
config = configuration.get_config()
config.start_rank = 0
if config.distributed.init_method is None:
infer_init_method(config)
if config.distributed.init_method is not None:
if torch.cuda.device_count() > 1 and not config.distributed.no_spawn:
config.start_rank = config.distributed.rank
config.distributed.rank = None
torch.multiprocessing.spawn(
fn=distributed_main,
args=(configuration, predict),
nprocs=torch.cuda.device_count(),
)
else:
distributed_main(0, configuration, predict)
elif config.distributed.world_size > 1:
assert config.distributed.world_size <= torch.cuda.device_count()
port = random.randint(10000, 20000)
config.distributed.init_method = f"tcp://localhost:{port}"
config.distributed.rank = None
torch.multiprocessing.spawn(
fn=distributed_main,
args=(configuration, predict),
nprocs=config.distributed.world_size,
)
else:
config.device_id = 0
main(configuration, predict=predict)
def run(predict=False):
setup_imports()
parser = flags.get_parser()
args = parser.parse_args()
print(args)
configuration = Configuration(args)
# Do set runtime args which can be changed by MMF
configuration.args = args
config = configuration.get_config()
config.start_rank = 0
if config.distributed.init_method is None:
infer_init_method(config)
if config.distributed.init_method is not None:
if torch.cuda.device_count() > 1 and not config.distributed.no_spawn:
config.start_rank = config.distributed.rank
config.distributed.rank = None
torch.multiprocessing.spawn(
fn=distributed_main,
args=(configuration, predict),
nprocs=torch.cuda.device_count(),
)
else:
distributed_main(0, configuration, predict)
elif config.distributed.world_size > 1:
assert config.distributed.world_size <= torch.cuda.device_count()
port = random.randint(10000, 20000)
config.distributed.init_method = "tcp://localhost:{port}".format(
port=port)
config.distributed.rank = None
torch.multiprocessing.spawn(
fn=distributed_main,
args=(configuration, predict),
nprocs=config.distributed.world_size,
)
else:
config.device_id = 0
main(configuration, predict)