def multiprocess_train(
rank, opt, port=61337, rank_offset=0, gpu=None, hostname='localhost'
):
with distributed_utils.distributed_context(
rank, opt, port, rank_offset, gpu, hostname
) as opt:
# Run the actual training
opt['multiprocessing'] = True
return single_train.TrainLoop(opt).train()
def multiprocess_eval(
rank, opt, port=61337, rank_offset=0, gpu=None, hostname='localhost'
):
"""
Run a multiprocessing evaluation.
Invoked by launch_and_eval, not instantiated directly.
"""
with distributed_utils.distributed_context(
rank, opt, port, rank_offset, gpu, hostname
) as opt:
return eval_model.eval_model(opt)
def multiprocess_eval(
rank, opt, port=61337, rank_offset=0, gpu=None, hostname='localhost'
):
"""
Run a multiprocessing evaluation.
Invoked by launch_and_eval, not instantiated directly.
"""
init_method = f'tcp://{hostname}:{port}'
with distributed_utils.distributed_context(
rank, opt, rank_offset, gpu, init_method=init_method
) as opt:
opt['multiprocessing'] = True
return eval_model.eval_model(opt)
def multiprocess_train(rank,
opt,
port=61337,
rank_offset=0,
gpu=None,
hostname='localhost'):
init_method = f"tcp://{hostname}:{port}"
with distributed_utils.distributed_context(rank,
opt,
rank_offset,
gpu,
init_method=init_method) as opt:
# Run the actual training
opt['multiprocessing'] = True
try:
return single_train.TrainLoop(opt).train()
except Exception:
import parlai.utils.logging as logging
logging.critical(traceback.format_exc())
logging.critical(
f"Got the above exception on worker {rank + rank_offset}. "
"This may cause hangs requiring manual killing of processes.")
raise