import os
import torch
from . import training_stats
def init():
if 'MASTER_ADDR' not in os.environ:
os.environ['MASTER_ADDR'] = 'localhost'
if 'MASTER_PORT' not in os.environ:
os.environ['MASTER_PORT'] = '29500'
if 'RANK' not in os.environ:
os.environ['RANK'] = '0'
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = '0'
if 'WORLD_SIZE' not in os.environ:
os.environ['WORLD_SIZE'] = '1'
backend = 'gloo' if os.name == 'nt' else 'nccl'
torch.distributed.init_process_group(backend=backend, init_method='env://')
torch.cuda.set_device(int(os.environ.get('LOCAL_RANK', '0')))
sync_device = torch.device('cuda') if get_world_size() > 1 else None
training_stats.init_multiprocessing(rank=get_rank(), sync_device=sync_device)
def get_rank():
return torch.distributed.get_rank() if torch.distributed.is_initialized() else 0
def get_world_size():
return torch.distributed.get_world_size() if torch.distributed.is_initialized() else 1
def should_stop():
return False
def update_progress(cur, total):
_ = cur, total
def print0(*args, **kwargs):
if get_rank() == 0:
print(*args, **kwargs)